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God hates the mixture of truth and error. Jacob discusses lukewarm churches of the current age.This teaching was originally taught on RTN TV's "Word for the Weekend" on February, 2, 2024 and can be found on RTN and Moriel's YouTube and ministry channels. Word for the Weekend streams live every Saturday.
7 noticias más importantes de los últimos 7 días sobre inteligencia artificial1. Meta crea un equipo secreto para dominar la IA con una inversión de 15.000 millonesMeta ha formado un equipo especial dedicado a desarrollar inteligencia artificial avanzada, con un gasto multimillonario, para mejorar sus capacidades en robótica y automatización, incluyendo robots que anticipan y planifican tareas autónomamente2. 2. DeepSeek y el Modelo R1: nuevo estándar en modelos de lenguaje en ChinaDeepSeek-R1, un modelo basado en arquitectura Mixture of Experts con 671 mil millones de parámetros, ha alcanzado un rendimiento comparable a GPT-4o y Claude Sonnet 3.5, destacando por su eficiencia y capacidades avanzadas de razonamiento3. 3. Alibaba transforma Quark en un asistente integral de IAAlibaba ha evolucionado su navegador Quark hacia un asistente de IA capaz de realizar tareas complejas como investigación, generación de imágenes y diagnósticos médicos preliminares, impulsado por su modelo Qwen con razonamiento avanzado3. 4. Estudio revela conductas dañinas de la IA hacia los humanosUna investigación reciente ha detectado riesgos poco estudiados en la interacción con chatbots de IA, mostrando que pueden generar conductas dañinas en las personas, lo que plantea nuevos desafíos éticos y de regulación4. 5. Microsoft impulsa Copilot con visión y voz para una IA más empáticaMicrosoft está renovando su asistente Copilot para dotarlo de capacidades de visión, audición y razonamiento avanzado, buscando una interacción más natural y empática con los usuarios1. 6. China implementará regulación que exige etiquetado claro de contenido generado por IAA partir de septiembre de 2025, China exigirá que todo contenido generado por inteligencia artificial esté claramente identificado, con el objetivo de aumentar la transparencia y confianza en estas tecnologías3. 7. OpenAI obligado a preservar indefinidamente las conversaciones de ChatGPTUna orden judicial obliga a OpenAI a conservar todas las conversaciones generadas por ChatGPT de forma indefinida, lo que marca un precedente en la regulación y supervisión del uso de IA conversacional2.Estas noticias reflejan los avances tecnológicos, la creciente regulación y los desafíos éticos que enfrenta la inteligencia artificial en la actualidad, tanto en Occidente como en China. Newsletter Marketing Radical: https://borjagiron.com/newsletterConviértete en un seguidor de este podcast: https://www.spreaker.com/podcast/noticias-marketing--5762806/support.
7 noticias más importantes de los últimos 7 días sobre inteligencia artificial1. Meta crea un equipo secreto para dominar la IA con una inversión de 15.000 millonesMeta ha formado un equipo especial dedicado a desarrollar inteligencia artificial avanzada, con un gasto multimillonario, para mejorar sus capacidades en robótica y automatización, incluyendo robots que anticipan y planifican tareas autónomamente2. 2. DeepSeek y el Modelo R1: nuevo estándar en modelos de lenguaje en ChinaDeepSeek-R1, un modelo basado en arquitectura Mixture of Experts con 671 mil millones de parámetros, ha alcanzado un rendimiento comparable a GPT-4o y Claude Sonnet 3.5, destacando por su eficiencia y capacidades avanzadas de razonamiento3. 3. Alibaba transforma Quark en un asistente integral de IAAlibaba ha evolucionado su navegador Quark hacia un asistente de IA capaz de realizar tareas complejas como investigación, generación de imágenes y diagnósticos médicos preliminares, impulsado por su modelo Qwen con razonamiento avanzado3. 4. Estudio revela conductas dañinas de la IA hacia los humanosUna investigación reciente ha detectado riesgos poco estudiados en la interacción con chatbots de IA, mostrando que pueden generar conductas dañinas en las personas, lo que plantea nuevos desafíos éticos y de regulación4. 5. Microsoft impulsa Copilot con visión y voz para una IA más empáticaMicrosoft está renovando su asistente Copilot para dotarlo de capacidades de visión, audición y razonamiento avanzado, buscando una interacción más natural y empática con los usuarios1. 6. China implementará regulación que exige etiquetado claro de contenido generado por IAA partir de septiembre de 2025, China exigirá que todo contenido generado por inteligencia artificial esté claramente identificado, con el objetivo de aumentar la transparencia y confianza en estas tecnologías3. 7. OpenAI obligado a preservar indefinidamente las conversaciones de ChatGPTUna orden judicial obliga a OpenAI a conservar todas las conversaciones generadas por ChatGPT de forma indefinida, lo que marca un precedente en la regulación y supervisión del uso de IA conversacional2. Estas noticias reflejan los avances tecnológicos, la creciente regulación y los desafíos éticos que enfrenta la inteligencia artificial en la actualidad, tanto en Occidente como en China. Newsletter Marketing Radical: https://borjagiron.com/newsletterConviértete en un seguidor de este podcast: https://www.spreaker.com/podcast/inteligencia-artificial-para-emprender--5863866/support.
The incredibly talented Carol Leifer joins us at the table! Everything Carol touches seems to turn to gold - Seinfeld, Curb Your Enthusiasm, and now Hacks. Carol shares behind the scenes stories of writing for each of these hit shows. She also discusses why kids can absolutely not be at her stand up shows. Enjoy! Check out Carol's new book How to Write a Speech at Barnes and Noble. For a limited time, Wildgrain is offering our listeners $30 off the first box - PLUS free Croissants in every box - when you go to Wildgrain.com/PAPA to start your subscription Get 50% Off Your One Month Trial with Trade, at drinktrade.com/PAPA Text PAPA to 64000 to get twenty percent off all IQBAR products, plus FREE shipping. ------------- 0:00:00 Intro 0:00:39 Patreon shout out 0:01:09 Wild Grain Ad 0:01:54 TomPapa.com 0:02:58 Bread and bombing on stage 0:05:31 Comedians are good in emergency situations 0:09:13 The loudest snack is Pirate's Booty 0:11:00 Corporates 0:12:33 Stand up before writing and being funny 0:16:00 First open mics 0:20:08 Carol's new book and giving speeches 0:29:15 Best writing job - Seinfeld 0:33:05 Larry David 0:35:00 Mixture of Jerry & Larry and idea generation 0:40:45 Trade Coffee Ad 0:43:27 Wild Grain Ad 0:45:30 IQ Bar Ad 0:48:44 Italian 0:53:04 Carol thinks Tom can't dance 0:55:25 Ketchup and ranch 0:56:45 Working on the Oscars 1:00:50 Uncomfortable moment 1:02:50 Writing for Hacks and other projects 1:08:35 Being a woman in comedy ------------- Tom Papa is a celebrated stand-up comedian with over 20 years in the industry. Watch Tom's new special "Home Free" out NOW on Netflix! Radio, Podcasts and more: https://linktr.ee/tompapa/ Website - http://tompapa.com/ Instagram - https://www.instagram.com/tompapa Tiktok - https://www.tiktok.com/@tompapa Facebook - https://www.facebook.com/comediantompapa Twitter - https://www.twitter.com/tompapa #tompapa #breakingbread #comedy #standup #standupcomedy #bread #seinfeld #curbyourenthusiasm Learn more about your ad choices. Visit megaphone.fm/adchoices
Everyone Counts by Dr. Jürgen Weimann - Der Podcast über Transformation mit Begeisterung
In dieser Folge spreche ich mit Henrik Klages, Managing Partner von TNG Technology Consulting, über die faszinierende und rasante Entwicklung großer Sprachmodelle (LLMs) – und was das für uns alle bedeutet. Henrik erklärt auf verständliche Weise, wie LLMs funktionieren, warum GPUs wichtiger als CPUs sind und wieso der Mythos vom „nächsten Wort“ die wahre Kraft dieser Systeme unterschätzt. Außerdem räumt er mit Irrtümern rund um KI auf und zeigt anhand konkreter Beispiele aus Praxis und Forschung, wie Unternehmen heute aktiv werden müssen, um nicht den Anschluss zu verlieren.
Dans le grand bal mondial de l'intelligence artificielle, la Chine avance à pas mesurés, mais assurés. Et l'un de ses fers de lance, DeepSeek, vient de marquer un nouveau point. La start-up, déjà repérée pour ses choix techniques efficaces et peu coûteux, vient de publier une mise à jour de son modèle de raisonnement sur la plateforme Hugging Face, haut lieu du partage de modèles IA. Nom de code : R1-0528.Une mise à jour qualifiée de « mineure » par ses créateurs. Mais dans les faits, les testeurs parlent de progrès sensibles, notamment sur la logique complexe et la génération de code. Sur des bancs d'essai comme LiveCodeBench, le modèle DeepSeek se hisse désormais juste derrière les modèles o4-mini et o3 d'OpenAI. Un résultat plus qu'honorable. Là où R1-0528 brille, c'est dans son raisonnement structuré. Il applique désormais la méthode dite de la "chaîne de pensée" : une démarche plus rigoureuse, où chaque étape de réflexion est explicitée avant de parvenir à une conclusion. Cette capacité à détailler son raisonnement améliore nettement la qualité des réponses, tout comme la cohérence des textes générés, débarrassés des bizarreries que l'on retrouvait parfois dans les versions précédentes.Autre évolution remarquée : la gestion des contextes longs. Avec une capacité d'attention jusqu'à 128 000 tokens, R1-0528 peut suivre un fil complexe pendant plus de 30 minutes. C'est une avancée cruciale pour les tâches qui demandent de la concentration sur la durée. Le revers de la médaille ? Un temps de réponse un peu plus long, mais jugé acceptable compte tenu des gains en précision. Côté architecture, DeepSeek reste fidèle à son modèle Mixture-of-Experts : 685 milliards de paramètres, dont seulement 37 milliards activés en simultané. Résultat : un modèle colossal, mais économe en ressources. Le coût d'entraînement du modèle R1 originel ? Moins de 6 millions de dollars. Une prouesse quand on sait que d'autres modèles similaires dépassent allègrement les centaines de millions. Enfin, DeepSeek reste fidèle à sa politique d'ouverture : le modèle est publié sous licence MIT, libre d'usage, même commercial. De quoi séduire développeurs indépendants et start-up, avec un accès simplifié via Hugging Face. Discrète mais redoutablement efficace, la Chine confirme qu'elle ne compte pas rester spectatrice de la révolution IA. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.
