Podcasts about Vae

Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at ocdevel.com/mlg/36 Try a walking desk - stay healthy & sharp while you learn & code Build the future of multi-agent software with AGNTCY. Thanks to T.J. Wilder from intrep.io for recording this episode! Fundamentals of Autoencoders Autoencoders are neural networks designed to reconstruct their input data by passing data through a compressed intermediate representation called a “code.” The architecture typically follows an hourglass shape: a wide input and output separated by a narrower bottleneck layer that enforces information compression. The encoder compresses input data into the code, while the decoder reconstructs the original input from this code. Comparison with Supervised Learning Unlike traditional supervised learning, where the output differs from the input (e.g., image classification), autoencoders use the same vector for both input and output. Use Cases: Dimensionality Reduction and Representation Autoencoders perform dimensionality reduction by learning compressed forms of high-dimensional data, making it easier to visualize and process data with many features. The compressed code can be used for clustering, visualization in 2D or 3D graphs, and input into subsequent machine learning models, saving computational resources and improving scalability. Feature Learning and Embeddings Autoencoders enable feature learning by extracting abstract representations from the input data, similar in concept to learned embeddings in large language models (LLMs). While effective for many data types, autoencoder-based encodings are less suited for variable-length text compared to LLM embeddings. Data Search, Clustering, and Compression By reducing dimensionality, autoencoders facilitate vector searches, efficient clustering, and similarity retrieval. The compressed codes enable lossy compression analogous to audio codecs like MP3, with the difference that autoencoders lack domain-specific optimizations for preserving perceptually important data. Reconstruction Fidelity and Loss Types Loss functions in autoencoders are defined to compare reconstructed outputs to original inputs, often using different loss types depending on input variable types (e.g., Boolean vs. continuous). Compression via autoencoders is typically lossy, meaning some information from the input is lost during reconstruction, and the areas of information lost may not be easily controlled. Outlier Detection and Noise Reduction Since reconstruction errors tend to move data toward the mean, autoencoders can be used to reduce noise and identify data outliers. Large reconstruction errors can signal atypical or outlier samples in the dataset. Denoising Autoencoders Denoising autoencoders are trained to reconstruct clean data from noisy inputs, making them valuable for applications in image and audio de-noising as well as signal smoothing. Iterative denoising as a principle forms the basis for diffusion models, where repeated application of a denoising autoencoder can gradually turn random noise into structured output. Data Imputation Autoencoders can aid in data imputation by filling in missing values: training on complete records and reconstructing missing entries for incomplete records using learned code representations. This approach leverages the model's propensity to output ‘plausible' values learned from overall data structure. Cryptographic Analogy The separation of encoding and decoding can draw parallels to encryption and decryption, though autoencoders are not intended or suitable for secure communication due to their inherent lossiness. Advanced Architectures: Sparse and Overcomplete Autoencoders Sparse autoencoders use constraints to encourage code representations with only a few active values, increasing interpretability and explainability. Overcomplete autoencoders have a code size larger than the input, often in applications that require extraction of distinct, interpretable features from complex model states. Interpretability and Research Example Research such as Anthropic's “Towards Monosemanticity” applies sparse autoencoders to the internal activations of language models to identify interpretable features correlated with concrete linguistic or semantic concepts. These models can be used to monitor and potentially control model behaviors (e.g., detecting specific language usage or enforcing safety constraints) by manipulating feature activations. Variational Autoencoders (VAEs) VAEs extend autoencoder architecture by encoding inputs as distributions (means and standard deviations) instead of point values, enforcing a continuous, normalized code space. Decoding from sampled points within this space enables synthetic data generation, as any point near the center of the code space corresponds to plausible data according to the model. VAEs for Synthetic Data and Rare Event Amplification VAEs are powerful in domains with sparse data or rare events (e.g., healthcare), allowing generation of synthetic samples representing underrepresented cases. They can increase model performance by augmenting datasets without requiring changes to existing model pipelines. Conditional Generative Techniques Conditional autoencoders extend VAEs by allowing controlled generation based on specified conditions (e.g., generating a house with a pool), through additional decoder inputs and conditional loss terms. Practical Considerations and Limitations Training autoencoders and their variants requires computational resources, and their stochastic training can produce differing code representations across runs. Lossy reconstruction, lack of domain-specific optimizations, and limited code interpretability restrict some use cases, particularly where exact data preservation or meaningful decompositions are required.

Vae soli!, dice el libro del Eclesiastés: ¡Pobre del que va solo! Pero nosotros nunca vamos solos porque una Persona divina nos ha sido dada. Habita en nosotros el Espíritu Santo, moviéndonos con inspiraciones y sus dones. Dentro de estos, pensemos en el superior, el de Sabiduría, que nos hace gustar las cosas divinas. Podemos preguntarnos si ese gozo de lo divino ha sido creciente en nuestra vida.

US-Präsident Trumps erste Auslandsreise führt nach Saudi-Arabien. "Saudi-Arabien geht es um Prestige, aber darüber hinaus um ganz harte sicherheitspolitische Interessen", sagt Nahost-Experte Guido Steinberg. Trump gehe es vor allem um Geld. Von WDR5.

Dans cet épisode, on discute avec Charles Ouimet, influenceur cycliste et créateur de contenu, des grandes tendances qui transforment l'industrie du vélo. Gravel, Vélo à Assistance Électrique (VAE), Direct-to-Consumer, retour des vélos aéro : on fait le tour des enjeux et des opportunités. Est-ce que les marques traditionnelles sont en train de perdre la game ? Est-ce que l'influence pèse plus que l'expertise et la connaissance? À écouter pour comprendre où s'en va l'industrie cycliste en 2025.

CardioNerds Critical Care Cardiology Council members Dr. Gurleen Kaur and Dr. Katie Vanchiere meet with Dr. Yash Patel, Dr. Akanksha, and Dr. Mohammed El Nayir from Trinity Health Ann Arbor. They discuss a case of pulmonary air embolism, RV failure, and cardiac arrest secondary to an ocular venous air embolism. Expert insights provided by Dr. Tanmay Swadia. Audio editing by CardioNerds Academy intern, Grace Qiu. A 36-year-old man with a history of multiple ocular surgeries, including a complex retinal detachment repair, suffered a post-vitrectomy collapse at home. He was found hypoxic, tachycardic, and hypotensive, later diagnosed with a pulmonary embolism from ocular venous air embolism leading to severe right heart failure. Despite a mild embolic burden, the cardiovascular response was profound, requiring advanced hemodynamic support, including an Impella RP device (Abiomed, Inc.). Multidisciplinary management, including fluid optimization, vasopressors and mechanical support to facilitate recovery. This case underscores the need for early recognition and individualized intervention in cases of ocular venous air embolism. US Cardiology Review is now the official journal of CardioNerds! Submit your manuscript here. CardioNerds Case Reports PageCardioNerds Episode PageCardioNerds AcademyCardionerds Healy Honor Roll CardioNerds Journal ClubSubscribe to The Heartbeat Newsletter!Check out CardioNerds SWAG!Become a CardioNerds Patron! Pearls- Clear Vision, Clouded Heart: Ocular Venous Air Embolism with Pulmonary Air Embolism, RV Failure, and Cardiac Arrest Hypoxia, hypotension and tachycardia in a patient following ocular instrumentation are classic findings suggestive of pulmonary embolism from possible air embolism. The diagnosis of RV failure is based on clinical presentation, echocardiographic findings (such as McConnell's sign), and invasive hemodynamic assessment via right heart catheterization. Mechanical circulatory support can be considered as a temporary measure for patients with refractory RV failure. Central Figure: Approach to Pulmonary Embolism with Acute RV Failure Notes - Clear Vision, Clouded Heart: Ocular Venous Air Embolism with Pulmonary Air Embolism, RV Failure, and Cardiac Arrest 1. What is an Ocular Venous Air Embolism (VAE), and how can it be managed in critically ill patients? An Ocular Venous Air Embolism is defined as the entry of air into the systemic venous circulation through the ocular venous circulation, often during vitrectomy procedures. Early diagnosis is key to preventing cardiovascular collapse in cases of Ocular Venous Air Embolism (VAE).  The goal is to stop further air entry. This can be done by covering the surgical site with saline-soaked dressings and checking for air entry points. Adjusting the operating table can help, especially with a reverse Trendelenburg position for lower-body procedures. The moment VAE is suspected, discontinue nitrous oxide and switch to 100% oxygen. This helps with oxygenation, speeds up nitrogen elimination, and shrinks air bubbles. Hyperbaric Oxygen Therapy can reduce bubble size and improve oxygenation, especially in cases of cerebral air embolism, when administered within 6 hours of the incident. Though delayed hyperbaric oxygen therapy can still offer benefits, the evidence is mixed. VAE increases right heart strain, so inotropic agents like dobutamine can help boost cardiac output, while norepinephrine supports ventricular function and systemic vascular resistance, but this may also worsen pulmonary resistance.  Aspiration of air via multi-orifice or Swan-Ganz catheters has limited success, with success rates ranging from 6% to 16%. In contrast, the Bunegin-Albin catheter has shown more promise, with a 30-60% success rate. Catheterization for acute VAE-induced hemodynamic compromise is controversial, and there's insufficient evidence to support its ...

Asia is the fastest growing region in the world. And there's more ways to invest in it now, than ever before. But with so much choice, comes a debate; what's strategy is best? Active, Passive or Thematic?We break it down in this episode: A list of the best ETF options for investing in Asia on the ASXWhich is the best performing?The case for Index, Thematic and Active investing in AsiaHow we invest in Asia; the products are in our portfoliosLinks Referenced:

Wir haben Pa M. K. Sinyan, Managing Partner bei Gallup, genau an dem Tag zu Gast, an dem der neue “State of the Global Workforce” erscheint. Unsere wichtigsten Take‑aways für euch: 1. Weltweit engagiert nur jede*r Fünfte seine ganze Energie im Job (21%) Wir sprechen hier von rund vier Milliarden Erwerbstätigen, und vier Fünftel davon fühlen sich emotional kaum verbunden, ein gigantischer Pool an ungenutzter Produktivität, Innovationsfreude und Lebensqualität. 2. Deutschland rutscht ab, das Engagement beträgt nur noch 9% Von 14% (2019) auf 9%: Nur jede*r Zehnte ist hierzulande noch mit Herz und Verstand bei der Arbeit. Das spüren wir in stagnierender Innovationskraft, hoher Wechselbereitschaft und steigender Krankheitsquote. 3. Führungskräfte brennen aus, besonders die jungen und weiblichen Gallup verzeichnet den stärksten Wohlfühl-Einbruch genau bei den Personen, die Teams leiten sollen. Junge Leaderinnen jonglieren Karriereaufbau, Remote-Komplexität und rasant neue Tech-Themen; weibliche Führungskräfte tragen oft zusätzliche Care-Lasten. 4. Onboarding‑Alarm: 40% der New Hires suchen nach weniger als 12 Monaten das Weite Wir investieren viel in Recruiting und verlieren die Talente, bevor sie wirklich Wert stiften. Fehlende klare Erwartungen, zu wenig persönliches Feedback und Remote‑Isolation wirken wie Austritts Beschleuniger. Pa mahnt: Wer die erste Lernkurve begleitet, bindet, wer sie verpasst, zahlt doppelt. 5. HRler*innen lieben ihren Job fühlen sich aber machtlos 87% der HR-Profis brennen für People-Themen, doch nur 17% spüren echten Einfluss. Die Folge: Gute Initiativen verhallen, wenn sie nicht mit Kennzahlen untermauert sind. 5. KI‑Gap in Deutschland: Nutzung ja, Skill‑Aufbau nein Über die Hälfte der Beschäftigten hat KI-Tools ausprobiert, doch nur ein Drittel bekommt Hilfe, diese Fähigkeiten strategisch auszubauen. Das verstärkt Ängste (“ersetzbar?”) statt Chancen (“entlastet!”). Ohne systematische Qualifizierung entsteht eine Zwei‑Klassen‑Workforce aus Early Adopter*innen und Verunsicherten. ​ 6. Mut schlägt Mittelmaß, VAE mit 28% Engagement Während wir in Deutschland bei 9% kleben, erzielen die Vereinigten Arabischen Emirate fast das Dreifache. Pa erklärt den Vorsprung mit einer "Achievement- Culture", klaren Zielen und schneller Adoption neuer Technologien. Das zeigt: Rahmenbedingungen sind wichtig, aber Leadership-Mindset und Risikofreude machen den Unterschied. ​ 7. Was nehmen wir mit: Wir feiern Pa Sinyans Klartext, weil er zeigt, wie viel Potenzial in guter Führung, datengetriebenen HR und einer mutigen Lernkultur steckt. Unsere Challenge an alle Hörer*innen: Nutzt die Freiräume, die Technologie & New‑Work‑Modelle bieten, um echte Verbindung, Vertrauen und Wachstum zu entfesseln, dann wird aus “good, bad & ugly” vielleicht bald “good, better, awesome”. Danke fürs Zuhören, und auf viele weitere Folgen! #Leadership #CorporateCulture #Family #Team #FutureOfWork #NewWork #Podcast #ZoomerMeetsBoomer LinkedIn: michaeltrautmann64 oskar-trautmann96

Dans cet épisode, on part à la découverte de l'univers de Quentin Joly, SRE chez Lucca, auteur du blog Une tasse de café et streamer passionné.On y parle :Du métier de Site Reliability Engineer, ses joies, ses galères et ses responsabilités.Des différences entre SRE et DevOps, avec une vraie réflexion terrain.De fonction publique vs secteur privé, de la VAE (validation des acquis), de reconversion et de salaires dans la tech.D'ergonomie, de claviers split, de neovim, tmux, et tous ces petits détails qui changent la vie d'un dev.Et surtout de partage de connaissance, de blog, de pédagogie, de la passion pour la tech sans condescendance.Le lien de la conf de Quentin: https://youtu.be/TbQ-rT__CY0?list=PLl0xIhYGSdm94h5lcrybZAsdGAfrpJx4y

durée : 00:29:44 - A votre service avec Nelly Sorbier et ses experts - François-Xavier Dauphin, vélociste à Valence, nous éclaire sur les vélos à assistance électrique (VAE) et les solutions adaptées pour tous, y compris pour les personnes à mobilité réduite. Découvrez les dernières innovations et conseils pratiques

La loi proposée par Gabriel Attal sur la justice des mineurs doit être votée au Sénat ce 25 mars. Elle propose une dérogation à l'excuse de minorité pour les jeunes délinquants multirécidivistes, la possibilité d'une comparution immédiate et la responsabilité civile solidaire des parents pour les dégâts causés par leurs enfants. Cette loi pourrait augmenter le nombre de mineurs en détention. Visite d'un établissement pour mineurs à Marseille, ville particulièrement touchée par le trafic de stupéfiants. De notre correspondante à Marseille, À la prison pour mineurs de Marseille, les portes s'ouvrent, exceptionnellement, pour laisser passer le député des Bouches-du-Rhône, Hendrik Davi. Il fait valoir son droit de visite.« On va voir quelles sont les conditions de détention des petits jeunes. On va voir les activités qu'ils peuvent avoir pour n'importe quel détenu. On va voir si les conditions de détention sont correctes. »Depuis les rangées de cellules qui donnent sur la cour, 50 adolescents de 13 à 17 ans essayent d'attirer l'attention du député. Idrisse a autrefois fait partie de ces jeunes incarcérés. Il a aujourd'hui 29 ans et explique l'engrenage.« Toute ma jeunesse, toute mon adolescence, je les ai passées dans les centres éducatifs fermés et la prison. La Busserine, c'est l'un des quartiers où il y a le plus de trafic. Et quand tu lèves la tête et que tu regardes autour de toi, tout le monde baigne dans le trafic pour subvenir à leur consommation, à leur style vestimentaire, à tout ça. Et puis petit à petit, je suis rentré dans le stup à guetter, à vendre et à monter de grades, jusqu'à voler et à braquer... »Sur lui, la détention a eu des effets positifs.« Oui, ça a été bien parce qu'il y a toujours des psychologues où ça te permet aussi de te poser, de faire une relecture un peu sur ta vie. Chez les mineurs, ça a été utile dans le sens où ils proposaient beaucoup de scolarité. Et aujourd'hui, je suis diplômé, moniteur éducateur, je fais une VAE pour être éducateur spécialisé. »À lire aussiMineurs privés de liberté en France: une situation préoccupante« La notion d'apprentissage et la notion d'éducation, elle est fondamentale »Mais ce jour-là à l'EPM, la direction l'avoue, il n'y a pas école à cause du manque de personnel. La situation n'est pas rare, elle est due à de nombreux arrêts maladie et des difficultés de recrutement. La juge pour enfant Laurence Bellon a suivi des mineurs pendant trente ans. « Quand vous êtes avec un mineur, la notion d'apprentissage, la notion d'éducation, elle est fondamentale. L'idée qu'en frappant l'enfant va comprendre. Ce n'est pas vrai. Vous avez là ceux qui sont en grande difficulté et ceux qui ne sont pas en grande difficulté, ils le deviennent en étant placés avec des gamins qui sont très difficiles. On s'est mis à souhaiter de plus en plus que les mineurs soient traités, je ne dis pas comme des majeurs, mais un peu comme des majeurs. »Des crédits qui favorisent l'enfermement, pourtant beaucoup plus coûteux que des places en foyer. Plusieurs syndicats appellent à manifester contre la loi Attal et pour une augmentation des moyens dans l'éducatif.À lire aussiFrance : la justice protège-t-elle suffisamment les mineurs ?À lire aussiLes enfants issus de l'immigration surreprésentés en prison

Fondée en 1965, l'IDRAC Business School repose sur une pédagogie innovante et immersive, favorisant l'alternance école/entreprise et un suivi personnalisé. Présente dans 12 campus en France, elle forme des managers agiles et visionnaires, adaptés aux transformations économiques et sociétales.Sa pédagogie repose sur l'accompagnement et l'encouragement, valorisant le droit à l'erreur et l'épanouissement personnel. L'école propose un soutien spécifique aux étudiants présentant des troubles DYS et mise sur l'apprentissage par l'action.Les programmes en blocs de compétences, construits avec des professionnels, préparent aux métiers de demain en intégrant des savoir-faire techniques, humains et comportementaux.Le Parcours Éducatif HEP, fondé sur Humanisme, Entrepreneuriat et Professionnalisme, développe cinq compétences clés : créativité, esprit critique, éthique, coopération et leadership, avec une reconnaissance via un système de badges.IDRAC Business School fait partie du réseau Compétences et Développement et propose des formations de Bac à Bac+5, accessibles aussi par la VAE. Forte de 38 000 alumni, elle prépare les étudiants à une intégration réussie dans un monde professionnel en mutation. ✅ DANS CET ÉPISODE NOUS ABORDONS :Le Programme Grande EcoleLe Bachelor Marketing et BusinessPour en savoir plus, rendez-vous sur le site officiel de l'école : https://www.ecoles-idrac.com/Episode sponsoriséℹ️ SUIVEZ L'ACTUALITÉ DE L'ORIENTATIONInscrivez-vous à l'Hebdo de l'orientation : https://azimut-orientation.com/abonnez-vous-a-la-newsletter/ (vous recevrez en cadeau un guide téléchargeable)

L'enorme quantità di informazioni che produciamo, in alcuni casi, non è sufficiente per alimentare le tecnologie in sviluppo negli ultimi anni, in particolare l'intelligenza artificiale. Come abbiamo più volte ripetuto, l'IA ha bisogno di questi dati per "imparare" ciò che deve fare. Ma perché migliaia di zettabyte non bastano? E qual è la soluzione a questo problema? In questa puntata proviamo a rispondere analizzando due strategie: la data augmentation e i dati sintetici, entrambe basate sulla generazione artificiale di dati, in tutto o in parte.Nella sezione delle notizie parliamo della sperimentazione delle bodycam, che continua a bordo dei treni, di Amazon che presenta il nuovo assistente Alexa+ e infine dell'insoddisfazione del clienti cinesi riguardo alla guida autonoma di Tesla.--Indice--00:00 - Introduzione00:55 - Continua la sperimentazione delle bodycam sui treni (IlPost.it, Matteo Gallo)02:09 - Amazon presenta il nuovo assistente Alexa+ (SmartWorld.it, Luca Martinelli)03:52 - Tesla arranca sulla guida autonoma in Cina (DMove.it, Davide Fasoli)05:33 - Come addestrare un'IA quando i dati non bastano (Luca Martinelli)16:03 - Conclusione--Contatti--• www.dentrolatecnologia.it• Instagram (@dentrolatecnologia)• Telegram (@dentrolatecnologia)• YouTube (@dentrolatecnologia)• redazione@dentrolatecnologia.it--Brani--• Ecstasy by Rabbit Theft• Time by Syn Cole

Bienvenue dans ce premier épisode de notre micro-série dédiée à la Validation des Acquis de l'Expérience (VAE), un dispositif qui permet de transformer les compétences acquises sur le terrain en un véritable tremplin pour l'évolution professionnelle ✨Les crèches Les Petites Canailles ont à cœur d'accompagner les professionnels dans leur montée en compétences, et cette série reflète cet engagement en explorant les différentes facettes du parcours VAE.Aujourd'hui, Licka Sarr reçoit Samantha Arnould, une professionnelle de la petite enfance qui vient tout juste de valider sa VAE pour devenir EJE. Samantha partagera avec nous son expérience personnelle, des premières démarches aux moments de doute, jusqu'à la joie de l'aboutissement.Bonne écoute !

Clement Bonnet discusses his novel approach to the ARC (Abstraction and Reasoning Corpus) challenge. Unlike approaches that rely on fine-tuning LLMs or generating samples at inference time, Clement's method encodes input-output pairs into a latent space, optimizes this representation with a search algorithm, and decodes outputs for new inputs. This end-to-end architecture uses a VAE loss, including reconstruction and prior losses. SPONSOR MESSAGES:***CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting!https://centml.ai/pricing/Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/***TRANSCRIPT + RESEARCH OVERVIEW:https://www.dropbox.com/scl/fi/j7m0gaz1126y594gswtma/CLEMMLST.pdf?rlkey=y5qvwq2er5nchbcibm07rcfpq&dl=0Clem and Matthew-https://www.linkedin.com/in/clement-bonnet16/https://github.com/clement-bonnethttps://mvmacfarlane.github.io/TOC1. LPN Fundamentals [00:00:00] 1.1 Introduction to ARC Benchmark and LPN Overview [00:05:05] 1.2 Neural Networks' Challenges with ARC and Program Synthesis [00:06:55] 1.3 Induction vs Transduction in Machine Learning2. LPN Architecture and Latent Space [00:11:50] 2.1 LPN Architecture and Latent Space Implementation [00:16:25] 2.2 LPN Latent Space Encoding and VAE Architecture [00:20:25] 2.3 Gradient-Based Search Training Strategy [00:23:39] 2.4 LPN Model Architecture and Implementation Details3. Implementation and Scaling [00:27:34] 3.1 Training Data Generation and re-ARC Framework [00:31:28] 3.2 Limitations of Latent Space and Multi-Thread Search [00:34:43] 3.3 Program Composition and Computational Graph Architecture4. Advanced Concepts and Future Directions [00:45:09] 4.1 AI Creativity and Program Synthesis Approaches [00:49:47] 4.2 Scaling and Interpretability in Latent Space ModelsREFS[00:00:05] ARC benchmark, Chollethttps://arxiv.org/abs/2412.04604[00:02:10] Latent Program Spaces, Bonnet, Macfarlanehttps://arxiv.org/abs/2411.08706[00:07:45] Kevin Ellis work on program generationhttps://www.cs.cornell.edu/~ellisk/[00:08:45] Induction vs transduction in abstract reasoning, Li et al.https://arxiv.org/abs/2411.02272[00:17:40] VAEs, Kingma, Wellinghttps://arxiv.org/abs/1312.6114[00:27:50] re-ARC, Hodelhttps://github.com/michaelhodel/re-arc[00:29:40] Grid size in ARC tasks, Chollethttps://github.com/fchollet/ARC-AGI[00:33:00] Critique of deep learning, Marcushttps://arxiv.org/vc/arxiv/papers/2002/2002.06177v1.pdf

Bienvenue dans ce premier épisode de notre micro-série dédiée à la Validation des Acquis de l'Expérience (VAE), un dispositif qui permet de transformer les compétences acquises sur le terrain en un véritable tremplin pour l'évolution professionnelle ✨Les crèches Les Petites Canailles ont à cœur d'accompagner les professionnels dans leur montée en compétences, et cette série reflète cet engagement en explorant les différentes facettes du parcours VAE.Aujourd'hui, Licka Sarr va explorer un autre point de vue essentiel : celui de l'accompagnement sur le terrain et notamment par les responsables des structures. Durant cet épisode, elle reçoit Marcia Santos Da Cruz, directrice engagée, qui soutient activement ses équipes dans leur démarche de VAE.Marcia nous partagera les actions concrètes qu'elle met en place pour encourager ses collaborateurs et les défis qu'elle rencontre dans cette dynamique d'accompagnement. Préparez-vous pour un échange riche et inspirant !Bonne écoute !

Bienvenue dans ce premier épisode de notre micro-série dédiée à la Validation des Acquis de l'Expérience (VAE), un dispositif qui permet de transformer les compétences acquises sur le terrain en un véritable tremplin pour l'évolution professionnelle ✨Aujourd'hui, Licka Sarr reçoit Charlotte Doussy, formatrice spécialisée dans les parcours VAE. Charlotte accompagne de nombreux professionnels de la petite enfance dans cette aventure, les guidant étape par étape pour valoriser leur expertise et obtenir leur diplôme

Vae and Anima continue to chase Stavias through their memories until they finally find their missing crewmate. Now all they have to do is break them out.

Vae and Anima dive into Stavias' nightmare with Tali providing backup as they rescue their friend.