In the time of Hosea, people mixed true worship with idol worship, and the same is still true today. In this message, Pastor Aaron Kennedy shares that God isn't looking for a halfway heart. He's calling us to a desert place, not to punish, but to restore our identity and affections.
This week, Taylor, Sandy and Taddea Richard discuss the panel's recent wilderness retreat, Joe Biden's nodule, Fan Bingbing's mother Bhumi bonanza, evil scientists' plan to “dim the sun,” Disney's Taliban collaboration, Taylor Swift's terrible testimony and much, much more!
Can Mistral make Europe a global AI contender? In episode 55 of Mixture of Experts, host Tim Hwang is joined by Chris Hay, Volkmar Uhlig and Kaoutar El Maghraoui to discuss the drop of Mistral Medium 3. Next, we analyze the AI chip sales both NVIDIA and AMD made to Saudi Arabia. Then, with IBM's new ITBench and OpenAI's HealthBench, we dive deeper into benchmarks for AI evaluation. Tune in to this week's Mixture of Experts for more! 00:01 – Intro 00:47 -- Mistral Medium 3 12:26 -- AI chips to Saudi Arabia 21:21 -- AI evaluation benchmarks 31:47 -- Amazon's AI-generated pause ads The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
C'est l'envers du décor d'une révolution numérique en marche. Alors que l'intelligence artificielle s'impose dans nos vies quotidiennes, son coût environnemental devient impossible à ignorer. D'après une étude publiée lundi par Greenly, spécialiste de la comptabilité carbone, les IA de dernière génération, ChatGPT en tête, consomment des quantités d'énergie vertigineuses. Le modèle GPT-4, développé par OpenAI, impressionne par ses capacités... mais inquiète par son empreinte. Avec 1 800 milliards de paramètres, soit dix fois plus que son prédécesseur, GPT-4 aurait multiplié par 20 sa consommation énergétique. Résultat : générer un million d'e-mails par mois à l'aide de l'outil produirait 7 138 tonnes de CO₂ par an, soit 4 300 allers-retours Paris-New York.Et ce n'est pas fini. D'après une étude de l'université Carnegie Mellon et de Hugging Face, chaque requête textuelle en IA consomme l'équivalent de 16 % d'une charge de smartphone. Pour une entreprise générant un million de réponses par mois, cela représente 514 tonnes de CO₂e par an. Les outils d'images, comme DALL-E, sont encore plus gourmands : une seule image générée équivaut à 60 fois plus d'émissions carbone qu'un texte, mobilisant 3,5 litres d'eau et l'énergie d'une recharge complète de smartphone.Mais une alternative pourrait émerger. Le modèle chinois DeepSeek propose une approche dite Mixture-of-Experts, qui active uniquement les sous-modèles nécessaires à chaque tâche. Résultat : une efficacité énergétique impressionnante. Son entraînement n'aurait requis que 2 000 puces NVIDIA H800, contre 25 000 pour GPT-4, selon les estimations. Un gain notable, certes, mais pas suffisant pour apaiser toutes les inquiétudes. Alexis Normand, PDG de Greenly, s'interroge :« Les géants de l'IA vont-ils enfin privilégier la sobriété, ou continuer à foncer sur la voie de la surenchère énergétique ? »Une question cruciale, alors que l'intelligence artificielle se place désormais au carrefour de la technologie et de l'écologie. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.
Has AI hallucination gotten out of control? In episode 54 of Mixture of Experts, host Tim Hwang is joined by Kate Soule, Skyler Speakman and Kaoutar El Maghraoui to analyze reasoning models and rising hallucinations. Next, as IBM Think 2025 wraps, the experts unpack the biggest highlights from IBM's biggest show of the year: new AI agents, Ferraris and ... penguins? Then, OpenAI is making moves with its acquisition of Windsurf. What does this mean? Tune in to this week's Mixture of Experts for more! 00:01 – Intro 01:12 – IBM Think 2025 09:27 – Reasoning models and hallucinations 19:23 – OpenAI Windsurf acquisition The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex task performance. Links Notes and resources at ocdevel.com/mlg/mlg34 Build the future of multi-agent software with AGNTCY Try a walking desk stay healthy & sharp while you learn & code Transformer Foundations and Scaling Laws Transformers: Introduced by the 2017 "Attention is All You Need" paper, transformers allow for parallel training and inference of sequences using self-attention, in contrast to the sequential nature of RNNs. Scaling Laws: Empirical research revealed that LLM performance improves predictably as model size (parameters), data size (training tokens), and compute are increased together, with diminishing returns if only one variable is scaled disproportionately. The "Chinchilla scaling law" (DeepMind, 2022) established the optimal model/data/compute ratio for efficient model performance: earlier large models like GPT-3 were undertrained relative to their size, whereas right-sized models with more training data (e.g., Chinchilla, LLaMA series) proved more compute and inference efficient. Emergent Abilities in LLMs Emergence: When trained beyond a certain scale, LLMs display abilities not present in smaller models, including: In-Context Learning (ICL): Performing new tasks based solely on prompt examples at inference time. Instruction Following: Executing natural language tasks not seen during training. Multi-Step Reasoning & Chain of Thought (CoT): Solving arithmetic, logic, or symbolic reasoning by generating intermediate reasoning steps. Discontinuity & Debate: These abilities appear abruptly in larger models, though recent research suggests that this could result from non-linearities in evaluation metrics rather than innate model properties. Architectural Evolutions: Mixture of Experts (MoE) MoE Layers: Modern LLMs often replace standard feed-forward layers with MoE structures. Composed of many independent "expert" networks specializing in different subdomains or latent structures. A gating network routes tokens to the most relevant experts per input, activating only a subset of parameters—this is called "sparse activation." Enables much larger overall models without proportional increases in compute per inference, but requires the entire model in memory and introduces new challenges like load balancing and communication overhead. Specialization & Efficiency: Experts learn different data/knowledge types, boosting model specialization and throughput, though care is needed to avoid overfitting and underutilization of specialists. The Three-Phase Training Process 1. Unsupervised Pre-Training: Next-token prediction on massive datasets—builds a foundation model capturing general language patterns. 2. Supervised Fine Tuning (SFT): Training on labeled prompt-response pairs to teach the model how to perform specific tasks (e.g., question answering, summarization, code generation). Overfitting and "catastrophic forgetting" are risks if not carefully managed. 3. Reinforcement Learning from Human Feedback (RLHF): Collects human preference data by generating multiple responses to prompts and then having annotators rank them. Builds a reward model (often PPO) based on these rankings, then updates the LLM to maximize alignment with human preferences (helpfulness, harmlessness, truthfulness). Introduces complexity and risk of reward hacking (specification gaming), where the model may exploit the reward system in unanticipated ways. Advanced Reasoning Techniques Prompt Engineering: The art/science of crafting prompts that elicit better model responses, shown to dramatically affect model output quality. Chain of Thought (CoT) Prompting: Guides models to elaborate step-by-step reasoning before arriving at final answers—demonstrably improves results on complex tasks. Variants include zero-shot CoT ("let's think step by step"), few-shot CoT with worked examples, self-consistency (voting among multiple reasoning chains), and Tree of Thought (explores multiple reasoning branches in parallel). Automated Reasoning Optimization: Frontier models selectively apply these advanced reasoning techniques, balancing compute costs with gains in accuracy and transparency. Optimization for Training and Inference Tradeoffs: The optimal balance between model size, data, and compute is determined not only for pretraining but also for inference efficiency, as lifetime inference costs may exceed initial training costs. Current Trends: Efficient scaling, model specialization (MoE), careful fine-tuning, RLHF alignment, and automated reasoning techniques define state-of-the-art LLM development.