Applications for the NYC AI Engineer Summit, focused on Agents at Work, are open!When we first started Latent Space, in the lightning round we'd always ask guests: “What's your favorite AI product?”. The majority would say Midjourney. The simple UI of prompt → very aesthetic image turned it into a $300M+ ARR bootstrapped business as it rode the first wave of AI image generation.In open source land, StableDiffusion was congregating around AUTOMATIC1111 as the de-facto web UI. Unlike Midjourney, which offered some flags but was mostly prompt-driven, A1111 let users play with a lot more parameters, supported additional modalities like img2img, and allowed users to load in custom models. If you're interested in some of the SD history, you can look at our episodes with Lexica, Replicate, and Playground.One of the people involved with that community was comfyanonymous, who was also part of the Stability team in 2023, decided to build an alternative called ComfyUI, now one of the fastest growing open source projects in generative images, and is now the preferred partner for folks like Black Forest Labs's Flux Tools on Day 1. The idea behind it was simple: “Everyone is trying to make easy to use interfaces. Let me try to make a powerful interface that's not easy to use.”Unlike its predecessors, ComfyUI does not have an input text box. Everything is based around the idea of a node: there's a text input node, a CLIP node, a checkpoint loader node, a KSampler node, a VAE node, etc. While daunting for simple image generation, the tool is amazing for more complex workflows since you can break down every step of the process, and then chain many of them together rather than manually switching between tools. You can also re-start execution halfway instead of from the beginning, which can save a lot of time when using larger models.To give you an idea of some of the new use cases that this type of UI enables:* Sketch something → Generate an image with SD from sketch → feed it into SD Video to animate* Generate an image of an object → Turn into a 3D asset → Feed into interactive experiences* Input audio → Generate audio-reactive videosTheir Examples page also includes some of the more common use cases like AnimateDiff, etc. They recently launched the Comfy Registry, an online library of different nodes that users can pull from rather than having to build everything from scratch. The project has >60,000 Github stars, and as the community grows, some of the projects that people build have gotten quite complex:The most interesting thing about Comfy is that it's not a UI, it's a runtime. You can build full applications on top of image models simply by using Comfy. You can expose Comfy workflows as an endpoint and chain them together just like you chain a single node. We're seeing the rise of AI Engineering applied to art.Major Tom's ComfyUI Resources from the Latent Space DiscordMajor shoutouts to Major Tom on the LS Discord who is a image generation expert, who offered these pointers:* “best thing about comfy is the fact it supports almost immediately every new thing that comes out - unlike A1111 or forge, which still don't support flux cnet for instance. It will be perfect tool when conflicting nodes will be resolved”* AP Workflows from Alessandro Perili are a nice example of an all-in-one train-evaluate-generate system built atop Comfy* ComfyUI YouTubers to learn from:* @sebastiankamph* @NerdyRodent* @OlivioSarikas* @sedetweiler* @pixaroma* ComfyUI Nodes to check out:* https://github.com/kijai/ComfyUI-IC-Light* https://github.com/MrForExample/ComfyUI-3D-Pack* https://github.com/PowerHouseMan/ComfyUI-AdvancedLivePortrait* https://github.com/pydn/ComfyUI-to-Python-Extension* https://github.com/THtianhao/ComfyUI-Portrait-Maker* https://github.com/ssitu/ComfyUI_NestedNodeBuilder* https://github.com/longgui0318/comfyui-magic-clothing* https://github.com/atmaranto/ComfyUI-SaveAsScript* https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID* https://github.com/AIFSH/ComfyUI-FishSpeech* https://github.com/coolzilj/ComfyUI-Photopea* https://github.com/lks-ai/anynode* Sarav: https://www.youtube.com/@mickmumpitz/videos ( applied stuff )* Sarav: https://www.youtube.com/@latentvision (technical, but infrequent)* look for comfyui node for https://github.com/magic-quill/MagicQuill* “Comfy for Video” resources* Kijai (https://github.com/kijai) pushing out support for Mochi, CogVideoX, AnimateDif, LivePortrait etc* Comfyui node support like LTX https://github.com/Lightricks/ComfyUI-LTXVideo , and HunyuanVideo* FloraFauna AI* Communities: https://www.reddit.com/r/StableDiffusion/, https://www.reddit.com/r/comfyui/Full YouTube EpisodeAs usual, you can find the full video episode on our YouTube (and don't forget to like and subscribe!)Timestamps* 00:00:04 Introduction of hosts and anonymous guest* 00:00:35 Origins of Comfy UI and early Stable Diffusion landscape* 00:02:58 Comfy's background and development of high-res fix* 00:05:37 Area conditioning and compositing in image generation* 00:07:20 Discussion on different AI image models (SD, Flux, etc.)* 00:11:10 Closed source model APIs and community discussions on SD versions* 00:14:41 LoRAs and textual inversion in image generation* 00:18:43 Evaluation methods in the Comfy community* 00:20:05 CLIP models and text encoders in image generation* 00:23:05 Prompt weighting and negative prompting* 00:26:22 Comfy UI's unique features and design choices* 00:31:00 Memory management in Comfy UI* 00:33:50 GPU market share and compatibility issues* 00:35:40 Node design and parameter settings in Comfy UI* 00:38:44 Custom nodes and community contributions* 00:41:40 Video generation models and capabilities* 00:44:47 Comfy UI's development timeline and rise to popularity* 00:48:13 Current state of Comfy UI team and future plans* 00:50:11 Discussion on other Comfy startups and potential text generation supportTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Small AI.swyx [00:00:12]: Hey everyone, we are in the Chroma Studio again, but with our first ever anonymous guest, Comfy Anonymous, welcome.Comfy [00:00:19]: Hello.swyx [00:00:21]: I feel like that's your full name, you just go by Comfy, right?Comfy [00:00:24]: Yeah, well, a lot of people just call me Comfy, even when they know my real name. Hey, Comfy.Alessio [00:00:32]: Swyx is the same. You know, not a lot of people call you Shawn.swyx [00:00:35]: Yeah, you have a professional name, right, that people know you by, and then you have a legal name. Yeah, it's fine. How do I phrase this? I think people who are in the know, know that Comfy is like the tool for image generation and now other multimodality stuff. I would say that when I first got started with Stable Diffusion, the star of the show was Automatic 111, right? And I actually looked back at my notes from 2022-ish, like Comfy was already getting started back then, but it was kind of like the up and comer, and your main feature was the flowchart. Can you just kind of rewind to that moment, that year and like, you know, how you looked at the landscape there and decided to start Comfy?Comfy [00:01:10]: Yeah, I discovered Stable Diffusion in 2022, in October 2022. And, well, I kind of started playing around with it. Yes, I, and back then I was using Automatic, which was what everyone was using back then. And so I started with that because I had, it was when I started, I had no idea like how Diffusion works. I didn't know how Diffusion models work, how any of this works, so.swyx [00:01:36]: Oh, yeah. What was your prior background as an engineer?Comfy [00:01:39]: Just a software engineer. Yeah. Boring software engineer.swyx [00:01:44]: But like any, any image stuff, any orchestration, distributed systems, GPUs?Comfy [00:01:49]: No, I was doing basically nothing interesting. Crud, web development? Yeah, a lot of web development, just, yeah, some basic, maybe some basic like automation stuff. Okay. Just. Yeah, no, like, no big companies or anything.swyx [00:02:08]: Yeah, but like already some interest in automations, probably a lot of Python.Comfy [00:02:12]: Yeah, yeah, of course, Python. But I wasn't actually used to like the Node graph interface before I started Comfy UI. It was just, I just thought it was like, oh, like, what's the best way to represent the Diffusion process in the user interface? And then like, oh, well. Well, like, naturally, oh, this is the best way I've found. And this was like with the Node interface. So how I got started was, yeah, so basic October 2022, just like I hadn't written a line of PyTorch before that. So it's completely new. What happened was I kind of got addicted to generating images.Alessio [00:02:58]: As we all did. Yeah.Comfy [00:03:00]: And then I started. I started experimenting with like the high-res fixed in auto, which was for those that don't know, the high-res fix is just since the Diffusion models back then could only generate that low-resolution. So what you would do, you would generate low-resolution image, then upscale, then refine it again. And that was kind of the hack to generate high-resolution images. I really liked generating. Like higher resolution images. So I was experimenting with that. And so I modified the code a bit. Okay. What happens if I, if I use different samplers on the second pass, I was edited the code of auto. So what happens if I use a different sampler? What happens if I use a different, like a different settings, different number of steps? And because back then the. The high-res fix was very basic, just, so. Yeah.swyx [00:04:05]: Now there's a whole library of just, uh, the upsamplers.Comfy [00:04:08]: I think, I think they added a bunch of, uh, of options to the high-res fix since, uh, since, since then. But before that was just so basic. So I wanted to go further. I wanted to try it. What happens if I use a different model for the second, the second pass? And then, well, then the auto code base was, wasn't good enough for. Like, it would have been, uh, harder to implement that in the auto interface than to create my own interface. So that's when I decided to create my own. And you were doing that mostly on your own when you started, or did you already have kind of like a subgroup of people? No, I was, uh, on my own because, because it was just me experimenting with stuff. So yeah, that was it. Then, so I started writing the code January one. 2023, and then I released the first version on GitHub, January 16th, 2023. That's how things got started.Alessio [00:05:11]: And what's, what's the name? Comfy UI right away or? Yeah.Comfy [00:05:14]: Comfy UI. The reason the name, my name is Comfy is people thought my pictures were comfy, so I just, uh, just named it, uh, uh, it's my Comfy UI. So yeah, that's, uh,swyx [00:05:27]: Is there a particular segment of the community that you targeted as users? Like more intensive workflow artists, you know, compared to the automatic crowd or, you know,Comfy [00:05:37]: This was my way of like experimenting with, uh, with new things, like the high risk fixed thing I mentioned, which was like in Comfy, the first thing you could easily do was just chain different models together. And then one of the first things, I think the first times it got a bit of popularity was when I started experimenting with the different, like applying. Prompts to different areas of the image. Yeah. I called it area conditioning, posted it on Reddit and it got a bunch of upvotes. So I think that's when, like, when people first learned of Comfy UI.swyx [00:06:17]: Is that mostly like fixing hands?Comfy [00:06:19]: Uh, no, no, no. That was just, uh, like, let's say, well, it was very, well, it still is kind of difficult to like, let's say you want a mountain, you have an image and then, okay. I'm like, okay. I want the mountain here and I want the, like a, a Fox here.swyx [00:06:37]: Yeah. So compositing the image. Yeah.Comfy [00:06:40]: My way was very easy. It was just like, oh, when you run the diffusion process, you kind of generate, okay. You do pass one pass through the diffusion, every step you do one pass. Okay. This place of the image with this brand, this space, place of the image with the other prop. And then. The entire image with another prop and then just average everything together, every step, and that was, uh, area composition, which I call it. And then, then a month later, there was a paper that came out called multi diffusion, which was the same thing, but yeah, that's, uh,Alessio [00:07:20]: could you do area composition with different models or because you're averaging out, you kind of need the same model.Comfy [00:07:26]: Could do it with, but yeah, I hadn't implemented it. For different models, but, uh, you, you can do it with, uh, with different models if you want, as long as the models share the same latent space, like we, we're supposed to ring a bell every time someone says, yeah, like, for example, you couldn't use like Excel and SD 1.5, because those have a different latent space, but like, uh, yeah, like SD 1.5 models, different ones. You could, you could do that.swyx [00:07:59]: There's some models that try to work in pixel space, right?Comfy [00:08:03]: Yeah. They're very slow. Of course. That's the problem. That that's the, the reason why stable diffusion actually became like popular, like, cause was because of the latent space.swyx [00:08:14]: Small and yeah. Because it used to be latent diffusion models and then they trained it up.Comfy [00:08:19]: Yeah. Cause a pixel pixel diffusion models are just too slow. So. Yeah.swyx [00:08:25]: Have you ever tried to talk to like, like stability, the latent diffusion guys, like, you know, Robin Rombach, that, that crew. Yeah.Comfy [00:08:32]: Well, I used to work at stability.swyx [00:08:34]: Oh, I actually didn't know. Yeah.Comfy [00:08:35]: I used to work at stability. I got, uh, I got hired, uh, in June, 2023.swyx [00:08:42]: Ah, that's the part of the story I didn't know about. Okay. Yeah.Comfy [00:08:46]: So the, the reason I was hired is because they were doing, uh, SDXL at the time and they were basically SDXL. I don't know if you remember it was a base model and then a refiner model. Basically they wanted to experiment, like chaining them together. And then, uh, they saw, oh, right. Oh, this, we can use this to do that. Well, let's hire that guy.swyx [00:09:10]: But they didn't, they didn't pursue it for like SD3. What do you mean? Like the SDXL approach. Yeah.Comfy [00:09:16]: The reason for that approach was because basically they had two models and then they wanted to publish both of them. So they, they trained one on. Lower time steps, which was the refiner model. And then they, the first one was trained normally. And then they went during their test, they realized, oh, like if we string these models together are like quality increases. So let's publish that. It worked. Yeah. But like right now, I don't think many people actually use the refiner anymore, even though it is actually a full diffusion model. Like you can use it on its own. And it's going to generate images. I don't think anyone, people have mostly forgotten about it. But, uh.Alessio [00:10:05]: Can we talk about models a little bit? So stable diffusion, obviously is the most known. I know flux has gotten a lot of traction. Are there any underrated models that people should use more or what's the state of the union?Comfy [00:10:17]: Well, the, the latest, uh, state of the art, at least, yeah, for images there's, uh, yeah, there's flux. There's also SD3.5. SD3.5 is two models. There's a, there's a small one, 2.5B and there's the bigger one, 8B. So it's, it's smaller than flux. So, and it's more, uh, creative in a way, but flux, yeah, flux is the best. People should give SD3.5 a try cause it's, uh, it's different. I won't say it's better. Well, it's better for some like specific use cases. Right. If you want some to make something more like creative, maybe SD3.5. If you want to make something more consistent and flux is probably better.swyx [00:11:06]: Do you ever consider supporting the closed source model APIs?Comfy [00:11:10]: Uh, well, they, we do support them as custom nodes. We actually have some, uh, official custom nodes from, uh, different. Ideogram.swyx [00:11:20]: Yeah. I guess DALI would have one. Yeah.Comfy [00:11:23]: That's, uh, it's just not, I'm not the person that handles that. Sure.swyx [00:11:28]: Sure. Quick question on, on SD. There's a lot of community discussion about the transition from SD1.5 to SD2 and then SD2 to SD3. People still like, you know, very loyal to the previous generations of SDs?Comfy [00:11:41]: Uh, yeah. SD1.5 then still has a lot of, a lot of users.swyx [00:11:46]: The last based model.Comfy [00:11:49]: Yeah. Then SD2 was mostly ignored. It wasn't, uh, it wasn't a big enough improvement over the previous one. Okay.swyx [00:11:58]: So SD1.5, SD3, flux and whatever else. SDXL. SDXL.Comfy [00:12:03]: That's the main one. Stable cascade. Stable cascade. That was a good model. But, uh, that's, uh, the problem with that one is, uh, it got, uh, like SD3 was announced one week after. Yeah.swyx [00:12:16]: It was like a weird release. Uh, what was it like inside of stability actually? I mean, statute of limitations. Yeah. The statute of limitations expired. You know, management has moved. So it's easier to talk about now. Yeah.Comfy [00:12:27]: And inside stability, actually that model was ready, uh, like three months before, but it got, uh, stuck in, uh, red teaming. So basically the product, if that model had released or was supposed to be released by the authors, then it would probably have gotten very popular since it's a, it's a step up from SDXL. But it got all of its momentum stolen. It got stolen by the SD3 announcement. So people kind of didn't develop anything on top of it, even though it's, uh, yeah. It was a good model, at least, uh, completely mostly ignored for some reason. Likeswyx [00:13:07]: I think the naming as well matters. It seemed like a branch off of the main, main tree of development. Yeah.Comfy [00:13:15]: Well, it was different researchers that did it. Yeah. Yeah. Very like, uh, good model. Like it's the Worcestershire authors. I don't know if I'm pronouncing it correctly. Yeah. Yeah. Yeah.swyx [00:13:28]: I actually met them in Vienna. Yeah.Comfy [00:13:30]: They worked at stability for a bit and they left right after the Cascade release.swyx [00:13:35]: This is Dustin, right? No. Uh, Dustin's SD3. Yeah.Comfy [00:13:38]: Dustin is a SD3 SDXL. That's, uh, Pablo and Dome. I think I'm pronouncing his name correctly. Yeah. Yeah. Yeah. Yeah. That's very good.swyx [00:13:51]: It seems like the community is very, they move very quickly. Yeah. Like when there's a new model out, they just drop whatever the current one is. And they just all move wholesale over. Like they don't really stay to explore the full capabilities. Like if, if the stable cascade was that good, they would have AB tested a bit more. Instead they're like, okay, SD3 is out. Let's go. You know?Comfy [00:14:11]: Well, I find the opposite actually. The community doesn't like, they only jump on a new model when there's a significant improvement. Like if there's a, only like a incremental improvement, which is what, uh, most of these models are going to have, especially if you, cause, uh, stay the same parameter count. Yeah. Like you're not going to get a massive improvement, uh, into like, unless there's something big that, that changes. So, uh. Yeah.swyx [00:14:41]: And how are they evaluating these improvements? Like, um, because there's, it's a whole chain of, you know, comfy workflows. Yeah. How does, how does one part of the chain actually affect the whole process?Comfy [00:14:52]: Are you talking on the model side specific?swyx [00:14:54]: Model specific, right? But like once you have your whole workflow based on a model, it's very hard to move.Comfy [00:15:01]: Uh, not, well, not really. Well, it depends on your, uh, depends on their specific kind of the workflow. Yeah.swyx [00:15:09]: So I do a lot of like text and image. Yeah.Comfy [00:15:12]: When you do change, like most workflows are kind of going to be complete. Yeah. It's just like, you might have to completely change your prompt completely change. Okay.swyx [00:15:24]: Well, I mean, then maybe the question is really about evals. Like what does the comfy community do for evals? Just, you know,Comfy [00:15:31]: Well, that they don't really do that. It's more like, oh, I think this image is nice. So that's, uh,swyx [00:15:38]: They just subscribe to Fofr AI and just see like, you know, what Fofr is doing. Yeah.Comfy [00:15:43]: Well, they just, they just generate like it. Like, I don't see anyone really doing it. Like, uh, at least on the comfy side, comfy users, they, it's more like, oh, generate images and see, oh, this one's nice. It's like, yeah, it's not, uh, like the, the more, uh, like, uh, scientific, uh, like, uh, like checking that's more on specifically on like model side. If, uh, yeah, but there is a lot of, uh, vibes also, cause it is a like, uh, artistic, uh, you can create a very good model that doesn't generate nice images. Cause most images on the internet are ugly. So if you, if that's like, if you just, oh, I have the best model at 10th giant, it's super smart. I created on all the, like I've trained on just all the images on the internet. The images are not going to look good. So yeah.Alessio [00:16:42]: Yeah.Comfy [00:16:43]: They're going to be very consistent. But yeah. People like, it's not going to be like the, the look that people are going to be expecting from, uh, from a model. So. Yeah.swyx [00:16:54]: Can we talk about LoRa's? Cause we thought we talked about models then like the next step is probably LoRa's. Before, I actually, I'm kind of curious how LoRa's entered the tool set of the image community because the LoRa paper was 2021. And then like, there was like other methods like textual inversion that was popular at the early SD stage. Yeah.Comfy [00:17:13]: I can't even explain the difference between that. Yeah. Textual inversions. That's basically what you're doing is you're, you're training a, cause well, yeah. Stable diffusion. You have the diffusion model, you have text encoder. So basically what you're doing is training a vector that you're going to pass to the text encoder. It's basically you're training a new word. Yeah.swyx [00:17:37]: It's a little bit like representation engineering now. Yeah.Comfy [00:17:40]: Yeah. Basically. Yeah. You're just, so yeah, if you know how like the text encoder works, basically you have, you take your, your words of your product, you convert those into tokens with the tokenizer and those are converted into vectors. Basically. Yeah. Each token represents a different vector. So each word presents a vector. And those, depending on your words, that's the list of vectors that get passed to the text encoder, which is just. Yeah. Yeah. I'm just a stack of, of attention. Like basically it's a very close to LLM architecture. Yeah. Yeah. So basically what you're doing is just training a new vector. We're saying, well, I have all these images and I want to know which word does that represent? And it's going to get like, you train this vector and then, and then when you use this vector, it hopefully generates. Like something similar to your images. Yeah.swyx [00:18:43]: I would say it's like surprisingly sample efficient in picking up the concept that you're trying to train it on. Yeah.Comfy [00:18:48]: Well, people have kind of stopped doing that even though back as like when I was at Stability, we, we actually did train internally some like textual versions on like T5 XXL actually worked pretty well. But for some reason, yeah, people don't use them. And also they might also work like, like, yeah, this is something and probably have to test, but maybe if you train a textual version, like on T5 XXL, it might also work with all the other models that use T5 XXL because same thing with like, like the textual inversions that, that were trained for SD 1.5, they also kind of work on SDXL because SDXL has the, has two text encoders. And one of them is the same as the, as the SD 1.5 CLIP-L. So those, they actually would, they don't work as strongly because they're only applied to one of the text encoders. But, and the same thing for SD3. SD3 has three text encoders. So it works. It's still, you can still use your textual version SD 1.5 on SD3, but it's just a lot weaker because now there's three text encoders. So it gets even more diluted. Yeah.swyx [00:20:05]: Do people experiment a lot on, just on the CLIP side, there's like Siglip, there's Blip, like do people experiment a lot on those?Comfy [00:20:12]: You can't really replace. Yeah.swyx [00:20:14]: Because they're trained together, right? Yeah.Comfy [00:20:15]: They're trained together. So you can't like, well, what I've seen people experimenting with is a long CLIP. So basically someone fine tuned the CLIP model to accept longer prompts.swyx [00:20:27]: Oh, it's kind of like long context fine tuning. Yeah.Comfy [00:20:31]: So, so like it's, it's actually supported in Core Comfy.swyx [00:20:35]: How long is long?Comfy [00:20:36]: Regular CLIP is 77 tokens. Yeah. Long CLIP is 256. Okay. So, but the hack that like you've, if you use stable diffusion 1.5, you've probably noticed, oh, it still works if I, if I use long prompts, prompts longer than 77 words. Well, that's because the hack is to just, well, you split, you split it up in chugs of 77, your whole big prompt. Let's say you, you give it like the massive text, like the Bible or something, and it would split it up in chugs of 77 and then just pass each one through the CLIP and then just cut anything together at the end. It's not ideal, but it actually works.swyx [00:21:26]: Like the positioning of the words really, really matters then, right? Like this is why order matters in prompts. Yeah.Comfy [00:21:33]: Yeah. Like it, it works, but it's, it's not ideal, but it's what people expect. Like if, if someone gives a huge prompt, they expect at least some of the concepts at the end to be like present in the image. But usually when they give long prompts, they, they don't, they like, they don't expect like detail, I think. So that's why it works very well.swyx [00:21:58]: And while we're on this topic, prompts waiting, negative comments. Negative prompting all, all sort of similar part of this layer of the stack. Yeah.Comfy [00:22:05]: The, the hack for that, which works on CLIP, like it, basically it's just for SD 1.5, well, for SD 1.5, the prompt waiting works well because CLIP L is a, is not a very deep model. So you have a very high correlation between, you have the input token, the index of the input token vector. And the output token, they're very, the concepts are very close, closely linked. So that means if you interpolate the vector from what, well, the, the way Comfy UI does it is it has, okay, you have the vector, you have an empty prompt. So you have a, a chunk, like a CLIP output for the empty prompt, and then you have the one for your prompt. And then it interpolates from that, depending on your prompt. Yeah.Comfy [00:23:07]: So that's how it, how it does prompt waiting. But this stops working the deeper your text encoder is. So on T5X itself, it doesn't work at all. So. Wow.swyx [00:23:20]: Is that a problem for people? I mean, cause I'm used to just move, moving up numbers. Probably not. Yeah.Comfy [00:23:25]: Well.swyx [00:23:26]: So you just use words to describe, right? Cause it's a bigger language model. Yeah.Comfy [00:23:30]: Yeah. So. Yeah. So honestly it might be good, but I haven't seen many complaints on Flux that it's not working. So, cause I guess people can sort of get around it with, with language. So. Yeah.swyx [00:23:46]: Yeah. And then coming back to LoRa's, now the, the popular way to, to customize models is LoRa's. And I saw you also support Locon and LoHa, which I've never heard of before.Comfy [00:23:56]: There's a bunch of, cause what, what the LoRa is essentially is. Instead of like, okay, you have your, your model and then you want to fine tune it. So instead of like, what you could do is you could fine tune the entire thing, but that's a bit heavy. So to speed things up and make things less heavy, what you can do is just fine tune some smaller weights, like basically two, two matrices that when you multiply like two low rank matrices and when you multiply them together, gives a, represents a difference between trained weights and your base weights. So by training those two smaller matrices, that's a lot less heavy. Yeah.Alessio [00:24:45]: And they're portable. So you're going to share them. Yeah. It's like easier. And also smaller.Comfy [00:24:49]: Yeah. That's the, how LoRa's work. So basically, so when, when inferencing you, you get an inference with them pretty efficiently, like how ComputeWrite does it. It just, when you use a LoRa, it just applies it straight on the weights so that there's only a small delay at the base, like before the sampling to when it applies the weights and then it just same speed as, as before. So for, for inference, it's, it's not that bad, but, and then you have, so basically all the LoRa types like LoHa, LoCon, everything, that's just different ways of representing that like. Basically, you can call it kind of like compression, even though it's not really compression, it's just different ways of represented, like just, okay, I want to train a different on the difference on the weights. What's the best way to represent that difference? There's the basic LoRa, which is just, oh, let's multiply these two matrices together. And then there's all the other ones, which are all different algorithms. So. Yeah.Alessio [00:25:57]: So let's talk about LoRa. Let's talk about what comfy UI actually is. I think most people have heard of it. Some people might've seen screenshots. I think fewer people have built very complex workflows. So when you started, automatic was like the super simple way. What were some of the choices that you made? So the node workflow, is there anything else that stands out as like, this was like a unique take on how to do image generation workflows?Comfy [00:26:22]: Well, I feel like, yeah, back then everyone was trying to make like easy to use interface. Yeah. So I'm like, well, everyone's trying to make an easy to use interface.swyx [00:26:32]: Let's make a hard to use interface.Comfy [00:26:37]: Like, so like, I like, I don't need to do that, everyone else doing it. So let me try something like, let me try to make a powerful interface that's not easy to use. So.swyx [00:26:52]: So like, yeah, there's a sort of node execution engine. Yeah. Yeah. And it actually lists, it has this really good list of features of things you prioritize, right? Like let me see, like sort of re-executing from, from any parts of the workflow that was changed, asynchronous queue system, smart memory management, like all this seems like a lot of engineering that. Yeah.Comfy [00:27:12]: There's a lot of engineering in the back end to make things, cause I was always focused on making things work locally very well. Cause that's cause I was using it locally. So everything. So there's a lot of, a lot of thought and working by getting everything to run as well as possible. So yeah. ConfUI is actually more of a back end, at least, well, not all the front ends getting a lot more development, but, but before, before it was, I was pretty much only focused on the backend. Yeah.swyx [00:27:50]: So v0.1 was only August this year. Yeah.Comfy [00:27:54]: With the new front end. Before there was no versioning. So yeah. Yeah. Yeah.swyx [00:27:57]: And so what was the big rewrite for the 0.1 and then the 1.0?Comfy [00:28:02]: Well, that's more on the front end side. That's cause before that it was just like the UI, what, cause when I first wrote it, I just, I said, okay, how can I make, like, I can do web development, but I don't like doing it. Like what's the easiest way I can slap a node interface on this. And then I found this library. Yeah. Like JavaScript library.swyx [00:28:26]: Live graph?Comfy [00:28:27]: Live graph.swyx [00:28:28]: Usually people will go for like react flow for like a flow builder. Yeah.Comfy [00:28:31]: But that seems like too complicated. So I didn't really want to spend time like developing the front end. So I'm like, well, oh, light graph. This has the whole node interface. So, okay. Let me just plug that into, to my backend.swyx [00:28:49]: I feel like if Streamlit or Gradio offered something that you would have used Streamlit or Gradio cause it's Python. Yeah.Comfy [00:28:54]: Yeah. Yeah. Yeah.Comfy [00:29:00]: Yeah.Comfy [00:29:14]: Yeah. logic and your backend logic and just sticks them together.swyx [00:29:20]: It's supposed to be easy for you guys. If you're a Python main, you know, I'm a JS main, right? Okay. If you're a Python main, it's supposed to be easy.Comfy [00:29:26]: Yeah, it's easy, but it makes your whole software a huge mess.swyx [00:29:30]: I see, I see. So you're mixing concerns instead of separating concerns?Comfy [00:29:34]: Well, it's because... Like frontend and backend. Frontend and backend should be well separated with a defined API. Like that's how you're supposed to do it. Smart people disagree. It just sticks everything together. It makes it easy to like a huge mess. And also it's, there's a lot of issues with Gradio. Like it's very good if all you want to do is just get like slap a quick interface on your, like to show off your ML project. Like that's what it's made for. Yeah. Like there's no problem using it. Like, oh, I have my, I have my code. I just wanted a quick interface on it. That's perfect. Like use Gradio. But if you want to make something that's like a real, like real software that will last a long time and will be easy to maintain, then I would avoid it. Yeah.swyx [00:30:32]: So your criticism is Streamlit and Gradio are the same. I mean, those are the same criticisms.Comfy [00:30:37]: Yeah, Streamlit I haven't used as much. Yeah, I just looked a bit.swyx [00:30:43]: Similar philosophy.Comfy [00:30:44]: Yeah, it's similar. It's just, it just seems to me like, okay, for quick, like AI demos, it's perfect.swyx [00:30:51]: Yeah. Going back to like the core tech, like asynchronous queues, slow re-execution, smart memory management, you know, anything that you were very proud of or was very hard to figure out?Comfy [00:31:00]: Yeah. The thing that's the biggest pain in the ass is probably the memory management. Yeah.swyx [00:31:05]: Were you just paging models in and out or? Yeah.Comfy [00:31:08]: Before it was just, okay, load the model, completely unload it. Then, okay, that, that works well when you, your model are small, but if your models are big and it takes sort of like, let's say someone has a, like a, a 4090, and the model size is 10 gigabytes, that can take a few seconds to like load and load, load and load, so you want to try to keep things like in memory, in the GPU memory as much as possible. What Comfy UI does right now is it. It tries to like estimate, okay, like, okay, you're going to sample this model, it's going to take probably this amount of memory, let's remove the models, like this amount of memory that's been loaded on the GPU and then just execute it. But so there's a fine line between just because try to remove the least amount of models that are already loaded. Because as fans, like Windows drivers, and one other problem is the NVIDIA driver on Windows by default, because there's a way to, there's an option to disable that feature, but by default it, like, if you start loading, you can overflow your GPU memory and then it's, the driver's going to automatically start paging to RAM. But the problem with that is it's, it makes everything extremely slow. So when you see people complaining, oh, this model, it works, but oh, s**t, it starts slowing down a lot, that's probably what's happening. So it's basically you have to just try to get, use as much memory as possible, but not too much, or else things start slowing down, or people get out of memory, and then just find, try to find that line where, oh, like the driver on Windows starts paging and stuff. Yeah. And the problem with PyTorch is it's, it's high levels, don't have that much fine-grained control over, like, specific memory stuff, so kind of have to leave, like, the memory freeing to, to Python and PyTorch, which is, can be annoying sometimes.swyx [00:33:32]: So, you know, I think one thing is, as a maintainer of this project, like, you're designing for a very wide surface area of compute, like, you even support CPUs.Comfy [00:33:42]: Yeah, well, that's... That's just, for PyTorch, PyTorch supports CPUs, so, yeah, it's just, that's not, that's not hard to support.swyx [00:33:50]: First of all, is there a market share estimate, like, is it, like, 70% NVIDIA, like, 30% AMD, and then, like, miscellaneous on Apple, Silicon, or whatever?Comfy [00:33:59]: For Comfy? Yeah. Yeah, and, yeah, I don't know the market share.swyx [00:34:03]: Can you guess?Comfy [00:34:04]: I think it's mostly NVIDIA. Right. Because, because AMD, the problem, like, AMD works horribly on Windows. Like, on Linux, it works fine. It's, it's lower than the price equivalent NVIDIA GPU, but it works, like, you can use it, you generate images, everything works. On Linux, on Windows, you might have a hard time, so, that's the problem, and most people, I think most people who bought AMD probably use Windows. They probably aren't going to switch to Linux, so... Yeah. So, until AMD actually, like, ports their, like, raw cam to, to Windows properly, and then there's actually PyTorch, I think they're, they're doing that, they're in the process of doing that, but, until they get it, they get a good, like, PyTorch raw cam build that works on Windows, it's, like, they're going to have a hard time. Yeah.Alessio [00:35:06]: We got to get George on it. Yeah. Well, he's trying to get Lisa Su to do it, but... Let's talk a bit about, like, the node design. So, unlike all the other text-to-image, you have a very, like, deep, so you have, like, a separate node for, like, clip and code, you have a separate node for, like, the case sampler, you have, like, all these nodes. Going back to, like, the making it easy versus making it hard, but, like, how much do people actually play with all the settings, you know? Kind of, like, how do you guide people to, like, hey, this is actually going to be very impactful versus this is maybe, like, less impactful, but we still want to expose it to you?Comfy [00:35:40]: Well, I try to... I try to expose, like, I try to expose everything or, but, yeah, at least for the, but for things, like, for example, for the samplers, like, there's, like, yeah, four different sampler nodes, which go in easiest to most advanced. So, yeah, if you go, like, the easy node, the regular sampler node, that's, you have just the basic settings. But if you use, like, the sampler advanced... If you use, like, the custom advanced node, that, that one you can actually, you'll see you have, like, different nodes.Alessio [00:36:19]: I'm looking it up now. Yeah. What are, like, the most impactful parameters that you use? So, it's, like, you know, you can have more, but, like, which ones, like, really make a difference?Comfy [00:36:30]: Yeah, they all do. They all have their own, like, they all, like, for example, yeah, steps. Usually you want steps, you want them to be as low as possible. But you want, if you're optimizing your workflow, you want to, you lower the steps until, like, the images start deteriorating too much. Because that, yeah, that's the number of steps you're running the diffusion process. So, if you want things to be faster, lower is better. But, yeah, CFG, that's more, you can kind of see that as the contrast of the image. Like, if your image looks too bursty. Then you can lower the CFG. So, yeah, CFG, that's how, yeah, that's how strongly the, like, the negative versus positive prompt. Because when you sample a diffusion model, it's basically a negative prompt. It's just, yeah, positive prediction minus negative prediction.swyx [00:37:32]: Contrastive loss. Yeah.Comfy [00:37:34]: It's positive minus negative, and the CFG does the multiplier. Yeah. Yeah. Yeah, so.Alessio [00:37:41]: What are, like, good resources to understand what the parameters do? I think most people start with automatic, and then they move over, and it's, like, snap, CFG, sampler, name, scheduler, denoise. Read it.Comfy [00:37:53]: But, honestly, well, it's more, it's something you should, like, try out yourself. I don't know, you don't necessarily need to know how it works to, like, what it does. Because even if you know, like, CFGO, it's, like, positive minus negative prompt. Yeah. So the only thing you know at CFG is if it's 1.0, then that means the negative prompt isn't applied. It also means sampling is two times faster. But, yeah. But other than that, it's more, like, you should really just see what it does to the images yourself, and you'll probably get a more intuitive understanding of what these things do.Alessio [00:38:34]: Any other nodes or things you want to shout out? Like, I know the animate diff IP adapter. Those are, like, some of the most popular ones. Yeah. What else comes to mind?Comfy [00:38:44]: Not nodes, but there's, like, what I like is when some people, sometimes they make things that use ComfyUI as their backend. Like, there's a plugin for Krita that uses ComfyUI as its backend. So you can use, like, all the models that work in Comfy in Krita. And I think I've tried it once. But I know a lot of people use it, and it's probably really nice, so.Alessio [00:39:15]: What's the craziest node that people have built, like, the most complicated?Comfy [00:39:21]: Craziest node? Like, yeah. I know some people have made, like, video games in Comfy with, like, stuff like that. So, like, someone, like, I remember, like, yeah, last, I think it was last year, someone made, like, a, like, Wolfenstein 3D in Comfy. Of course. And then one of the inputs was, oh, you can generate a texture, and then it changes the texture in the game. So you can plug it to, like, the workflow. And there's a lot of, if you look there, there's a lot of crazy things people do, so. Yeah.Alessio [00:39:59]: And now there's, like, a node register that people can use to, like, download nodes. Yeah.Comfy [00:40:04]: Like, well, there's always been the, like, the ComfyUI manager. Yeah. But we're trying to make this more, like, I don't know, official, like, with, yeah, with the node registry. Because before the node registry, the, like, okay, how did your custom node get into ComfyUI manager? That's the guy running it who, like, every day he searched GitHub for new custom nodes and added dev annually to his custom node manager. So we're trying to make it less effortless. So we're trying to make it less effortless for him, basically. Yeah.Alessio [00:40:40]: Yeah. But I was looking, I mean, there's, like, a YouTube download node. There's, like, this is almost like, you know, a data pipeline more than, like, an image generation thing at this point. It's, like, you can get data in, you can, like, apply filters to it, you can generate data out.Comfy [00:40:54]: Yeah. You can do a lot of different things. Yeah. So I'm thinking, I think what I did is I made it easy to make custom nodes. So I think that helped a lot. I think that helped a lot for, like, the ecosystem because it is very easy to just make a node. So, yeah, a bit too easy sometimes. Then we have the issue where there's a lot of custom node packs which share similar nodes. But, well, that's, yeah, something we're trying to solve by maybe bringing some of the functionality into the core. Yeah. Yeah. Yeah.Alessio [00:41:36]: And then there's, like, video. People can do video generation. Yeah.Comfy [00:41:40]: Video, that's, well, the first video model was, like, stable video diffusion, which was last, yeah, exactly last year, I think. Like, one year ago. But that wasn't a true video model. So it was...swyx [00:41:55]: It was, like, moving images? Yeah.Comfy [00:41:57]: I generated video. What I mean by that is it's, like, it's still 2D Latents. It's basically what I'm trying to do. So what they did is they took SD2, and then they added some temporal attention to it, and then trained it on videos and all. So it's kind of, like, animated, like, same idea, basically. Why I say it's not a true video model is that you still have, like, the 2D Latents. Like, a true video model, like Mochi, for example, would have 3D Latents. Mm-hmm.Alessio [00:42:32]: Which means you can, like, move through the space, basically. It's the difference. You're not just kind of, like, reorienting. Yeah.Comfy [00:42:39]: And it's also, well, it's also because you have a temporal VAE. Mm-hmm. Also, like, Mochi has a temporal VAE that compresses on, like, the temporal direction, also. So that's something you don't have with, like, yeah, animated diff and stable video diffusion. They only, like, compress spatially, not temporally. Mm-hmm. Right. So, yeah. That's why I call that, like, true video models. There's, yeah, there's actually a few of them, but the one I've implemented in comfy is Mochi, because that seems to be the best one so far. Yeah.swyx [00:43:15]: We had AJ come and speak at the stable diffusion meetup. The other open one I think I've seen is COG video. Yeah.Comfy [00:43:21]: COG video. Yeah. That one's, yeah, it also seems decent, but, yeah. Chinese, so we don't use it. No, it's fine. It's just, yeah, I could. Yeah. It's just that there's a, it's not the only one. There's also a few others, which I.swyx [00:43:36]: The rest are, like, closed source, right? Like, Cling. Yeah.Comfy [00:43:39]: Closed source, there's a bunch of them. But I mean, open. I've seen a few of them. Like, I can't remember their names, but there's COG videos, the big, the big one. Then there's also a few of them that released at the same time. There's one that released at the same time as SSD 3.5, same day, which is why I don't remember the name.swyx [00:44:02]: We should have a release schedule so we don't conflict on each of these things. Yeah.Comfy [00:44:06]: I think SD 3.5 and Mochi released on the same day. So everything else was kind of drowned, completely drowned out. So for some reason, lots of people picked that day to release their stuff.Comfy [00:44:21]: Yeah. Which is, well, shame for those. And I think Omnijet also released the same day, which also seems interesting. Yeah. Yeah.Alessio [00:44:30]: What's Comfy? So you are Comfy. And then there's like, comfy.org. I know we do a lot of things for, like, news research and those guys also have kind of like a more open source thing going on. How do you work? Like you mentioned, you mostly work on like, the core piece of it. And then what...Comfy [00:44:47]: Maybe I should fade it in because I, yeah, I feel like maybe, yeah, I only explain part of the story. Right. Yeah. Maybe I should explain the rest. So yeah. So yeah. Basically, January, that's when the first January 2023, January 16, 2023, that's when Amphi was first released to the public. Then, yeah, did a Reddit post about the area composition thing somewhere in, I don't remember exactly, maybe end of January, beginning of February. And then someone, a YouTuber, made a video about it, like Olivio, he made a video about Amphi in March 2023. I think that's when it was a real burst of attention. And by that time, I was continuing to develop it and it was getting, people were starting to use it more, which unfortunately meant that I had first written it to do like experiments, but then my time to do experiments went down. It started going down, because people were actually starting to use it then. Like, I had to, and I said, well, yeah, time to add all these features and stuff. Yeah, and then I got hired by Stability June, 2023. Then I made, basically, yeah, they hired me because they wanted the SD-XL. So I got the SD-XL working very well withітhe UI, because they were experimenting withámphi.house.com. Actually, the SDX, how the SDXL released worked is they released, for some reason, like they released the code first, but they didn't release the model checkpoint. So they released the code. And then, well, since the research was related to code, I released the code in Compute 2. And then the checkpoints were basically early access. People had to sign up and they only allowed a lot of people from edu emails. Like if you had an edu email, like they gave you access basically to the SDXL 0.9. And, well, that leaked. Right. Of course, because of course it's going to leak if you do that. Well, the only way people could easily use it was with Comfy. So, yeah, people started using. And then I fixed a few of the issues people had. So then the big 1.0 release happened. And, well, Comfy UI was the only way a lot of people could actually run it on their computers. Because it just like automatic was so like inefficient and bad that most people couldn't actually, like it just wouldn't work. Like because he did a quick implementation. So people were forced. To use Comfy UI, and that's how it became popular because people had no choice.swyx [00:47:55]: The growth hack.Comfy [00:47:56]: Yeah.swyx [00:47:56]: Yeah.Comfy [00:47:57]: Like everywhere, like people who didn't have the 4090, they had like, who had just regular GPUs, they didn't have a choice.Alessio [00:48:05]: So yeah, I got a 4070. So think of me. And so today, what's, is there like a core Comfy team or?Comfy [00:48:13]: Uh, yeah, well, right now, um, yeah, we are hiring. Okay. Actually, so right now core, like, um, the core core itself, it's, it's me. Uh, but because, uh, the reason where folks like all the focus has been mostly on the front end right now, because that's the thing that's been neglected for a long time. So, uh, so most of the focus right now is, uh, all on the front end, but we are, uh, yeah, we will soon get, uh, more people to like help me with the actual backend stuff. Yeah. So, no, I'm not going to say a hundred percent because that's why once the, once we have our V one release, which is because it'd be the package, come fee-wise with the nice interface and easy to install on windows and hopefully Mac. Uh, yeah. Yeah. Once we have that, uh, we're going to have to, lots of stuff to do on the backend side and also the front end side, but, uh.Alessio [00:49:14]: What's the release that I'm on the wait list. What's the timing?Comfy [00:49:18]: Uh, soon. Uh, soon. Yeah, I don't want to promise a release date. We do have a release date we're targeting, but I'm not sure if it's public. Yeah, and we're still going to continue doing the open source, making MPUI the best way to run stable infusion models. At least the open source side, it's going to be the best way to run models locally. But we will have a few things to make money from it, like cloud inference or that type of thing. And maybe some things for some enterprises.swyx [00:50:08]: I mean, a few questions on that. How do you feel about the other comfy startups?Comfy [00:50:11]: I mean, I think it's great. They're using your name. Yeah, well, it's better they use comfy than they use something else. Yeah, that's true. It's fine. We're going to try not to... We don't want to... We want people to use comfy. Like I said, it's better that people use comfy than something else. So as long as they use comfy, I think it helps the ecosystem. Because more people, even if they don't contribute directly, the fact that they are using comfy means that people are more likely to join the ecosystem. So, yeah.swyx [00:50:57]: And then would you ever do text?Comfy [00:50:59]: Yeah, well, you can already do text with some custom nodes. So, yeah, it's something we like. Yeah, it's something I've wanted to eventually add to core, but it's more like not a very... It's a very high priority. But because a lot of people use text for prompt enhancement and other things like that. So, yeah, it's just that my focus has always been on diffusion models. Yeah, unless some text diffusion model comes out.swyx [00:51:30]: Yeah, David Holtz is investing a lot in text diffusion.Comfy [00:51:34]: Yeah, well, if a good one comes out, then we'll probably implement it since it fits with the whole...swyx [00:51:39]: Yeah, I mean, I imagine it's going to be a close source to Midjourney. Yeah.Comfy [00:51:43]: Well, if an open one comes out, then I'll probably implement it.Alessio [00:51:54]: Cool, comfy. Thanks so much for coming on. This was fun. Bye. Get full access to Latent Space at www.latent.space/subscribe