We are celebrating MoE podcast's one year anniversary! In episode 53 of Mixture of Experts, host Tim Hwang is joined by the O.G. panel of experts from our pilot—Chris Hay, Shobhit Varshney and Kush Varshney. This week, we cover some exciting announcements at LlamaCon. Then, we discuss some new Chinese AI models from Qwen3 to the rumored DeepSeek-R2. Next, J.P. Morgan's CISO, Patrick Opet, released “An open letter to our third-party suppliers,” covering the need for AI security. Are we doomed? Finally, we look back at some of the topics we discussed in episode 1—the Rabbit AI device, GPT-2 chatbot, Apple Intelligence—after all that, who was the first person to say “agents” on the podcast? Tune in to find out, on today's one-year celebration of Mixture of Experts. 00:00 -- Intro00:38 -- LlamaCon10:34 -- Qwen3 and DeepSeek-R223:23 -- J.P. Morgan's open letter 39:45 -- One year of MoEThe opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
Join Tommy Shaughnessy from Delphi Ventures as he hosts Sam Lehman, Principal at Symbolic Capital and AI researcher, for a deep dive into the Reinforcement Learning (RL) renaissance and its implications for decentralized AI. Sam recently authored a widely discussed post, "The World's RL Gym", exploring the evolution of AI scaling and the exciting potential of decentralized networks for training next-generation models. The World's RL Gym: https://www.symbolic.capital/writing/the-worlds-rl-gym
Is OpenAI going to enter the social media game? In episode 52 of Mixture of Experts host, Tim Hwang is joined by Gabe Goodhart, Kate Soule and Marina Danilevsky. First, Sam Altman is rumored to be testing an internal prototype social network; why is this a potential next move for the AI giant? Next, for our paper of the week, we analyze Anthropic's study on chain-of-thought reasoning, “Reasoning Models Don't Always Say What They Think.” Then, AI scraping puts a strain on Wikimedia; what's the impact of this? Finally, China held a humanoid robot half-marathon, where humans raced alongside robot competitors. Who wins this AI race? All that and more on today's Mixture of Experts. 00:41 -- OpenAI social network 10:02 -- Anthropic's reasoning study 20:56 -- AI bots strain Wikimedia 31:33 -- Humanoid half-marathon The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
Kevin Kelly has spent more time thinking about the future than almost anyone else.From VR in the 1980s to the blockchain in the 2000s—and now generative AI—Kevin has spent a lifetime journeying to the frontiers of technology, only to return with rich stories about what's next.Today, as Wired's senior maverick, his project for 2025 is to outline what the next century looks like in a world shaped by new technologies like AI and genetic engineering. He's a personal hero of mine—not to mention a fellow Annie Dillard fan—and it was a privilege to have him on the show. We get into:How you can predict the future. According to Kevin, the draw of new frontiers—from the first edition of Burning Man and remote corners of Asia, to the early days of the internet and AI—isn't staying at the edge forever; it's returning with a story to tell.Why history is so important to help you understand the future To stay grounded while exploring what's new, Kevin balances the thrill of the future with the wisdom of the past. He pairs AI research with reading about history, and playing with an AI tool by retreating to his workshop to make something with his hands.From 1,000 true fans to an audience of one. Rather than creating for an audience, Kevin has been using LLMs to explore his own imagination. After realizing that da Vinci, Martin Luther, and Columbus were alive at the same time, he asked ChatGPT to imagine them snowed in at a hotel together, and the prompt spiraled into an epic saga, co-written with AI. But he has no plans to publish it because the joy was in creating something just for himself.What the history of electricity can teach us about AI. Kevin draws a parallel between AI and the early days of electricity. We could produce electric sparks long before we understood the forces that created them, and now we're building intelligent machines without really understanding what intelligence is.Why Kevin sees intelligence as a mosaic—not a monolith. Kevin believes intelligence isn't a single force, but a compound of many cognitive elements. He draws from Marvin Minsky's “society of mind”—the theory that the mind is made up of smaller agents working together—and sees echoes of this in the Mixture of Experts architecture used in some models today.Your competitive advantage is being yourself. Don't aim to be the best—aim to be the only. Kevin realized the stories no one else at Wired wanted to write were often the ones he was suited for, and trusting that instinct led to some of his best work.This is a must-watch for anyone who wants to make sense of AI through the lens of history, learn how to spot the future before it arrives, or grew up reading Wired.If you found this episode interesting, please like, subscribe, comment, and share! To hear more from Dan:Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper SponsorsVanta: Get $1,000 off of Vanta at https://www.vanta.com/every and automate up to 90% of the work for SOC 2, ISO 27001, and more.Attio: Go to https://www.attio.com/every and get 15% off your first year on your AI-powered CRM.Timestamps:Introduction: 00:00:50Why Kevin and I love Annie Dillard: 00:01:10Learn how to predict the future like Kevin: 00:12:50What the history of electricity can teach us about AI: 00:16:08How Kevin thinks about the nature of intelligence: 00:20:11Kevin's advice on discovering your competitive advantage: 00:27:21The story of how Kevin assembled a bench of star writers for Wired: 00:31:07How Kevin used ChatGPT to co-create a book: 00:36:17Using AI as a mirror for your mind: 00:40:45What Kevin learned from betting on VR in the 1980s: 00:45:16Links to resources mentioned:Kevin Kelly: @kevin2kellyKelly's books: https://kk.org/books Annie Dillard books that Kelly and Dan discuss: Pilgrim at Tinker Creek, Teaching a Stone to Talk, Holy the Firm, The Writing LifeDillard's account of the total eclipse: "Total Eclipse"
OpenAI just dropped o3 and o4-mini! In episode 51 of Mixture of Experts host, Tim Hwang is joined by Chris Hay, Vyoma Gajjar and special guest John Willis, Owner of Botchagalupe Technologies. Today, we analyze Sam Altman's new AI models, o3 and o4-mini. Next, Google announced that by Q3 you can run Gemini on-prem; what does this mean for enterprise AI adoption? Then, John is on the show today to take us through AI evaluation tools and why we need them. Finally, NVIDIA is planning to move AI chip manufacturing to the U.S. Can they pull this off? All that and more on today's Mixture of Experts. 00:01 – Intro 00:56 – OpenAI o3 and o4 mini 14:57 – Google Gemini on-prem 23:43 – AI evaluation tools 34:59 – NVIDIA's U.S. chip manufacturing The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
IBM z17 is here! In episode 50 of Mixture of Experts, host Tim Hwang is joined by Kate Soule, Shobhit Varshney and Hillery Hunter to debrief the launch of a new mainframe with robust AI infrastructure. Next, Meta dropped Llama 4 over the weekend;, how's it going? Then, Shobhit is recording live from Google Cloud Next in Las Vegas, along with Gemini 2.5 Pro. What are some of the most exciting announcements? Finally, the Pew Research Center shows perception of AI, how does this impact the industry? All that and more on today's 50th Mixture of Experts. 00:01 -- Intro 00:55 -- IBM z17 11:42 -- Llama 4 25:02 -- Google Cloud Next 2025 34:29 -- Pew's research on perception of AI The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Explore the new features of IBM z17: https://www.ibm.com/products/z17 Read the Pew Research: https://www.pewresearch.org/internet/2025/04/03/how-the-us-public-and-ai-experts-view-artificial-intelligence/ Subscribe for AI updates: https://ibm.biz/Think_newsletter Visit Mixture of Experts podcast page to learn more AI content: https://www.ibm.com/think/podcasts/mixture-of-experts
Send us a textIn this thought-provoking episode of Sidecar Sync, Mallory and Amith dig into the fascinating world of semiconductors and how a historic joint venture between Intel and TSMC is reshaping the global tech landscape. They explore the underlying tensions between vertically integrated business models and specialization — a conversation that holds key lessons for association leaders navigating change in the age of AI. From reflections on the Innovation Hub Chicago event to an insightful breakdown of Llama 4's powerful capabilities, this episode is a timely reminder that adaptability is everything — in both tech and associations.
Will OpenAI be fully open source by 2027? In episode 49 of Mixture of Experts, host Tim Hwang is joined by Aaron Baughman, Ash Minhas and Chris Hay to analyze Sam Altman's latest move towards open source. Next, we explore Anthropic's mechanistic interpretability results and the progress the AI research community is making. Then, can Apple catch up? We analyze the latest critiques on Apple Intelligence. Finally, Amazon enters the chat with AI agents. How does this elevate the competition? All that and more on today's Mixture of Experts.00:01 -- Introduction00:48 -- OpenAI goes open 11:36 -- Anthropic interpretability results 24:55 -- Daring Fireball on Apple Intelligence 34:22 -- Amazon's AI agentsThe opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.Subscribe for AI updates: https://www.ibm.com/account/reg/us-en/signup?formid=news-urx-52120Learn more about artificial intelligence → https://www.ibm.com/think/artificial-intelligenceVisit Mixture of Experts podcast page to learn more AI content → https://www.ibm.com/think/podcasts/mixture-of-experts
What's the best open-source model? In episode 48 of Mixture of Experts, host Tim Hwang is joined by Kate Soule, Kush Varshney and Skyler Speakman to explore the future of open-source AI models. First, we chat about the release of DeepSeek-V3-0324. Then, more announcements coming out of Google including Gemini Canvas and Gemini 2.5. Next, Extropic has entered the chat with a thermodynamic chip. Finally, AI image generation is on the rise as OpenAI released GPT-4o image generation. All that, and more on today's Mixture of Experts. 00:01 – Intro 00:42– DeepSeek-V3-0324 09:48 – Gemini 2.5 and Canvas 21:27– Extropic's thermodynamic chip 30:20 – OpenAI image generation The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
What's the most exciting announcement coming out of NVIDIA GTC? In episode 47 of Mixture of Experts, host Tim Hwang is joined by Nathalie Baracaldo, Kaoutar El Maghraoui and Vyoma Gajjar. First, we dive into the latest announcements from NVIDIA GTC, including the Groot N1 model for humanoid robotics. Next, Baidu released some new AI reasoning models, and they're not open source? Then, for our paper of the week we discuss the flaws of Chain-of-Thought reasoning. Finally, Gemini Flash 2.0 has released image generation models for developer experimentation., Iis Google catching up on the AI game? Tune -in to today's Mixture of Experts to find out! 00:01 – Intro 01:27– NVIDIA GTC 14:18– New Baidu AI models 21:19– Chain-of-Thought reasoning 32:18 – Gemini image generation The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
March 18, 2025 Ezra 9:1-15; Ps. 31:9-18; Prov. 11:16-17; I Cor. 5:9-13
Is Manus a second DeepSeek moment? In episode 46 of Mixture of Experts, host Tim Hwang is joined by Chris Hay, Kaoutar El Maghraoui and Vyoma Gajjar to talk Manus! Next, the rise of vibe coding—what started as a joke has now become a thing? Then, we dive deep into the future of scaling laws. Finally, Perplexity is teaming up with Deutsche Telekom to release an AI phone—what's the motivation here? Tune-in to today's Mixture of Experts to find out more! 00:01 – Intro 00:37 -- Manus 14:09 – Vibe coding 30:13 – Scaling laws 39:07 – Perplexity's AI phone The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
When can we expect quantum to reach consumer devices? In episode 45 of Mixture of Experts, host Tim Hwang is joined by special guest, Blake Johnson, to debrief the quantum noise in the news. Blake helps us understand the intersection between quantum and AI and how far we are from this technology. Then, veteran experts Chris Hay and Volkmar Uhlig hash out some other news in AI this week. We cover Anthropic's Model Context Protocol, CoreWeave filing for an IPO and Sesame AI's new voice companion. All that and more on today's Mixture of Experts! 00:01 – Intro 01:06 – Quantum leap 20:08 -- Model Context Protocol 28:24 -- CoreWeave IPO 40:12 -- Sesame AI voice companion The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
The 365 Days of Astronomy, the daily podcast of the International Year of Astronomy 2009
Hosted by Chris Beckett & Shane Ludtke, two amateur astronomers in Saskatchewan. actualastronomy@gmail.com The Observer's Calendar for March 2025 on Episode 472 of the Actual Astronomy podcast. I'm Chris and joining me is Shane. We are amateur astronomers who love looking up at the night sky and this podcast is for everyone who enjoys going out under the stars. March 4th is Pancake Tuesday March 5 - Moon 0.6-degrees N of Pleiades but 6-7 degrees E of M45 for us March 6 - Lunar X & V visible March 7 - Lunar straight wall and Walther Sunrise Ray visible on Moon March 8 - Mercury at greatest evening elongation 18-degrees from Sun in W. & Mars 1.7 degrees S of Moon March 9 - Jewelled Handle Visible on Moon March 11 - 2 Satellites Visible on Jupiter at 8:42 pm EST March 12 - Asteroid 8 Flora at opposition m=9.5 - Discovered by Hind in 1847 is is the innermost large asteroid and the seventh brightest. Name was proposed by John Herschel for the latin goddess of flowers and gardens. Parent of the Flora family of asteroids. Mixture of silicate rock, nickel-iron metal. March 12 - also, - Wargetin Pancake Visible on Moon March 13 - M 93 well placed this evening March 14 - Lunar Eclipse for NA - Just before Midnight on the 13…for us it's best around 2:45 CST. March 20 - Spring Equinox March 22 - Zodiacal Light becomes visible for a. Couple weeks in W evening sky March 23 - large tides this week March 24 - Mare Orientale visible on Moon - 6am March 27 - 2579 nebula and cluster well placed for observing this evening - Galaxy NGC 2784 March 28 - Friday, best weekend this year for Messier Marathon March 29 - Partial Solar Eclipse - Centred on Northern Labrador and Baffin Island. - Gegenschein visible from a very Dark Site high in S at midnight March 30 - More Large Tides - Sirius B, “The Pup” - Current separation about 11 arc seconds max in 50 years. https://www.rasc.ca/sirius-b-observing-challenge Concluding Listener Message: Please subscribe and share the show with other stargazers you know and send us show ideas, observations and questions to actualastronomy@gmail.com We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs. Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too! Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations. Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.