Happy holidays! We'll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all all our LS supporters who helped fund the gorgeous venue and A/V production!For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver.The single most requested domain was computer vision, and we could think of no one better to help us recap 2024 than our friends at Roboflow, who was one of our earliest guests in 2023 and had one of this year's top episodes in 2024 again. Roboflow has since raised a $40m Series B!LinksTheir slides are here:All the trends and papers they picked:* Isaac Robinson* Sora (see our Video Diffusion pod) - extending diffusion from images to video* SAM 2: Segment Anything in Images and Videos (see our SAM2 pod) - extending prompted masks to full video object segmentation* DETR Dominancy: DETRs show Pareto improvement over YOLOs* RT-DETR: DETRs Beat YOLOs on Real-time Object Detection* LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection* D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement* Peter Robicheaux* MMVP (Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs)* * Florence 2 (Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks) * PalíGemma / PaliGemma 2* PaliGemma: A versatile 3B VLM for transfer* PaliGemma 2: A Family of Versatile VLMs for Transfer* AlMv2 (Multimodal Autoregressive Pre-training of Large Vision Encoders) * Vik Korrapati - MoondreamFull Talk on YouTubeWant more content like this? Like and subscribe to stay updated on our latest talks, interviews, and podcasts.Transcript/Timestamps[00:00:00] Intro[00:00:05] AI Charlie: welcome to Latent Space Live, our first mini conference held at NeurIPS 2024 in Vancouver. This is Charlie, your AI co host. When we were thinking of ways to add value to our academic conference coverage, we realized that there was a lack of good talks, just recapping the best of 2024, going domain by domain.[00:00:36] AI Charlie: We sent out a survey to the over 900 of you. who told us what you wanted, and then invited the best speakers in the Latent Space Network to cover each field. 200 of you joined us in person throughout the day, with over 2, 200 watching live online. Our second featured keynote is The Best of Vision 2024, with Peter Robichaud and Isaac [00:01:00] Robinson of Roboflow, with a special appearance from Vic Corrapati of Moondream.[00:01:05] AI Charlie: When we did a poll of our attendees, the highest interest domain of the year was vision. And so our first port of call was our friends at Roboflow. Joseph Nelson helped us kickstart our vision coverage in episode 7 last year, and this year came back as a guest host with Nikki Ravey of Meta to cover segment Anything 2.[00:01:25] AI Charlie: Roboflow have consistently been the leaders in open source vision models and tooling. With their SuperVision library recently eclipsing PyTorch's Vision library. And Roboflow Universe hosting hundreds of thousands of open source vision datasets and models. They have since announced a 40 million Series B led by Google Ventures.[00:01:46] AI Charlie: Woohoo.[00:01:48] Isaac's picks[00:01:48] Isaac Robinson: Hi, we're Isaac and Peter from Roboflow, and we're going to talk about the best papers of 2024 in computer vision. So, for us, we defined best as what made [00:02:00] the biggest shifts in the space. And to determine that, we looked at what are some major trends that happened and what papers most contributed to those trends.[00:02:09] Isaac Robinson: So I'm going to talk about a couple trends, Peter's going to talk about a trend, And then we're going to hand it off to Moondream. So, the trends that I'm interested in talking about are These are a major transition from models that run on per image basis to models that run using the same basic ideas on video.[00:02:28] Isaac Robinson: And then also how debtors are starting to take over the real time object detection scene from the YOLOs, which have been dominant for years.[00:02:37] Sora, OpenSora and Video Vision vs Generation[00:02:37] Isaac Robinson: So as a highlight we're going to talk about Sora, which from my perspective is the biggest paper of 2024, even though it came out in February. Is the what?[00:02:48] Isaac Robinson: Yeah. Yeah. So just it's a, SORA is just a a post. So I'm going to fill it in with details from replication efforts, including open SORA and related work, such as a stable [00:03:00] diffusion video. And then we're also going to talk about SAM2, which applies the SAM strategy to video. And then how debtors, These are the improvements in 2024 to debtors that are making them a Pareto improvement to YOLO based models.[00:03:15] Isaac Robinson: So to start this off, we're going to talk about the state of the art of video generation at the end of 2023, MagVIT MagVIT is a discrete token, video tokenizer akin to VQ, GAN, but applied to video sequences. And it actually outperforms state of the art handcrafted video compression frameworks.[00:03:38] Isaac Robinson: In terms of the bit rate versus human preference for quality and videos generated by autoregressing on these discrete tokens generate some pretty nice stuff, but up to like five seconds length and, you know, not super detailed. And then suddenly a few months later we have this, which when I saw it, it was totally mind blowing to me.[00:03:59] Isaac Robinson: 1080p, [00:04:00] a whole minute long. We've got light reflecting in puddles. That's reflective. Reminds me of those RTX demonstrations for next generation video games, such as Cyberpunk, but with better graphics. You can see some issues in the background if you look closely, but they're kind of, as with a lot of these models, the issues tend to be things that people aren't going to pay attention to unless they're looking for.[00:04:24] Isaac Robinson: In the same way that like six fingers on a hand. You're not going to notice is a giveaway unless you're looking for it. So yeah, as we said, SORA does not have a paper. So we're going to be filling it in with context from the rest of the computer vision scene attempting to replicate these efforts. So the first step, you have an LLM caption, a huge amount of videos.[00:04:48] Isaac Robinson: This, this is a trick that they introduced in Dolly 3, where they train a image captioning model to just generate very high quality captions for a huge corpus and then train a diffusion model [00:05:00] on that. Their Sora and their application efforts also show a bunch of other steps that are necessary for good video generation.[00:05:09] Isaac Robinson: Including filtering by aesthetic score and filtering by making sure the videos have enough motion. So they're not just like kind of the generators not learning to just generate static frames. So. Then we encode our video into a series of space time latents. Once again, SORA, very sparse in details.[00:05:29] Isaac Robinson: So the replication related works, OpenSORA actually uses a MAG VIT V2 itself to do this, but swapping out the discretization step with a classic VAE autoencoder framework. They show that there's a lot of benefit from getting the temporal compression, which makes a lot of sense as the Each sequential frames and videos have mostly redundant information.[00:05:53] Isaac Robinson: So by compressing against, compressing in the temporal space, you allow the latent to hold [00:06:00] a lot more semantic information while avoiding that duplicate. So, we've got our spacetime latents. Possibly via, there's some 3D VAE, presumably a MAG VATV2 and then you throw it into a diffusion transformer.[00:06:19] Isaac Robinson: So I think it's personally interesting to note that OpenSORA is using a MAG VATV2, which originally used an autoregressive transformer decoder to model the latent space, but is now using a diffusion diffusion transformer. So it's still a transformer happening. Just the question is like, is it?[00:06:37] Isaac Robinson: Parameterizing the stochastic differential equation is, or parameterizing a conditional distribution via autoregression. It's also it's also worth noting that most diffusion models today, the, the very high performance ones are switching away from the classic, like DDPM denoising diffusion probability modeling framework to rectified flows.[00:06:57] Isaac Robinson: Rectified flows have a very interesting property that as [00:07:00] they converge, they actually get closer to being able to be sampled with a single step. Which means that in practice, you can actually generate high quality samples much faster. Major problem of DDPM and related models for the past four years is just that they require many, many steps to generate high quality samples.[00:07:22] Isaac Robinson: So, and naturally, the third step is throwing lots of compute at the problem. So I didn't, I never figured out how to manage to get this video to loop, but we see very little compute, medium compute, lots of compute. This is so interesting because the the original diffusion transformer paper from Facebook actually showed that, in fact, the specific hyperparameters of the transformer didn't really matter that much.[00:07:48] Isaac Robinson: What mattered was that you were just increasing the amount of compute that the model had. So, I love how in the, once again, little blog posts, they don't even talk about [00:08:00] like the specific hyperparameters. They say, we're using a diffusion transformer, and we're just throwing more compute at it, and this is what happens.[00:08:08] Isaac Robinson: OpenSora shows similar results. The primary issue I think here is that no one else has 32x compute budget. So we end up with these we end up in the middle of the domain and most of the related work, which is still super, super cool. It's just a little disappointing considering the context. So I think this is a beautiful extension of the framework that was introduced in 22 and 23 for these very high quality per image generation and then extending that to videos.[00:08:39] Isaac Robinson: It's awesome. And it's GA as of Monday, except no one can seem to get access to it because they keep shutting down the login.[00:08:46] SAM and SAM2[00:08:46] Isaac Robinson: The next, so next paper I wanted to talk about is SAM. So we at Roboflow allow users to label data and train models on that data. Sam, for us, has saved our users 75 years of [00:09:00] labeling time.[00:09:00] Isaac Robinson: We are the, to the best of my knowledge, the largest SAM API that exists. We also, SAM also allows us to have our users train just pure bounding box regression models and use those to generate high quality masks which has the great side effect of requiring less training data to have a meaningful convergence.[00:09:20] Isaac Robinson: So most people are data limited in the real world. So anything that requires less data to get to a useful thing is that super useful. Most of our users actually run their object per frame object detectors on every frame in a video, or maybe not most, but many, many. And so Sam follows into this category of taking, Sam 2 falls into this category of taking something that really really works and applying it to a video which has the wonderful benefit of being plug and play with most of our Many of our users use cases.[00:09:53] Isaac Robinson: We're, we're still building out a sufficiently mature pipeline to take advantage of that, but it's, it's in the works. [00:10:00] So here we've got a great example. We can click on cells and then follow them. You even notice the cell goes away and comes back and we can still keep track of it which is very challenging for existing object trackers.[00:10:14] Isaac Robinson: High level overview of how SAM2 works. We there's a simple pipeline here where we can give, provide some type of prompt and it fills out the rest of the likely masks for that object throughout the rest of the video. So here we're giving a bounding box in the first frame, a set of positive negative points, or even just a simple mask.[00:10:36] Isaac Robinson: I'm going to assume people are somewhat familiar with SAM. So I'm going to just give a high level overview of how SAM works. You have an image encoder that runs on every frame. SAM two can be used on a single image, in which case the only difference between SAM two and SAM is that image encoder, which Sam used a standard VIT [00:11:00] Sam two replaced that with a hara hierarchical encoder, which gets approximately the same results, but leads to a six times faster inference, which is.[00:11:11] Isaac Robinson: Excellent, especially considering how in a trend of 23 was replacing the VAT with more efficient backbones. In the case where you're doing video segmentation, the difference is that you actually create a memory bank and you cross attend the features from the image encoder based on the memory bank.[00:11:31] Isaac Robinson: So the feature set that is created is essentially well, I'll go more into it in a couple of slides, but we take the features from the past couple frames, plus a set of object pointers and the set of prompts and use that to generate our new masks. Then we then fuse the new masks for this frame with the.[00:11:57] Isaac Robinson: Image features and add that to the memory bank. [00:12:00] It's, well, I'll say more in a minute. The just like SAM, the SAM2 actually uses a data engine to create its data set in that people are, they assembled a huge amount of reference data, used people to label some of it and train the model used the model to label more of it and asked people to refine the predictions of the model.[00:12:20] Isaac Robinson: And then ultimately the data set is just created from the engine Final output of the model on the reference data. It's very interesting. This paradigm is so interesting to me because it unifies a model in a dataset in a way that is very unique. It seems unlikely that another model could come in and have such a tight.[00:12:37] Isaac Robinson: So brief overview of how the memory bank works, the paper did not have a great visual, so I'm just, I'm going to fill in a bit more. So we take the last couple of frames from our video. And we take the last couple of frames from our video attend that, along with the set of prompts that we provided, they could come from the future, [00:13:00] they could come from anywhere in the video, as well as reference object pointers, saying, by the way, here's what we've found so far attending to the last few frames has the interesting benefit of allowing it to model complex object motion without actually[00:13:18] Isaac Robinson: By limiting the amount of frames that you attend to, you manage to keep the model running in real time. This is such an interesting topic for me because one would assume that attending to all of the frames is super essential, or having some type of summarization of all the frames is super essential for high performance.[00:13:35] Isaac Robinson: But we see in their later ablation that that actually is not the case. So here, just to make sure that there is some benchmarking happening, we just compared to some of the stuff that's came out prior, and indeed the SAM2 strategy does improve on the state of the art. This ablation deep in their dependencies was super interesting to me.[00:13:59] Isaac Robinson: [00:14:00] We see in section C, the number of memories. One would assume that increasing the count of memories would meaningfully increase performance. And we see that it has some impact, but not the type that you'd expect. And that it meaningfully decreases speed, which justifies, in my mind, just having this FIFO queue of memories.[00:14:20] Isaac Robinson: Although in the future, I'm super interested to see A more dedicated summarization of all of the last video, not just a stacking of the last frames. So that another extension of beautiful per frame work into the video domain.[00:14:42] Realtime detection: DETRs > YOLO[00:14:42] Isaac Robinson: The next trend I'm interested in talking about is this interesting at RoboFlow, we're super interested in training real time object detectors.[00:14:50] Isaac Robinson: Those are bread and butter. And so we're doing a lot to keep track of what is actually happening in that space. We are finally starting to see something change. So, [00:15:00] for years, YOLOs have been the dominant way of doing real time object detection, and we can see here that they've essentially stagnated.[00:15:08] Isaac Robinson: The performance between 10 and 11 is not meaningfully different, at least, you know, in this type of high level chart. And even from the last couple series, there's not. A major change so YOLOs have hit a plateau, debtors have not. So we can look here and see the YOLO series has this plateau. And then these RT debtor, LW debtor, and Define have meaningfully changed that plateau so that in fact, the best Define models are plus 4.[00:15:43] Isaac Robinson: 6 AP on Cocoa at the same latency. So three major steps to accomplish this. The first RT deditor, which is technically a 2023 paper preprint, but published officially in 24, so I'm going to include that. I hope that's okay. [00:16:00] That is showed that RT deditor showed that we could actually match or out speed YOLOs.[00:16:04] Isaac Robinson: And then LWdebtor showed that pre training is hugely effective on debtors and much less so on YOLOs. And then DeFine added the types of bells and whistles that we expect from these types, this, this arena. So the major improvements that RTdebtor shows was Taking the multi scale features that debtors typically pass into their encoder and decoupling them into a much more efficient transformer encoder.[00:16:30] Isaac Robinson: The transformer is of course, quadratic complexity. So decreasing the amount of stuff that you pass in at once is super helpful for increasing your runtime or increasing your throughput. So that change basically brought us up to yellow speed and then they do a hardcore analysis on. Benchmarking YOLOs, including the NMS step.[00:16:54] Isaac Robinson: Once you once you include the NMS in the latency calculation, you see that in fact, these debtors [00:17:00] are outperforming, at least this time, the the, the YOLOs that existed. Then LW debtor goes in and suggests that in fact, the frame, the huge boost here is from pre training. So, this is the define line, and this is the define line without pre training.[00:17:19] Isaac Robinson: It's within range, it's still an improvement over the YOLOs, but Really huge boost comes from the benefit of pre training. When YOLOx came out in 2021, they showed that they got much better results by having a much, much longer training time, but they found that when they did that, they actually did not benefit from pre training.[00:17:40] Isaac Robinson: So, you see in this graph from LWdebtor, in fact, YOLOs do have a real benefit from pre training, but it goes away as we increase the training time. Then, the debtors converge much faster. LWdebtor trains for only 50 epochs, RTdebtor is 60 epochs. So, one could assume that, in fact, [00:18:00] the entire extra gain from pre training is that you're not destroying your original weights.[00:18:06] Isaac Robinson: By relying on this long training cycle. And then LWdebtor also shows superior performance to our favorite data set, Roboflow 100 which means that they do better on the real world, not just on Cocoa. Then Define throws all the bells and whistles at it. Yellow models tend to have a lot of very specific complicated loss functions.[00:18:26] Isaac Robinson: This Define brings that into the debtor world and shows consistent improvement on a variety of debtor based frameworks. So bring these all together and we see that suddenly we have almost 60 AP on Cocoa while running in like 10 milliseconds. Huge, huge stuff. So we're spending a lot of time trying to build models that work better with less data and debtors are clearly becoming a promising step in that direction.[00:18:56] Isaac Robinson: The, what we're interested in seeing [00:19:00] from the debtors in this, this trend to next is. Codetter and the models that are currently sitting on the top of the leaderboard for large scale inference scale really well as you switch out the backbone. We're very interested in seeing and having people publish a paper, potentially us, on what happens if you take these real time ones and then throw a Swingy at it.[00:19:23] Isaac Robinson: Like, do we have a Pareto curve that extends from the real time domain all the way up to the super, super slow but high performance domain? We also want to see people benchmarking in RF100 more, because that type of data is what's relevant for most users. And we want to see more pre training, because pre training works now.[00:19:43] Isaac Robinson: It's super cool.[00:19:48] Peter's Picks[00:19:48] Peter Robicheaux: Alright, so, yeah, so in that theme one of the big things that we're focusing on is how do we get more out of our pre trained models. And one of the lenses to look at this is through sort of [00:20:00] this, this new requirement for like, how Fine grained visual details and your representations that are extracted from your foundation model.[00:20:08] Peter Robicheaux: So it's sort of a hook for this Oh, yeah, this is just a list of all the the papers that I'm going to mention I just want to make sure I set an actual paper so you can find it later[00:20:18] MMVP (Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs)[00:20:18] Peter Robicheaux: Yeah, so sort of the big hook here is that I make the claim that LLMs can't see if you go to if you go to Claude or ChatGPT you ask it to see this Watch and tell me what time it is, it fails, right?[00:20:34] Peter Robicheaux: And so you could say, like, maybe, maybe the Like, this is, like, a very classic test of an LLM, but you could say, Okay, maybe this, this image is, like, too zoomed out, And it just, like, it'll do better if we increase the resolution, And it has easier time finding these fine grained features, Like, where the watch hands are pointing.[00:20:53] Peter Robicheaux: Nodice. And you can say, okay, well, maybe the model just doesn't know how to tell time from knowing the position of the hands. But if you actually prompt [00:21:00] it textually, it's very easy for it to tell the time. So this to me is proof that these LLMs literally cannot see the position of the watch hands and it can't see those details.[00:21:08] Peter Robicheaux: So the question is sort of why? And for you anthropic heads out there, cloud fails too. So the, the, my first pick for best paper of 2024 Envision is this MMVP paper, which tries to investigate the Why do LLMs not have the ability to see fine grained details? And so, for instance, it comes up with a lot of images like this, where you ask it a question that seems very visually apparent to us, like, which way is the school bus facing?[00:21:32] Peter Robicheaux: And it gets it wrong, and then, of course, it makes up details to support its wrong claim. And so, the process by which it finds these images is sort of contained in its hypothesis for why it can't. See these details. So it hypothesizes that models that have been initialized with, with Clip as their vision encoder, they don't have fine grained details and the, the features extracted using Clip because Clip sort of doesn't need to find these fine grained [00:22:00] details to do its job correctly, which is just to match captions and images, right?[00:22:04] Peter Robicheaux: And sort of at a high level, even if ChatGPT wasn't initialized with Clip and wasn't trained contrastively at all. The vision encoder wasn't trained contrastively at all. Still, in order to do its job of capturing the image it could do a pretty good job without actually finding the exact position of all the objects and visual features in the image, right?[00:22:21] Peter Robicheaux: So This paper finds a set of difficult images for these types of models. And the way it does it is it looks for embeddings that are similar in clip space, but far in DynaV2 space. So DynaV2 is a foundation model that was trained self supervised purely on image data. And it kind of uses like some complex student teacher framework, but essentially, and like, it patches out like certain areas of the image or like crops with certain areas of the image and tries to make sure that those have consistent representations, which is a way for it to learn very fine grained visual features.[00:22:54] Peter Robicheaux: And so if you take things that are very close in clip space and very far in DynaV2 space, you get a set of images [00:23:00] that Basically, pairs of images that are hard for a chat GPT and other big language models to distinguish. So, if you then ask it questions about this image, well, as you can see from this chart, it's going to answer the same way for both images, right?[00:23:14] Peter Robicheaux: Because to, to, from the perspective of the vision encoder, they're the same image. And so if you ask a question like, how many eyes does this animal have? It answers the same for both. And like all these other models, including Lava do the same thing, right? And so this is the benchmark that they create, which is like finding clip, like clip line pairs, which is pairs of images that are similar in clip space and creating a data set of multiple choice questions based off of those.[00:23:39] Peter Robicheaux: And so how do these models do? Well, really bad. Lava, I think, So, so, chat2BT and Jim and I do a little bit better than random guessing, but, like, half of the performance of humans who find these problems to be very easy. Lava is, interestingly, extremely negatively correlated with this dataset. It does much, much, much, much worse [00:24:00] than random guessing, which means that this process has done a very good job of identifying hard images for, for Lava, specifically.[00:24:07] Peter Robicheaux: And that's because Lava is basically not trained for very long and is initialized from Clip, and so You would expect it to do poorly on this dataset. So, one of the proposed solutions that this paper attempts is by basically saying, Okay, well if clip features aren't enough, What if we train the visual encoder of the language model also on dyno features?[00:24:27] Peter Robicheaux: And so it, it proposes two different ways of doing this. One, additively which is basically interpolating between the two features, and then one is interleaving, which is just kind of like training one on the combination of both features. So there's this really interesting trend when you do the additive mixture of features.[00:24:45] Peter Robicheaux: So zero is all clip features and one is all DynaV2 features. So. It, as you, so I think it's helpful to look at the right most chart first, which is as you increase the number of DynaV2 features, your model does worse and worse and [00:25:00] worse on the actual language modeling task. And that's because DynaV2 features were trained completely from a self supervised manner and completely in image space.[00:25:08] Peter Robicheaux: It knows nothing about text. These features aren't really compatible with these text models. And so you can train an adapter all you want, but it seems that it's in such an alien language that it's like a very hard optimization for this. These models to solve. And so that kind of supports what's happening on the left, which is that, yeah, it gets better at answering these questions if as you include more dyna V two features up to a point, but then you, when you oversaturate, it completely loses its ability to like.[00:25:36] Peter Robicheaux: Answer language and do language tasks. So you can also see with the interleaving, like they essentially double the number of tokens that are going into these models and just train on both, and it still doesn't really solve the MMVP task. It gets Lava 1. 5 above random guessing by a little bit, but it's still not close to ChachiPT or, you know, Any like human performance, obviously.[00:25:59] Peter Robicheaux: [00:26:00] So clearly this proposed solution of just using DynaV2 features directly, isn't going to work. And basically what that means is that as a as a vision foundation model, DynaV2 is going to be insufficient for language tasks, right?[00:26:14] Florence 2 (Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks)[00:26:14] Peter Robicheaux: So my next pick for best paper of 2024 would be Florence 2, which tries to solve this problem by incorporating not only This dimension of spatial hierarchy, which is to say pixel level understanding, but also in making sure to include what they call semantic granularity, which ends up, the goal is basically to have features that are sufficient for finding objects in the image, so they're, they're, they have enough pixel information, but also can be talked about and can be reasoned about.[00:26:44] Peter Robicheaux: And that's on the semantic granularity axis. So here's an example of basically three different paradigms of labeling that they do. So they, they create a big dataset. One is text, which is just captioning. And you would expect a model that's trained [00:27:00] only on captioning to have similar performance like chat2BT and like not have spatial hierarchy, not have features that are meaningful at the pixel level.[00:27:08] Peter Robicheaux: And so they add another type, which is region text pairs, which is essentially either classifying a region or You're doing object detection or doing instance segmentation on that region or captioning that region. And then they have text phrased region annotations, which is essentially a triple. And basically, not only do you have a region that you've described, you also find it's like, It's placed in a descriptive paragraph about the image, which is basically trying to introduce even more like semantic understanding of these regions.[00:27:39] Peter Robicheaux: And so like, for instance, if you're saying a woman riding on the road, right, you have to know what a woman is and what the road is and that she's on top of it. And that's, that's basically composing a bunch of objects in this visual space, but also thinking about it semantically, right? And so the way that they do this is they take basically they just dump Features from a vision encoder [00:28:00] straight into a encoder decoder transformer.[00:28:03] Peter Robicheaux: And then they train a bunch of different tasks like object detection and so on as a language task. And I think that's one of the big things that we saw in 2024 is these, these vision language models operating in, on pixel space linguistically. So they introduced a bunch of new tokens to point to locations and[00:28:22] Peter Robicheaux: So how does it work? How does it actually do? We can see if you look at the graph on the right, which is using the, the Dino, the the Dino framework your, your pre trained Florence 2 models transfer very, very well. They get 60%, 60 percent map on Cocoa, which is like approaching state of the art and they train[00:28:42] Vik Korrapati: with, and they[00:28:43] Peter Robicheaux: train with a much more more efficiently.[00:28:47] Peter Robicheaux: So they, they converge a lot faster, which both of these things are pointing to the fact that they're actually leveraging their pre trained weights effectively. So where is it falling short? So these models, I forgot to mention, Florence is a 0. 2 [00:29:00] billion and a 0. 7 billion parameter count. So they're very, very small in terms of being a language model.[00:29:05] Peter Robicheaux: And I think that. This framework, you can see saturation. So, what this graph is showing is that if you train a Florence 2 model purely on the image level and region level annotations and not including the pixel level annotations, like this, segmentation, it actually performs better as an object detector.[00:29:25] Peter Robicheaux: And what that means is that it's not able to actually learn all the visual tasks that it's trying to learn because it doesn't have enough capacity.[00:29:32] PalíGemma / PaliGemma 2[00:29:32] Peter Robicheaux: So I'd like to see this paper explore larger model sizes, which brings us to our next big paper of 2024 or two papers. So PolyGemma came out earlier this year.[00:29:42] Peter Robicheaux: PolyGemma 2 was released, I think like a week or two ago. Oh, I forgot to mention, you can actually train You can, like, label text datasets on RoboFlow and you can train a Florence 2 model and you can actually train a PolyGemma 2 model on RoboFlow, which we got into the platform within, like, 14 hours of release, which I was really excited about.[00:29:59] Peter Robicheaux: So, anyway, so [00:30:00] PolyGemma 2, so PolyGemma is essentially doing the same thing, but instead of doing an encoder decoder, it just dumps everything into a decoder only transformer model. But it also introduced the concept of location tokens to point to objects in pixel space. PolyGemma 2, so PolyGemma uses Gemma as the language encoder, and it uses Gemma2B.[00:30:17] Peter Robicheaux: PolyGemma 2 introduces using multiple different sizes of language encoders. So, the way that they sort of get around having to do encoder decoder is they use the concept of prefix loss. Which basically means that when it's generating, tokens autoregressively, it's all those tokens in the prefix, which is like the image that it's looking at and like a description of the task that it's trying to do.[00:30:41] Peter Robicheaux: They're attending to each other fully, full attention. Which means that, you know, it can sort of. Find high level it's easier for the, the prefix to color, to color the output of the suffix and also to just find like features easily. So this is sort of [00:31:00] an example of like one of the tasks that was trained on, which is like, you describe the task in English and then you give it all these, like, You're asking for it to segment these two classes of objects, and then it finds, like, their locations using these tokens, and it finds their masks using some encoding of the masks into tokens.[00:31:24] Peter Robicheaux: And, yeah, so, one of my critiques, I guess, of PolyGemma 1, at least, is that You find that performance saturates as a pre trained model after only 300 million examples seen. So, what this graph is representing is each blue dot is a performance on some downstream task. And you can see that after seeing 300 million examples, It sort of does equally well on all of the downtrend tasks that they tried it on, which was a lot as 1 billion examples, which to me also kind of suggests a lack of capacity for this model.[00:31:58] Peter Robicheaux: PolyGemma2, [00:32:00] you can see the results on object detection. So these were transferred to to Coco. And you can see that this sort of also points to an increase in capacity being helpful to the model. You can see as. Both the resolution increases, and the parameter count of the language model increases, performance increases.[00:32:16] Peter Robicheaux: So resolution makes sense, obviously, it helps to find small images, or small objects in the image. But it also makes sense for another reason, which is that it kind of gives the model a thinking register, and it gives it more tokens to, like, process when making its predictions. But yeah, you could, you could say, oh, 43.[00:32:30] Peter Robicheaux: 6, that's not that great, like Florence 2 got 60. But this is not Training a dino or a debtor on top of this language or this image encoder. It's doing the raw language modeling task on Cocoa. So it doesn't have any of the bells and whistles. It doesn't have any of the fancy losses. It doesn't even have bipartite graph matching or anything like that.[00:32:52] Peter Robicheaux: Okay, the big result and one of the reasons that I was really excited about this paper is that they blow everything else away [00:33:00] on MMVP. I mean, 47. 3, sure, that's nowhere near human accuracy, which, again, is 94%, but for a, you know, a 2 billion language, 2 billion parameter language model to be chat2BT, that's quite the achievement.[00:33:12] Peter Robicheaux: And that sort of brings us to our final pick for paper of the year, which is AIMV2. So, AIMV2 sort of says, okay, Maybe this language model, like, maybe coming up with all these specific annotations to find features and with high fidelity and pixel space isn't actually necessary. And we can come up with an even simpler, more beautiful idea for combining you know, image tokens and pixel tokens in a way that's interfaceable for language tasks.[00:33:44] Peter Robicheaux: And this is nice because it can scale, you can come up with lots more data if you don't have to come up with all these annotations, right? So the way that it works. is it does something very, very similar to PolyGemo, where you have a vision encoder that dumps image tokens into a decoder only transformer.[00:33:59] Peter Robicheaux: But [00:34:00] the interesting thing is that it also autoregressively tries to learn the mean squared error of the image tokens. So instead of having to come up with fancy object detection or semantic, or segment, or segmentation labels, you can just try to reconstruct the image and have it learn fine grained features that way.[00:34:16] Peter Robicheaux: And it does this in kind of, I think, a beautiful way that's kind of compatible with the PolyGemma line of thinking, which is randomly sampling a prefix line of thinking Prefix length and using only this number of image tokens as the prefix. And so doing a similar thing with the causal. So the causal with prefix is the, the attention mask on the right.[00:34:35] Peter Robicheaux: So it's doing full block attention with some randomly sampled number of image tokens to then reconstruct the rest of the image and the downstream caption for that image. And so, This is the dataset that they train on. It's image or internet scale data, very high quality data created by the data filtering networks paper, essentially which is maybe The best clip data that exists.[00:34:59] Peter Robicheaux: [00:35:00] And we can see that this is finally a model that doesn't saturate. It's even at the highest parameter count, it's, it appears to be, oh, at the highest parameter account, it appears to be improving in performance with more and more samples seen. And so you can sort of think that. You know, if we just keep bumping the parameter count and increasing the example scene, which is the, the, the line of thinking for language models, then it'll keep getting better.[00:35:27] Peter Robicheaux: So how does it actually do at finding, oh, it also improves with resolution, which you would expect for a model that This is the ImageNet classification accuracy, but yeah, it does better if you increase the resolution, which means that it's actually leveraging and finding fine grained visual features.[00:35:44] Peter Robicheaux: And so how does that actually do compared to CLIP on Cocoa? Well, you can see that if you slap a transformer detection head on it, Entry now in Cocoa, it's just 60. 2, which is also within spitting distance of Soda, which means that it does a very good job of [00:36:00] finding visual features, but you could say, okay, well, wait a second.[00:36:03] Peter Robicheaux: Clip got to 59. 1, so. Like, how does this prove your claim at all? Because doesn't that mean like clip, which is known to be clip blind and do badly on MMVP, it's able to achieve a very high performance on fine, on this fine grained visual features task of object detection, well, they train on like, Tons of data.[00:36:24] Peter Robicheaux: They train on like objects, 365, Cocoa, Flickr and everything else. And so I think that this benchmark doesn't do a great job of selling how good of a pre trained model MV2 is. And we would like to see the performance on fewer data as examples and not trained to convergence on object detection. So seeing it in the real world on like a dataset, like RoboFlow 100, I think would be quite interesting.[00:36:48] Peter Robicheaux: And our, our, I guess our final, final pick for paper of 2024 would be Moondream. So introducing Vic to talk about that.[00:36:54] swyx: But overall, that was exactly what I was looking for. Like best of 2024, an amazing job. Yeah, you can, [00:37:00] if there's any other questions while Vic gets set up, like vision stuff,[00:37:07] swyx: yeah,[00:37:11] swyx: Vic, go ahead. Hi,[00:37:13] Vik Korrapati / Moondream[00:37:13] question: well, while we're getting set up, hi, over here, thanks for the really awesome talk. One of the things that's been weird and surprising is that the foundation model companies Even these MLMs, they're just like worse than RT Tether at detection still. Like, if you wanted to pay a bunch of money to auto label your detection dataset, If you gave it to OpenAI or Cloud, that would be like a big waste.[00:37:37] question: So I'm curious, just like, even Pali Gemma 2, like is worse. So, so I'm curious to hear your thoughts on like, how come, Nobody's cracked the code on like a generalist that really you know, beats a specialist model in computer vision like they have in in LLM land.[00:38:00][00:38:01] Isaac Robinson: Okay. It's a very, very interesting question. I think it depends on the specific domain. For image classification, it's basically there. In the, in AIMv2 showed, a simple attentional probe on the pre trained features gets like 90%, which is as well as anyone does. The, the, the, the bigger question, like, why isn't it transferring to object detection, especially like real time object detection.[00:38:25] Isaac Robinson: I think, in my mind, there are two answers. One is, object detection is really, really, really the architectures are super domain specific. You know, we see these, all these super, super complicated things, and it's not super easy to, to, to build something that just transfers naturally like that, whereas image classification, you know, clip pre training transfers super, super quickly.[00:38:48] Isaac Robinson: And the other thing is, until recently, the real time object detectors didn't even really benefit from pre training. Like, you see the YOLOs that are like, essentially saturated, showing very little [00:39:00] difference with pre training improvements, with using pre trained model at all. It's not surprising, necessarily, that People aren't looking at the effects of better and better pre training on real time detection.[00:39:12] Isaac Robinson: Maybe that'll change in the next year. Does that answer your question?[00:39:17] Peter Robicheaux: Can you guys hear me? Yeah, one thing I want to add is just like, or just to summarize, basically, is that like, Until 2024, you know, we haven't really seen a combination of transformer based object detectors and fancy losses, and PolyGemma suffers from the same problem, which is basically to say that these ResNet, or like the convolutional models, they have all these, like, extreme optimizations for doing object detection, but essentially, I think it's kind of been shown now that convolution models like just don't benefit from pre training and just don't like have the level of intelligence of transformer models.[00:39:56] swyx: Awesome. Hi,[00:39:59] Vik Korrapati: can [00:40:00] you hear me?[00:40:01] swyx: Cool. I hear you. See you. Are you sharing your screen?[00:40:04] Vik Korrapati: Hi. Might have forgotten to do that. Let me do[00:40:07] swyx: that. Sorry, should have done[00:40:08] Vik Korrapati: that.[00:40:17] swyx: Here's your screen. Oh, classic. You might have to quit zoom and restart. What? It's fine. We have a capture of your screen.[00:40:34] swyx: So let's get to it.[00:40:35] Vik Korrapati: Okay, easy enough.[00:40:49] Vik Korrapati: All right. Hi, everyone. My name is Vic. I've been working on Moondream for almost a year now. Like Shawn mentioned, I just went and looked and it turns out the first version I released December [00:41:00] 29, 2023. It's been a fascinating journey. So Moonbeam started off as a tiny vision language model. Since then, we've expanded scope a little bit to also try and build some tooling, client libraries, et cetera, to help people really deploy it.[00:41:13] Vik Korrapati: Unlike traditional large models that are focused at assistant type use cases, we're laser focused on building capabilities that developers can, sorry, it's yeah, we're basically focused on building capabilities that developers can use to build vision applications that can run anywhere. So, in a lot of cases for vision more so than for text, you really care about being able to run on the edge, run in real time, etc.[00:41:40] Vik Korrapati: So That's really important. We have we have different output modalities that we support. There's query where you can ask general English questions about an image and get back human like answers. There's captioning, which a lot of our users use for generating synthetic datasets to then train diffusion models and whatnot.[00:41:57] Vik Korrapati: We've done a lot of work to minimize those sessions there. [00:42:00] So that's. Use lot. We have open vocabulary object detection built in similar to a couple of more recent models like Palagem, et cetera, where rather than having to train a dedicated model, you can just say show me soccer balls in this image or show me if there are any deer in this image, it'll detect it.[00:42:14] Vik Korrapati: More recently, earlier this month, we released pointing capability where if all you're interested in is the center of an object you can just ask it to point out where that is. This is very useful when you're doing, you know, I automation type stuff. Let's see, LA we, we have two models out right now.[00:42:33] Vik Korrapati: There's a general purpose to be para model, which runs fair. Like it's, it's it's fine if you're running on server. It's good for our local Amma desktop friends and it can run on flagship, flagship mobile phones, but it never. so much for joining us today, and we'll see you in the [00:43:00] next one. Less memory even with our not yet fully optimized inference client.[00:43:06] Vik Korrapati: So the way we built our 0. 5b model was to start with the 2 billion parameter model and prune it while doing continual training to retain performance. We, our objective during the pruning was to preserve accuracy across a broad set of benchmarks. So the way we went about it was to estimate the importance of different components of the model, like attention heads, channels MLP rows and whatnot using basically a technique based on the gradient.[00:43:37] Vik Korrapati: I'm not sure how much people want to know details. We'll be writing a paper about this, but feel free to grab me if you have more questions. Then we iteratively prune a small chunk that will minimize loss and performance retrain the model to recover performance and bring it back. The 0. 5b we released is more of a proof of concept that this is possible.[00:43:54] Vik Korrapati: I think the thing that's really exciting about this is it makes it possible for for developers to build using the 2B param [00:44:00] model and just explore, build their application, and then once they're ready to deploy figure out what exactly they need out of the model and prune those capabilities into a smaller form factor that makes sense for their deployment target.[00:44:12] Vik Korrapati: So yeah, very excited about that. Let me talk to you folks a little bit about another problem I've been working on recently, which is similar to the clocks example we've been talking about. We had a customer reach out who was talking about, like, who had a bunch of gauges out in the field. This is very common in manufacturing and oil and gas, where you have a bunch of analog devices that you need to monitor.[00:44:34] Vik Korrapati: It's expensive to. And I was like, okay, let's have humans look at that and monitor stuff and make sure that the system gets shut down when the temperature goes over 80 or something. So I was like, yeah, this seems easy enough. Happy to, happy to help you distill that. Let's, let's get it going. Turns out our model couldn't do it at all.[00:44:51] Vik Korrapati: I went and looked at other open source models to see if I could just generate a bunch of data and learn from that. Did not work either. So I was like, let's look at what the folks with [00:45:00] hundreds of billions of dollars in market cap have to offer. And yeah, that doesn't work either. My hypothesis is that like the, the way these models are trained are using a large amount of image text data scraped from the internet.[00:45:15] Vik Korrapati: And that can be biased. In the case of gauges, most gauge images aren't gauges in the wild, they're product images. Detail images like these, where it's always set to zero. It's paired with an alt text that says something like GIVTO, pressure sensor, PSI, zero to 30 or something. And so the models are fairly good at picking up those details.[00:45:35] Vik Korrapati: It'll tell you that it's a pressure gauge. It'll tell you what the brand is, but it doesn't really learn to pay attention to the needle over there. And so, yeah, that's a gap we need to address. So naturally my mind goes to like, let's use synthetic data to, Solve this problem. That works, but it's problematic because it turned out we needed millions of synthetic gauge images to get to reasonable performance.[00:45:57] Vik Korrapati: And thinking about it, reading a gauge is like [00:46:00] not a one, like it's not a zero short process in our minds, right? Like if you had to tell me the reading in Celsius for this, Real world gauge. There's two dials on there. So first you have to figure out which one you have to be paying attention to, like the inner one or the outer one.[00:46:14] Vik Korrapati: You look at the tip of the needle, you look at what labels it's between, and you count how many and do some math to figure out what that probably is. So what happens if we just add that as a Chain of thought to give the model better understanding of the different sub, to allow the model to better learn the subtasks it needs to perform to accomplish this goal.[00:46:37] Vik Korrapati: So you can see in this example, this was actually generated by the latest version of our model. It's like, okay, Celsius is the inner scale. It's between 50 and 60. There's 10 ticks. So the second tick, it's a little debatable here, like there's a weird shadow situation going on, the dial is off, so I don't know what the ground truth is, but it works okay.[00:46:57] Vik Korrapati: There's points on there that are, the points [00:47:00] over there are actually grounded. I don't know if this is easy to see, but when I click on those, there's a little red dot that moves around on the image. The model actually has to predict where this points are, I was already trying to do this with bounding boxes, but then Malmo came out with pointing capabilities.[00:47:15] Vik Korrapati: And it's like pointing is a much better paradigm to to represent this. We see pretty good results. This one's actually for clock reading. I couldn't find our chart for gauge reading at the last minute. So the light. Blue chart is with our rounded chain of thought. This measures, we have, we built a clock reading benchmark about 500 images.[00:47:37] Vik Korrapati: This measures accuracy on that. You can see it's a lot more sample efficient when you're using the chain of thought to model. Another big benefit from this approach is like, you can kind of understand how the model is. it and how it's failing. So in this example, the actual correct reading is 54 Celsius, the model output [00:48:00] 56, not too bad but you can actually go and see where it messed up. Like it got a lot of these right, except instead of saying it was on the 7th tick, it actually predicted that it was the 8th tick and that's why it went with 56.[00:48:14] Vik Korrapati: So now that you know that this. Failing in this way, you can adjust how you're doing the chain of thought to maybe say like, actually count out each tick from 40, instead of just trying to say it's the eighth tick. Or you might say like, okay, I see that there's that middle thing, I'll count from there instead of all the way from 40.[00:48:31] Vik Korrapati: So helps a ton. The other thing I'm excited about is a few short prompting or test time training with this. Like if a customer has a specific gauge that like we're seeing minor errors on, they can give us a couple of examples where like, if it's miss detecting the. Needle, they can go in and correct that in the chain of thought.[00:48:49] Vik Korrapati: And hopefully that works the next time. Now, exciting approach, we only apply it to clocks and gauges. The real question is, is it going to generalize? Probably, like, there's some science [00:49:00] from text models that when you train on a broad number of tasks, it does generalize. And I'm seeing some science with our model as well.[00:49:05] Vik Korrapati: So, in addition to the image based chain of thought stuff, I also added some spelling based chain of thought to help it understand better understand OCR, I guess. I don't understand why everyone doesn't do this, by the way. Like, it's trivial benchmark question. It's Very, very easy to nail. But I also wanted to support it for stuff like license plate, partial matching, like, hey, does any license plate in this image start with WHA or whatever?[00:49:29] Vik Korrapati: So yeah, that sort of worked. All right, that, that ends my story about the gauges. If you think about what's going on over here it's interesting that like LLMs are showing enormous. Progress in reasoning, especially with the latest set of models that we've seen, but we're not really seeing, I have a feeling that VLMs are lagging behind, as we can see with these tasks that should be very simple for a human to do [00:50:00] that are very easy to find VLMs failing at.[00:50:04] Vik Korrapati: My hypothesis on why this is the case is because On the internet, there's a ton of data that talks about how to reason. There's books about how to solve problems. There's books critiquing the books about how to solve problems. But humans are just so good at perception that we never really talk about it.[00:50:20] Vik Korrapati: Like, maybe in art books where it's like, hey, to show that that mountain is further away, you need to desaturate it a bit or whatever. But the actual data on how to, like, look at images is, isn't really present. Also, the Data we have is kind of sketched. The best source of data we have is like image all text pairs on the internet and that's pretty low quality.[00:50:40] Vik Korrapati: So yeah, I, I think our solution here is really just we need to teach them how to operate on individual tasks and figure out how to scale that out. All right. Yep. So conclusion. At Moondream we're trying to build amazing PLMs that run everywhere. Very hard problem. Much work ahead, but we're making a ton of progress and I'm really excited [00:51:00] about If anyone wants to chat about more technical details about how we're doing this or interest in collaborating, please, please hit me up.[00:51:08] Isaac Robinson: Yeah,[00:51:09] swyx: like, I always, when people say, when people say multi modality, like, you know, I always think about vision as the first among equals in all the modalities. So, I really appreciate having the experts in the room. Get full access to Latent Space at www.latent.space/subscribe