Dj Bully B -Essence of Soul - Divine Mixture -4-3-25 -
Is pre-training dead? In this bonus episode of Mixture of Experts, guest host Bryan Casey is joined by Kate Soule and Chris Hay. On Thursday, Sam Altman dropped GPT-4.5 just after we wrapped our weekly recording. We got a few of our veteran experts on the podcast to analyze OpenAI's largest and “best” chat model yet. What's the hype? Tune-in to this bonus episode to find out! 00:01 – Intro 00:25 – GPT-4.5 The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
Granite 3.2 is officially here! In episode 44 of Mixture of Experts, host Tim Hwang is joined by Kate Soule, Maya Murad and Kaoutar El Maghraoui to debrief a few big AI announcements. Last week we covered small vision-language models (VLMs), and this week Granite 3.2 dropped with new VLMs, enhanced reasoning capabilities, and more! Kate takes us under the hood to understand the new features and how they were created. Next, Anthropic dropped a new intelligence model, Claude 3.7 Sonnet, and a new agentic coding tool, Claude Code. Why did Anthropic release these separately? Then, as we cannot have an episode without covering agents, Maya takes us through the new BeeAI agents! Finally, can fine tuning on a malicious task lead to much broader misalignment? Our experts analyze a new paper released on ‘Emergent misalignment.' All that and more on this week's episode! 00:01 – Intro 00:41 – Claude 3.7 Sonnet 11:58 – BeeAI agents 20:11– Granite 3.2 29:23 – Emergent misalignment The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
Dj Bully B - The Essence of Soul - 100% Independent Music Mixture 26/2/25
This Week in Machine Learning & Artificial Intelligence (AI) Podcast
Today, we're joined by Ron Diamant, chief architect for Trainium at Amazon Web Services, to discuss hardware acceleration for generative AI and the design and role of the recently released Trainium2 chip. We explore the architectural differences between Trainium and GPUs, highlighting its systolic array-based compute design, and how it balances performance across key dimensions like compute, memory bandwidth, memory capacity, and network bandwidth. We also discuss the Trainium tooling ecosystem including the Neuron SDK, Neuron Compiler, and Neuron Kernel Interface (NKI). We also dig into the various ways Trainum2 is offered, including Trn2 instances, UltraServers, and UltraClusters, and access through managed services like AWS Bedrock. Finally, we cover sparsity optimizations, customer adoption, performance benchmarks, support for Mixture of Experts (MoE) models, and what's next for Trainium. The complete show notes for this episode can be found at https://twimlai.com/go/720.
Paul Maurice, Florida Panthers Head Coach, joins the show! Did 4 Nations rock the entire world? Some Canadian and USA 11th province banter. And the Stanley Cup is the only thing on the Panthers mind?!
What is all the hype around Deep Research? In episode 43 of Mixture of Experts, host Tim Hwang is joined by Kate Soule, Volkmar Uhlig and Shobhit Varshney. This week, we discuss reasoning model features coming out of companies like OpenAI's Deep Research, Google Gemini, Perplexity, xAI's Grok-3 and more! Next, OpenAI is rumored to release an inference chip, but how likely is this to be a success in the AI chip game? Then, we analyze the capabilities of small vision-language models (VLMs). Finally, a startup, Firecrawl, released a job posting in search of an AI agent. Is this the future for AI tools in the workforce? Tune-in to today's Mixture of Experts to find out. 00:01 – Intro 00:35 – Deep Research 11:58 – OpenAI inference chip 22:17 – Small VLMs 32:31 – AI agent job posting The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
Live from Paris, Tim Hwang is at the AI Action Summit 2025. In episode 42 of Mixture of Experts, we welcome Anastasia Stasenko, CEO and Co-Founder of pleias along with our veteran experts Marina Danilevsky and Chris Hay. Last week, we touched on some potential conversations at the Paris AI Summit, this week we recap what actually happened. Is AI safety improving Globally? Next, for our paper of the week, we breakdown s1: Simple test-time scaling. Then, Sam Altman is back with another blog, “Three Observations,” what do our experts have to say? Finally, what can we learn from Anthropic's Economic Index? All that and more on today's Mixture of Experts. 00:01 – Intro 00:42 – Paris AI Summit 11:10 – s1: Simple test-time scaling 19:32 – Sam Altman's “Three Observations” 30:41 – Anthropic's Economic Index The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Resources:Read the paper about s1: Simple test-time scaling: https://arxiv.org/abs/2501.19393Read Sam Altman's "Three Observations": https://blog.samaltman.com/three-observationsRead Anthropic's Economic Index: https://www.anthropic.com/economic-indexRead more about AGI: https://www.ibm.com/think/topics/artificial-general-intelligence
This week I welcome on the show two of the most important technologists ever, in any field.Jeff Dean is Google's Chief Scientist, and through 25 years at the company, has worked on basically the most transformative systems in modern computing: from MapReduce, BigTable, Tensorflow, AlphaChip, to Gemini.Noam Shazeer invented or co-invented all the main architectures and techniques that are used for modern LLMs: from the Transformer itself, to Mixture of Experts, to Mesh Tensorflow, to Gemini and many other things.We talk about their 25 years at Google, going from PageRank to MapReduce to the Transformer to MoEs to AlphaChip – and maybe soon to ASI.My favorite part was Jeff's vision for Pathways, Google's grand plan for a mutually-reinforcing loop of hardware and algorithmic design and for going past autoregression. That culminates in us imagining *all* of Google-the-company, going through one huge MoE model.And Noam just bites every bullet: 100x world GDP soon; let's get a million automated researchers running in the Google datacenter; living to see the year 3000.SponsorsScale partners with major AI labs like Meta, Google Deepmind, and OpenAI. Through Scale's Data Foundry, labs get access to high-quality data to fuel post-training, including advanced reasoning capabilities. If you're an AI researcher or engineer, learn about how Scale's Data Foundry and research lab, SEAL, can help you go beyond the current frontier at scale.com/dwarkesh.Curious how Jane Street teaches their new traders? They use Figgie, a rapid-fire card game that simulates the most exciting parts of markets and trading. It's become so popular that Jane Street hosts an inter-office Figgie championship every year. Download from the app store or play on your desktop at figgie.com.Meter wants to radically improve the digital world we take for granted. They're developing a foundation model that automates network management end-to-end. To do this, they just announced a long-term partnership with Microsoft for tens of thousands of GPUs, and they're recruiting a world class AI research team. To learn more, go to meter.com/dwarkesh.Advertisers:To sponsor a future episode, visit: dwarkeshpatel.com/p/advertise.Timestamps00:00:00 - Intro00:02:44 - Joining Google in 199900:05:36 - Future of Moore's Law00:10:21 - Future TPUs00:13:13 - Jeff's undergrad thesis: parallel backprop00:15:10 - LLMs in 200700:23:07 - “Holy s**t” moments00:29:46 - AI fulfills Google's original mission00:34:19 - Doing Search in-context00:38:32 - The internal coding model00:39:49 - What will 2027 models do?00:46:00 - A new architecture every day?00:49:21 - Automated chip design and intelligence explosion00:57:31 - Future of inference scaling01:03:56 - Already doing multi-datacenter runs01:22:33 - Debugging at scale01:26:05 - Fast takeoff and superalignment01:34:40 - A million evil Jeff Deans01:38:16 - Fun times at Google01:41:50 - World compute demand in 203001:48:21 - Getting back to modularity01:59:13 - Keeping a giga-MoE in-memory02:04:09 - All of Google in one model02:12:43 - What's missing from distillation02:18:03 - Open research, pros and cons02:24:54 - Going the distance Get full access to Dwarkesh Podcast at www.dwarkeshpatel.com/subscribe
What does Sam Altman have up his sleeve? In episode 41 of Mixture of Experts, join host Tim Hwang along with experts Nathalie Baracaldo, Marina Danilevsky and Chris Hay. Last week, we covered all things DeepSeek, and this week OpenAI has some new releases to share. Today, the experts dissect deep research and o3-mini. Next, our host Tim Hwang is travelling to AI Action Summit, he asks our experts what we can expect coming out of the event. Then, we talk about Anthropic's Constitutional Classifiers. Finally, Microsoft is creating a unit to study AI's impact, what does this mean? Find out all this and more on Mixture of Experts. 00:01 – intro 00:41 – Open AI deep research and o3-mini 13:51 – AI Action Summit 20:17 – Anthropic's Constitutional Classifiers 28:54 – Microsoft AI Impact team The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Subscribe for AI updatesLearn more about artificial intelligenceDeepSeek's reasoning AI shows power of small models, efficiently trainedVisit Mixture of Experts podcast page to learn more AI content
From the beginning of time, a war has raged over humanity—one that seeks to distort, defile, and ultimately sever our connection to God. The Fallen Sons of God abandoned their divine purpose, descending to earth and corrupting its people through deception and genetic manipulation. Their offspring, the Nephilim, were more than just giants of old; they embodied an agenda to erase the image of God from humanity. Though their physical presence faded, their influence remains—woven into the fabric of our world through mind control and ideological deception. But darkness does not have the final say. In this episode of the Revelations Podcast, host Reagan Kramer welcomes back Dr. Laura Sanger, a researcher, author, speaker, and clinical psychologist with a deep passion for awakening people to the spiritual battle at hand. Together, they dive into the spiritual war between the sons of God and the forces of darkness, tracing its origins from biblical times to its modern-day manifestations. They discuss the erosion of biblical truth, the dangers of gender ideology, transhumanism, and the corrupt systems that seek to enslave future generations. Whether you're new to these concepts or looking to equip yourself for the days ahead, this conversation will challenge and inspire you to step into your identity as a son or daughter of God.Here are three reasons why you should listen to this episode:Learn the hidden truths behind the Nephilim agenda and how it impacts our world todayGain practical insights on how to rise up as a son or daughter of God, equipped with spiritual authority to combat these dark forces.Reflect on the urgency of spiritual maturity and the call to live a victorious life aligned with God's truth in perilous times.Become Part of Our Mission! Support The Revelations Podcast:Your support fuels our mission to share transformative messages of hope and faith. Click here to learn how you can contribute and be part of this growing community!ResourcesMore from the Revelations Podcast hosted by Reagan Kramer: Website | Instagram | Apple Podcast | YoutubeListen to our previous episode with Dr. Laura Sanger, “Fighting the Nephilim Agenda with our Authority in Christ”"The Roots of the Federal Reserve" by Dr. Laura Sanger"Generation Hoodwinked" by Dr. Laura Sanger"From Transgender to Transhuman" — by Martin Rothblatt"Future Humans" — Children's BookLaura Sanger: Website | Instagram | Youtube | RumbleLaura's Telegram: @laurasanger444hzBible VersesEcclesiastes 10:20Mark 41 Corinthians 14:20John 14:10John 7:16-18John 12:49-50 Galatians 4:1,7Romans 8:14Ephesians 5:112 Timothy 4:3-4This Episode is brought to you by Advanced Medicine AlternativesGet back to the active life you love through natural & regenerative musculoskeletal healing: https://www.georgekramermd.com/Episode Highlights[0:50] Introduction and Background of Dr. Laura SangerReagan Kramer welcomes back Dr. Laura Sanger to The Revelations Podcast to shed light on the hidden spiritual war shaping our world today.With a Ph.D. in Clinical Psychology and a Master of Arts in Theology from Fuller Theological Seminary, her work bridges biblical revelation and scholarly research. Her books, The Roots of the Federal Reserve and Generation Hoodwinked uncover deep-seated deceptions designed to enslave humanity.A recent gathering at Blurry Con provided an opportunity to reconnect with like-minded individuals and reaffirm the urgency of exposing these dark forces.[5:28] Dr. Laura's Vision and MissionA dream and vision she had in May 2020 led to the title Generation Hoodwinked, revealing a world where AI and spiritual oppression silence the voices of future generations.In the vision, Jesus led Dr. Sanger into an underground cavern where children were trapped in cages, symbolizing the control systems designed to enslave them.The Nephilim agenda thrives on deception, and exposing it is essential to breaking its power.Ephesians 5:11 and 2 Timothy 4:3-4 serve as guiding scriptures in this mission, urging believers to stand against false doctrines and wake up to the battle at hand.[11:43] The Battle of the Sons of GodLong ago, the Fallen Sons of God abandoned their heavenly domain, descending to corrupt humanity and unleash the Nephilim agenda.Their goal was to defile the human genome and stage an insurrection against God's divine order.Though Jesus secured victory through His death and resurrection, the war still rages in the spiritual realm.The need for God-fearing believers to rise up has never been greater, as deception seeks to strip humanity of its divine identity.Spiritual warfare is not passive—strongholds must be torn down, and the authority of Christ must be wielded with boldness.[15:38] Defining the Sons of GodNot all believers walk in the full authority of the Sons of God.Romans 8:14 states that those led by the Spirit are the true sons, yet many remain trapped in self-reliance rather than surrendering to divine direction.Cultural norms encourage independence, but spiritual maturity requires complete dependence on Jesus.Obedience to the Holy Spirit is the mark of a true Son of God, distinguishing those who move in divine authority from those merely going through the motions of faith.[20:28] Laura: “Sons of God are not their own person. They don't make their own decisions. They are fully surrendered to the Father's will.”The invitation to step into sonship is available to all—but it requires a willingness to follow God without hesitation.[27:13] Mixture and SyncretismThe mixing of truth with deception opens doors to bondage, preventing believers from being led by the soul rather than the Spirit.Operating from the soul—through emotions and human reasoning—rather than the Spirit leads to misguided intentions, no matter how well-meaning.Syncretism, the blending of Christian faith with pagan influences, is rampant in modern culture, from Halloween celebrations to the normalization of ideologies that distort God's design.Spiritual purity demands discernment, and the removal of compromise is essential to living victoriously in Christ.[30:12] Laura: “The Fallen sons of God, they mix their seed with human seed to birth the Nephilim. And so giving room to mixture, what that does is that allows us to take the bait that causes many of us to become hoodwinked”[36:28] The Nephilim Agenda and TransgenderismA systematic effort to erase human identity is at play, progressing from transgender ideology to full-scale transhumanism.Dr. Laura describes how this movement is being fueled by the United Nations and comprehensive sexuality education (CSE).She highlights the harmful effects of CSE on children, including promoting sexual stimulation and normalizing bestiality.The long-term effects of puberty blockers and gender-affirming surgeries on children's development and mental health are not acts of liberation but of enslavement[48:04] The Impact of Media and TechnologyMedia and technology are not just entertainment but tools of indoctrination.Future Humans for example, a bestselling children's book, subtly introduces transhumanist ideals by showcasing technological modifications.Movies, music, and television shows create fantasies that reinforce the allure of enhanced abilities, steering the next generation toward a post-human reality.The Nephilim agenda thrives on deception; its end goal is to wipe out humanity and cut at the heart of the Kingdom of God.[50:50] Laura: “The Nephilim agenda is really about defiling the human genome so much that we can't have relationship with Jesus anymore”[52:48] The Role of the Sons of God in Spiritual WarfareThe Sons of God are warriors, called to push back the forces of darkness with unwavering faith.The Hebrew phrase Rak Chazak Amats embodies the strength and courage required to stand in battle.Dr. Laura highlights the importance of the Sons of God in arising and maturing to become heirs of God and walking in their inheritance.As deception intensifies, Dr Laura encourages listeners to find Jesus in the secret place to develop an intimate relationship and learn His voice.[1:05:54] Practical Steps to Become a Son or Daughter of GodVictory begins in the secret place, where intimacy with Jesus is cultivated.Dr Laura emphasizes the importance of distinguishing between the true Holy Spirit and false voices in the church and media.Recognizing this requires deep connection with the True Shepherd, and daily communion with Him to ensure that fear and deception lose their grip.As the episode closes, Dr. Laura prays for listeners, asking for protection, boldness, and the empowerment to walk as Sons of God in a world desperately in need of truth.About Laura SangerDr. Laura Sanger is a researcher, author, speaker, and clinical psychologist dedicated to equipping believers with the knowledge and spiritual tools needed to navigate the unseen battle against darkness. As the founder of No Longer Enslaved, her mission is to awaken people to the pervasive influence of the Nephilim agenda and empower them to walk in their God-given authority.With a Ph.D. in Clinical Psychology and a Master of Arts in Theology from Fuller Theological Seminary, Dr. Laura Sanger combines scholarly research with biblical revelation to expose the hidden forces shaping our world. As the author of books such as Generation Hoodwinked: The Impact of the Nephilim Agenda Today, she unravels the deep-seated deception embedded in financial systems, transhumanism, and ideological warfare. Dr. Sanger has shared her insights on platforms across the globe, equipping believers to discern false narratives, break free from spiritual bondage, and step into their true identity in Christ. Her teachings emphasize the importance of spiritual maturity, exposing darkness, and wielding the weapons of our warfare with boldness.Connect with Dr. Laura Sanger and learn more about her conferences and resources at No Longer Enslaved.Enjoyed this Episode?If you did, subscribe and share it with your friends!Post a review and share it! If you found our deep dive into the spiritual influences on mental health insightful, we'd love to hear your thoughts. Leave a review and share this episode with friends and family. Step into your God-given authority and awaken as a Son of God. Expose deception, break free from spiritual bondage, and walk boldly in the truth of Christ.Have any questions? You can connect with me on Instagram.Thank you for tuning in! For more updates, tune in on Apple Podcasts.