“What are you going through?” This was one of the central animating questions in Simone Weil's thought that pushed her beyond philosophy into action. Weil believed that genuinely asking this question of the other, particularly the afflicted other, then truly listening and prayerfully attending, would move us toward an enactment of justice and love.Simone Weil believed that any suffering that can be ameliorated, should be.In this episode, Part 2 of our short series on How to Read Simone Weil, Cynthia Wallace (Associate Professor of English at St. Thomas More College at the University of Saskatchewan), and author of The Literary Afterlives of Simone Weil: Feminism, Justice, and the Challenge of Religion and Evan Rosa discuss the risky self-giving way of Simone Weil; her incredible literary influence, particularly on late 20th century feminist writers; the possibility of redemptive suffering; the morally complicated territory of self-sacrificial care and the way that has traditionally fallen to women and minorities; what it means to make room and practicing hospitality for the afflicted other; hunger; the beauty of vulnerability; and that grounding question for Simone Weil political ethics, “What are you going through?”We're in our second episode of a short series exploring How to Read Simone Weil. She's the author of Gravity and Grace, The Need for Roots, and Waiting for God—among many other essays, letters, and notes—and a deep and lasting influence that continues today.In this series, we're exploring Simone Weil the Mystic, Simone Weil the Activist, Simone Weil the Existentialist. And what we'll see is that so much of her spiritual, political, and philosophical life, are deeply unified in her way of being and living and dying.And on that note, before we go any further, I need to issue a correction from our previous episode in which I erroneously stated that Weil died in France. And I want to thank subscriber and listener Michael for writing and correcting me.Actually she died in England in 1943, having ambivalently fled France in 1942 when it was already under Nazi occupation—first to New York, then to London to work with the Free French movement and be closer to her home.And as I went back to fix my research, I began to realize just how important her place of death was. She died in a nursing home outside London. In Kent, Ashford to be precise. She had become very sick, and in August 1943 was moved to the Grosvenor Sanitorium.The manner and location of her death matter because it's arguable that her death by heart failure was not a self-starving suicide (as the coroner reported), but rather, her inability to eat was a complication rising from tuberculosis, combined with her practice of eating no more than the meager rations her fellow Frenchmen lived on under Nazi occupation.Her biographer Richard Rees wrote: "As for her death, whatever explanation one may give of it will amount in the end to saying that she died of love.In going back over the details of her death, I found a 1977 New York Times article by Elizabeth Hardwick, and I'll quote at length, as it offers a very fitting entry into this week's episode on her life of action, solidarity, and identification with and attention to the affliction of others.“Simone Weil, one of the most brilliant, and original minds of 20th century France, died at the age of 34 in a nursing home near London. The coroner issued a verdict of suicide, due to voluntary starvation—an action undertaken at least in part out of wish not to eat more than the rations given her compatriots in France under the German occupation. The year of her death was 1943.“The willed deprivation of her last period was not new; indeed refusal seems to have been a part of her character since infancy. What sets her apart from our current ascetics with their practice of transcendental meditation, diet, vegetarianism, ashram simplicities, yoga is that with them the deprivations and rigors‐are undergone for the pay‐off—for tranquility, for thinness, for the hope of a long life—or frequently, it seems, to fill the hole of emptiness so painful to the narcissist. With Simone Well it was entirely the opposite.“It was her wish, or her need, to undergo misery, affliction and deprivation because such had been the lot of mankind throughout history. Her wish was not to feel better, but to honor the sufferings of the lowest. Thus around 1935, when she was 25 years old, this woman of transcendent intellectual gifts and the widest learning, already very frail and suffering from severe headaches, was determined to undertake a year of work in a factory. The factories, the assembly lines, were then the modem equivalent of “slavery,” and she survived in her own words as “forever a slave.” What she went through at the factory “marked me in so lasting a manner that still today when any human being, whoever he may be and in whatever circumstances, speaks to me without brutality, I cannot help having the impression teat there must be a mistake....”[Her contemporary] “Simone de Beauvoir tells of meeting her when they were preparing for examinations to enter a prestigious private school. ‘She intrigued me because of her great reputation for intelligence and her bizarre outfits. ... A great famine had broken out in China, and I was told that when she heard the news she had wept. . . . I envied her for having a heart that could beat round the world.'“In London her health vanished, even though the great amount of writing she did right up to the time she went to the hospital must have come from those energies of the dying we do not understand—the energies of certain chosen dying ones, that is. Her behavior in the hospital, her refusal and by now her Inability to eat, vexed and bewildered the staff. Her sense of personal accountability to the world's suffering had reached farther than sense could follow.”Last week, we heard from Eric Springsted, one of the co-founders of the American Weil Society and author of Simone Weil for the Twenty-First Century.Next week, we'll explore Simone Weil the Existentialist—with philosopher Deborah Casewell, author of Monotheism & Existentialism and Co-Director of the Simone Weil Research Network in the UK.But this week we're looking at Simone Weil the Activist—her perspectives on redemptive suffering, her longing for justice, and her lasting influence on feminist writers. With me is Cynthia Wallace, associate professor of English at St. Thomas More College at the University of Saskatchewan, and author of The Literary Afterlives of Simone Weil: Feminism, Justice, and the Challenge of Religion.This is unique because it's learning how to read Simone Weil from some of her closest readers and those she influenced, including poets and writers such as Adrienne Rich, Denise Levertov, and Annie Dillard.About Cynthia WallaceCynthia Wallace is Associate Professor of English at St. Thomas More College at the University of Saskatchewan, and author of The Literary Afterlives of Simone Weil: Feminism, Justice, and the Challenge of Religion, as well as **Of Women Borne: A Literary Ethics of Suffering.About Simone WeilSimone Weil (1909–1943) was a French philosopher, mystic, and political activist. She's the author of Gravity and Grace, The Need for Roots, and Waiting for God—among many other essays, letters, and notes.Show NotesCynthia Wallace (Associate Professor of English at St. Thomas More College at the University of Saskatchewan), and author of The Literary Afterlives of Simone Weil: Feminism, Justice, and the Challenge of ReligionElizabeth Hardwick, “A woman of transcendent intellect who assumed the sufferings of humanity” (New York Times, Jan 23, 1977)Of Women Borne: A Literary Ethics of SufferingThe hard work of productive tensionSimone Weil on homework: “Reflections on the Right Use of School Studies with a View to the Love of God”Open, patient, receptive waiting in school studies — same skill as prayer“What are you going through?” Then you listen.Union organizerWaiting for God and Gravity & GraceVulnerability and tendernessJustice and Feminism, and “making room for the other”Denise Levertov's  ”Mass for the Day of St. Thomas Didymus”“Levertov wrote herself into Catholic conversion”“after pages and pages of struggle, she finally says: “So be it. Come rag of pungent quiverings,  dim star, let's try  if something human still can shield you, spark of remote light.”“And so she  argues that God isn't  particularly active in the world that we have, except for when we open ourselves to these chances of divine encounter.”“ Her imagination of God is different from how I think  a lot of contemporary Western   people think about an all powerful, all knowing God. Vae thinks about God as having done exactly what she's asking us to do, which is to make room for the other to exist in a way that requires us to give up power.”Exploiting self-emptying, particularly of women“Exposing the degree to which women have been disproportionately expected to sacrifice themselves.”Disproportionate self-sacrifice of women and in particular women of colorAdrienne Rich, Of Woman Borne: ethics that care for the otherThe distinction between suffering and afflictionAdrienne Rich's poem, “Hunger”Embodiment“ You have to follow both sides to the kind of limit of their capacity for thought, and then see what you find in that untidy both-and-ness.”Annie Dillard's expansive attentivenessPilgrim at Tinker Creek and attending to the world: “ to bear witness to the world in a way that tells the truth about what is brutal in the world, while also telling the truth about what is glorious  in the world.”“She's suspicious of our imaginations because she doesn't want us to distract  ourselves from contemplating the void.”Dillard, For the Time Being (1999) on natural evil and injusticeGoing from attention to creation“Reading writers writing about writing”Joan Didion: “I write entirely to find out what I'm thinking, what I'm looking at, what I see and what it means, what I want and what I fear.”Writing as both creation and discoveryFriendship and “ we let the other person be who they are instead of trying to make them who we want them to be.”The joy of creativity—pleasure and desire“ Simone Weil argues that suffering that can be ameliorated should be.”“ What is possible through shared practices of attention?”The beauty of vulnerability and the blossoms of fruit trees“What it takes for us to be fed”Need for ourselves, each other, and the divineProduction NotesThis podcast featured Cynthia WallaceEdited and Produced by Evan RosaHosted by Evan RosaProduction Assistance by Emily Brookfield, Liz Vukovic, and Kacie BarrettA Production of the Yale Center for Faith & Culture at Yale Divinity School https://faith.yale.edu/aboutSupport For the Life of the World podcast by giving to the Yale Center for Faith & Culture: https://faith.yale.edu/give

⁠13 ⁠But he that shall persevere to the end, he shall be saved.qui autem perseveraverit usque in finem, hic salvus erit. ⁠ 14 ⁠And this gospel of the kingdom, shall be preached in the whole world, for a testimony to all nations, and then shall the consummation come.Et praedicabitur hoc Evangelium regni in universo orbe, in testimonium omnibus gentibus : et tunc veniet consummatio. ⁠ 15 ⁠When therefore you shall see the abomination of desolation, which was spoken of by Daniel the prophet, standing in the holy place: he that readeth let him understand.Cum ergo videritis abominationem desolationis, quae dicta est a Daniele propheta, stantem in loco sancto, qui legit, intelligat : ⁠ 16 ⁠Then they that are in Judea, let them flee to the mountains:tunc qui in Judaea sunt, fugiant ad montes : ⁠ 17 ⁠And he that is on the housetop, let him not come down to take any thing out of his house:et qui in tecto, non descendat tollere aliquid de domo sua : ⁠ 18 ⁠And he that is in the field, let him not go back to take his coat.et qui in agro, non revertatur tollere tunicam suam. ⁠ 19 ⁠And woe to them that are with child, and that give suck in those days.Vae autem praegnantibus et nutrientibus in illis diebus! ⁠ 20 ⁠But pray that your flight be not in the winter, or on the sabbath.Orate autem ut non fiat fuga vestra in hieme, vel sabbato : ⁠ 21 ⁠For there shall be then great tribulation, such as hath not been from the beginning of the world until now, neither shall be.erit enim tunc tribulatio magna, qualis non fuit ab initio mundi usque modo, neque fiet. ⁠ 22 ⁠And unless those days had been shortened, no flesh should be saved: but for the sake of the elect those days shall be shortened.Et nisi breviati fuissent dies illi, non fieret salva omnis caro : sed propter electos breviabuntur dies illi. ⁠ 23 ⁠Then if any man shall say to you: Lo here is Christ, or there, do not believe him.Tunc si quis vobis dixerit : Ecce hic est Christus, aut illic : nolite credere. ⁠ 24 ⁠For there shall arise false Christs and false prophets, and shall shew great signs and wonders, insomuch as to deceive (if possible) even the elect.Surgent enim pseudochristi, et pseudoprophetae : et dabunt signa magna, et prodigia, ita ut in errorem inducantur ( si fieri potest) etiam electi. ⁠ 25 ⁠Behold I have told it to you, beforehand.Ecce praedixi vobis. ⁠ 26 ⁠If therefore they shall say to you: Behold he is in the desert, go ye not out: Behold he is in the closets, believe it not.Si ergo dixerint vobis : Ecce in deserto est, nolite exire; Ecce in penetralibus, nolite credere. ⁠ 27 ⁠For as lightning cometh out of the east, and appeareth even into the west: so shall the coming of the Son of man be.Sicut enim fulgur exit ab oriente, et paret usque in occidentem : ita erit et adventus Filii hominis. ⁠ 28 ⁠Wheresoever the body shall be, there shall the eagles also be gathered together.Ubicumque fuerit corpus, illic congregabuntur et aquilae. ⁠ 29 ⁠And immediately after the tribulation of those days, the sun shall be darkened and the moon shall not give her light, and the stars shall fall from heaven, and the powers of heaven shall be moved:Statim autem post tribulationem dierum illorum sol obscurabitur, et luna non dabit lumen suum, et stellae cadent de caelo, et virtutes caelorum commovebuntur : ⁠ 30 ⁠And then shall appear the sign of the Son of man in heaven: and then shall all tribes of the earth mourn: and they shall see the Son of man coming in the clouds of heaven with much power and majesty.et tunc parebit signum Filii hominis in caelo : et tunc plangent omnes tribus terrae : et videbunt Filium hominis venientem in nubibus caeli cum virtute multa et majestate. ⁠ 31 ⁠And he shall send his angels with a trumpet, and a great voice: and they shall gather together his elect from the four winds, from the farthest parts of the heavens to the utmost bounds of them.Et mittet angelos suos cum tuba, et voce magna : et congregabunt electos ejus a quatuor ventis, a summis caelorum usque ad terminos eorum. ⁠ 32 ⁠And from the fig tree learn a parable: When the branch thereof is now tender, and the leaves come forth, you know that summer is nigh.Ab arbore autem fici discite parabolam : cum jam ramus ejus tener fuerit, et folia nata, scitis quia prope est aestas : ⁠ 33 ⁠So you also, when you shall see all these things, know ye that it is nigh, even at the doors.ita et vos cum videritis haec omnia, scitote quia prope est, in januis. ⁠ 34 ⁠Amen I say to you, that this generation shall not pass, till all these things be done.Amen dico vobis, quia non praeteribit generatio haec, donec omnia haec fiant. ⁠ 35 ⁠Heaven and earth shall pass, but my words shall not pass.Caelum et terra transibunt, verba autem mea non praeteribunt. Jesus foretells the destruction of the world, and His second Advent, when all nations shall see the eternal Judge coming with power and majesty in the clouds of heaven.

13 But he that shall persevere to the end, he shall be saved.qui autem perseveraverit usque in finem, hic salvus erit.  14 And this gospel of the kingdom, shall be preached in the whole world, for a testimony to all nations, and then shall the consummation come.Et praedicabitur hoc Evangelium regni in universo orbe, in testimonium omnibus gentibus : et tunc veniet consummatio.  15 When therefore you shall see the abomination of desolation, which was spoken of by Daniel the prophet, standing in the holy place: he that readeth let him understand.Cum ergo videritis abominationem desolationis, quae dicta est a Daniele propheta, stantem in loco sancto, qui legit, intelligat :  16 Then they that are in Judea, let them flee to the mountains:tunc qui in Judaea sunt, fugiant ad montes :  17 And he that is on the housetop, let him not come down to take any thing out of his house:et qui in tecto, non descendat tollere aliquid de domo sua :  18 And he that is in the field, let him not go back to take his coat.et qui in agro, non revertatur tollere tunicam suam.  19 And woe to them that are with child, and that give suck in those days.Vae autem praegnantibus et nutrientibus in illis diebus!  20 But pray that your flight be not in the winter, or on the sabbath.Orate autem ut non fiat fuga vestra in hieme, vel sabbato :  21 For there shall be then great tribulation, such as hath not been from the beginning of the world until now, neither shall be.erit enim tunc tribulatio magna, qualis non fuit ab initio mundi usque modo, neque fiet.  22 And unless those days had been shortened, no flesh should be saved: but for the sake of the elect those days shall be shortened.Et nisi breviati fuissent dies illi, non fieret salva omnis caro : sed propter electos breviabuntur dies illi.  23 Then if any man shall say to you: Lo here is Christ, or there, do not believe him.Tunc si quis vobis dixerit : Ecce hic est Christus, aut illic : nolite credere.  24 For there shall arise false Christs and false prophets, and shall shew great signs and wonders, insomuch as to deceive (if possible) even the elect.Surgent enim pseudochristi, et pseudoprophetae : et dabunt signa magna, et prodigia, ita ut in errorem inducantur ( si fieri potest) etiam electi.  25 Behold I have told it to you, beforehand.Ecce praedixi vobis.  26 If therefore they shall say to you: Behold he is in the desert, go ye not out: Behold he is in the closets, believe it not.Si ergo dixerint vobis : Ecce in deserto est, nolite exire; Ecce in penetralibus, nolite credere.  27 For as lightning cometh out of the east, and appeareth even into the west: so shall the coming of the Son of man be.Sicut enim fulgur exit ab oriente, et paret usque in occidentem : ita erit et adventus Filii hominis.  28 Wheresoever the body shall be, there shall the eagles also be gathered together.Ubicumque fuerit corpus, illic congregabuntur et aquilae.  29 And immediately after the tribulation of those days, the sun shall be darkened and the moon shall not give her light, and the stars shall fall from heaven, and the powers of heaven shall be moved:Statim autem post tribulationem dierum illorum sol obscurabitur, et luna non dabit lumen suum, et stellae cadent de caelo, et virtutes caelorum commovebuntur :  30 And then shall appear the sign of the Son of man in heaven: and then shall all tribes of the earth mourn: and they shall see the Son of man coming in the clouds of heaven with much power and majesty.et tunc parebit signum Filii hominis in caelo : et tunc plangent omnes tribus terrae : et videbunt Filium hominis venientem in nubibus caeli cum virtute multa et majestate.  31 And he shall send his angels with a trumpet, and a great voice: and they shall gather together his elect from the four winds, from the farthest parts of the heavens to the utmost bounds of them.Et mittet angelos suos cum tuba, et voce magna : et congregabunt electos ejus a quatuor ventis, a summis caelorum usque ad terminos eorum.  32 And from the fig tree learn a parable: When the branch thereof is now tender, and the leaves come forth, you know that summer is nigh.Ab arbore autem fici discite parabolam : cum jam ramus ejus tener fuerit, et folia nata, scitis quia prope est aestas :  33 So you also, when you shall see all these things, know ye that it is nigh, even at the doors.ita et vos cum videritis haec omnia, scitote quia prope est, in januis.  34 Amen I say to you, that this generation shall not pass, till all these things be done.Amen dico vobis, quia non praeteribit generatio haec, donec omnia haec fiant.  35 Heaven and earth shall pass, but my words shall not pass.Caelum et terra transibunt, verba autem mea non praeteribunt. Jesus foretells the destruction of the world, and His second Advent, when all nations shall see the eternal Judge coming with power and majesty in the clouds of heaven.

 15 When therefore you shall see the abomination of desolation, which was spoken of by Daniel the prophet, standing in the holy place: he that readeth let him understand.Cum ergo videritis abominationem desolationis, quae dicta est a Daniele propheta, stantem in loco sancto, qui legit, intelligat :  16 Then they that are in Judea, let them flee to the mountains:tunc qui in Judaea sunt, fugiant ad montes :  17 And he that is on the housetop, let him not come down to take any thing out of his house:et qui in tecto, non descendat tollere aliquid de domo sua :  18 And he that is in the field, let him not go back to take his coat.et qui in agro, non revertatur tollere tunicam suam.  19 And woe to them that are with child, and that give suck in those days.Vae autem praegnantibus et nutrientibus in illis diebus!  20 But pray that your flight be not in the winter, or on the sabbath.Orate autem ut non fiat fuga vestra in hieme, vel sabbato :  21 For there shall be then great tribulation, such as hath not been from the beginning of the world until now, neither shall be.erit enim tunc tribulatio magna, qualis non fuit ab initio mundi usque modo, neque fiet.  22 And unless those days had been shortened, no flesh should be saved: but for the sake of the elect those days shall be shortened.Et nisi breviati fuissent dies illi, non fieret salva omnis caro : sed propter electos breviabuntur dies illi.  23 Then if any man shall say to you: Lo here is Christ, or there, do not believe him.Tunc si quis vobis dixerit : Ecce hic est Christus, aut illic : nolite credere.  24 For there shall arise false Christs and false prophets, and shall shew great signs and wonders, insomuch as to deceive (if possible) even the elect.Surgent enim pseudochristi, et pseudoprophetae : et dabunt signa magna, et prodigia, ita ut in errorem inducantur ( si fieri potest) etiam electi.  25 Behold I have told it to you, beforehand.Ecce praedixi vobis.  26 If therefore they shall say to you: Behold he is in the desert, go ye not out: Behold he is in the closets, believe it not.Si ergo dixerint vobis : Ecce in deserto est, nolite exire; Ecce in penetralibus, nolite credere.  27 For as lightning cometh out of the east, and appeareth even into the west: so shall the coming of the Son of man be.Sicut enim fulgur exit ab oriente, et paret usque in occidentem : ita erit et adventus Filii hominis.  28 Wheresoever the body shall be, there shall the eagles also be gathered together.Ubicumque fuerit corpus, illic congregabuntur et aquilae.  29 And immediately after the tribulation of those days, the sun shall be darkened and the moon shall not give her light, and the stars shall fall from heaven, and the powers of heaven shall be moved:Statim autem post tribulationem dierum illorum sol obscurabitur, et luna non dabit lumen suum, et stellae cadent de caelo, et virtutes caelorum commovebuntur :  30 And then shall appear the sign of the Son of man in heaven: and then shall all tribes of the earth mourn: and they shall see the Son of man coming in the clouds of heaven with much power and majesty.et tunc parebit signum Filii hominis in caelo : et tunc plangent omnes tribus terrae : et videbunt Filium hominis venientem in nubibus caeli cum virtute multa et majestate.  31 And he shall send his angels with a trumpet, and a great voice: and they shall gather together his elect from the four winds, from the farthest parts of the heavens to the utmost bounds of them.Et mittet angelos suos cum tuba, et voce magna : et congregabunt electos ejus a quatuor ventis, a summis caelorum usque ad terminos eorum.  32 And from the fig tree learn a parable: When the branch thereof is now tender, and the leaves come forth, you know that summer is nigh.Ab arbore autem fici discite parabolam : cum jam ramus ejus tener fuerit, et folia nata, scitis quia prope est aestas :  33 So you also, when you shall see all these things, know ye that it is nigh, even at the doors.ita et vos cum videritis haec omnia, scitote quia prope est, in januis.  34 Amen I say to you, that this generation shall not pass, till all these things be done.Amen dico vobis, quia non praeteribit generatio haec, donec omnia haec fiant.  35 Heaven and earth shall pass, but my words shall not pass.Caelum et terra transibunt, verba autem mea non praeteribunt

 13- But he that shall persevere to the end, he shall be savedqui autem perseveraverit usque in finem, hic salvus erit.  [Matthew 24:13]  14 And this gospel of the kingdom, shall be preached in the whole world, for a testimony to all nations, and then shall the consummation come.Et praedicabitur hoc Evangelium regni in universo orbe, in testimonium omnibus gentibus : et tunc veniet consummatio.  15 When therefore you shall see the abomination of desolation, which was spoken of by Daniel the prophet, standing in the holy place: he that readeth let him understand.Cum ergo videritis abominationem desolationis, quae dicta est a Daniele propheta, stantem in loco sancto, qui legit, intelligat :  16 Then they that are in Judea, let them flee to the mountains:tunc qui in Judaea sunt, fugiant ad montes :  17 And he that is on the housetop, let him not come down to take any thing out of his house:et qui in tecto, non descendat tollere aliquid de domo sua :  18 And he that is in the field, let him not go back to take his coat.et qui in agro, non revertatur tollere tunicam suam.  19 And woe to them that are with child, and that give suck in those days.Vae autem praegnantibus et nutrientibus in illis diebus!  20 But pray that your flight be not in the winter, or on the sabbath.Orate autem ut non fiat fuga vestra in hieme, vel sabbato :  21 For there shall be then great tribulation, such as hath not been from the beginning of the world until now, neither shall be.erit enim tunc tribulatio magna, qualis non fuit ab initio mundi usque modo, neque fiet.  22 And unless those days had been shortened, no flesh should be saved: but for the sake of the elect those days shall be shortened.Et nisi breviati fuissent dies illi, non fieret salva omnis caro : sed propter electos breviabuntur dies illi.  23 Then if any man shall say to you: Lo here is Christ, or there, do not believe him.Tunc si quis vobis dixerit : Ecce hic est Christus, aut illic : nolite credere.  24 For there shall arise false Christs and false prophets, and shall shew great signs and wonders, insomuch as to deceive (if possible) even the elect.Surgent enim pseudochristi, et pseudoprophetae : et dabunt signa magna, et prodigia, ita ut in errorem inducantur ( si fieri potest) etiam electi.  25 Behold I have told it to you, beforehand.Ecce praedixi vobis.  26 If therefore they shall say to you: Behold he is in the desert, go ye not out: Behold he is in the closets, believe it not.Si ergo dixerint vobis : Ecce in deserto est, nolite exire; Ecce in penetralibus, nolite credere.  27 For as lightning cometh out of the east, and appeareth even into the west: so shall the coming of the Son of man be.Sicut enim fulgur exit ab oriente, et paret usque in occidentem : ita erit et adventus Filii hominis.  28 Wheresoever the body shall be, there shall the eagles also be gathered together.Ubicumque fuerit corpus, illic congregabuntur et aquilae.  29 And immediately after the tribulation of those days, the sun shall be darkened and the moon shall not give her light, and the stars shall fall from heaven, and the powers of heaven shall be moved:Statim autem post tribulationem dierum illorum sol obscurabitur, et luna non dabit lumen suum, et stellae cadent de caelo, et virtutes caelorum commovebuntur :  30 And then shall appear the sign of the Son of man in heaven: and then shall all tribes of the earth mourn: and they shall see the Son of man coming in the clouds of heaven with much power and majesty.et tunc parebit signum Filii hominis in caelo : et tunc plangent omnes tribus terrae : et videbunt Filium hominis venientem in nubibus caeli cum virtute multa et majestate.  31 And he shall send his angels with a trumpet, and a great voice: and they shall gather together his elect from the four winds, from the farthest parts of the heavens to the utmost bounds of them.Et mittet angelos suos cum tuba, et voce magna : et congregabunt electos ejus a quatuor ventis, a summis caelorum usque ad terminos eorum.  32 And from the fig tree learn a parable: When the branch thereof is now tender, and the leaves come forth, you know that summer is nigh.Ab arbore autem fici discite parabolam : cum jam ramus ejus tener fuerit, et folia nata, scitis quia prope est aestas :  33 So you also, when you shall see all these things, know ye that it is nigh, even at the doors.ita et vos cum videritis haec omnia, scitote quia prope est, in januis.  34 Amen I say to you, that this generation shall not pass, till all these things be done.Amen dico vobis, quia non praeteribit generatio haec, donec omnia haec fiant.  35 Heaven and earth shall pass, but my words shall not pass.Caelum et terra transibunt, verba autem mea non praeteribunt.