They say you either have charisma or you don't, but Charlie Houpert proves charisma can be built, and reveals the secret code to mastering it for success in love, work, and friendship Charlie Houpert is the co-founder of the confidence-building online platform, ‘Charisma on Command'. He is the author of books such as, ‘The Anti Pick Up Line: Real Habits To Naturally Attract Stunning Women' and ‘Charisma On Command: Inspire, Impress, and Energize Everyone You Meet'. In this conversation, Charlie and Steven discuss topics such as, how to stop feeling awkward in social situations, the ultimate body language hack to build trust, how to become instantly likeable, and how to master the art of persuasion. 00:00 Intro 02:25 What Is It You Do? 04:39 How Much Will These Skills Shift Someone's Life? 06:35 Is It Something You Can Learn? 07:15 Your YouTube Channel 09:37 I Was Shy and Introverted—How I Changed 12:47 What Did You Think of Yourself in the Early Years? 15:22 What Was the Biggest Difference in You? 17:32 First Impressions 21:07 Engineer the Conversation You Want to Have 24:38 How to Get Out of Small Talk 26:05 Flirt With the World 27:55 Prey vs. Predator Movements 35:02 The Confidence Trick Before Talking to a Big Crowd 37:02 Do We Underestimate the Ways We Communicate? 41:11 Is Talking About Yourself a Bad Thing? 43:22 How to Connect With Someone in a Normal Interaction 47:40 How to Figure Out if an Interaction Is Real 50:19 People Controlling the Narratives That Reach You 52:18 Narcissists and Sociopaths 55:28 What Billion-Dollar Business Would You Build and Not Sell? 01:01:20 Six Charismatic Mindsets 01:03:16 Elon Musk Salute 01:06:13 The Media Has Made Saying Sorry the Wrong Thing to Do 01:08:26 Ads 01:09:24 Is Trump Charismatic? 01:14:22 Impeccable Honesty and Integrity 01:18:06 I Don't Need to Convince Anyone of Anything 01:20:43 I Proactively Share My Purpose 01:23:46 Be the First to Humanize the Interaction 01:26:13 Charismatic Types of People 01:31:23 Obama's Charisma 01:32:26 The Importance of Charisma 01:33:43 Ads 01:35:40 How to Use These Skills to Get a Job or Promotion 01:41:07 What Are Women Attracted to in Your Opinion? 01:45:08 Are People Testing to See if You Have Standards? 01:49:21 Five Habits That Make People Instantly Dislike You 01:53:56 Speaking Like a Leader 01:54:46 Pausing Instead of Using Filler Words 01:56:12 Does Body Language Matter When Speaking? 01:57:35 The Fundamentals of Being Confident 01:59:19 What's the Most Important Thing You're Doing to Increase Your Well-Being? 02:02:53 What Are the Mixture of Emotions You Feel? 02:08:19 Is There Anything You Wish You Could Have Said to That Boy? Follow Charlie: Instagram - https://g2ul0.app.link/sX0XNx4tBQb Charisma on Command - https://g2ul0.app.link/Bo2XEO2tBQb You can purchase Charlie's book, ‘Charisma On Command: Inspire, Impress, and Energize Everyone You Meet', here: https://g2ul0.app.link/DoIMBn9tBQb Watch the episodes on Youtube - https://g2ul0.app.link/DOACEpisodes My new book! 'The 33 Laws Of Business & Life' is out now - https://g2ul0.app.link/DOACBook You can purchase the The Diary Of A CEO Conversation Cards: Second Edition, here: https://g2ul0.app.link/f31dsUttKKb Follow me: https://g2ul0.app.link/gnGqL4IsKKb Sponsors: Linkedin Ads - https://www.linkedin.com/DIARY NordVPN - https://NORDVPN.COM/DOAC ZOE - http://joinzoe.com with code BARTLETT10 for 10% off Learn more about your ad choices. Visit megaphone.fm/adchoices
This is the second part of episode 10 of Effortless Podcast, hosts Dheeraj Pandey and Amit Prakash sit down with Alex Dimakis, a renowned AI researcher and professor, to discuss one of the biggest breakthroughs in open AI models—DeepSeek R1. They explore how DeepSeek's innovations in reasoning, reinforcement learning, and efficiency optimizations are reshaping the AI landscape.The conversation covers the shift from large, proprietary AI models to open-source alternatives, the role of post-training fine-tuning, and how reinforcement learning (GRPO) enables reasoning capabilities in LLMs. They also dive into KV caching, mixture of experts, multi-token prediction, and what this means for NVIDIA, hardware players, and AI startups.Key Topics & Timestamps:[00:00] - Introduction & Why DeepSeek Matters[01:30] - DeepSeek R1: Open-Source AI Disrupting the Industry[03:00] - Has China Become an AI Innovator?[07:30] - Open Weights vs. Open Data: What Really Matters?[10:00] - KV Caching, Mixture of Experts & Model Optimizations[21:00] - How Reinforcement Learning (GRPO) Enables Reasoning[32:00] - Why OpenAI is Keeping Its Reasoning Traces Hidden[45:00] - The Impact of AI on NVIDIA & Hardware Demand[1:02:00] - AGI: Language Models vs. Multimodal AI[1:15:00] - The Future of AI: Fine-Tuning, Open-Source & Specialized ModelsHosts:Dheeraj Pandey: Co-founder and CEO at DevRev, formerly Co-founder and CEO of Nutanix. A tech visionary with a deep interest in AI and systems thinking.Amit Prakash: Co-founder and CTO at ThoughtSpot, formerly at Google AdSense and Bing, with extensive expertise in analytics and large-scale systems.Guest:Alex Dimakis: Professor at UC Berkeley and co-founder of Bespoke Labs, Alex has made significant contributions to deep learning, machine learning infrastructure, and the development of AI reasoning frameworks.Follow the Hosts and the Guest:Dheeraj Pandey:LinkedIn - https://www.linkedin.com/in/dpandeyTwitter - https://x.com/dheerajAmit Prakash:LinkedIn - https://www.linkedin.com/in/amit-prak...Twitter - https://x.com/amitp42Alex Dimakis:LinkedIn - https://www.linkedin.com/in/alex-dima...Twitter - https://x.com/AlexGDimakisShare Your Thoughts:Have questions, comments, or ideas for future episodes? Email us at EffortlessPodcastHQ@gmail.comDon't forget to Like, Comment, and Subscribe for more in-depth discussions on AI, technology, and innovation!
Let's bust some early myths about DeepSeek. In episode 40 of Mixture of Experts, join host Tim Hwang along with experts Aaron Baughman, Chris Hay and Kate Soule. Last week, we covered the release of DeepSeek-R1; now that the entire world is up to speed, let's separate the facts from the hype. Next, what is model distillation and why does it matter for competition in AI? Finally, Sam Altman among other tech CEOs shared his response to DeepSeek. Will R1 radically change the open-source strategy of other tech giants? Find out all this and more on Mixture of Experts. 00:01 – Intro 00:41 – DeepSeek facts vs hype 21:00 – Model distillation 31:21 – Open source and OpenAI The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity.
One last Gold sponsor slot is available for the AI Engineer Summit in NYC. Our last round of invites is going out soon - apply here - If you are building AI agents or AI eng teams, this will be the single highest-signal conference of the year for you!While the world melts down over DeepSeek, few are talking about the OTHER notable group of former hedge fund traders who pivoted into AI and built a remarkably profitable consumer AI business with a tiny team with incredibly cracked engineering team — Chai Research. In short order they have:* Started a Chat AI company well before Noam Shazeer started Character AI, and outlasted his departure.* Crossed 1m DAU in 2.5 years - William updates us on the pod that they've hit 1.4m DAU now, another +40% from a few months ago. Revenue crossed >$22m. * Launched the Chaiverse model crowdsourcing platform - taking 3-4 week A/B testing cycles down to 3-4 hours, and deploying >100 models a week.While they're not paying million dollar salaries, you can tell they're doing pretty well for an 11 person startup:The Chai Recipe: Building infra for rapid evalsRemember how the central thesis of LMarena (formerly LMsys) is that the only comprehensive way to evaluate LLMs is to let users try them out and pick winners?At the core of Chai is a mobile app that looks like Character AI, but is actually the largest LLM A/B testing arena in the world, specialized on retaining chat users for Chai's usecases (therapy, assistant, roleplay, etc). It's basically what LMArena would be if taken very, very seriously at one company (with $1m in prizes to boot):Chai publishes occasional research on how they think about this, including talks at their Palo Alto office:William expands upon this in today's podcast (34 mins in):Fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours.In Crowdsourcing the leap to Ten Trillion-Parameter AGI, William describes Chai's routing as a recommender system, which makes a lot more sense to us than previous pitches for model routing startups:William is notably counter-consensus in a lot of his AI product principles:* No streaming: Chats appear all at once to allow rejection sampling* No voice: Chai actually beat Character AI to introducing voice - but removed it after finding that it was far from a killer feature.* Blending: “Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model.” (that's it!)But chief above all is the recommender system.We also referenced Exa CEO Will Bryk's concept of SuperKnowlege:Full Video versionOn YouTube. please like and subscribe!Timestamps* 00:00:04 Introductions and background of William Beauchamp* 00:01:19 Origin story of Chai AI* 00:04:40 Transition from finance to AI* 00:11:36 Initial product development and idea maze for Chai* 00:16:29 User psychology and engagement with AI companions* 00:20:00 Origin of the Chai name* 00:22:01 Comparison with Character AI and funding challenges* 00:25:59 Chai's growth and user numbers* 00:34:53 Key inflection points in Chai's growth* 00:42:10 Multi-modality in AI companions and focus on user-generated content* 00:46:49 Chaiverse developer platform and model evaluation* 00:51:58 Views on AGI and the nature of AI intelligence* 00:57:14 Evaluation methods and human feedback in AI development* 01:02:01 Content creation and user experience in Chai* 01:04:49 Chai Grant program and company culture* 01:07:20 Inference optimization and compute costs* 01:09:37 Rejection sampling and reward models in AI generation* 01:11:48 Closing thoughts and recruitmentTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and today we're in the Chai AI office with my usual co-host, Swyx.swyx [00:00:14]: Hey, thanks for having us. It's rare that we get to get out of the office, so thanks for inviting us to your home. We're in the office of Chai with William Beauchamp. Yeah, that's right. You're founder of Chai AI, but previously, I think you're concurrently also running your fund?William [00:00:29]: Yep, so I was simultaneously running an algorithmic trading company, but I fortunately was able to kind of exit from that, I think just in Q3 last year. Yeah, congrats. Yeah, thanks.swyx [00:00:43]: So Chai has always been on my radar because, well, first of all, you do a lot of advertising, I guess, in the Bay Area, so it's working. Yep. And second of all, the reason I reached out to a mutual friend, Joyce, was because I'm just generally interested in the... ...consumer AI space, chat platforms in general. I think there's a lot of inference insights that we can get from that, as well as human psychology insights, kind of a weird blend of the two. And we also share a bit of a history as former finance people crossing over. I guess we can just kind of start it off with the origin story of Chai.William [00:01:19]: Why decide working on a consumer AI platform rather than B2B SaaS? So just quickly touching on the background in finance. Sure. Originally, I'm from... I'm from the UK, born in London. And I was fortunate enough to go study economics at Cambridge. And I graduated in 2012. And at that time, everyone in the UK and everyone on my course, HFT, quant trading was really the big thing. It was like the big wave that was happening. So there was a lot of opportunity in that space. And throughout college, I'd sort of played poker. So I'd, you know, I dabbled as a professional poker player. And I was able to accumulate this sort of, you know, say $100,000 through playing poker. And at the time, as my friends would go work at companies like ChangeStreet or Citadel, I kind of did the maths. And I just thought, well, maybe if I traded my own capital, I'd probably come out ahead. I'd make more money than just going to work at ChangeStreet.swyx [00:02:20]: With 100k base as capital?William [00:02:22]: Yes, yes. That's not a lot. Well, it depends what strategies you're doing. And, you know, there is an advantage. There's an advantage to being small, right? Because there are, if you have a 10... Strategies that don't work in size. Exactly, exactly. So if you have a fund of $10 million, if you find a little anomaly in the market that you might be able to make 100k a year from, that's a 1% return on your 10 million fund. If your fund is 100k, that's 100% return, right? So being small, in some sense, was an advantage. So started off, and the, taught myself Python, and machine learning was like the big thing as well. Machine learning had really, it was the first, you know, big time machine learning was being used for image recognition, neural networks come out, you get dropout. And, you know, so this, this was the big thing that's going on at the time. So I probably spent my first three years out of Cambridge, just building neural networks, building random forests to try and predict asset prices, right, and then trade that using my own money. And that went well. And, you know, if you if you start something, and it goes well, you You try and hire more people. And the first people that came to mind was the talented people I went to college with. And so I hired some friends. And that went well and hired some more. And eventually, I kind of ran out of friends to hire. And so that was when I formed the company. And from that point on, we had our ups and we had our downs. And that was a whole long story and journey in itself. But after doing that for about eight or nine years, on my 30th birthday, which was four years ago now, I kind of took a step back to just evaluate my life, right? This is what one does when one turns 30. You know, I just heard it. I hear you. And, you know, I looked at my 20s and I loved it. It was a really special time. I was really lucky and fortunate to have worked with this amazing team, been successful, had a lot of hard times. And through the hard times, learned wisdom and then a lot of success and, you know, was able to enjoy it. And so the company was making about five million pounds a year. And it was just me and a team of, say, 15, like, Oxford and Cambridge educated mathematicians and physicists. It was like the real dream that you'd have if you wanted to start a quant trading firm. It was like...swyx [00:04:40]: Your own, all your own money?William [00:04:41]: Yeah, exactly. It was all the team's own money. We had no customers complaining to us about issues. There's no investors, you know, saying, you know, they don't like the risk that we're taking. We could. We could really run the thing exactly as we wanted it. It's like Susquehanna or like Rintec. Yeah, exactly. Yeah. And they're the companies that we would kind of look towards as we were building that thing out. But on my 30th birthday, I look and I say, OK, great. This thing is making as much money as kind of anyone would really need. And I thought, well, what's going to happen if we keep going in this direction? And it was clear that we would never have a kind of a big, big impact on the world. We can enrich ourselves. We can make really good money. Everyone on the team would be paid very, very well. Presumably, I can make enough money to buy a yacht or something. But this stuff wasn't that important to me. And so I felt a sort of obligation that if you have this much talent and if you have a talented team, especially as a founder, you want to be putting all that talent towards a good use. I looked at the time of like getting into crypto and I had a really strong view on crypto, which was that as far as a gambling device. This is like the most fun form of gambling invented in like ever super fun, I thought as a way to evade monetary regulations and banking restrictions. I think it's also absolutely amazing. So it has two like killer use cases, not so much banking the unbanked, but everything else, but everything else to do with like the blockchain and, and you know, web, was it web 3.0 or web, you know, that I, that didn't, it didn't really make much sense. And so instead of going into crypto, which I thought, even if I was successful, I'd end up in a lot of trouble. I thought maybe it'd be better to build something that governments wouldn't have a problem with. I knew that LLMs were like a thing. I think opening. I had said they hadn't released GPT-3 yet, but they'd said GPT-3 is so powerful. We can't release it to the world or something. Was it GPT-2? And then I started interacting with, I think Google had open source, some language models. They weren't necessarily LLMs, but they, but they were. But yeah, exactly. So I was able to play around with, but nowadays so many people have interacted with the chat GPT, they get it, but it's like the first time you, you can just talk to a computer and it talks back. It's kind of a special moment and you know, everyone who's done that goes like, wow, this is how it should be. Right. It should be like, rather than having to type on Google and search, you should just be able to ask Google a question. When I saw that I read the literature, I kind of came across the scaling laws and I think even four years ago. All the pieces of the puzzle were there, right? Google had done this amazing research and published, you know, a lot of it. Open AI was still open. And so they'd published a lot of their research. And so you really could be fully informed on, on the state of AI and where it was going. And so at that point I was confident enough, it was worth a shot. I think LLMs are going to be the next big thing. And so that's the thing I want to be building in, in that space. And I thought what's the most impactful product I can possibly build. And I thought it should be a platform. So I myself love platforms. I think they're fantastic because they open up an ecosystem where anyone can contribute to it. Right. So if you think of a platform like a YouTube, instead of it being like a Hollywood situation where you have to, if you want to make a TV show, you have to convince Disney to give you the money to produce it instead, anyone in the world can post any content they want to YouTube. And if people want to view it, the algorithm is going to promote it. Nowadays. You can look at creators like Mr. Beast or Joe Rogan. They would have never have had that opportunity unless it was for this platform. Other ones like Twitter's a great one, right? But I would consider Wikipedia to be a platform where instead of the Britannica encyclopedia, which is this, it's like a monolithic, you get all the, the researchers together, you get all the data together and you combine it in this, in this one monolithic source. Instead. You have this distributed thing. You can say anyone can host their content on Wikipedia. Anyone can contribute to it. And anyone can maybe their contribution is they delete stuff. When I was hearing like the kind of the Sam Altman and kind of the, the Muskian perspective of AI, it was a very kind of monolithic thing. It was all about AI is basically a single thing, which is intelligence. Yeah. Yeah. The more intelligent, the more compute, the more intelligent, and the more and better AI researchers, the more intelligent, right? They would speak about it as a kind of erased, like who can get the most data, the most compute and the most researchers. And that would end up with the most intelligent AI. But I didn't believe in any of that. I thought that's like the total, like I thought that perspective is the perspective of someone who's never actually done machine learning. Because with machine learning, first of all, you see that the performance of the models follows an S curve. So it's not like it just goes off to infinity, right? And the, the S curve, it kind of plateaus around human level performance. And you can look at all the, all the machine learning that was going on in the 2010s, everything kind of plateaued around the human level performance. And we can think about the self-driving car promises, you know, how Elon Musk kept saying the self-driving car is going to happen next year, it's going to happen next, next year. Or you can look at the image