Wacker Chemie AG Company Presentation: Key Takeaways Introduction to Wacker ChemieJoerg Hoffmann, Head of Investor Relations at Wacker Chemie AG, presented the company's business model, core segments, markets, and strategies. Wacker Chemie, a leader in specialty chemicals and advanced materials, reported €6.4 billion in sales and €824 million in EBITDA for 2023, employing over 16,000 people globally. The company's innovation, sustainability, and leadership across its four business segments—Silicones, Polymers, Polysilicon, and Biosolutions—were emphasized. Wacker's technologies enable advances in modern computing, artificial intelligence, and renewable energy. The company consistently ranks #1 or #2 globally in its segments, leveraging vertically integrated operations and global reach for competitive advantage. Business Segments Overview SiliconesWacker's largest segment contributes significantly to revenue, with 85% of silicone sales from high-margin specialities. Silicones are used in industries such as construction, healthcare, automotive, and renewable energy. Applications include adhesives, insulation, e-mobility components, and coatings. Wacker's integrated production process ensures high-quality, specialized products. PolymersThis segment focuses on sustainable, water-based binders and adhesives used in paints, construction materials, and food packaging. Key innovations include dispersible polymer powders (DPP) and vinyl acetate-ethylene (VAE)-based adhesives, addressing urbanization and green building trends by replacing plastics with environmentally friendly materials. PolysiliconWacker is a global leader in high-purity polysilicon for semiconductor and solar applications. Nearly half of all computer microchips use Wacker's polysilicon, which supports technologies like AI and renewable energy. The segment's focus on premium-quality products strengthens its leadership in semiconductor and solar markets. BiosolutionsThe fastest-growing segment, Biosolutions delivers advanced medicines, biopharmaceuticals, and nutraceuticals. It focuses on trends like personalized medicine with innovations such as mRNA and pDNA manufacturing. Wacker's proprietary technologies enable cost-efficient, scalable production for pharmaceutical partners. Sustainability and Integrated OperationsSustainability is core to Wacker's strategy, with a commitment to reducing CO2 emissions by 50% by 2030. Integrated production processes and renewable energy sources, such as hydropower, enhance efficiency. Over two-thirds of Wacker's portfolio addresses customer sustainability needs, such as water-based adhesives and CO2-neutral materials. The SustainaBalance® strategy focuses on creating products with low environmental footprints while enabling sustainable technologies. Applications include wind turbines, solar panels, and energy-efficient building solutions. Growth Drivers and Strategic Vision Wacker's strategy emphasizes value over volume, targeting €10 billion in sales by 2030, an EBITDA margin above 20%, and a ROCE of 2x the cost of capital. Growth drivers include urbanization, energy efficiency, and the shift to renewables. Innovation and customer-centricity are key priorities, supported by a global network of technical centers delivering localized solutions. ▶️ Other videos: Elevator Pitch: https://seat11a.com/investor-relations-elevator-pitch/ Company Presentation: https://seat11a.com/investor-relations-company-presentation/ Deep Dive Presentation: https://seat11a.com/investor-relations-deep-dive/ Financial Results Presentation: https://seat11a.com/investor-relations-financial-results/ ESG Presentation: https://seat11a.com/investor-relations-esg/ T&C This publication is for informational purposes only and does not constitute investment advice. By using this website, you agree to our terms and conditions outlined on www.seat11a.com/legal and www.seat11a.com/imprint.

1 At that hour the disciples came to Jesus, saying: Who thinkest thou is the greater in the kingdom of heaven?In illa hora accesserunt discipuli ad Jesum, dicentes : Quis, putas, major est in regno caelorum?  2 And Jesus calling unto him a little child, set him in the midst of them,Et advocans Jesus parvulum, statuit eum in medio eorum,  3 And said: Amen I say to you, unless you be converted, and become as little children, you shall not enter into the kingdom of heaven.et dixit : Amen dico vobis, nisi conversi fueritis, et efficiamini sicut parvuli, non intrabitis in regnum caelorum.  4 Whosoever therefore shall humble himself as this little child, he is the greater in the kingdom of heaven.Quicumque ergo humiliaverit se sicut parvulus iste, hic est major in regno caelorum.  5 And he that shall receive one such little child in my name, receiveth me.Et qui susceperit unum parvulum talem in nomine meo, me suscipit :  6 But he that shall scandalize one of these little ones that believe in me, it were better for him that a millstone should be hanged about his neck, and that he should be drowned in the depth of the sea.qui autem scandalizaverit unum de pusillis istis, qui in me credunt, expedit ei ut suspendatur mola asinaria in collo ejus, et demergatur in profundum maris.  7 Woe to the world because of scandals. For it must needs be that scandals come: but nevertheless woe to that man by whom the scandal cometh.Vae mundo a scandalis! Necesse est enim ut veniant scandala : verumtamen vae homini illi, per quem scandalum venit.  8 And if thy hand, or thy foot scandalize thee, cut it off, and cast it from thee. It is better for thee to go into life maimed or lame, than having two hands or two feet, to be cast into everlasting fire.Si autem manus tua, vel pes tuus scandalizat te, abscide eum, et projice abs te : bonum tibi est ad vitam ingredi debilem, vel claudum, quam duas manus vel duos pedes habentem mitti in ignem aeternum.  9 And if thy eye scandalize thee, pluck it out, and cast it from thee. It is better for thee having one eye to enter into life, than having two eyes to be cast into hell fire.Et si oculus tuus scandalizat te, erue eum, et projice abs te : bonum tibi est cum uno oculo in vitam intrare, quam duos oculos habentem mitti in gehennam ignis.  10 See that you despise not one of these little ones: for I say to you, that their angels in heaven always see the face of my Father who is in heaven.Videte ne contemnatis unum ex his pusillis : dico enim vobis, quia angeli eorum in caelis semper vident faciem Patris mei, qui in caelis est God's love for us was not satisfied with giving us His Son, Jesus, for our Redeemer, and Mary for our Advocate; He has been pleased to give us also His Angels to be our guardians: "He hath given His Angels charge over thee: to keep thee in all thy ways" (Ps. 90. 20). These holy spirits and princes of heaven are always present with us, and assist us in all our actions. And on this account, out of regard to our guardian angles, we ought carefully to refrain from every action which can displease them.

 1 At that hour the disciples came to Jesus, saying: Who thinkest thou is the greater in the kingdom of heaven?In illa hora accesserunt discipuli ad Jesum, dicentes : Quis, putas, major est in regno caelorum?  2 And Jesus calling unto him a little child, set him in the midst of them,Et advocans Jesus parvulum, statuit eum in medio eorum,  3 And said: Amen I say to you, unless you be converted, and become as little children, you shall not enter into the kingdom of heaven.et dixit : Amen dico vobis, nisi conversi fueritis, et efficiamini sicut parvuli, non intrabitis in regnum caelorum.  4 Whosoever therefore shall humble himself as this little child, he is the greater in the kingdom of heaven.Quicumque ergo humiliaverit se sicut parvulus iste, hic est major in regno caelorum.  5 And he that shall receive one such little child in my name, receiveth me.Et qui susceperit unum parvulum talem in nomine meo, me suscipit :  6 But he that shall scandalize one of these little ones that believe in me, it were better for him that a millstone should be hanged about his neck, and that he should be drowned in the depth of the sea.qui autem scandalizaverit unum de pusillis istis, qui in me credunt, expedit ei ut suspendatur mola asinaria in collo ejus, et demergatur in profundum maris.  7 Woe to the world because of scandals. For it must needs be that scandals come: but nevertheless woe to that man by whom the scandal cometh.Vae mundo a scandalis! Necesse est enim ut veniant scandala : verumtamen vae homini illi, per quem scandalum venit.  8 And if thy hand, or thy foot scandalize thee, cut it off, and cast it from thee. It is better for thee to go into life maimed or lame, than having two hands or two feet, to be cast into everlasting fire.Si autem manus tua, vel pes tuus scandalizat te, abscide eum, et projice abs te : bonum tibi est ad vitam ingredi debilem, vel claudum, quam duas manus vel duos pedes habentem mitti in ignem aeternum.  9 And if thy eye scandalize thee, pluck it out, and cast it from thee. It is better for thee having one eye to enter into life, than having two eyes to be cast into hell fire.Et si oculus tuus scandalizat te, erue eum, et projice abs te : bonum tibi est cum uno oculo in vitam intrare, quam duos oculos habentem mitti in gehennam ignis.  10 See that you despise not one of these little ones: for I say to you, that their angels in heaven always see the face of my Father who is in heaven.Videte ne contemnatis unum ex his pusillis : dico enim vobis, quia angeli eorum in caelis semper vident faciem Patris mei, qui in caelis est. Christ teaches us to practice humility, to beware of scandal, and to flee the occasions of sin, for our Guardian Angels see the face of God.

Vroeger was een 'Intel Inside'-sticker op je laptop of computer nog een pronkstuk. Tegenwoordig heeft 'Intel Inside' een heel andere betekenis. Het is een energie-slurpend, netstroom-verslaafd apparaat geworden. En dat is aan de cijfers van Intel te merken. Het krijgt het maar niet voor elkaar om weer relevant te worden in de chipsector. Concurrenten zien het als een kans om zich in de bezigheden van Intel te mengen. Chipdesigner Qualcomm wil het volledige bedrijf opkopen, en investeringsfonds Apollo biedt ook miljarden. Een andere overname lijkt ook steeds dichterbij. Commerzbank verzet zich koste wat het kost tegen een fusie met het Italiaanse UniCredit, en haalde zelfs de Duitse overheid erbij. Toch wist UniCredit ruim een vijfde van de aandelen van de Duitse bank te bemachtigen. En daar blijft het niet bij, want de Italianen hebben zich al opgegeven om nog meer belang in handen te krijgen. We hebben het ook nog over de autosector, en dan niet die van Duitsland. Want de Amerikaanse overheid ziet ook de concurrentie uit China en is bereid nogal ver te gaan om de eigen autobouwers te redden. De VS willen zo goed als alle auto's die uit China komen compleet gaan verbieden op Amerikaanse wegen. Ze hebben er alleen niet aan gedacht dat veel Amerikaanse autobouwers hun fabrieken in China hebben staan.See omnystudio.com/listener for privacy information.

Vae victis! This week, we explore the first entry in the "Legacy of Kain" series, Blood Omen. Submitted by our producer level patron Danny Acme. An action adventure vampiric romp of revenge and annihilation from 1996. Super Garbage Day DiscordSuper Garbage Day PatreonVanfernal's Retro Stream ShowOur Facebook GroupSupport the Show.Show Links: https://linktr.ee/supergarbagedayHosted by: B-Ross and Vanfernal Produced and edited by: B-Ross Email us at: supergarbageday@gmail.com

Episode 456 avec Aurélien et Benoît.La revue de presse :• C comme Canon (0:04:51) : Canon R5 II et R1. Nouveaux hybrides haut et moyen de gamme. (Source : canon.be) • C comme Cheval (0:14:54) : Connaissez vous Morgane Surquart ? Les dessous du cheval sur la Seine ! (Sources : francetvinfo.fr et mmprocess.fr) • E comme Energie (0:23:09) : IA moins gourmande. Sans multiplication matricielle. (Sources : ucsc.edu et developpez.com) • F comme Fou (0:31:46) : Tout Wikipedia en poche ! Internet-in-a-box : cela peut sauver des vies. (Sources : wikimedia.org, mdwiki.org et youtube.com) • G comme Google (0:39:13) : Est un monopole. Et enfreint la loi pour le rester. (Sources : msn.com, levif.be et theguardian.com) • T comme TikTok (0:45:33) : Fin de l'application Tiktok lite en France. Quand l'UE arrive à protéger ces concitoyens ! (Sources : lemonde.fr et 20minutes.fr) • T comme Transition (0:53:36) : Vers ZephyrOS. ARM cesse le développement de mbed, son OS IoT, Arduino bascule vers le RTOS Zephyr. (Sources : mbed.com et arduino.cc) • V comme VAE (1:00:14) : DJI veut concurencer les acteurs majeurs du VAE. Quand DJI s'attaque à l'industrie allemande des équipementiers. (Sources : caradisiac.com et dji.com) Retrouvez toutes nos informations, liens, versions du podcast via notre site : LesTechnos.be

Latest advances have achieved realistic virtual try-on (VTON) through localized garment inpainting using latent diffusion models, significantly enhancing consumers' online shopping experience. However, existing VTON technologies neglect the need for merchants to showcase garments comprehensively, including flexible control over garments, optional faces, poses, and scenes. To address this issue, we define a virtual dressing (VD) task focused on generating freely editable human images with fixed garments and optional conditions. Meanwhile, we design a comprehensive affinity metric index (CAMI) to evaluate the consistency between generated images and reference garments. Then, we propose IMAGDressing-v1, which incorporates a garment UNet that captures semantic features from CLIP and texture features from VAE. We present a hybrid attention module, including a frozen self-attention and a trainable cross-attention, to integrate garment features from the garment UNet into a frozen denoising UNet, ensuring users can control different scenes through text. IMAGDressing-v1 can be combined with other extension plugins, such as ControlNet and IP-Adapter, to enhance the diversity and controllability of generated images. Furthermore, to address the lack of data, we release the interactive garment pairing (IGPair) dataset, containing over 300,000 pairs of clothing and dressed images, and establish a standard pipeline for data assembly. Extensive experiments demonstrate that our IMAGDressing-v1 achieves state-of-the-art human image synthesis performance under various controlled conditions. The code and model will be available at https://github.com/muzishen/IMAGDressing. 2024: Fei Shen, Xin Jiang, Xin He, Hu Ye, Cong Wang, Xiaoyu Du, Zechao Li, Jinghui Tang https://arxiv.org/pdf/2407.12705v1

Vae en ondeursigtige algoritmes druk aanlyngebruikers in inligtingborrels in en versterk vooroordele insluitend rassisme, vrouehaat en diskriminasie van alle soorte. Dit is die mening van Antonio Guterres, sekretaris-generaal van die Verenigde Nasies. Hy sê tegnologie-reuse moet verantwoordelikheid aanvaar.

Una diferencia entre el sacerdote diocesano y el religioso: que este último tiene una regla (la de san Benito, la de santo Domingo, etc.), y el que no tiene reglamento necesita absolutamente la acción del Espíritu Santo. Vae soli!, dice la Escritura: no dudemos que no nos conviene andar sin la ayuda, la fortaleza, la luz del Espíritu Santo. Busquemos ser movidos desde dentro, para no ser manipulados por los objetos exteriores.

Christ teaches humility, to beware of scandal, and to flee the occasions of sin:  At that hour the disciples came to Jesus, saying: Who thinkest thou is the greater in the kingdom of heaven?In illa hora accesserunt discipuli ad Jesum, dicentes : Quis, putas, major est in regno caelorum?  2 And Jesus calling unto him a little child, set him in the midst of them,Et advocans Jesus parvulum, statuit eum in medio eorum,  3 And said: Amen I say to you, unless you be converted, and become as little children, you shall not enter into the kingdom of heaven.et dixit : Amen dico vobis, nisi conversi fueritis, et efficiamini sicut parvuli, non intrabitis in regnum caelorum.  4 Whosoever therefore shall humble himself as this little child, he is the greater in the kingdom of heaven.Quicumque ergo humiliaverit se sicut parvulus iste, hic est major in regno caelorum.  5 And he that shall receive one such little child in my name, receiveth me.Et qui susceperit unum parvulum talem in nomine meo, me suscipit :  6 But he that shall scandalize one of these little ones that believe in me, it were better for him that a millstone should be hanged about his neck, and that he should be drowned in the depth of the sea.qui autem scandalizaverit unum de pusillis istis, qui in me credunt, expedit ei ut suspendatur mola asinaria in collo ejus, et demergatur in profundum maris.  7 Woe to the world because of scandals. For it must needs be that scandals come: but nevertheless woe to that man by whom the scandal cometh.Vae mundo a scandalis! Necesse est enim ut veniant scandala : verumtamen vae homini illi, per quem scandalum venit.  8 And if thy hand, or thy foot scandalize thee, cut it off, and cast it from thee. It is better for thee to go into life maimed or lame, than having two hands or two feet, to be cast into everlasting fire.Si autem manus tua, vel pes tuus scandalizat te, abscide eum, et projice abs te : bonum tibi est ad vitam ingredi debilem, vel claudum, quam duas manus vel duos pedes habentem mitti in ignem aeternum.  9 And if thy eye scandalize thee, pluck it out, and cast it from thee. It is better for thee having one eye to enter into life, than having two eyes to be cast into hell fire.Et si oculus tuus scandalizat te, erue eum, et projice abs te : bonum tibi est cum uno oculo in vitam intrare, quam duos oculos habentem mitti in gehennam ignis.  10 See that you despise not one of these little ones: for I say to you, that their angels in heaven always see the face of my Father who is in heaven.Videte ne contemnatis unum ex his pusillis : dico enim vobis, quia angeli eorum in caelis semper vident faciem Patris mei, qui in caelis est. St John Baptist studied theology at the Sorbonne. Inspired by God to give a Christian education to the poor, he founded the Brother of the Christian Schools which soon spread throughout the world. In private life he treated himself with extreme rigour, and died full of merits and years in A.D 1719.

Vae soli!, dice el libro del Eclesiastés: ¡Pobre del que va solo! Pero nosotros nunca vamos así porque una Persona divina nos ha sido dada. Habita en nosotros el Espíritu Santo, y nos mueve con sus mociones y sus dones. Dentro de estos, pensemos en el superior, el de Sabiduría, que nos hace gustar las cosas de Dios. Podemos preguntarnos si el gozo de lo divino ha sido creciente en nuestra vida.

Things are quiet as the Opal Star helps escort the Goliath to the Nova Station outpost, allowing the party some time to work on personal projects and enjoy some downtime.We finally meet Lex, who informs Spectra that Vae might be a cybermancer. Spectra promises to help Vae get a hang of those abilities.Then they decide to pull the ghostship gambit to lure out the mystery ship that has been following them through a very empty stretch of space.Sound effects by: Zapsplat, Tabletop AudioTheme music by: https://chesterstudios.net/, Headshot by Nicholas JeudyCheck us out: https://pseudonymsocial.com/Follow us: https://twitter.com/PseudonymSocialSupport us: https://www.patreon.com/pseudonymsocial/Buy me a coffee: https://www.ko-fi.com/briannajean

Aujourd'hui Licka Sarr reçoit Miléna.De Gestionnaire RH à Assistante maternelle, Miléna nous parle de son parcours et de sa révélation pour la pédagogie infantile, du soin de la famille au point d'en faire un choix de carrière qui accapare jusqu'à sa vie familiale. En perpétuelle évolution, elle nous parle aussi de sa VAE d'EJE, Incroyable ! Avec autant de plaisir et pour la 2e fois, je vous laisse en compagnie de Miléna

Our next 2 big events are AI UX and the World's Fair. Join and apply to speak/sponsor!Due to timing issues we didn't have an interview episode to share with you this week, but not to worry, we have more than enough “weekend special” content in the backlog for you to get your Latent Space fix, whether you like thinking about the big picture, or learning more about the pod behind the scenes, or talking Groq and GPUs, or AI Leadership, or Personal AI. Enjoy!AI BreakdownThe indefatigable NLW had us back on his show for an update on the Four Wars, covering Sora, Suno, and the reshaped GPT-4 Class Landscape:and a longer segment on AI Engineering trends covering the future LLM landscape (Llama 3, GPT-5, Gemini 2, Claude 4), Open Source Models (Mistral, Grok), Apple and Meta's AI strategy, new chips (Groq, MatX) and the general movement from baby AGIs to vertical Agents:Thursday Nights in AIWe're also including swyx's interview with Josh Albrecht and Ali Rohde to reintroduce swyx and Latent Space to a general audience, and engage in some spicy Q&A:Dylan Patel on GroqWe hosted a private event with Dylan Patel of SemiAnalysis (our last pod here):Not all of it could be released so we just talked about our Groq estimates:Milind Naphade - Capital OneIn relation to conversations at NeurIPS and Nvidia GTC and upcoming at World's Fair, we also enjoyed chatting with Milind Naphade about his AI Leadership work at IBM, Cisco, Nvidia, and now leading the AI Foundations org at Capital One. We covered:* Milind's learnings from ~25 years in machine learning * His first paper citation was 24 years ago* Lessons from working with Jensen Huang for 6 years and being CTO of Metropolis * Thoughts on relevant AI research* GTC takeaways and what makes NVIDIA specialIf you'd like to work on building solutions rather than platform (as Milind put it), his Applied AI Research team at Capital One is hiring, which falls under the Capital One Tech team.Personal AI MeetupIt all started with a meme:Within days of each other, BEE, FRIEND, EmilyAI, Compass, Nox and LangFriend were all launching personal AI wearables and assistants. So we decided to put together a the world's first Personal AI meetup featuring creators and enthusiasts of wearables. The full video is live now, with full show notes within.Timestamps* [00:01:13] AI Breakdown Part 1* [00:02:20] Four Wars* [00:13:45] Sora* [00:15:12] Suno* [00:16:34] The GPT-4 Class Landscape* [00:17:03] Data War: Reddit x Google* [00:21:53] Gemini 1.5 vs Claude 3* [00:26:58] AI Breakdown Part 2* [00:27:33] Next Frontiers: Llama 3, GPT-5, Gemini 2, Claude 4* [00:31:11] Open Source Models - Mistral, Grok* [00:34:13] Apple MM1* [00:37:33] Meta's $800b AI rebrand* [00:39:20] AI Engineer landscape - from baby AGIs to vertical Agents* [00:47:28] Adept episode - Screen Multimodality* [00:48:54] Top Model Research from January Recap* [00:53:08] AI Wearables* [00:57:26] Groq vs Nvidia month - GPU Chip War* [01:00:31] Disagreements* [01:02:08] Summer 2024 Predictions* [01:04:18] Thursday Nights in AI - swyx* [01:33:34] Dylan Patel - Semianalysis + Latent Space Live Show* [01:34:58] GroqTranscript[00:00:00] swyx: Welcome to the Latent Space Podcast Weekend Edition. This is Charlie, your AI co host. Swyx and Alessio are off for the week, making more great content. We have exciting interviews coming up with Elicit, Chroma, Instructor, and our upcoming series on NSFW, Not Safe for Work AI. In today's episode, we're collating some of Swyx and Alessio's recent appearances, all in one place for you to find.[00:00:32] swyx: In part one, we have our first crossover pod of the year. In our listener survey, several folks asked for more thoughts from our two hosts. In 2023, Swyx and Alessio did crossover interviews with other great podcasts like the AI Breakdown, Practical AI, Cognitive Revolution, Thursday Eye, and Chinatalk, all of which you can find in the Latentspace About page.[00:00:56] swyx: NLW of the AI Breakdown asked us back to do a special on the 4Wars framework and the AI engineer scene. We love AI Breakdown as one of the best examples Daily podcasts to keep up on AI news, so we were especially excited to be back on Watch out and take[00:01:12] NLW: care[00:01:13] AI Breakdown Part 1[00:01:13] NLW: today on the AI breakdown. Part one of my conversation with Alessio and Swix from Latent Space.[00:01:19] NLW: All right, fellas, welcome back to the AI Breakdown. How are you doing? I'm good. Very good. With the last, the last time we did this show, we were like, oh yeah, let's do check ins like monthly about all the things that are going on and then. Of course, six months later, and, you know, the, the, the world has changed in a thousand ways.[00:01:36] NLW: It's just, it's too busy to even, to even think about podcasting sometimes. But I, I'm super excited to, to be chatting with you again. I think there's, there's a lot to, to catch up on, just to tap in, I think in the, you know, in the beginning of 2024. And, and so, you know, we're gonna talk today about just kind of a, a, a broad sense of where things are in some of the key battles in the AI space.[00:01:55] NLW: And then the, you know, one of the big things that I, that I'm really excited to have you guys on here for us to talk about where, sort of what patterns you're seeing and what people are actually trying to build, you know, where, where developers are spending their, their time and energy and, and, and any sort of, you know, trend trends there, but maybe let's start I guess by checking in on a framework that you guys actually introduced, which I've loved and I've cribbed a couple of times now, which is this sort of four wars of the, of the AI stack.[00:02:20] Four Wars[00:02:20] NLW: Because first, since I have you here, I'd love, I'd love to hear sort of like where that started gelling. And then and then maybe we can get into, I think a couple of them that are you know, particularly interesting, you know, in the, in light of[00:02:30] swyx: some recent news. Yeah, so maybe I'll take this one. So the four wars is a framework that I came up around trying to recap all of 2023.[00:02:38] swyx: I tried to write sort of monthly recap pieces. And I was trying to figure out like what makes one piece of news last longer than another or more significant than another. And I think it's basically always around battlegrounds. Wars are fought around limited resources. And I think probably the, you know, the most limited resource is talent, but the talent expresses itself in a number of areas.[00:03:01] swyx: And so I kind of focus on those, those areas at first. So the four wars that we cover are the data wars, the GPU rich, poor war, the multi modal war, And the RAG and Ops War. And I think you actually did a dedicated episode to that, so thanks for covering that. Yeah, yeah.[00:03:18] NLW: Not only did I do a dedicated episode, I actually used that.[00:03:22] NLW: I can't remember if I told you guys. I did give you big shoutouts. But I used it as a framework for a presentation at Intel's big AI event that they hold each year, where they have all their folks who are working on AI internally. And it totally resonated. That's amazing. Yeah, so, so, what got me thinking about it again is specifically this inflection news that we recently had, this sort of, you know, basically, I can't imagine that anyone who's listening wouldn't have thought about it, but, you know, inflection is a one of the big contenders, right?[00:03:53] NLW: I think probably most folks would have put them, you know, just a half step behind the anthropics and open AIs of the world in terms of labs, but it's a company that raised 1. 3 billion last year, less than a year ago. Reed Hoffman's a co founder Mustafa Suleyman, who's a co founder of DeepMind, you know, so it's like, this is not a a small startup, let's say, at least in terms of perception.[00:04:13] NLW: And then we get the news that basically most of the team, it appears, is heading over to Microsoft and they're bringing in a new CEO. And you know, I'm interested in, in, in kind of your take on how much that reflects, like hold aside, I guess, you know, all the other things that it might be about, how much it reflects this sort of the, the stark.[00:04:32] NLW: Brutal reality of competing in the frontier model space right now. And, you know, just the access to compute.[00:04:38] Alessio: There are a lot of things to say. So first of all, there's always somebody who's more GPU rich than you. So inflection is GPU rich by startup standard. I think about 22, 000 H100s, but obviously that pales compared to the, to Microsoft.[00:04:55] Alessio: The other thing is that this is probably good news, maybe for the startups. It's like being GPU rich, it's not enough. You know, like I think they were building something pretty interesting in, in pi of their own model of their own kind of experience. But at the end of the day, you're the interface that people consume as end users.[00:05:13] Alessio: It's really similar to a lot of the others. So and we'll tell, talk about GPT four and cloud tree and all this stuff. GPU poor, doing something. That the GPU rich are not interested in, you know we just had our AI center of excellence at Decibel and one of the AI leads at one of the big companies was like, Oh, we just saved 10 million and we use these models to do a translation, you know, and that's it.[00:05:39] Alessio: It's not, it's not a GI, it's just translation. So I think like the inflection part is maybe. A calling and a waking to a lot of startups then say, Hey, you know, trying to get as much capital as possible, try and get as many GPUs as possible. Good. But at the end of the day, it doesn't build a business, you know, and maybe what inflection I don't, I don't, again, I don't know the reasons behind the inflection choice, but if you say, I don't want to build my own company that has 1.[00:06:05] Alessio: 3 billion and I want to go do it at Microsoft, it's probably not a resources problem. It's more of strategic decisions that you're making as a company. So yeah, that was kind of my. I take on it.[00:06:15] swyx: Yeah, and I guess on my end, two things actually happened yesterday. It was a little bit quieter news, but Stability AI had some pretty major departures as well.[00:06:25] swyx: And you may not be considering it, but Stability is actually also a GPU rich company in the sense that they were the first new startup in this AI wave to brag about how many GPUs that they have. And you should join them. And you know, Imadis is definitely a GPU trader in some sense from his hedge fund days.[00:06:43] swyx: So Robin Rhombach and like the most of the Stable Diffusion 3 people left Stability yesterday as well. So yesterday was kind of like a big news day for the GPU rich companies, both Inflection and Stability having sort of wind taken out of their sails. I think, yes, it's a data point in the favor of Like, just because you have the GPUs doesn't mean you can, you automatically win.[00:07:03] swyx: And I think, you know, kind of I'll echo what Alessio says there. But in general also, like, I wonder if this is like the start of a major consolidation wave, just in terms of, you know, I think that there was a lot of funding last year and, you know, the business models have not been, you know, All of these things worked out very well.[00:07:19] swyx: Even inflection couldn't do it. And so I think maybe that's the start of a small consolidation wave. I don't think that's like a sign of AI winter. I keep looking for AI winter coming. I think this is kind of like a brief cold front. Yeah,[00:07:34] NLW: it's super interesting. So I think a bunch of A bunch of stuff here.[00:07:38] NLW: One is, I think, to both of your points, there, in some ways, there, there had already been this very clear demarcation between these two sides where, like, the GPU pores, to use the terminology, like, just weren't trying to compete on the same level, right? You know, the vast majority of people who have started something over the last year, year and a half, call it, were racing in a different direction.[00:07:59] NLW: They're trying to find some edge somewhere else. They're trying to build something different. If they're, if they're really trying to innovate, it's in different areas. And so it's really just this very small handful of companies that are in this like very, you know, it's like the coheres and jaspers of the world that like this sort of, you know, that are that are just sort of a little bit less resourced than, you know, than the other set that I think that this potentially even applies to, you know, everyone else that could clearly demarcate it into these two, two sides.[00:08:26] NLW: And there's only a small handful kind of sitting uncomfortably in the middle, perhaps. Let's, let's come back to the idea of, of the sort of AI winter or, you know, a cold front or anything like that. So this is something that I, I spent a lot of time kind of thinking about and noticing. And my perception is that The vast majority of the folks who are trying to call for sort of, you know, a trough of disillusionment or, you know, a shifting of the phase to that are people who either, A, just don't like AI for some other reason there's plenty of that, you know, people who are saying, You Look, they're doing way worse than they ever thought.[00:09:03] NLW: You know, there's a lot of sort of confirmation bias kind of thing going on. Or two, media that just needs a different narrative, right? Because they're sort of sick of, you know, telling the same story. Same thing happened last summer, when every every outlet jumped on the chat GPT at its first down month story to try to really like kind of hammer this idea that that the hype was too much.[00:09:24] NLW: Meanwhile, you have, you know, just ridiculous levels of investment from enterprises, you know, coming in. You have, you know, huge, huge volumes of, you know, individual behavior change happening. But I do think that there's nothing incoherent sort of to your point, Swyx, about that and the consolidation period.[00:09:42] NLW: Like, you know, if you look right now, for example, there are, I don't know, probably 25 or 30 credible, like, build your own chatbot. platforms that, you know, a lot of which have, you know, raised funding. There's no universe in which all of those are successful across, you know, even with a, even, even with a total addressable market of every enterprise in the world, you know, you're just inevitably going to see some amount of consolidation.[00:10:08] NLW: Same with, you know, image generators. There are, if you look at A16Z's top 50 consumer AI apps, just based on, you know, web traffic or whatever, they're still like I don't know, a half. Dozen or 10 or something, like, some ridiculous number of like, basically things like Midjourney or Dolly three. And it just seems impossible that we're gonna have that many, you know, ultimately as, as, as sort of, you know, going, going concerned.[00:10:33] NLW: So, I don't know. I, I, I think that the, there will be inevitable consolidation 'cause you know. It's, it's also what kind of like venture rounds are supposed to do. You're not, not everyone who gets a seed round is supposed to get to series A and not everyone who gets a series A is supposed to get to series B.[00:10:46] NLW: That's sort of the natural process. I think it will be tempting for a lot of people to try to infer from that something about AI not being as sort of big or as as sort of relevant as, as it was hyped up to be. But I, I kind of think that's the wrong conclusion to come to.[00:11:02] Alessio: I I would say the experimentation.[00:11:04] Alessio: Surface is a little smaller for image generation. So if you go back maybe six, nine months, most people will tell you, why would you build a coding assistant when like Copilot and GitHub are just going to win everything because they have the data and they have all the stuff. If you fast forward today, A lot of people use Cursor everybody was excited about the Devin release on Twitter.[00:11:26] Alessio: There are a lot of different ways of attacking the market that are not completion of code in the IDE. And even Cursors, like they evolved beyond single line to like chat, to do multi line edits and, and all that stuff. Image generation, I would say, yeah, as a, just as from what I've seen, like maybe the product innovation has slowed down at the UX level and people are improving the models.[00:11:50] Alessio: So the race is like, how do I make better images? It's not like, how do I make the user interact with the generation process better? And that gets tough, you know? It's hard to like really differentiate yourselves. So yeah, that's kind of how I look at it. And when we think about multimodality, maybe the reason why people got so excited about Sora is like, oh, this is like a completely It's not a better image model.[00:12:13] Alessio: This is like a completely different thing, you know? And I think the creative mind It's always looking for something that impacts the viewer in a different way, you know, like they really want something different versus the developer mind. It's like, Oh, I, I just, I have this like very annoying thing I want better.[00:12:32] Alessio: I have this like very specific use cases that I want to go after. So it's just different. And that's why you see a lot more companies in image generation. But I agree with you that. If you fast forward there, there's not going to be 10 of them, you know, it's probably going to be one or[00:12:46] swyx: two. Yeah, I mean, to me, that's why I call it a war.[00:12:49] swyx: Like, individually, all these companies can make a story that kind of makes sense, but collectively, they cannot all be true. Therefore, they all, there is some kind of fight over limited resources here. Yeah, so[00:12:59] NLW: it's interesting. We wandered very naturally into sort of another one of these wars, which is the multimodality kind of idea, which is, you know, basically a question of whether it's going to be these sort of big everything models that end up winning or whether, you know, you're going to have really specific things, you know, like something, you know, Dolly 3 inside of sort of OpenAI's larger models versus, you know, a mid journey or something like that.[00:13:24] NLW: And at first, you know, I was kind of thinking like, For most of the last, call it six months or whatever, it feels pretty definitively both and in some ways, you know, and that you're, you're seeing just like great innovation on sort of the everything models, but you're also seeing lots and lots happen at sort of the level of kind of individual use cases.[00:13:45] Sora[00:13:45] NLW: But then Sora comes along and just like obliterates what I think anyone thought you know, where we were when it comes to video generation. So how are you guys thinking about this particular battle or war at the moment?[00:13:59] swyx: Yeah, this was definitely a both and story, and Sora tipped things one way for me, in terms of scale being all you need.[00:14:08] swyx: And the benefit, I think, of having multiple models being developed under one roof. I think a lot of people aren't aware that Sora was developed in a similar fashion to Dolly 3. And Dolly3 had a very interesting paper out where they talked about how they sort of bootstrapped their synthetic data based on GPT 4 vision and GPT 4.[00:14:31] swyx: And, and it was just all, like, really interesting, like, if you work on one modality, it enables you to work on other modalities, and all that is more, is, is more interesting. I think it's beneficial if it's all in the same house, whereas the individual startups who don't, who sort of carve out a single modality and work on that, definitely won't have the state of the art stuff on helping them out on synthetic data.[00:14:52] swyx: So I do think like, The balance is tilted a little bit towards the God model companies, which is challenging for the, for the, for the the sort of dedicated modality companies. But everyone's carving out different niches. You know, like we just interviewed Suno ai, the sort of music model company, and, you know, I don't see opening AI pursuing music anytime soon.[00:15:12] Suno[00:15:12] swyx: Yeah,[00:15:13] NLW: Suno's been phenomenal to play with. Suno has done that rare thing where, which I think a number of different AI product categories have done, where people who don't consider themselves particularly interested in doing the thing that the AI enables find themselves doing a lot more of that thing, right?[00:15:29] NLW: Like, it'd be one thing if Just musicians were excited about Suno and using it but what you're seeing is tons of people who just like music all of a sudden like playing around with it and finding themselves kind of down that rabbit hole, which I think is kind of like the highest compliment that you can give one of these startups at the[00:15:45] swyx: early days of it.[00:15:46] swyx: Yeah, I, you know, I, I asked them directly, you know, in the interview about whether they consider themselves mid journey for music. And he had a more sort of nuanced response there, but I think that probably the business model is going to be very similar because he's focused on the B2C element of that. So yeah, I mean, you know, just to, just to tie back to the question about, you know, You know, large multi modality companies versus small dedicated modality companies.[00:16:10] swyx: Yeah, highly recommend people to read the Sora blog posts and then read through to the Dali blog posts because they, they strongly correlated themselves with the same synthetic data bootstrapping methods as Dali. And I think once you make those connections, you're like, oh, like it, it, it is beneficial to have multiple state of the art models in house that all help each other.[00:16:28] swyx: And these, this, that's the one thing that a dedicated modality company cannot do.[00:16:34] The GPT-4 Class Landscape[00:16:34] NLW: So I, I wanna jump, I wanna kind of build off that and, and move into the sort of like updated GPT-4 class landscape. 'cause that's obviously been another big change over the last couple months. But for the sake of completeness, is there anything that's worth touching on with with sort of the quality?[00:16:46] NLW: Quality data or sort of a rag ops wars just in terms of, you know, anything that's changed, I guess, for you fundamentally in the last couple of months about where those things stand.[00:16:55] swyx: So I think we're going to talk about rag for the Gemini and Clouds discussion later. And so maybe briefly discuss the data piece.[00:17:03] Data War: Reddit x Google[00:17:03] swyx: I think maybe the only new thing was this Reddit deal with Google for like a 60 million dollar deal just ahead of their IPO, very conveniently turning Reddit into a AI data company. Also, very, very interestingly, a non exclusive deal, meaning that Reddit can resell that data to someone else. And it probably does become table stakes.[00:17:23] swyx: A lot of people don't know, but a lot of the web text dataset that originally started for GPT 1, 2, and 3 was actually scraped from GitHub. from Reddit at least the sort of vote scores. And I think, I think that's a, that's a very valuable piece of information. So like, yeah, I think people are figuring out how to pay for data.[00:17:40] swyx: People are suing each other over data. This, this, this war is, you know, definitely very, very much heating up. And I don't think, I don't see it getting any less intense. I, you know, next to GPUs, data is going to be the most expensive thing in, in a model stack company. And. You know, a lot of people are resorting to synthetic versions of it, which may or may not be kosher based on how far along or how commercially blessed the, the forms of creating that synthetic data are.[00:18:11] swyx: I don't know if Alessio, you have any other interactions with like Data source companies, but that's my two cents.[00:18:17] Alessio: Yeah yeah, I actually saw Quentin Anthony from Luther. ai at GTC this week. He's also been working on this. I saw Technium. He's also been working on the data side. I think especially in open source, people are like, okay, if everybody is putting the gates up, so to speak, to the data we need to make it easier for people that don't have 50 million a year to get access to good data sets.[00:18:38] Alessio: And Jensen, at his keynote, he did talk about synthetic data a little bit. So I think that's something that we'll definitely hear more and more of in the enterprise, which never bodes well, because then all the, all the people with the data are like, Oh, the enterprises want to pay now? Let me, let me put a pay here stripe link so that they can give me 50 million.[00:18:57] Alessio: But it worked for Reddit. I think the stock is up. 40 percent today after opening. So yeah, I don't know if it's all about the Google deal, but it's obviously Reddit has been one of those companies where, hey, you got all this like great community, but like, how are you going to make money? And like, they try to sell the avatars.[00:19:15] Alessio: I don't know if that it's a great business for them. The, the data part sounds as an investor, you know, the data part sounds a lot more interesting than, than consumer[00:19:25] swyx: cosmetics. Yeah, so I think, you know there's more questions around data you know, I think a lot of people are talking about the interview that Mira Murady did with the Wall Street Journal, where she, like, just basically had no, had no good answer for where they got the data for Sora.[00:19:39] swyx: I, I think this is where, you know, there's, it's in nobody's interest to be transparent about data, and it's, it's kind of sad for the state of ML and the state of AI research but it is what it is. We, we have to figure this out as a society, just like we did for music and music sharing. You know, in, in sort of the Napster to Spotify transition, and that might take us a decade.[00:19:59] swyx: Yeah, I[00:20:00] NLW: do. I, I agree. I think, I think that you're right to identify it, not just as that sort of technical problem, but as one where society has to have a debate with itself. Because I think that there's, if you rationally within it, there's Great kind of points on all side, not to be the sort of, you know, person who sits in the middle constantly, but it's why I think a lot of these legal decisions are going to be really important because, you know, the job of judges is to listen to all this stuff and try to come to things and then have other judges disagree.[00:20:24] NLW: And, you know, and have the rest of us all debate at the same time. By the way, as a total aside, I feel like the synthetic data right now is like eggs in the 80s and 90s. Like, whether they're good for you or bad for you, like, you know, we, we get one study that's like synthetic data, you know, there's model collapse.[00:20:42] NLW: And then we have like a hint that llama, you know, to the most high performance version of it, which was one they didn't release was trained on synthetic data. So maybe it's good. It's like, I just feel like every, every other week I'm seeing something sort of different about whether it's a good or bad for, for these models.[00:20:56] swyx: Yeah. The branding of this is pretty poor. I would kind of tell people to think about it like cholesterol. There's good cholesterol, bad cholesterol. And you can have, you know, good amounts of both. But at this point, it is absolutely without a doubt that most large models from here on out will all be trained as some kind of synthetic data and that is not a bad thing.[00:21:16] swyx: There are ways in which you can do it poorly. Whether it's commercial, you know, in terms of commercial sourcing or in terms of the model performance. But it's without a doubt that good synthetic data is going to help your model. And this is just a question of like where to obtain it and what kinds of synthetic data are valuable.[00:21:36] swyx: You know, if even like alpha geometry, you know, was, was a really good example from like earlier this year.[00:21:42] NLW: If you're using the cholesterol analogy, then my, then my egg thing can't be that far off. Let's talk about the sort of the state of the art and the, and the GPT 4 class landscape and how that's changed.[00:21:53] Gemini 1.5 vs Claude 3[00:21:53] NLW: Cause obviously, you know, sort of the, the two big things or a couple of the big things that have happened. Since we last talked, we're one, you know, Gemini first announcing that a model was coming and then finally it arriving, and then very soon after a sort of a different model arriving from Gemini and and Cloud three.[00:22:11] NLW: So I guess, you know, I'm not sure exactly where the right place to start with this conversation is, but, you know, maybe very broadly speaking which of these do you think have made a bigger impact? Thank you.[00:22:20] Alessio: Probably the one you can use, right? So, Cloud. Well, I'm sure Gemini is going to be great once they let me in, but so far I haven't been able to.[00:22:29] Alessio: I use, so I have this small podcaster thing that I built for our podcast, which does chapters creation, like named entity recognition, summarization, and all of that. Cloud Tree is, Better than GPT 4. Cloud2 was unusable. So I use GPT 4 for everything. And then when Opus came out, I tried them again side by side and I posted it on, on Twitter as well.[00:22:53] Alessio: Cloud is better. It's very good, you know, it's much better, it seems to me, it's much better than GPT 4 at doing writing that is more, you know, I don't know, it just got good vibes, you know, like the GPT 4 text, you can tell it's like GPT 4, you know, it's like, it always uses certain types of words and phrases and, you know, maybe it's just me because I've now done it for, you know, So, I've read like 75, 80 generations of these things next to each other.[00:23:21] Alessio: Clutter is really good. I know everybody is freaking out on twitter about it, my only experience of this is much better has been on the podcast use case. But I know that, you know, Quran from from News Research is a very big opus pro, pro opus person. So, I think that's also It's great to have people that actually care about other models.[00:23:40] Alessio: You know, I think so far to a lot of people, maybe Entropic has been the sibling in the corner, you know, it's like Cloud releases a new model and then OpenAI releases Sora and like, you know, there are like all these different things, but yeah, the new models are good. It's interesting.[00:23:55] NLW: My my perception is definitely that just, just observationally, Cloud 3 is certainly the first thing that I've seen where lots of people.[00:24:06] NLW: They're, no one's debating evals or anything like that. They're talking about the specific use cases that they have, that they used to use chat GPT for every day, you know, day in, day out, that they've now just switched over. And that has, I think, shifted a lot of the sort of like vibe and sentiment in the space too.[00:24:26] NLW: And I don't necessarily think that it's sort of a A like full you know, sort of full knock. Let's put it this way. I think it's less bad for open AI than it is good for anthropic. I think that because GPT 5 isn't there, people are not quite willing to sort of like, you know get overly critical of, of open AI, except in so far as they're wondering where GPT 5 is.[00:24:46] NLW: But I do think that it makes, Anthropic look way more credible as a, as a, as a player, as a, you know, as a credible sort of player, you know, as opposed to to, to where they were.[00:24:57] Alessio: Yeah. And I would say the benchmarks veil is probably getting lifted this year. I think last year. People were like, okay, this is better than this on this benchmark, blah, blah, blah, because maybe they did not have a lot of use cases that they did frequently.[00:25:11] Alessio: So it's hard to like compare yourself. So you, you defer to the benchmarks. I think now as we go into 2024, a lot of people have started to use these models from, you know, from very sophisticated things that they run in production to some utility that they have on their own. Now they can just run them side by side.[00:25:29] Alessio: And it's like, Hey, I don't care that like. The MMLU score of Opus is like slightly lower than GPT 4. It just works for me, you know, and I think that's the same way that traditional software has been used by people, right? Like you just strive for yourself and like, which one does it work, works best for you?[00:25:48] Alessio: Like nobody looks at benchmarks outside of like sales white papers, you know? And I think it's great that we're going more in that direction. We have a episode with Adapt coming out this weekend. I'll and some of their model releases, they specifically say, We do not care about benchmarks, so we didn't put them in, you know, because we, we don't want to look good on them.[00:26:06] Alessio: We just want the product to work. And I think more and more people will, will[00:26:09] swyx: go that way. Yeah. I I would say like, it does take the wind out of the sails for GPT 5, which I know where, you know, Curious about later on. I think anytime you put out a new state of the art model, you have to break through in some way.[00:26:21] swyx: And what Claude and Gemini have done is effectively take away any advantage to saying that you have a million token context window. Now everyone's just going to be like, Oh, okay. Now you just match the other two guys. And so that puts An insane amount of pressure on what gpt5 is going to be because it's just going to have like the only option it has now because all the other models are multimodal all the other models are long context all the other models have perfect recall gpt5 has to match everything and do more to to not be a flop[00:26:58] AI Breakdown Part 2[00:26:58] NLW: hello friends back again with part two if you haven't heard part one of this conversation i suggest you go check it out but to be honest they are kind of actually separable In this conversation, we get into a topic that I think Alessio and Swyx are very well positioned to discuss, which is what developers care about right now, what people are trying to build around.[00:27:16] NLW: I honestly think that one of the best ways to see the future in an industry like AI is to try to dig deep on what developers and entrepreneurs are attracted to build, even if it hasn't made it to the news pages yet. So consider this your preview of six months from now, and let's dive in. Let's bring it to the GPT 5 conversation.[00:27:33] Next Frontiers: Llama 3, GPT-5, Gemini 2, Claude 4[00:27:33] NLW: I mean, so, so I think that that's a great sort of assessment of just how the stakes have been raised, you know is your, I mean, so I guess maybe, maybe I'll, I'll frame this less as a question, just sort of something that, that I, that I've been watching right now, the only thing that makes sense to me with how.[00:27:50] NLW: Fundamentally unbothered and unstressed OpenAI seems about everything is that they're sitting on something that does meet all that criteria, right? Because, I mean, even in the Lex Friedman interview that, that Altman recently did, you know, he's talking about other things coming out first. He's talking about, he's just like, he, listen, he, he's good and he could play nonchalant, you know, if he wanted to.[00:28:13] NLW: So I don't want to read too much into it, but. You know, they've had so long to work on this, like unless that we are like really meaningfully running up against some constraint, it just feels like, you know, there's going to be some massive increase, but I don't know. What do you guys think?[00:28:28] swyx: Hard to speculate.[00:28:29] swyx: You know, at this point, they're, they're pretty good at PR and they're not going to tell you anything that they don't want to. And he can tell you one thing and change their minds the next day. So it's, it's, it's really, you know, I've always said that model version numbers are just marketing exercises, like they have something and it's always improving and at some point you just cut it and decide to call it GPT 5.[00:28:50] swyx: And it's more just about defining an arbitrary level at which they're ready and it's up to them on what ready means. We definitely did see some leaks on GPT 4. 5, as I think a lot of people reported and I'm not sure if you covered it. So it seems like there might be an intermediate release. But I did feel, coming out of the Lex Friedman interview, that GPT 5 was nowhere near.[00:29:11] swyx: And you know, it was kind of a sharp contrast to Sam talking at Davos in February, saying that, you know, it was his top priority. So I find it hard to square. And honestly, like, there's also no point Reading too much tea leaves into what any one person says about something that hasn't happened yet or has a decision that hasn't been taken yet.[00:29:31] swyx: Yeah, that's, that's my 2 cents about it. Like, calm down, let's just build .[00:29:35] Alessio: Yeah. The, the February rumor was that they were gonna work on AI agents, so I don't know, maybe they're like, yeah,[00:29:41] swyx: they had two agent two, I think two agent projects, right? One desktop agent and one sort of more general yeah, sort of GPTs like agent and then Andre left, so he was supposed to be the guy on that.[00:29:52] swyx: What did Andre see? What did he see? I don't know. What did he see?[00:29:56] Alessio: I don't know. But again, it's just like the rumors are always floating around, you know but I think like, this is, you know, we're not going to get to the end of the year without Jupyter you know, that's definitely happening. I think the biggest question is like, are Anthropic and Google.[00:30:13] Alessio: Increasing the pace, you know, like it's the, it's the cloud four coming out like in 12 months, like nine months. What's the, what's the deal? Same with Gemini. They went from like one to 1. 5 in like five days or something. So when's Gemini 2 coming out, you know, is that going to be soon? I don't know.[00:30:31] Alessio: There, there are a lot of, speculations, but the good thing is that now you can see a world in which OpenAI doesn't rule everything. You know, so that, that's the best, that's the best news that everybody got, I would say.[00:30:43] swyx: Yeah, and Mistral Large also dropped in the last month. And, you know, not as, not quite GPT 4 class, but very good from a new startup.[00:30:52] swyx: So yeah, we, we have now slowly changed in landscape, you know. In my January recap, I was complaining that nothing's changed in the landscape for a long time. But now we do exist in a world, sort of a multipolar world where Cloud and Gemini are legitimate challengers to GPT 4 and hopefully more will emerge as well hopefully from meta.[00:31:11] Open Source Models - Mistral, Grok[00:31:11] NLW: So speak, let's actually talk about sort of the open source side of this for a minute. So Mistral Large, notable because it's, it's not available open source in the same way that other things are, although I think my perception is that the community has largely given them Like the community largely recognizes that they want them to keep building open source stuff and they have to find some way to fund themselves that they're going to do that.[00:31:27] NLW: And so they kind of understand that there's like, they got to figure out how to eat, but we've got, so, you know, there there's Mistral, there's, I guess, Grok now, which is, you know, Grok one is from, from October is, is open[00:31:38] swyx: sourced at, yeah. Yeah, sorry, I thought you thought you meant Grok the chip company.[00:31:41] swyx: No, no, no, yeah, you mean Twitter Grok.[00:31:43] NLW: Although Grok the chip company, I think is even more interesting in some ways, but and then there's the, you know, obviously Llama3 is the one that sort of everyone's wondering about too. And, you know, my, my sense of that, the little bit that, you know, Zuckerberg was talking about Llama 3 earlier this year, suggested that, at least from an ambition standpoint, he was not thinking about how do I make sure that, you know, meta content, you know, keeps, keeps the open source thrown, you know, vis a vis Mistral.[00:32:09] NLW: He was thinking about how you go after, you know, how, how he, you know, releases a thing that's, you know, every bit as good as whatever OpenAI is on at that point.[00:32:16] Alessio: Yeah. From what I heard in the hallways at, at GDC, Llama 3, the, the biggest model will be, you 260 to 300 billion parameters, so that that's quite large.[00:32:26] Alessio: That's not an open source model. You know, you cannot give people a 300 billion parameters model and ask them to run it. You know, it's very compute intensive. So I think it is, it[00:32:35] swyx: can be open source. It's just, it's going to be difficult to run, but that's a separate question.[00:32:39] Alessio: It's more like, as you think about what they're doing it for, you know, it's not like empowering the person running.[00:32:45] Alessio: llama. On, on their laptop, it's like, oh, you can actually now use this to go after open AI, to go after Anthropic, to go after some of these companies at like the middle complexity level, so to speak. Yeah. So obviously, you know, we estimate Gentala on the podcast, they're doing a lot here, they're making PyTorch better.[00:33:03] Alessio: You know, they want to, that's kind of like maybe a little bit of a shorted. Adam Bedia, in a way, trying to get some of the CUDA dominance out of it. Yeah, no, it's great. The, I love the duck destroying a lot of monopolies arc. You know, it's, it's been very entertaining. Let's bridge[00:33:18] NLW: into the sort of big tech side of this, because this is obviously like, so I think actually when I did my episode, this was one of the I added this as one of as an additional war that, that's something that I'm paying attention to.[00:33:29] NLW: So we've got Microsoft's moves with inflection, which I think pretend, potentially are being read as A shift vis a vis the relationship with OpenAI, which also the sort of Mistral large relationship seems to reinforce as well. We have Apple potentially entering the race, finally, you know, giving up Project Titan and and, and kind of trying to spend more effort on this.[00:33:50] NLW: Although, Counterpoint, we also have them talking about it, or there being reports of a deal with Google, which, you know, is interesting to sort of see what their strategy there is. And then, you know, Meta's been largely quiet. We kind of just talked about the main piece, but, you know, there's, and then there's spoilers like Elon.[00:34:07] NLW: I mean, you know, what, what of those things has sort of been most interesting to you guys as you think about what's going to shake out for the rest of this[00:34:13] Apple MM1[00:34:13] swyx: year? I'll take a crack. So the reason we don't have a fifth war for the Big Tech Wars is that's one of those things where I just feel like we don't cover differently from other media channels, I guess.[00:34:26] swyx: Sure, yeah. In our anti interestness, we actually say, like, we try not to cover the Big Tech Game of Thrones, or it's proxied through Twitter. You know, all the other four wars anyway, so there's just a lot of overlap. Yeah, I think absolutely, personally, the most interesting one is Apple entering the race.[00:34:41] swyx: They actually released, they announced their first large language model that they trained themselves. It's like a 30 billion multimodal model. People weren't that impressed, but it was like the first time that Apple has kind of showcased that, yeah, we're training large models in house as well. Of course, like, they might be doing this deal with Google.[00:34:57] swyx: I don't know. It sounds very sort of rumor y to me. And it's probably, if it's on device, it's going to be a smaller model. So something like a Jemma. It's going to be smarter autocomplete. I don't know what to say. I'm still here dealing with, like, Siri, which hasn't, probably hasn't been updated since God knows when it was introduced.[00:35:16] swyx: It's horrible. I, you know, it, it, it makes me so angry. So I, I, one, as an Apple customer and user, I, I'm just hoping for better AI on Apple itself. But two, they are the gold standard when it comes to local devices, personal compute and, and trust, like you, you trust them with your data. And. I think that's what a lot of people are looking for in AI, that they have, they love the benefits of AI, they don't love the downsides, which is that you have to send all your data to some cloud somewhere.[00:35:45] swyx: And some of this data that we're going to feed AI is just the most personal data there is. So Apple being like one of the most trusted personal data companies, I think it's very important that they enter the AI race, and I hope to see more out of them.[00:35:58] Alessio: To me, the, the biggest question with the Google deal is like, who's paying who?[00:36:03] Alessio: Because for the browsers, Google pays Apple like 18, 20 billion every year to be the default browser. Is Google going to pay you to have Gemini or is Apple paying Google to have Gemini? I think that's, that's like what I'm most interested to figure out because with the browsers, it's like, it's the entry point to the thing.[00:36:21] Alessio: So it's really valuable to be the default. That's why Google pays. But I wonder if like the perception in AI is going to be like, Hey. You just have to have a good local model on my phone to be worth me purchasing your device. And that was, that's kind of drive Apple to be the one buying the model. But then, like Shawn said, they're doing the MM1 themselves.[00:36:40] Alessio: So are they saying we do models, but they're not as good as the Google ones? I don't know. The whole thing is, it's really confusing, but. It makes for great meme material on on Twitter.[00:36:51] swyx: Yeah, I mean, I think, like, they are possibly more than OpenAI and Microsoft and Amazon. They are the most full stack company there is in computing, and so, like, they own the chips, man.[00:37:05] swyx: Like, they manufacture everything so if, if, if there was a company that could do that. You know, seriously challenge the other AI players. It would be Apple. And it's, I don't think it's as hard as self driving. So like maybe they've, they've just been investing in the wrong thing this whole time. We'll see.[00:37:21] swyx: Wall Street certainly thinks[00:37:22] NLW: so. Wall Street loved that move, man. There's a big, a big sigh of relief. Well, let's, let's move away from, from sort of the big stuff. I mean, the, I think to both of your points, it's going to.[00:37:33] Meta's $800b AI rebrand[00:37:33] NLW: Can I, can[00:37:34] swyx: I, can I, can I jump on factoid about this, this Wall Street thing? I went and looked at when Meta went from being a VR company to an AI company.[00:37:44] swyx: And I think the stock I'm trying to look up the details now. The stock has gone up 187% since Lamo one. Yeah. Which is $830 billion in market value created in the past year. . Yeah. Yeah.[00:37:57] NLW: It's, it's, it's like, remember if you guys haven't Yeah. If you haven't seen the chart, it's actually like remarkable.[00:38:02] NLW: If you draw a little[00:38:03] swyx: arrow on it, it's like, no, we're an AI company now and forget the VR thing.[00:38:10] NLW: It's it, it is an interesting, no, it's, I, I think, alessio, you called it sort of like Zuck's Disruptor Arc or whatever. He, he really does. He is in the midst of a, of a total, you know, I don't know if it's a redemption arc or it's just, it's something different where, you know, he, he's sort of the spoiler.[00:38:25] NLW: Like people loved him just freestyle talking about why he thought they had a better headset than Apple. But even if they didn't agree, they just loved it. He was going direct to camera and talking about it for, you know, five minutes or whatever. So that, that's a fascinating shift that I don't think anyone had on their bingo card, you know, whatever, two years ago.[00:38:41] NLW: Yeah. Yeah,[00:38:42] swyx: we still[00:38:43] Alessio: didn't see and fight Elon though, so[00:38:45] swyx: that's what I'm really looking forward to. I mean, hey, don't, don't, don't write it off, you know, maybe just these things take a while to happen. But we need to see and fight in the Coliseum. No, I think you know, in terms of like self management, life leadership, I think he has, there's a lot of lessons to learn from him.[00:38:59] swyx: You know he might, you know, you might kind of quibble with, like, the social impact of Facebook, but just himself as a in terms of personal growth and, and, you know, Per perseverance through like a lot of change and you know, everyone throwing stuff his way. I think there's a lot to say about like, to learn from, from Zuck, which is crazy 'cause he's my age.[00:39:18] swyx: Yeah. Right.[00:39:20] AI Engineer landscape - from baby AGIs to vertical Agents[00:39:20] NLW: Awesome. Well, so, so one of the big things that I think you guys have, you know, distinct and, and unique insight into being where you are and what you work on is. You know, what developers are getting really excited about right now. And by that, I mean, on the one hand, certainly, you know, like startups who are actually kind of formalized and formed to startups, but also, you know, just in terms of like what people are spending their nights and weekends on what they're, you know, coming to hackathons to do.[00:39:45] NLW: And, you know, I think it's a, it's a, it's, it's such a fascinating indicator for, for where things are headed. Like if you zoom back a year, right now was right when everyone was getting so, so excited about. AI agent stuff, right? Auto, GPT and baby a GI. And these things were like, if you dropped anything on YouTube about those, like instantly tens of thousands of views.[00:40:07] NLW: I know because I had like a 50,000 view video, like the second day that I was doing the show on YouTube, you know, because I was talking about auto GPT. And so anyways, you know, obviously that's sort of not totally come to fruition yet, but what are some of the trends in what you guys are seeing in terms of people's, people's interest and, and, and what people are building?[00:40:24] Alessio: I can start maybe with the agents part and then I know Shawn is doing a diffusion meetup tonight. There's a lot of, a lot of different things. The, the agent wave has been the most interesting kind of like dream to reality arc. So out of GPT, I think they went, From zero to like 125, 000 GitHub stars in six weeks, and then one year later, they have 150, 000 stars.[00:40:49] Alessio: So there's kind of been a big plateau. I mean, you might say there are just not that many people that can start it. You know, everybody already started it. But the promise of, hey, I'll just give you a goal, and you do it. I think it's like, amazing to get people's imagination going. You know, they're like, oh, wow, this This is awesome.[00:41:08] Alessio: Everybody, everybody can try this to do anything. But then as technologists, you're like, well, that's, that's just like not possible, you know, we would have like solved everything. And I think it takes a little bit to go from the promise and the hope that people show you to then try it yourself and going back to say, okay, this is not really working for me.[00:41:28] Alessio: And David Wong from Adept, you know, they in our episode, he specifically said. We don't want to do a bottom up product. You know, we don't want something that everybody can just use and try because it's really hard to get it to be reliable. So we're seeing a lot of companies doing vertical agents that are narrow for a specific domain, and they're very good at something.[00:41:49] Alessio: Mike Conover, who was at Databricks before, is also a friend of Latentspace. He's doing this new company called BrightWave doing AI agents for financial research, and that's it, you know, and they're doing very well. There are other companies doing it in security, doing it in compliance, doing it in legal.[00:42:08] Alessio: All of these things that like, people, nobody just wakes up and say, Oh, I cannot wait to go on AutoGPD and ask it to do a compliance review of my thing. You know, just not what inspires people. So I think the gap on the developer side has been the more bottom sub hacker mentality is trying to build this like very Generic agents that can do a lot of open ended tasks.[00:42:30] Alessio: And then the more business side of things is like, Hey, If I want to raise my next round, I can not just like sit around the mess, mess around with like super generic stuff. I need to find a use case that really works. And I think that that is worth for, for a lot of folks in parallel, you have a lot of companies doing evals.[00:42:47] Alessio: There are dozens of them that just want to help you measure how good your models are doing. Again, if you build evals, you need to also have a restrained surface area to actually figure out whether or not it's good, right? Because you cannot eval anything on everything under the sun. So that's another category where I've seen from the startup pitches that I've seen, there's a lot of interest in, in the enterprise.[00:43:11] Alessio: It's just like really. Fragmented because the production use cases are just coming like now, you know, there are not a lot of long established ones to, to test against. And so does it, that's kind of on the virtual agents and then the robotic side it's probably been the thing that surprised me the most at NVIDIA GTC, the amount of robots that were there that were just like robots everywhere.[00:43:33] Alessio: Like, both in the keynote and then on the show floor, you would have Boston Dynamics dogs running around. There was, like, this, like fox robot that had, like, a virtual face that, like, talked to you and, like, moved in real time. There were industrial robots. NVIDIA did a big push on their own Omniverse thing, which is, like, this Digital twin of whatever environments you're in that you can use to train the robots agents.[00:43:57] Alessio: So that kind of takes people back to the reinforcement learning days, but yeah, agents, people want them, you know, people want them. I give a talk about the, the rise of the full stack employees and kind of this future, the same way full stack engineers kind of work across the stack. In the future, every employee is going to interact with every part of the organization through agents and AI enabled tooling.[00:44:17] Alessio: This is happening. It just needs to be a lot more narrow than maybe the first approach that we took, which is just put a string in AutoGPT and pray. But yeah, there's a lot of super interesting stuff going on.[00:44:27] swyx: Yeah. Well, he Let's recover a lot of stuff there. I'll separate the robotics piece because I feel like that's so different from the software world.[00:44:34] swyx: But yeah, we do talk to a lot of engineers and you know, that this is our sort of bread and butter. And I do agree that vertical agents have worked out a lot better than the horizontal ones. I think all You know, the point I'll make here is just the reason AutoGPT and maybe AGI, you know, it's in the name, like they were promising AGI.[00:44:53] swyx: But I think people are discovering that you cannot engineer your way to AGI. It has to be done at the model level and all these engineering, prompt engineering hacks on top of it weren't really going to get us there in a meaningful way without much further, you know, improvements in the models. I would say, I'll go so far as to say, even Devin, which is, I would, I think the most advanced agent that we've ever seen, still requires a lot of engineering and still probably falls apart a lot in terms of, like, practical usage.[00:45:22] swyx: Or it's just, Way too slow and expensive for, you know, what it's, what it's promised compared to the video. So yeah, that's, that's what, that's what happened with agents from, from last year. But I, I do, I do see, like, vertical agents being very popular and, and sometimes you, like, I think the word agent might even be overused sometimes.[00:45:38] swyx: Like, people don't really care whether or not you call it an AI agent, right? Like, does it replace boring menial tasks that I do That I might hire a human to do, or that the human who is hired to do it, like, actually doesn't really want to do. And I think there's absolutely ways in sort of a vertical context that you can actually go after very routine tasks that can be scaled out to a lot of, you know, AI assistants.[00:46:01] swyx: So, so yeah, I mean, and I would, I would sort of basically plus one what let's just sit there. I think it's, it's very, very promising and I think more people should work on it, not less. Like there's not enough people. Like, we, like, this should be the, the, the main thrust of the AI engineer is to look out, look for use cases and, and go to a production with them instead of just always working on some AGI promising thing that never arrives.[00:46:21] swyx: I,[00:46:22] NLW: I, I can only add that so I've been fiercely making tutorials behind the scenes around basically everything you can imagine with AI. We've probably done, we've done about 300 tutorials over the last couple of months. And the verticalized anything, right, like this is a solution for your particular job or role, even if it's way less interesting or kind of sexy, it's like so radically more useful to people in terms of intersecting with how, like those are the ways that people are actually.[00:46:50] NLW: Adopting AI in a lot of cases is just a, a, a thing that I do over and over again. By the way, I think that's the same way that even the generalized models are getting adopted. You know, it's like, I use midjourney for lots of stuff, but the main thing I use it for is YouTube thumbnails every day. Like day in, day out, I will always do a YouTube thumbnail, you know, or two with, with Midjourney, right?[00:47:09] NLW: And it's like you can, you can start to extrapolate that across a lot of things and all of a sudden, you know, a AI doesn't. It looks revolutionary because of a million small changes rather than one sort of big dramatic change. And I think that the verticalization of agents is sort of a great example of how that's[00:47:26] swyx: going to play out too.[00:47:28] Adept episode - Screen Multimodality[00:47:28] swyx: So I'll have one caveat here, which is I think that Because multi modal models are now commonplace, like Cloud, Gemini, OpenAI, all very very easily multi modal, Apple's easily multi modal, all this stuff. There is a switch for agents for sort of general desktop browsing that I think people so much for joining us today, and we'll see you in the next video.[00:48:04] swyx: Version of the the agent where they're not specifically taking in text or anything They're just watching your screen just like someone else would and and I'm piloting it by vision And you know in the the episode with David that we'll have dropped by the time that this this airs I think I think that is the promise of adept and that is a promise of what a lot of these sort of desktop agents Are and that is the more general purpose system That could be as big as the browser, the operating system, like, people really want to build that foundational piece of software in AI.[00:48:38] swyx: And I would see, like, the potential there for desktop agents being that, that you can have sort of self driving computers. You know, don't write the horizontal piece out. I just think we took a while to get there.[00:48:48] NLW: What else are you guys seeing that's interesting to you? I'm looking at your notes and I see a ton of categories.[00:48:54] Top Model Research from January Recap[00:48:54] swyx: Yeah so I'll take the next two as like as one category, which is basically alternative architectures, right? The two main things that everyone following AI kind of knows now is, one, the diffusion architecture, and two, the let's just say the, Decoder only transformer architecture that is popularized by GPT.[00:49:12] swyx: You can read, you can look on YouTube for thousands and thousands of tutorials on each of those things. What we are talking about here is what's next, what people are researching, and what could be on the horizon that takes the place of those other two things. So first of all, we'll talk about transformer architectures and then diffusion.[00:49:25] swyx: So transformers the, the two leading candidates are effectively RWKV and the state space models the most recent one of which is Mamba, but there's others like the Stripe, ENA, and the S four H three stuff coming out of hazy research at Stanford. And all of those are non quadratic language models that scale the promise to scale a lot better than the, the traditional transformer.[00:49:47] swyx: That this might be too theoretical for most people right now, but it's, it's gonna be. It's gonna come out in weird ways, where, imagine if like, Right now the talk of the town is that Claude and Gemini have a million tokens of context and like whoa You can put in like, you know, two hours of video now, okay But like what if you put what if we could like throw in, you know, two hundred thousand hours of video?[00:50:09] swyx: Like how does that change your usage of AI? What if you could throw in the entire genetic sequence of a human and like synthesize new drugs. Like, well, how does that change things? Like, we don't know because we haven't had access to this capability being so cheap before. And that's the ultimate promise of these two models.[00:50:28] swyx: They're not there yet but we're seeing very, very good progress. RWKV and Mamba are probably the, like, the two leading examples, both of which are open source that you can try them today and and have a lot of progress there. And the, the, the main thing I'll highlight for audio e KV is that at, at the seven B level, they seem to have beat LAMA two in all benchmarks that matter at the same size for the same amount of training as an open source model.[00:50:51] swyx: So that's exciting. You know, they're there, they're seven B now. They're not at seven tb. We don't know if it'll. And then the other thing is diffusion. Diffusions and transformers are are kind of on the collision course. The original stable diffusion already used transformers in in parts of its architecture.[00:51:06] swyx: It seems that transformers are eating more and more of those layers particularly the sort of VAE layer. So that's, the Diffusion Transformer is what Sora is built on. The guy who wrote the Diffusion Transformer paper, Bill Pebbles, is, Bill Pebbles is the lead tech guy on Sora. So you'll just see a lot more Diffusion Transformer stuff going on.[00:51:25] swyx: But there's, there's more sort of experimentation with diffusion. I'm holding a meetup actually here in San Francisco that's gonna be like the state of diffusion, which I'm pretty excited about. Stability's doing a lot of good work. And if you look at the, the architecture of how they're creating Stable Diffusion 3, Hourglass Diffusion, and the inconsistency models, or SDXL Turbo.[00:51:45] swyx: All of these are, like, very, very interesting innovations on, like, the original idea of what Stable Diffusion was. So if you think that it is expensive to create or slow to create Stable Diffusion or an AI generated art, you are not up to date with the latest models. If you think it is hard to create text and images, you are not up to date with the latest models.[00:52:02] swyx: And people still are kind of far behind. The last piece of which is the wildcard I always kind of hold out, which is text diffusion. So Instead of using autogenerative or autoregressive transformers, can you use text to diffuse? So you can use diffusion models to diffuse and create entire chunks of text all at once instead of token by token.[00:52:22] swyx: And that is something that Midjourney confirmed today, because it was only rumored the past few months. But they confirmed today that they were looking into. So all those things are like very exciting new model architectures that are, Maybe something that we'll, you'll see in production two to three years from now.[00:52:37] swyx: So the couple of the trends[00:52:38] NLW: that I want to just get your takes on, because they're sort of something that, that seems like they're coming up are one sort of these, these wearable, you know, kind of passive AI experiences where they're absorbing a lot of what's going on around you and then, and then kind of bringing things back.[00:52:53] NLW: And then the, the other one that I, that I wanted to see if you guys had thoughts on were sort of this next generation of chip companies. Obviously there's a huge amount of emphasis. On on hardware and silicon and, and, and different ways of doing things, but, y

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We are running an end of year survey for our listeners! Please let us know any feedback you have, what episodes resonated with you, and guest requests for 2024! Survey link here!Before language models became all the rage in November 2022, image generation was the hottest space in AI (it was the subject of our first piece on Latent Space!) In our interview with Sharif Shameem from Lexica we talked through the launch of StableDiffusion and the early days of that space. At the time, the toolkit was still pretty rudimentary: Lexica made it easy to search images, you had the AUTOMATIC1111 Web UI to generate locally, some HuggingFace spaces that offered inference, and eventually DALL-E 2 through OpenAI's platform, but not much beyond basic text-to-image workflows.Today's guest, Suhail Doshi, is trying to solve this with Playground AI, an image editor reimagined with AI in mind. Some of the differences compared to traditional text-to-image workflows:* Real-time preview rendering using consistency: as you change your prompt, you can see changes in real-time before doing a final rendering of it.* Style filtering: rather than having to prompt exactly how you'd like an image to look, you can pick from a whole range of filters both from Playground's model as well as Stable Diffusion (like RealVis, Starlight XL, etc). We talk about this at 25:46 in the podcast.* Expand prompt: similar to DALL-E3, Playground will do some prompt tuning for you to get better results in generation. Unlike DALL-E3, you can turn this off at any time if you are a prompting wizard* Image editing: after generation, you have tools like a magic eraser, inpainting pencil, etc. This makes it easier to do a full workflow in Playground rather than switching to another tool like Photoshop.Outside of the product, they have also trained a new model from scratch, Playground v2, which is fully open source and open weights and allows for commercial usage. They benchmarked the model against SDXL across 1,000 prompts and found that humans preferred the Playground generation 70% of the time. They had similar results on PartiPrompts:They also created a new benchmark, MJHQ-30K, for “aesthetic quality”:We introduce a new benchmark, MJHQ-30K, for automatic evaluation of a model's aesthetic quality. The benchmark computes FID on a high-quality dataset to gauge aesthetic quality.We curate the high-quality dataset from Midjourney with 10 common categories, each category with 3K samples. Following common practice, we use aesthetic score and CLIP score to ensure high image quality and high image-text alignment. Furthermore, we take extra care to make the data diverse within each category.Suhail was pretty open with saying that Midjourney is currently the best product for imagine generation out there, and that's why they used it as the base for this benchmark. I think it's worth comparing yourself to maybe the best thing and try to find like a really fair way of doing that. So I think more people should try to do that. I definitely don't think you should be kind of comparing yourself on like some Google model or some old SD, Stable Diffusion model and be like, look, we beat Stable Diffusion 1.5. I think users ultimately want care, how close are you getting to the thing that people mostly agree with? [00:23:47]We also talked a lot about Suhail's founder journey from starting Mixpanel in 2009, then going through YC again with Mighty, and eventually sunsetting that to pivot into Playground. Enjoy!Show Notes* Suhail's Twitter* “Starting my road to learn AI”* Bill Gates book trip* Playground* Playground v2 Announcement* $40M raise announcement* “Running infra dev ops for 24 A100s”* Mixpanel* Mighty* “I decided to stop working on Mighty”* Fast.ai* CivitTimestamps* [00:00:00] Intros* [00:02:59] Being early in ML at Mixpanel* [00:04:16] Pivoting from Mighty to Playground and focusing on generative AI* [00:07:54] How DALL-E 2 inspired Mighty* [00:09:19] Reimagining the graphics editor with AI* [00:17:34] Training the Playground V2 model from scratch to advance generative graphics* [00:21:11] Techniques used to improve Playground V2 like data filtering and model tuning* [00:25:21] Releasing the MJHQ30K benchmark to evaluate generative models* [00:30:35] The limitations of current models for detailed image editing tasks* [00:34:06] Using post-generation user feedback to create better benchmarks* [00:38:28] Concerns over potential misuse of powerful generative models* [00:41:54] Rethinking the graphics editor user experience in the AI era* [00:45:44] Integrating consistency models into Playground using preview rendering* [00:47:23] Interacting with the Stable Diffusion LoRAs community* [00:51:35] Running DevOps on A100s* [00:53:12] Startup ideas?TranscriptAlessio: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO-in-Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI. [00:00:15]Swyx: Hey, and today in the studio we have Suhail Doshi, welcome. [00:00:18]Suhail: Yeah, thanks. Thanks for having me. [00:00:20]Swyx: So among many things, you're a CEO and co-founder of Mixpanel, and I think about three years ago you left to start Mighty, and more recently, I think about a year ago, transitioned into Playground, and you've just announced your new round. How do you like to be introduced beyond that? [00:00:34]Suhail: Just founder of Playground is fine, yeah, prior co-founder and CEO of Mixpanel. [00:00:40]Swyx: Yeah, awesome. I'd just like to touch on Mixpanel a little bit, because it's obviously one of the more successful analytics companies we previously had amplitude on, and I'm curious if you had any reflections on the interaction of that amount of data that people would want to use for AI. I don't know if there's still a part of you that stays in touch with that world. [00:00:59]Suhail: Yeah, I mean, the short version is that maybe back in like 2015 or 2016, I don't really remember exactly, because it was a while ago, we had an ML team at Mixpanel, and I think this is when maybe deep learning or something really just started getting kind of exciting, and we were thinking that maybe given that we had such vast amounts of data, perhaps we could predict things. So we built two or three different features, I think we built a feature where we could predict whether users would churn from your product. We made a feature that could predict whether users would convert, we built a feature that could do anomaly detection, like if something occurred in your product, that was just very surprising, maybe a spike in traffic in a particular region, can we tell you that that happened? Because it's really hard to like know everything that's going on with your data, can we tell you something surprising about your data? And we tried all of these various features, most of it boiled down to just like, you know, using logistic regression, and it never quite seemed very groundbreaking in the end. And so I think, you know, we had a four or five person ML team, and I think we never expanded it from there. And I did all these Fast AI courses trying to learn about ML. And that was the- That's the first time you did fast AI. Yeah, that was the first time I did fast AI. Yeah, I think I've done it now three times, maybe. [00:02:12]Swyx: Oh, okay. [00:02:13]Suhail: I didn't know it was the third. No, no, just me reviewing it, it's maybe three times, but yeah. [00:02:16]Swyx: You mentioned prediction, but honestly, like it's also just about the feedback, right? The quality of feedback from users, I think it's useful for anyone building AI applications. [00:02:25]Suhail: Yeah. Yeah, I think I haven't spent a lot of time thinking about Mixpanel because it's been a long time, but sometimes I'm like, oh, I wonder what we could do now. And then I kind of like move on to whatever I'm working on, but things have changed significantly since. [00:02:39]Swyx: And then maybe we'll touch on Mighty a little bit. Mighty was very, very bold. My framing of it was, you will run our browsers for us because everyone has too many tabs open. I have too many tabs open and slowing down your machines that you can do it better for us in a centralized data center. [00:02:51]Suhail: Yeah, we were first trying to make a browser that we would stream from a data center to your computer at extremely low latency, but the real objective wasn't trying to make a browser or anything like that. The real objective was to try to make a new kind of computer. And the thought was just that like, you know, we have these computers in front of us today and we upgrade them or they run out of RAM or they don't have enough RAM or not enough disk or, you know, there's some limitation with our computers, perhaps like data locality is a problem. Why do I need to think about upgrading my computer ever? And so, you know, we just had to kind of observe that like, well, actually it seems like a lot of applications are just now in the browser, you know, it's like how many real desktop applications do we use relative to the number of applications we use in the browser? So it's just this realization that actually like, you know, the browser was effectively becoming more or less our operating system over time. And so then that's why we kind of decided to go, hmm, maybe we can stream the browser. Fortunately, the idea did not work for a couple of different reasons, but the objective is try to make sure new computer. [00:03:50]Swyx: Yeah, very, very bold. [00:03:51]Alessio: Yeah, and I was there at YC Demo Day when you first announced it. It was, I think, the last or one of the last in-person ones, at Pier34 in Mission Bay. How do you think about that now when everybody wants to put some of these models in people's machines and some of them want to stream them in, do you think there's maybe another wave of the same problem before it was like browser apps too slow, now it's like models too slow to run on device? [00:04:16]Suhail: Yeah. I mean, I've obviously pivoted away from Mighty, but a lot of what I somewhat believed at Mighty, maybe why I'm so excited about AI and what's happening, a lot of what Mighty was about was like moving compute somewhere else, right? Right now, applications, they get limited quantities of memory, disk, networking, whatever your home network has, et cetera. You know, what if these applications could somehow, if we could shift compute, and then these applications have vastly more compute than they do today. Right now it's just like client backend services, but you know, what if we could change the shape of how applications could interact with things? And it's changed my thinking. In some ways, AI has like a bit of a continuation of my belief that like perhaps we can really shift compute somewhere else. One of the problems with Mighty was that JavaScript is single-threaded in the browser. And what we learned, you know, the reason why we kind of abandoned Mighty was because I didn't believe we could make a new kind of computer. We could have made some kind of enterprise business, probably it could have made maybe a lot of money, but it wasn't going to be what I hoped it was going to be. And so once I realized that most of a web app is just going to be single-threaded JavaScript, then the only thing you could do largely withstanding changing JavaScript, which is a fool's errand most likely, make a better CPU, right? And there's like three CPU manufacturers, two of which sell, you know, big ones, you know, AMD, Intel, and then of course like Apple made the M1. And it's not like single-threaded CPU core performance, single-core performance was increasing very fast, it's plateauing rapidly. And even these different companies were not doing as good of a job, you know, sort of with the continuation of Moore's law. But what happened in AI was that you got like, if you think of the AI model as like a computer program, like just like a compiled computer program, it is literally built and designed to do massive parallel computations. And so if you could take like the universal approximation theorem to its like kind of logical complete point, you know, you're like, wow, I can get, make computation happen really rapidly and parallel somewhere else, you know, so you end up with these like really amazing models that can like do anything. It just turned out like perhaps the new kind of computer would just simply be shifted, you know, into these like really amazing AI models in reality. Yeah. [00:06:30]Swyx: Like I think Andrej Karpathy has always been, has been making a lot of analogies with the LLMOS. [00:06:34]Suhail: I saw his video and I watched that, you know, maybe two weeks ago or something like that. I was like, oh man, this, I very much resonate with this like idea. [00:06:41]Swyx: Why didn't I see this three years ago? [00:06:43]Suhail: Yeah. I think, I think there still will be, you know, local models and then there'll be these very large models that have to be run in data centers. I think it just depends on kind of like the right tool for the job, like any engineer would probably care about. But I think that, you know, by and large, like if the models continue to kind of keep getting bigger, you're always going to be wondering whether you should use the big thing or the small, you know, the tiny little model. And it might just depend on like, you know, do you need 30 FPS or 60 FPS? Maybe that would be hard to do, you know, over a network. [00:07:13]Swyx: You tackled a much harder problem latency wise than the AI models actually require. Yeah. [00:07:18]Suhail: Yeah. You can do quite well. You can do quite well. You definitely did 30 FPS video streaming, did very crazy things to make that work. So I'm actually quite bullish on the kinds of things you can do with networking. [00:07:30]Swyx: Maybe someday you'll come back to that at some point. But so for those that don't know, you're very transparent on Twitter. Very good to follow you just to learn your insights. And you actually published a postmortem on Mighty that people can read up on and willing to. So there was a bit of an overlap. You started exploring the AI stuff in June 2022, which is when you started saying like, I'm taking fast AI again. Maybe, was there more context around that? [00:07:54]Suhail: Yeah. I think I was kind of like waiting for the team at Mighty to finish up, you know, something. And I was like, okay, well, what can I do? I guess I will make some kind of like address bar predictor in the browser. So we had, you know, we had forked Chrome and Chromium. And I was like, you know, one thing that's kind of lame is that like this browser should be like a lot better at predicting what I might do, where I might want to go. It struck me as really odd that, you know, Chrome had very little AI actually or ML inside this browser. For a company like Google, you'd think there's a lot. Code is actually just very, you know, it's just a bunch of if then statements is more or less the address bar. So it seemed like a pretty big opportunity. And that's also where a lot of people interact with the browser. So, you know, long story short, I was like, hmm, I wonder what I could build here. So I started to take some AI courses and review the material again and get back to figuring it out. But I think that was somewhat serendipitous because right around April was, I think, a very big watershed moment in AI because that's when Dolly 2 came out. And I think that was the first truly big viral moment for generative AI. [00:08:59]Swyx: Because of the avocado chair. [00:09:01]Suhail: Yeah, exactly. [00:09:02]Swyx: It wasn't as big for me as Stable Diffusion. [00:09:04]Suhail: Really? [00:09:05]Swyx: Yeah, I don't know. Dolly was like, all right, that's cool. [00:09:07]Suhail: I don't know. Yeah. [00:09:09]Swyx: I mean, they had some flashy videos, but it didn't really register. [00:09:13]Suhail: That moment of images was just such a viral novel moment. I think it just blew people's mind. Yeah. [00:09:19]Swyx: I mean, it's the first time I encountered Sam Altman because they had this Dolly 2 hackathon and they opened up the OpenAI office for developers to walk in back when it wasn't as much of a security issue as it is today. I see. Maybe take us through the journey to decide to pivot into this and also choosing images. Obviously, you were inspired by Dolly, but there could be any number of AI companies and businesses that you could start and why this one, right? [00:09:45]Suhail: Yeah. So I think at that time, Mighty and OpenAI was not quite as popular as it is all of a sudden now these days, but back then they had a lot more bandwidth to kind of help anybody. And so we had been talking with the team there around trying to see if we could do really fast low latency address bar prediction with GPT-3 and 3.5 and that kind of thing. And so we were sort of figuring out how could we make that low latency. I think that just being able to talk to them and kind of being involved gave me a bird's eye view into a bunch of things that started to happen. Latency first was the Dolly 2 moment, but then stable diffusion came out and that was a big moment for me as well. And I remember just kind of like sitting up one night thinking, I was like, you know, what are the kinds of companies one could build? Like what matters right now? One thing that I observed is that I find a lot of inspiration when I'm working in a field in something and then I can identify a bunch of problems. Like for Mixpanel, I was an intern at a company and I just noticed that they were doing all this data analysis. And so I thought, hmm, I wonder if I could make a product and then maybe they would use it. And in this case, you know, the same thing kind of occurred. It was like, okay, there are a bunch of like infrastructure companies that put a model up and then you can use their API, like Replicate is a really good example of that. There are a bunch of companies that are like helping you with training, model optimization, Mosaic at the time, and probably still, you know, was doing stuff like that. So I just started listing out like every category of everything, of every company that was doing something interesting. I started listing out like weights and biases. I was like, oh man, weights and biases is like this great company. Do I want to compete with that company? I might be really good at competing with that company because of Mixpanel because it's so much of like analysis. But I was like, no, I don't want to do anything related to that. That would, I think that would be too boring now at this point. So I started to list out all these ideas and one thing I observed was that at OpenAI, they had like a playground for GPT-3, right? All it was is just like a text box more or less. And then there were some settings on the right, like temperature and whatever. [00:11:41]Swyx: Top K. [00:11:42]Suhail: Yeah, top K. You know, what's your end stop sequence? I mean, that was like their product before GPT, you know, really difficult to use, but fun if you're like an engineer. And I just noticed that their product kind of was evolving a little bit where the interface kind of was getting a little bit more complex. They had like a way where you could like generate something in the middle of a sentence and all those kinds of things. And I just thought to myself, I was like, everything is just like this text box and you generate something and that's about it. And stable diffusion had kind of come out and it was all like hugging face and code. Nobody was really building any UI. And so I had this kind of thing where I wrote prompt dash like question mark in my notes and I didn't know what was like the product for that at the time. I mean, it seems kind of trite now, but I just like wrote prompt. What's the thing for that? Manager. Prompt manager. Do you organize them? Like, do you like have a UI that can play with them? Yeah. Like a library. What would you make? And so then, of course, then you thought about what would the modalities be given that? How would you build a UI for each kind of modality? And so there are a couple of people working on some pretty cool things. And I basically chose graphics because it seemed like the most obvious place where you could build a really powerful, complex UI. That's not just only typing a box. It would very much evolve beyond that. Like what would be the best thing for something that's visual? Probably something visual. Yeah. I think that just that progression kind of happened and it just seemed like there was a lot of effort going into language, but not a lot of effort going into graphics. And then maybe the very last thing was, I think I was talking to Aditya Ramesh, who was the co-creator of DALL-E 2 and Sam. And I just kind of went to these guys and I was just like, hey, are you going to make like a UI for this thing? Like a true UI? Are you going to go for this? Are you going to make a product? For DALL-E. Yeah. For DALL-E. Yeah. Are you going to do anything here? Because if you are going to do it, just let me know and I will stop and I'll go do something else. But if you're not going to do anything, I'll just do it. And so we had a couple of conversations around what that would look like. And then I think ultimately they decided that they were going to focus on language primarily. And I just felt like it was going to be very underinvested in. Yes. [00:13:46]Swyx: There's that sort of underinvestment from OpenAI, but also it's a different type of customer than you're used to, presumably, you know, and Mixpanel is very good at selling to B2B and developers will figure on you or not. Yeah. Was that not a concern? [00:14:00]Suhail: Well, not so much because I think that, you know, right now I would say graphics is in this very nascent phase. Like most of the customers are just like hobbyists, right? Yeah. Like it's a little bit of like a novel toy as opposed to being this like very high utility thing. But I think ultimately, if you believe that you could make it very high utility, the probably the next customers will end up being B2B. It'll probably not be like a consumer. There will certainly be a variation of this idea that's in consumer. But if your quest is to kind of make like something that surpasses human ability for graphics, like ultimately it will end up being used for business. So I think it's maybe more of a progression. In fact, for me, it's maybe more like Mixpanel started out as SMB and then very much like ended up starting to grow up towards enterprise. So for me, I think it will be a very similar progression. But yeah, I mean, the reason why I was excited about it is because it was a creative tool. I make music and it's AI. It's like something that I know I could stay up till three o'clock in the morning doing. Those are kind of like very simple bars for me. [00:14:56]Alessio: So you mentioned Dolly, Stable Diffusion. You just had Playground V2 come out two days ago. Yeah, two days ago. [00:15:02]Suhail: Two days ago. [00:15:03]Alessio: This is a model you train completely from scratch. So it's not a cheap fine tune on something. You open source everything, including the weights. Why did you decide to do it? I know you supported Stable Diffusion XL in Playground before, right? Yep. What made you want to come up with V2 and maybe some of the interesting, you know, technical research work you've done? [00:15:24]Suhail: Yeah. So I think that we continue to feel like graphics and these foundation models for anything really related to pixels, but also definitely images continues to be very underinvested. It feels a little like graphics is in like this GPT-2 moment, right? Like even GPT-3, even when GPT-3 came out, it was exciting, but it was like, what are you going to use this for? Yeah, we'll do some text classification and some semantic analysis and maybe it'll sometimes like make a summary of something and it'll hallucinate. But no one really had like a very significant like business application for GPT-3. And in images, we're kind of stuck in the same place. We're kind of like, okay, I write this thing in a box and I get some cool piece of artwork and the hands are kind of messed up and sometimes the eyes are a little weird. Maybe I'll use it for a blog post, you know, that kind of thing. The utility feels so limited. And so, you know, and then we, you sort of look at Stable Diffusion and we definitely use that model in our product and our users like it and use it and love it and enjoy it, but it hasn't gone nearly far enough. So we were kind of faced with the choice of, you know, do we wait for progress to occur or do we make that progress happen? So yeah, we kind of embarked on a plan to just decide to go train these things from scratch. And I think the community has given us so much. The community for Stable Diffusion I think is one of the most vibrant communities on the internet. It's like amazing. It feels like, I hope this is what like Homebrew Club felt like when computers like showed up because it's like amazing what that community will do and it moves so fast. I've never seen anything in my life and heard other people's stories around this where an academic research paper comes out and then like two days later, someone has sample code for it. And then two days later, there's a model. And then two days later, it's like in nine products, you know, they're all competing with each other. It's incredible to see like math symbols on an academic paper go to well-designed features in a product. So I think the community has done so much. So I think we wanted to give back to the community kind of on our way. Certainly we would train a better model than what we gave out on Tuesday, but we definitely felt like there needs to be some kind of progress in these open source models. The last kind of milestone was in July when Stable Diffusion Excel came out, but there hasn't been anything really since. Right. [00:17:34]Swyx: And there's Excel Turbo now. [00:17:35]Suhail: Well, Excel Turbo is like this distilled model, right? So it's like lower quality, but fast. You have to decide, you know, what your trade off is there. [00:17:42]Swyx: It's also a consistency model. [00:17:43]Suhail: I don't think it's a consistency model. It's like it's they did like a different thing. Yeah. I think it's like, I don't want to get quoted for this, but it's like something called ad like adversarial or something. [00:17:52]Swyx: That's exactly right. [00:17:53]Suhail: I've read something about that. Maybe it's like closer to GANs or something, but I didn't really read the full paper. But yeah, there hasn't been quite enough progress in terms of, you know, there's no multitask image model. You know, the closest thing would be something called like EmuEdit, but there's no model for that. It's just a paper that's within meta. So we did that and we also gave out pre-trained weights, which is very rare. Usually you just get the aligned model and then you have to like see if you can do anything with it. So we actually gave out, there's like a 256 pixel pre-trained stage and a 512. And we did that for academic research because we come across people all the time in academia, they have access to like one A100 or eight at best. And so if we can give them kind of like a 512 pre-trained model, our hope is that there'll be interesting novel research that occurs from that. [00:18:38]Swyx: What research do you want to happen? [00:18:39]Suhail: I would love to see more research around things that users care about tend to be things like character consistency. [00:18:45]Swyx: Between frames? [00:18:46]Suhail: More like if you have like a face. Yeah, yeah. Basically between frames, but more just like, you know, you have your face and it's in one image and then you want it to be like in another. And users are very particular and sensitive to faces changing because we know we're trained on faces as humans. Not seeing a lot of innovation, enough innovation around multitask editing. You know, there are two things like instruct pics to pics and then the EmuEdit paper that are maybe very interesting, but we certainly are not pushing the fold on that in that regard. All kinds of things like around that rotation, you know, being able to keep coherence across images, style transfer is still very limited. Just even reasoning around images, you know, what's going on in an image, that kind of thing. Things are still very, very underpowered, very nascent. So therefore the utility is very, very limited. [00:19:32]Alessio: On the 1K Prompt Benchmark, you are 2.5x prefer to Stable Diffusion XL. How do you get there? Is it better images in the training corpus? Can you maybe talk through the improvements in the model? [00:19:44]Suhail: I think they're still very early on in the recipe, but I think it's a lot of like little things and you know, every now and then there are some big important things like certainly your data quality is really, really important. So we spend a lot of time thinking about that. But I would say it's a lot of things that you kind of clean up along the way as you train your model. Everything from captions to the data that you align with after pre-train to how you're picking your data sets, how you filter your data sets. I feel like there's a lot of work in AI that doesn't really feel like AI. It just really feels like just data set filtering and systems engineering and just like, you know, and the recipe is all there, but it's like a lot of extra work to do that. I think we plan to do a Playground V 2.1, maybe either by the end of the year or early next year. And we're just like watching what the community does with the model. And then we're just going to take a lot of the things that they're unhappy about and just like fix them. You know, so for example, like maybe the eyes of people in an image don't feel right. They feel like they're a little misshapen or they're kind of blurry feeling. That's something that we already know we want to fix. So I think in that case, it's going to be about data quality. Or maybe you want to improve the kind of the dynamic range of color. You know, we want to make sure that that's like got a good range in any image. So what technique can we use there? There's different things like offset noise, pyramid noise, terminal zero, SNR, like there are all these various interesting things that you can do. So I think it's like a lot of just like tricks. Some are tricks, some are data, and some is just like cleaning. [00:21:11]Swyx: Specifically for faces, it's very common to use a pipeline rather than just train the base model more. Do you have a strong belief either way on like, oh, they should be separated out to different stages for like improving the eyes, improving the face or enhance or whatever? Or do you think like it can all be done in one model? [00:21:28]Suhail: I think we will make a unified model. Yeah, I think it will. I think we'll certainly in the end, ultimately make a unified model. There's not enough research about this. Maybe there is something out there that we haven't read. There are some bottlenecks, like for example, in the VAE, like the VAEs are ultimately like compressing these things. And so you don't know. And then you might have like a big informational information bottleneck. So maybe you would use a pixel based model, perhaps. I think we've talked to people, everyone from like Rombach to various people, Rombach trained stable diffusion. I think there's like a big question around the architecture of these things. It's still kind of unknown, right? Like we've got transformers and we've got like a GPT architecture model, but then there's this like weird thing that's also seemingly working with diffusion. And so, you know, are we going to use vision transformers? Are we going to move to pixel based models? Is there a different kind of architecture? We don't really, I don't think there have been enough experiments. Still? Oh my God. [00:22:21]Swyx: Yeah. [00:22:22]Suhail: That's surprising. I think it's very computationally expensive to do a pipeline model where you're like fixing the eyes and you're fixing the mouth and you're fixing the hands. [00:22:29]Swyx: That's what everyone does as far as I understand. [00:22:31]Suhail: I'm not exactly sure what you mean, but if you mean like you get an image and then you will like make another model specifically to fix a face, that's fairly computationally expensive. And I think it's like not probably not the right way. Yeah. And it doesn't generalize very well. Now you have to pick all these different things. [00:22:45]Swyx: Yeah. You're just kind of glomming things on together. Yeah. Like when I look at AI artists, like that's what they do. [00:22:50]Suhail: Ah, yeah, yeah, yeah. They'll do things like, you know, I think a lot of ARs will do control net tiling to do kind of generative upscaling of all these different pieces of the image. Yeah. And I think these are all just like, they're all hacks ultimately in the end. I mean, it just to me, it's like, let's go back to where we were just three years, four years ago with where deep learning was at and where language was that, you know, it's the same thing. It's like we were like, okay, well, I'll just train these very narrow models to try to do these things and kind of ensemble them or pipeline them to try to get to a best in class result. And here we are with like where the models are gigantic and like very capable of solving huge amounts of tasks when given like lots of great data. [00:23:28]Alessio: You also released a new benchmark called MJHQ30K for automatic evaluation of a model's aesthetic quality. I have one question. The data set that you use for the benchmark is from Midjourney. Yes. You have 10 categories. How do you think about the Playground model, Midjourney, like, are you competitors? [00:23:47]Suhail: There are a lot of people, a lot of people in research, they like to compare themselves to something they know they can beat, right? Maybe this is the best reason why it can be helpful to not be a researcher also sometimes like I'm not trained as a researcher, I don't have a PhD in anything AI related, for example. But I think if you care about products and you care about your users, then the most important thing that you want to figure out is like everyone has to acknowledge that Midjourney is very good. They are the best at this thing. I'm happy to admit that. I have no problem admitting that. Just easy. It's very visual to tell. So I think it's incumbent on us to try to compare ourselves to the thing that's best, even if we lose, even if we're not the best. At some point, if we are able to surpass Midjourney, then we only have ourselves to compare ourselves to. But on First Blush, I think it's worth comparing yourself to maybe the best thing and try to find like a really fair way of doing that. So I think more people should try to do that. I definitely don't think you should be kind of comparing yourself on like some Google model or some old SD, Stable Diffusion model and be like, look, we beat Stable Diffusion 1.5. I think users ultimately want care, how close are you getting to the thing that people mostly agree with? So we put out that benchmark for no other reason to say like, this seems like a worthy thing for us to at least try, for people to try to get to. And then if we surpass it, great, we'll come up with another one. [00:25:06]Alessio: Yeah, no, that's awesome. And you killed Stable Diffusion Excel and everything. In the benchmark chart, it says Playground V2 1024 pixel dash aesthetic. Do you have kind of like, yeah, style fine tunes or like what's the dash aesthetic for? [00:25:21]Suhail: We debated this, maybe we named it wrong or something, but we were like, how do we help people realize the model that's aligned versus the models that weren't? Because we gave out pre-trained models, we didn't want people to like use those. So that's why they're called base. And then the aesthetic model, yeah, we wanted people to pick up the thing that makes things pretty. Who wouldn't want the thing that's aesthetic? But if there's a better name, we're definitely open to feedback. No, no, that's cool. [00:25:46]Alessio: I was using the product. You also have the style filter and you have all these different styles. And it seems like the styles are tied to the model. So there's some like SDXL styles, there's some Playground V2 styles. Can you maybe give listeners an overview of how that works? Because in language, there's not this idea of like style, right? Versus like in vision model, there is, and you cannot get certain styles in different [00:26:11]Suhail: models. [00:26:12]Alessio: So how do styles emerge and how do you categorize them and find them? [00:26:15]Suhail: Yeah, I mean, it's so fun having a community where people are just trying a model. Like it's only been two days for Playground V2. And we actually don't know what the model's capable of and not capable of. You know, we certainly see problems with it. But we have yet to see what emergent behavior is. I mean, we've just sort of discovered that it takes about like a week before you start to see like new things. I think like a lot of that style kind of emerges after that week, where you start to see, you know, there's some styles that are very like well known to us, like maybe like pixel art is a well known style. Photorealism is like another one that's like well known to us. But there are some styles that cannot be easily named. You know, it's not as simple as like, okay, that's an anime style. It's very visual. And in the end, you end up making up the name for what that style represents. And so the community kind of shapes itself around these different things. And so if anyone that's into stable diffusion and into building anything with graphics and stuff with these models, you know, you might have heard of like Proto Vision or Dream Shaper, some of these weird names, but they're just invented by these authors. But they have a sort of je ne sais quoi that, you know, appeals to users. [00:27:26]Swyx: Because it like roughly embeds to what you what you want. [00:27:29]Suhail: I guess so. I mean, it's like, you know, there's one of my favorite ones that's fine tuned. It's not made by us. It's called like Starlight XL. It's just this beautiful model. It's got really great color contrast and visual elements. And the users love it. I love it. And it's so hard. I think that's like a very big open question with graphics that I'm not totally sure how we'll solve. I don't know. It's, it's like an evolving situation too, because styles get boring, right? They get fatigued. Like it's like listening to the same style of pop song. I try to relate to graphics a little bit like with music, because I think it gives you a little bit of a different shape to things. Like it's not as if we just have pop music, rap music and country music, like all of these, like the EDM genre alone has like sub genres. And I think that's very true in graphics and painting and art and anything that we're doing. There's just these sub genres, even if we can't quite always name them. But I think they are emergent from the community, which is why we're so always happy to work with the community. [00:28:26]Swyx: That is a struggle. You know, coming back to this, like B2B versus B2C thing, B2C, you're going to have a huge amount of diversity and then it's going to reduce as you get towards more sort of B2B type use cases. I'm making this up here. So like you might be optimizing for a thing that you may eventually not need. [00:28:42]Suhail: Yeah, possibly. Yeah, possibly. I think like a simple thing with startups is that I worry sometimes by trying to be overly ambitious and like really scrutinizing like what something is in its most nascent phase that you miss the most ambitious thing you could have done. Like just having like very basic curiosity with something very small can like kind of lead you to something amazing. Like Einstein definitely did that. And then he like, you know, he basically won all the prizes and got everything he wanted and then basically did like kind of didn't really. He can dismiss quantum and then just kind of was still searching, you know, for the unifying theory. And he like had this quest. I think that happens a lot with like Nobel Prize people. I think there's like a term for it that I forget. I actually wanted to go after a toy almost intentionally so long as that I could see, I could imagine that it would lead to something very, very large later. Like I said, it's very hobbyist, but you need to start somewhere. You need to start with something that has a big gravitational pull, even if these hobbyists aren't likely to be the people that, you know, have a way to monetize it or whatever, even if they're, but they're doing it for fun. So there's something, something there that I think is really important. But I agree with you that, you know, in time we will absolutely focus on more utilitarian things like things that are more related to editing feats that are much harder. And so I think like a very simple use case is just, you know, I'm not a graphics designer. It seems like very simple that like you, if we could give you the ability to do really complex graphics without skill, wouldn't you want that? You know, like my wife the other day was set, you know, said, I wish Playground was better. When are you guys going to have a feature where like we could make my son, his name's Devin, smile when he was not smiling in the picture for the holiday card. Right. You know, just being able to highlight his, his mouth and just say like, make him smile. Like why can't we do that with like high fidelity and coherence, little things like that, all the way to putting you in completely different scenarios. [00:30:35]Swyx: Is that true? Can we not do that in painting? [00:30:37]Suhail: You can do in painting, but the quality is just so bad. Yeah. It's just really terrible quality. You know, it's like you'll do it five times and it'll still like kind of look like crooked or just artifact. Part of it's like, you know, the lips on the face, there's such little information there. So small that the models really struggle with it. Yeah. [00:30:55]Swyx: Make the picture smaller and you don't see it. That's my trick. I don't know. [00:30:59]Suhail: Yeah. Yeah. That's true. Or, you know, you could take that region and make it really big and then like say it's a mouth and then like shrink it. It feels like you're wrestling with it more than it's doing something that kind of surprises you. [00:31:12]Swyx: Yeah. It feels like you are very much the internal tastemaker, like you carry in your head this vision for what a good art model should look like. Do you find it hard to like communicate it to like your team and other people? Just because it's obviously it's hard to put into words like we just said. [00:31:26]Suhail: Yeah. It's very hard to explain. Images have such high bitrate compared to just words and we don't have enough words to describe these things. It's not terribly difficult. I think everyone on the team, if they don't have good kind of like judgment taste or like an eye for some of these things, they're like steadily building it because they have no choice. Right. So in that realm, I don't worry too much, actually. Like everyone is kind of like learning to get the eye is what I would call it. But I also have, you know, my own narrow taste. Like I don't represent the whole population either. [00:31:59]Swyx: When you benchmark models, you know, like this benchmark we're talking about, we use FID. Yeah. Input distance. OK. That's one measure. But like it doesn't capture anything you just said about smiles. [00:32:08]Suhail: Yeah. FID is generally a bad metric. It's good up to a point and then it kind of like is irrelevant. Yeah. [00:32:14]Swyx: And then so are there any other metrics that you like apart from vibes? I'm always looking for alternatives to vibes because vibes don't scale, you know. [00:32:22]Suhail: You know, it might be fun to kind of talk about this because it's actually kind of fresh. So up till now, we haven't needed to do a ton of like benchmarking because we hadn't trained our own model and now we have. So now what? What does that mean? How do we evaluate it? And, you know, we're kind of like living with the last 48, 72 hours of going, did the way that we benchmark actually succeed? [00:32:43]Swyx: Did it deliver? [00:32:44]Suhail: Right. You know, like I think Gemini just came out. They just put out a bunch of benchmarks. But all these benchmarks are just an approximation of how you think it's going to end up with real world performance. And I think that's like very fascinating to me. So if you fake that benchmark, you'll still end up in a really bad scenario at the end of the day. And so, you know, one of the benchmarks we did was we kind of curated like a thousand prompts. And I think that's kind of what we published in our blog post, you know, of all these tasks that we a lot of some of them are curated by our team where we know the models all suck at it. Like my favorite prompt that no model is really capable of is a horse riding an astronaut, the inverse one. And it's really, really hard to do. [00:33:22]Swyx: Not in data. [00:33:23]Suhail: You know, another one is like a giraffe underneath a microwave. How does that work? Right. There's so many of these little funny ones. We do. We have prompts that are just like misspellings of things. Yeah. We'll figure out if the models will figure it out. [00:33:36]Swyx: They should embed to the same space. [00:33:39]Suhail: Yeah. And just like all these very interesting weirdo things. And so we have so many of these and then we kind of like evaluate whether the models are any good at it. And the reality is that they're all bad at it. And so then you're just picking the most aesthetic image. We're still at the beginning of building like the best benchmark we can that aligns most with just user happiness, I think, because we're not we're not like putting these in papers and trying to like win, you know, I don't know, awards at ICCV or something if they have awards. You could. [00:34:05]Swyx: That's absolutely a valid strategy. [00:34:06]Suhail: Yeah, you could. But I don't think it could correlate necessarily with the impact we want to have on humanity. I think we're still evolving whatever our benchmarks are. So the first benchmark was just like very difficult tasks that we know the models are bad at. Can we come up with a thousand of these, whether they're hand rated and some of them are generated? And then can we ask the users, like, how do we do? And then we wanted to use a benchmark like party prompts. We mostly did that so people in academia could measure their models against ours versus others. But yeah, I mean, fit is pretty bad. And I think in terms of vibes, it's like you put out the model and then you try to see like what users make. And I think my sense is that we're going to take all the things that we notice that the users kind of were failing at and try to find like new ways to measure that, whether that's like a smile or, you know, color contrast or lighting. One benefit of Playground is that we have users making millions of images every single day. And so we can just ask them for like a post generation feedback. Yeah, we can just ask them. We can just say, like, how good was the lighting here? How was the subject? How was the background? [00:35:06]Swyx: Like a proper form of like, it's just like you make it, you come to our site, you make [00:35:10]Suhail: an image and then we say, and then maybe randomly you just say, hey, you know, like, how was the color and contrast of this image? And you say it was not very good, just tell us. So I think I think we can get like tens of thousands of these evaluations every single day to truly measure real world performance as opposed to just like benchmark performance. I would like to publish hopefully next year. I think we will try to publish a benchmark that anyone could use, that we evaluate ourselves on and that other people can, that we think does a good job of approximating real world performance because we've tried it and done it and noticed that it did. Yeah. I think we will do that. [00:35:45]Swyx: I personally have a few like categories that I consider special. You know, you know, you have like animals, art, fashion, food. There are some categories which I consider like a different tier of image. Top among them is text in images. How do you think about that? So one of the big wow moments for me, something I've been looking out for the entire year is just the progress of text and images. Like, can you write in an image? Yeah. And Ideogram came out recently, which had decent but not perfect text and images. Dolly3 had improved some and all they said in their paper was that they just included more text in the data set and it just worked. I was like, that's just lazy. But anyway, do you care about that? Because I don't see any of that in like your sample. Yeah, yeah. [00:36:27]Suhail: The V2 model was mostly focused on image quality versus like the feature of text synthesis. [00:36:33]Swyx: Well, as a business user, I care a lot about that. [00:36:35]Suhail: Yeah. Yeah. I'm very excited about text synthesis. And yeah, I think Ideogram has done a good job of maybe the best job. Dolly has like a hit rate. Yes. You know, like sometimes it's Egyptian letters. Yeah. I'm very excited about text synthesis. You know, I don't have much to say on it just yet. You know, you don't want just text effects. I think where this has to go is it has to be like you could like write little tiny pieces of text like on like a milk carton. That's maybe not even the focal point of a scene. I think that's like a very hard task that, you know, if you could do something like that, then there's a lot of other possibilities. Well, you don't have to zero shot it. [00:37:09]Swyx: You can just be like here and focus on this. [00:37:12]Suhail: Sure. Yeah, yeah. Definitely. Yeah. [00:37:16]Swyx: Yeah. So I think text synthesis would be very exciting. I'll also flag that Max Wolf, MiniMaxxier, which you must have come across his work. He's done a lot of stuff about using like logo masks that then map onto food and vegetables. And it looks like text, which can be pretty fun. [00:37:29]Suhail: That's the wonderful thing about like the open source community is that you get things like control net and then you see all these people do these just amazing things with control net. And then you wonder, I think from our point of view, we sort of go that that's really wonderful. But how do we end up with like a unified model that can do that? What are the bottlenecks? What are the issues? The community ultimately has very limited resources. And so they need these kinds of like workaround research ideas to get there. But yeah. [00:37:55]Swyx: Are techniques like control net portable to your architecture? [00:37:58]Suhail: Definitely. Yeah. We kept the Playground V2 exactly the same as SDXL. Not because not out of laziness, but just because we knew that the community already had tools. You know, all you have to do is maybe change a string in your code and then, you know, retrain a control net for it. So it was very intentional to do that. We didn't want to fragment the community with different architectures. Yeah. [00:38:16]Swyx: So basically, I'm going to go over three more categories. One is UIs, like app UIs, like mock UIs. Third is not safe for work, and then copyrighted stuff. I don't know if you care to comment on any of those. [00:38:28]Suhail: I think the NSFW kind of like safety stuff is really important. I kind of think that one of the biggest risks kind of going into maybe the U.S. election year will probably be very interrelated with like graphics, audio, video. I think it's going to be very hard to explain, you know, to a family relative who's not kind of in our world. And our world is like sometimes very, you know, we think it's very big, but it's very tiny compared to the rest of the world. Some people like there's still lots of humanity who have no idea what chat GPT is. And I think it's going to be very hard to explain, you know, to your uncle, aunt, whoever, you know, hey, I saw President Biden say this thing on a video, you know, I can't believe, you know, he said that. I think that's going to be a very troubling thing going into the world next year, the year after. [00:39:12]Swyx: That's more like a risk thing, like deepfakes, faking, political faking. But there's a lot of studies on how for most businesses, you don't want to train on not safe for work images, except that it makes you really good at bodies. [00:39:24]Suhail: Personally, we filter out NSFW type of images in our data set so that it's, you know, so our safety filter stuff doesn't have to work as hard. [00:39:32]Swyx: But you've heard this argument that not safe for work images are very good at human anatomy, which you do want to be good at. [00:39:38]Suhail: It's not like necessarily a bad thing to train on that data. It's more about like how you go and use it. That's why I was kind of talking about safety, you know, in part, because there are very terrible things that can happen in the world. If you have an extremely powerful graphics model, you know, suddenly like you can kind of imagine, you know, now if you can like generate nudes and then there's like you could do very character consistent things with faces, like what does that lead to? Yeah. And so I tend to think more what occurs after that, right? Even if you train on, let's say, you know, new data, if it does something to kind of help, there's nothing wrong with the human anatomy, it's very valid for a model to learn that. But then it's kind of like, how does that get used? And, you know, I won't bring up all of the very, very unsavory, terrible things that we see on a daily basis on the site, but I think it's more about what occurs. And so we, you know, we just recently did like a big sprint on safety. It's very difficult with graphics and art, right? Because there is tasteful art that has nudity, right? They're all over in museums, like, you know, there's very valid situations for that. And then there's the things that are the gray line of that, you know, what I might not find tasteful, someone might be like, that is completely tasteful, right? And then there are things that are way over the line. And then there are things that maybe you or, you know, maybe I would be okay with, but society isn't, you know? So where does that kind of end up on the spectrum of things? I think it's really hard with art. Sometimes even if you have like things that are not nude, if a child goes to your site, scrolls down some images, you know, classrooms of kids, you know, using our product, it's a really difficult problem. And it stretches mostly culture, society, politics, everything. [00:41:14]Alessio: Another favorite topic of our listeners is UX and AI. And I think you're probably one of the best all-inclusive editors for these things. So you don't just have the prompt, images come out, you pray, and now you do it again. First, you let people pick a seed so they can kind of have semi-repeatable generation. You also have, yeah, you can pick how many images and then you leave all of them in the canvas. And then you have kind of like this box, the generation box, and you can even cross between them and outpaint. There's all these things. How did you get here? You know, most people are kind of like, give me text, I give you image. You know, you're like, these are all the tools for you. [00:41:54]Suhail: Even though we were trying to make a graphics foundation model, I think we think that we're also trying to like re-imagine like what a graphics editor might look like given the change in technology. So, you know, I don't think we're trying to build Photoshop, but it's the only thing that we could say that people are largely familiar with. Oh, okay, there's Photoshop. What would Photoshop compare itself to pre-computer? I don't know, right? It's like, or kind of like a canvas, but you know, there's these menu options and you can use your mouse. What's a mouse? So I think that we're trying to re-imagine what a graphics editor might look like, not just for the fun of it, but because we kind of have no choice. Like there's this idea in image generation where you can generate images. That's like a super weird thing. What is that in Photoshop, right? You have to wait right now for the time being, but the wait is worth it often for a lot of people because they can't make that with their own skills. So I think it goes back to, you know, how we started the company, which was kind of looking at GPT-3's Playground, that the reason why we're named Playground is a homage to that actually. And, you know, it's like, shouldn't these products be more visual? These prompt boxes are like a terminal window, right? We're kind of at this weird point where it's just like MS-DOS. I remember my mom using MS-DOS and I memorized the keywords, like DIR, LS, all those things, right? It feels a little like we're there, right? Prompt engineering, parentheses to say beautiful or whatever, waits the word token more in the model or whatever. That's like super strange. I think a large portion of humanity would agree that that's not user-friendly, right? So how do we think about the products to be more user-friendly? Well, sure, you know, sure, it would be nice if I wanted to get rid of, like, the headphones on my head, you know, it'd be nice to mask it and then say, you know, can you remove the headphones? You know, if I want to grow, expand the image, you know, how can we make that feel easier without typing lots of words and being really confused? I don't even think we've nailed the UI UX yet. Part of that is because we're still experimenting. And part of that is because the model and the technology is going to get better. And whatever felt like the right UX six months ago is going to feel very broken now. So that's a little bit of how we got there is kind of saying, does everything have to be like a prompt in a box? Or can we do things that make it very intuitive for users? [00:44:03]Alessio: How do you decide what to give access to? So you have things like an expand prompt, which Dally 3 just does. It doesn't let you decide whether you should or not. [00:44:13]Swyx: As in, like, rewrites your prompts for you. [00:44:15]Suhail: Yeah, for that feature, I think once we get it to be cheaper, we'll probably just give it up. We'll probably just give it away. But we also decided something that might be a little bit different. We noticed that most of image generation is just, like, kind of casual. You know, it's in WhatsApp. It's, you know, it's in a Discord bot somewhere with Majorny. It's in ChatGPT. One of the differentiators I think we provide is at the expense of just lots of users necessarily. Mainstream consumers is that we provide as much, like, power and tweakability and configurability as possible. So the only reason why it's a toggle, because we know that users might want to use it and might not want to use it. There's some really powerful power user hobbyists that know what they're doing. And then there's a lot of people that just want something that looks cool, but they don't know how to prompt. And so I think a lot of Playground is more about going after that core user base that, like, knows, has a little bit more savviness and how to use these tools. You know, the average Dell user is probably not going to use ControlNet. They probably don't even know what that is. And so I think that, like, as the models get more powerful, as there's more tooling, hopefully you'll imagine a new sort of AI-first graphics editor that's just as, like, powerful and configurable as Photoshop. And you might have to master a new kind of tool. [00:45:28]Swyx: There's so many things I could go bounce off of. One, you mentioned about waiting. We have to kind of somewhat address the elephant in the room. Consistency models have been blowing up the past month. How do you think about integrating that? Obviously, there's a lot of other companies also trying to beat you to that space as well. [00:45:44]Suhail: I think we were the first company to integrate it. Ah, OK. [00:45:47]Swyx: Yeah. I didn't see your demo. [00:45:49]Suhail: Oops. Yeah, yeah. Well, we integrated it in a different way. OK. There are, like, 10 companies right now that have kind of tried to do, like, interactive editing, where you can, like, draw on the left side and then you get an image on the right side. We decided to kind of, like, wait and see whether there's, like, true utility on that. We have a different feature that's, like, unique in our product that is called preview rendering. And so you go to the product and you say, you know, we're like, what is the most common use case? The most common use case is you write a prompt and then you get an image. But what's the most annoying thing about that? The most annoying thing is, like, it feels like a slot machine, right? You're like, OK, I'm going to put it in and maybe I'll get something cool. So we did something that seemed a lot simpler, but a lot more relevant to how users already use these products, which is preview rendering. You toggle it on and it will show you a render of the image. And then graphics tools already have this. Like, if you use Cinema 4D or After Effects or something, it's called viewport rendering. And so we try to take something that exists in the real world that has familiarity and say, OK, you're going to get a rough sense of an early preview of this thing. And then when you're ready to generate, we're going to try to be as coherent about that image that you saw. That way, you're not spending so much time just like pulling down the slot machine lever. I think we were the first company to actually ship a quick LCM thing. Yeah, we were very excited about it. So we shipped it very quick. Yeah. [00:47:03]Swyx: Well, the demos I've been seeing, it's not like a preview necessarily. They're almost using it to animate their generations. Like, because you can kind of move shapes. [00:47:11]Suhail: Yeah, yeah, they're like doing it. They're animating it. But they're sort of showing, like, if I move a moon, you know, can I? [00:47:17]Swyx: I don't know. To me, it unlocks video in a way. [00:47:20]Suhail: Yeah. But the video models are already so much better than that. Yeah. [00:47:23]Swyx: There's another one, which I think is general ecosystem of Loras, right? Civit is obviously the most popular repository of Loras. How do you think about interacting with that ecosystem? [00:47:34]Suhail: The guy that did Lora, not the guy that invented Loras, but the person that brought Loras to Stable Diffusion actually works with us on some projects. His name is Simu. Shout out to Simu. And I think Loras are wonderful. Obviously, fine tuning all these Dreambooth models and such, it's just so heavy. And it's obvious in our conversation around styles and vibes, it's very hard to evaluate the artistry of these things. Loras give people this wonderful opportunity to create sub-genres of art. And I think they're amazing. Any graphics tool, any kind of thing that's expressing art has to provide some level of customization to its user base that goes beyond just typing Greg Rakowski in a prompt. We have to give more than that. It's not like users want to type these real artist names. It's that they don't know how else to get an image that looks interesting. They truly want originality and uniqueness. And I think Loras provide that. And they provide it in a very nice, scalable way. I hope that we find something even better than Loras in the long term, because there are still weaknesses to Loras, but I think they do a good job for now. Yeah. [00:48:39]Swyx: And so you would never compete with Civit? You would just kind of let people import? [00:48:43]Suhail: Civit's a site where all these things get kind of hosted by the community, right? And so, yeah, we'll often pull down some of the best things there. I think when we have a significantly better model, we will certainly build something that gets closer to that. Again, I go back to saying just I still think this is very nascent. Things are very underpowered, right? Loras are not easy to train. They're easy for an engineer. It sure would be nicer if I could just pick five or six reference images, right? And they might even be five or six different reference images that are not... They're just very different. They communicate a style, but they're actually like... It's like a mood board, right? And you have to be kind of an engineer almost to train these Loras or go to some site and be technically savvy, at least. It seems like it'd be much better if I could say, I love this style. Here are five images and you tell the model, like, this is what I want. And the model gives you something that's very aligned with what your style is, what you're talking about. And it's a style you couldn't even communicate, right? There's n

Wir beginnen den ersten Teil des Programms mit einer Diskussion über die politische Zukunft des Gazastreifens. Danach sprechen wir über die Organisation der Klimakonferenz COP28 und die dort getroffenen Aussagen. Im wissenschaftlichen Teil des Programms diskutieren wir heute über ein einzigartiges Experiment, bei dem die Ergebnisse verschiedener Ernährungsweisen von 22 Paaren genetisch identischer Zwillinge miteinander verglichen wurden. Und zum Schluss werden wir über ChatGPT sprechen. Was hat der Chatbot, den es jetzt seit einem Jahr gibt, der Welt gebracht? Im zweiten Teil unseres Programms, „Trending in Germany“, sprechen wir heute über die Abschiedsshow des Moderators Thomas Gottschalk von „WETTEN DASS..?“, die von 12,1 Millionen Zuschauern verfolgt wurde. Außerdem sprechen wir über die Auszeichnung der trendigsten Stadtviertel weltweit, die das Time Out Magazine vor kurzem vergeben hat. Deutschland ist mit dem Berliner Stadtbezirk Neukölln vertreten. Diese Wahl hat einige überrascht, da Neukölln von vielen als ein Problembezirk angesehen wird. Die politische Zukunft des Gazastreifens bleibt unklar Chef eines Ölkonzerns aus den VAE leitet die UN-Klimakonferenz Neue Studie vergleicht unterschiedliche Ernährungsweisen von Zwillingen Ein Jahr nach der Veröffentlichung von ChatGPT hält die Kontroverse um das Programm an Thomas Gottschalk verabschiedet sich von WETTEN DASS..? Neukölln ist mit dabei

Wir beginnen den ersten Teil des Programms mit einer Diskussion über die politische Zukunft des Gazastreifens. Danach sprechen wir über die Organisation der Klimakonferenz COP28 und die dort getroffenen Aussagen. Im wissenschaftlichen Teil des Programms diskutieren wir heute über ein einzigartiges Experiment, bei dem die Ergebnisse verschiedener Ernährungsweisen von 22 Paaren genetisch identischer Zwillinge miteinander verglichen wurden. Und zum Schluss werden wir über ChatGPT sprechen. Was hat der Chatbot, den es jetzt seit einem Jahr gibt, der Welt gebracht? Im zweiten Teil unseres Programms, „Trending in Germany“, sprechen wir heute über die Abschiedsshow des Moderators Thomas Gottschalk von „WETTEN DASS..?“, die von 12,1 Millionen Zuschauern verfolgt wurde. Außerdem sprechen wir über die Auszeichnung der trendigsten Stadtviertel weltweit, die das Time Out Magazine vor kurzem vergeben hat. Deutschland ist mit dem Berliner Stadtbezirk Neukölln vertreten. Diese Wahl hat einige überrascht, da Neukölln von vielen als ein Problembezirk angesehen wird. Die politische Zukunft des Gazastreifens bleibt unklar Chef eines Ölkonzerns aus den VAE leitet die UN-Klimakonferenz Neue Studie vergleicht unterschiedliche Ernährungsweisen von Zwillingen Ein Jahr nach der Veröffentlichung von ChatGPT hält die Kontroverse um das Programm an Thomas Gottschalk verabschiedet sich von WETTEN DASS..? Neukölln ist mit dabei

Die ölreichen Vereinigten Arabischen Emirate sind Gastgeber der COP28, der “Conference of the Parties”, die Klimakonferenz, die die UN jedes Jahr organisieren. Und sie steht unter keinem guten Stern. Die Klimaziele, die 2015 in Paris vereinbart wurden, sind nur noch schwer erreichbar. Und auch das politische Weltklima hat sich zum Schlechten verändert. Wir schauen uns die gastgebenden Emirate genauer an - ein Land voller Widersprüche, weltoffen nach außen, nach innen autoritär. Einerseits wird in den VAE die Öl- und Gasförderung massiv weiter ausgebaut, andererseits stehen Mega-Solarparks in der Wüste, und in vielen Bereichen setzt das Land auf grüne Technik. Aber wie grün ist der Staat am Golf wirklich? Wir sprechen mit Julika Oldenburg, Journalistin und Autorin mehrerer Bücher über die VAE, mit Sebastian Sons von der Deutschen Gesellschaft für Auswärtige Politik und mit unseren für die Region zuständigen Korrespondentinnen und Korrespondenten. Podcast-Tipp: Auf der COP werden die großen Themen rund ums Klima besprochen. Aber wie sieht es eigentlich im Kleinen aus? In der „NDR Info Redezeit“ geht es darum, was ein persönlicher Verzicht für das Klima eigentlich bringt. Gäste: Prof. Dr. Anita Engels (Soziologin und Klimaforscherin, Universität Hamburg) und Lea-Maria Rhein (Aktivistin und Sprecherin der Klimaprotest-Organisation "Letzte Generation") Moderation: Nina Zimmermann https://www.ardaudiothek.de/episode/redezeit/un-klimakonferenz-was-bringt-persoenlicher-verzicht-fuer-das-klima/ndr-info/12952023/

#DrKenyattaCavil #SportsLab #HBCUsports"Inside the HBCU Sports Lab" episode 463 with Dr. Kenyatta Cavil, Mike Washington & Charles Bishop radio show. Today's show will be a good one as Dr. Cavil, BCSN SportsWrap's AD Drew and Joshua Sims Sr (HBCU Nightly) recap Week 13 in HBCU football.TOPICS:HBCU SIAC Mid-Major DIVISION Game of Week- NCAA Division II PlayoffsSecond RoundNCAA Division II Playoffs– SIAC – Saturday, November 25th(SAC) No. 11 (No. 4) Lenoir-Rhyne Bears (12-1, 8-1) defeats (SIAC) No. 4 / HBCU No. 1 (No. 1) Benedict Tigers (11-1, 8-0), 35-25, Finalhttps://benedicttigers.com/sports/football/stats/2023/lenoir-rhyne-university/boxscore/5749-HBCU MEAC MAJOR DIVISION Game of Week-Richmond, VAE. Claiborne Robins StadiumNCAA FCS Playoff First Round– CAA / MEAC – Saturday, November 25th, 2:00pm CT – ESPN+The (HWCU) (NCAA FCS No. 25) Richmond Spiders (9-3, 6-2) defeats the (NCAA FCS No. 13) No. 2 North Carolina Central Eagles (10-2, 4-1), 49-27, Final- DR. CAVIL'S INSIDE THE HBCU HUDDLE REPORTHOUSTON– Dr. Cavil's 2023 HBCU Major Division Football Games of the Week – Week 13-HBCU CLASSIC MAJOR DIVISION Game of Week-Montgomery, ALHornets StadiumTurkey Day Classic– SIAC / SWAC – Thursday, November 23rd, 2:30pm CT – ESPNUThe (Major) No. 6 Alabama State Hornets (7-4, 5-3) (Mid-Major) defeat No. 7 Tuskegee Golden Tigers (7-4, 6-2), 41-7, Finalhttps://www.espn.com/college-football/boxscore/_/gameId/401540124-HBCU SWAC MAJOR DIVISION Game of Week-New Orleans, LAMercedes Stadium– SWAC – Saturday, November 25th, 2:00pm CT – NBCThe No. 9 Southern Jaguars (6-5, 5-3) defeat No. 10 Grambling State Tigers (5-6, 4-4), 27-22, Finalhttps://www.espn.com/college-football/boxscore/_/gameId/401540159@InsidetheHBCUSportsLab on Facebook Live and Spreaker.‬Donations welcome at CashApp $JafusCavil

#DrKenyattaCavil #SportsLab #HBCUsports"Inside the HBCU Sports Lab" episode 462 with Dr. Kenyatta Cavil, Mike Washington & Charles Bishop radio show. Today's show will be a good one as Dr. Cavil plus BCSN SportsWrap's AD Drew and Bryan Fulford discuss the Week 12 HBCU Marching Sport (the bands) poll rankings and preview Week 13 HBCU football match-ups.TOPICS:Jackson State Upsets Missouri on the Road 73-72 from SWAC.orgThree HBCU coaches named Eddie Robinson Coach of the Year finalists from HBCUSports.comJackson State paces all Division I HBCUs, FCS schools in home attendance from HBCUsports.comNCCU Eagles to Face Richmond Spiders in First Round of NCAA FCS Playoffs from MEACSports.comBand of The Year announces top five contendersThe ESPN Band of The Year committee has tabulated the cumulative scores as it narrows down two bands in each categoryMEAC announces 2024 Hall of Fame Induction ClassThe MEAC Hall of Fame class includes current NFL star Javon Hargrave as well as a world champion sprinter and legendary coachesDR. CAVIL'S INSIDE THE HBCU HUDDLE REPORTDr. Cavil's 2023 HBCU Mid-Major Marching Sport (The Bands) Top 7 Poll Rankings – Week 12The Miles Purple Marching Machine Remain No. 1 in Week 12DR. CAVIL'S INSIDE THE HBCU HUDDLE REPORTDr. Cavil's 2023 HBCU Major Division Football Games of the Week – Week 13-HBCU CLASSIC MAJOR DIVISION Game of Week-Montgomery, ALHornets StadiumTurkey Day Classic– SIAC / SWAC – Thursday, November 23rd, 2:30pm CT – ESPNU(Major) No. 6 Alabama State Hornets (7-4, 6-3) defeat (Mid-Major) No. 7 Tuskegee Golden Tigers (7-4, 6-3), 41-3 Final.https://www.espn.com/college-football/boxscore/_/gameId/401540124-HBCU INDEPENDENT/NON-CONFERENCE MAJOR DIVISION Game of Week--HBCU SWAC MAJOR DIVISION Game of Week-New Orleans, LAMercedes Stadium– SWAC – Saturday, November 25th, 2:00pm CT – NBCNo. 10 Grambling State Tigers (5-5, 4-3) at No. 9 Southern Jaguars (5-5, 4-3)DR. CAVIL'S INSIDE THE HBCU HUDDLE REPORTDr. Cavil's 2023 HBCU Major Division Marching Sport (The Bands) Top 7 Poll Rankings – Week 12The Norfolk State – The Spartan Legion Continues at the Top in the Rankings in Week 12Band of The Year announces top five contendersThe ESPN Band of The Year committee has tabulated the cumulative scores as it narrows down two bands in each category, In the Major Division-HBCU SIAC Mid-Major DIVISION Game of Week-Second RoundNCAA Division II Playoffs– SIAC – Saturday, November 25th(SAC) No. 11 (No. 4) Lenoir-Rhyne Bears (11-1, 8-1) at (SIAC) No. 4 / HBCU No. 1 (No. 1) Benedict Tigers(11-0, 8-0)-HBCU MEAC MAJOR DIVISION Game of Week-Richmond, VAE. Claiborne Robins StadiumNCAA FCS Playoff First Round– CAA / MEAC – Saturday, November 25th, 2:00pm CT – ESPN+(HWCU) (NCAA FCS No. 25) Richmond Spiders (8-3, 6-2) at (NCAA FCS No. 13) No. 2 North Carolina CentralEagles (10-1, 4-1)@InsidetheHBCUSportsLab on Facebook Live and Spreaker.‬Donations welcome at CashApp $JafusCavil

#DrKenyattaCavil #SportsLab #HBCUsports"Inside the HBCU Sports Lab" episode 461 with Dr. Kenyatta Cavil, Mike Washington & Charles Bishop radio show. Today's show will be a good one as Dr. Cavil, Mike, and Charles discuss the latest in HBCU news and sports plus discuss key Week 13 HBCU football match-ups.TOPICS:The Sooner Athletic Conference unveiled their All-ConferenceMid-Eastern Athletic Conference Weekly Football HonorsSWAC Football Weekly Honors: Nov. 20Howard highlights football Saturday by claiming the MEAC Regular Season Championship titlePanthers hold Alabama State to 16 rushing yards in 21-14 victory to earn the Western Division Championship TitleCoppin State claims their first ever MEAC Volleyball ChampionshipJackson State Claims SWAC Volleyball Tournament TitleNCCU Eagles to Face Richmond Spiders in First Round of NCAA FCS Playoffs from MEACSports.comDR. CAVIL'S INSIDE THE HBCU HUDDLE REPORTDr. Cavil's 2023 HBCU Mid-Major Division Football Top 7 Poll Rankings – Week 12The Benedict Tigers Maintains Unanimous No. 1 Spot in the Rankings in Week 12-HBCU SIAC Mid-Major DIVISION Game of Week-NCAA Division II PlayoffsSecond RoundNCAA Division II Playoffs– SIAC – Saturday, November 25th(SAC) No. 11 (No. 4) Lenoir-Rhyne Bears (11-1, 8-1) at (SIAC) No. 4 / HBCU No. 1 (No. 1) Benedict Tigers(11-0, 8-0)-HBCU MEAC MAJOR DIVISION Game of Week-Richmond, VAE. Claiborne Robins StadiumNCAA FCS Playoff First Round– CAA / MEAC – Saturday, November 25th, 2:00pm CT – ESPN+(HWCU) (NCAA FCS No. 25) Richmond Spiders (8-3, 6-2) at (NCAA FCS No. 13) No. 2 North Carolina CentralEagles (10-1, 4-1)DR. CAVIL'S INSIDE THE HBCU HUDDLE REPORTDr. Cavil's 2023 HBCU Major Division Football Poll Rankings – Week 12The Florida A&M Rattlers Remain Unanimous No. 1, in Ranking in Week 12-HBCU INDEPENDENT/NON-CONFERENCE MAJOR DIVISION Game of Week-Montgomery, ALHornets StadiumTurkey Day Classic– SIAC / SWAC – Thursday, November 23rd, 2:30pm CT – ESPNU(Mid-Major) No. 7 Tuskegee Golden Tigers (7-3, 6-2) at (Major) No. 6 Alabama State Hornets (6-4, 5-3)-HBCU CLASSIC MAJOR DIVISION Game of Week--HBCU SWAC MAJOR DIVISION Game of Week-New Orleans, LAMercedes Stadium– SWAC – Saturday, November 25th, 2:00pm CT – NBCNo. 10 Grambling State Tigers (5-5, 4-3) at No. 9 Southern Jaguars (5-5, 4-3)@InsidetheHBCUSportsLab on Facebook Live and Spreaker.‬Donations welcome at CashApp $JafusCavil