recognition, the speech recognition. You can look at. All of these things, there was almost nothing that went superhuman, except for something like AlphaGo. And we can speak about why AlphaGo was able to go like super superhuman. So I thought the most likely thing was going to be this, I thought it's not going to be a monolithic thing. That's like an encyclopedia Britannica. I thought it must be a distributed thing. And I actually liked to look at the world of finance for what I think a mature machine learning ecosystem would look like. So, yeah. So finance is a machine learning ecosystem because all of these quant trading firms are running machine learning algorithms, but they're running it on a centralized platform like a marketplace. And it's not the case that there's one giant quant trading company of all the data and all the quant researchers and all the algorithms and compute, but instead they all specialize. So one will specialize on high frequency training. Another will specialize on mid frequency. Another one will specialize on equity. Another one will specialize. And I thought that's the way the world works. That's how it is. And so there must exist a platform where a small team can produce an AI for a unique purpose. And they can iterate and build the best thing for that, right? And so that was the vision for Chai. So we wanted to build a platform for LLMs.Alessio [00:11:36]: That's kind of the maybe inside versus contrarian view that led you to start the company. Yeah. And then what was maybe the initial idea maze? Because if somebody told you that was the Hugging Face founding story, people might believe it. It's kind of like a similar ethos behind it. How did you land on the product feature today? And maybe what were some of the ideas that you discarded that initially you thought about?William [00:11:58]: So the first thing we built, it was fundamentally an API. So nowadays people would describe it as like agents, right? But anyone could write a Python script. They could submit it to an API. They could send it to the Chai backend and we would then host this code and execute it. So that's like the developer side of the platform. On their Python script, the interface was essentially text in and text out. An example would be the very first bot that I created. I think it was a Reddit news bot. And so it would first, it would pull the popular news. Then it would prompt whatever, like I just use some external API for like Burr or GPT-2 or whatever. Like it was a very, very small thing. And then the user could talk to it. So you could say to the bot, hi bot, what's the news today? And it would say, this is the top stories. And you could chat with it. Now four years later, that's like perplexity or something. That's like the, right? But back then the models were first of all, like really, really dumb. You know, they had an IQ of like a four year old. And users, there really wasn't any demand or any PMF for interacting with the news. So then I was like, okay. Um. So let's make another one. And I made a bot, which was like, you could talk to it about a recipe. So you could say, I'm making eggs. Like I've got eggs in my fridge. What should I cook? And it'll say, you should make an omelet. Right. There was no PMF for that. No one used it. And so I just kept creating bots. And so every single night after work, I'd be like, okay, I like, we have AI, we have this platform. I can create any text in textile sort of agent and put it on the platform. And so we just create stuff night after night. And then all the coders I knew, I would say, yeah, this is what we're going to do. And then I would say to them, look, there's this platform. You can create any like chat AI. You should put it on. And you know, everyone's like, well, chatbots are super lame. We want absolutely nothing to do with your chatbot app. No one who knew Python wanted to build on it. I'm like trying to build all these bots and no consumers want to talk to any of them. And then my sister who at the time was like just finishing college or something, I said to her, I was like, if you want to learn Python, you should just submit a bot for my platform. And she, she built a therapy for me. And I was like, okay, cool. I'm going to build a therapist bot. And then the next day I checked the performance of the app and I'm like, oh my God, we've got 20 active users. And they spent, they spent like an average of 20 minutes on the app. I was like, oh my God, what, what bot were they speaking to for an average of 20 minutes? And I looked and it was the therapist bot. And I went, oh, this is where the PMF is. There was no demand for, for recipe help. There was no demand for news. There was no demand for dad jokes or pub quiz or fun facts or what they wanted was they wanted the therapist bot. the time I kind of reflected on that and I thought, well, if I want to consume news, the most fun thing, most fun way to consume news is like Twitter. It's not like the value of there being a back and forth, wasn't that high. Right. And I thought if I need help with a recipe, I actually just go like the New York times has a good recipe section, right? It's not actually that hard. And so I just thought the thing that AI is 10 X better at is a sort of a conversation right. That's not intrinsically informative, but it's more about an opportunity. You can say whatever you want. You're not going to get judged. If it's 3am, you don't have to wait for your friend to text back. It's like, it's immediate. They're going to reply immediately. You can say whatever you want. It's judgment-free and it's much more like a playground. It's much more like a fun experience. And you could see that if the AI gave a person a compliment, they would love it. It's much easier to get the AI to give you a compliment than a human. From that day on, I said, okay, I get it. Humans want to speak to like humans or human like entities and they want to have fun. And that was when I started to look less at platforms like Google. And I started to look more at platforms like Instagram. And I was trying to think about why do people use Instagram? And I could see that I think Chai was, was filling the same desire or the same drive. If you go on Instagram, typically you want to look at the faces of other humans, or you want to hear about other people's lives. So if it's like the rock is making himself pancakes on a cheese plate. You kind of feel a little bit like you're the rock's friend, or you're like having pancakes with him or something, right? But if you do it too much, you feel like you're sad and like a lonely person, but with AI, you can talk to it and tell it stories and tell you stories, and you can play with it for as long as you want. And you don't feel like you're like a sad, lonely person. You feel like you actually have a friend.Alessio [00:16:29]: And what, why is that? Do you have any insight on that from using it?William [00:16:33]: I think it's just the human psychology. I think it's just the idea that, with old school social media. You're just consuming passively, right? So you'll just swipe. If I'm watching TikTok, just like swipe and swipe and swipe. And even though I'm getting the dopamine of like watching an engaging video, there's this other thing that's building my head, which is like, I'm feeling lazier and lazier and lazier. And after a certain period of time, I'm like, man, I just wasted 40 minutes. I achieved nothing. But with AI, because you're interacting, you feel like you're, it's not like work, but you feel like you're participating and contributing to the thing. You don't feel like you're just. Consuming. So you don't have a sense of remorse basically. And you know, I think on the whole people, the way people talk about, try and interact with the AI, they speak about it in an incredibly positive sense. Like we get people who say they have eating disorders saying that the AI helps them with their eating disorders. People who say they're depressed, it helps them through like the rough patches. So I think there's something intrinsically healthy about interacting that TikTok and Instagram and YouTube doesn't quite tick. From that point on, it was about building more and more kind of like human centric AI for people to interact with. And I was like, okay, let's make a Kanye West bot, right? And then no one wanted to talk to the Kanye West bot. And I was like, ah, who's like a cool persona for teenagers to want to interact with. And I was like, I was trying to find the influencers and stuff like that, but no one cared. Like they didn't want to interact with the, yeah. And instead it was really just the special moment was when we said the realization that developers and software engineers aren't interested in building this sort of AI, but the consumers are right. And rather than me trying to guess every day, like what's the right bot to submit to the platform, why don't we just create the tools for the users to build it themselves? And so nowadays this is like the most obvious thing in the world, but when Chai first did it, it was not an obvious thing at all. Right. Right. So we took the API for let's just say it was, I think it was GPTJ, which was this 6 billion parameter open source transformer style LLM. We took GPTJ. We let users create the prompt. We let users select the image and we let users choose the name. And then that was the bot. And through that, they could shape the experience, right? So if they said this bot's going to be really mean, and it's going to be called like bully in the playground, right? That was like a whole category that I never would have guessed. Right. People love to fight. They love to have a disagreement, right? And then they would create, there'd be all these romantic archetypes that I didn't know existed. And so as the users could create the content that they wanted, that was when Chai was able to, to get this huge variety of content and rather than appealing to, you know, 1% of the population that I'd figured out what they wanted, you could appeal to a much, much broader thing. And so from that moment on, it was very, very crystal clear. It's like Chai, just as Instagram is this social media platform that lets people create images and upload images, videos and upload that, Chai was really about how can we let the users create this experience in AI and then share it and interact and search. So it's really, you know, I say it's like a platform for social AI.Alessio [00:20:00]: Where did the Chai name come from? Because you started the same path. I was like, is it character AI shortened? You started at the same time, so I was curious. The UK origin was like the second, the Chai.William [00:20:15]: We started way before character AI. And there's an interesting story that Chai's numbers were very, very strong, right? So I think in even 20, I think late 2022, was it late 2022 or maybe early 2023? Chai was like the number one AI app in the app store. So we would have something like 100,000 daily active users. And then one day we kind of saw there was this website. And we were like, oh, this website looks just like Chai. And it was the character AI website. And I think that nowadays it's, I think it's much more common knowledge that when they left Google with the funding, I think they knew what was the most trending, the number one app. And I think they sort of built that. Oh, you found the people.swyx [00:21:03]: You found the PMF for them.William [00:21:04]: We found the PMF for them. Exactly. Yeah. So I worked a year very, very hard. And then they, and then that was when I learned a lesson, which is that if you're VC backed and if, you know, so Chai, we'd kind of ran, we'd got to this point, I was the only person who'd invested. I'd invested maybe 2 million pounds in the business. And you know, from that, we were able to build this thing, get to say a hundred thousand daily active users. And then when character AI came along, the first version, we sort of laughed. We were like, oh man, this thing sucks. Like they don't know what they're building. They're building the wrong thing anyway, but then I saw, oh, they've raised a hundred million dollars. Oh, they've raised another hundred million dollars. And then our users started saying, oh guys, your AI sucks. Cause we were serving a 6 billion parameter model, right? How big was the model that character AI could afford to serve, right? So we would be spending, let's say we would spend a dollar per per user, right? Over the, the, you know, the entire lifetime.swyx [00:22:01]: A dollar per session, per chat, per month? No, no, no, no.William [00:22:04]: Let's say we'd get over the course of the year, we'd have a million users and we'd spend a million dollars on the AI throughout the year. Right. Like aggregated. Exactly. Exactly. Right. They could spend a hundred times that. So people would say, why is your AI much dumber than character AIs? And then I was like, oh, okay, I get it. This is like the Silicon Valley style, um, hyper scale business. And so, yeah, we moved to Silicon Valley and, uh, got some funding and iterated and built the flywheels. And, um, yeah, I, I'm very proud that we were able to compete with that. Right. So, and I think the reason we were able to do it was just customer obsession. And it's similar, I guess, to how deep seek have been able to produce such a compelling model when compared to someone like an open AI, right? So deep seek, you know, their latest, um, V2, yeah, they claim to have spent 5 million training it.swyx [00:22:57]: It may be a bit more, but, um, like, why are you making it? Why are you making such a big deal out of this? Yeah. There's an agenda there. Yeah. You brought up deep seek. So we have to ask you had a call with them.William [00:23:07]: We did. We did. We did. Um, let me think what to say about that. I think for one, they have an amazing story, right? So their background is again in finance.swyx [00:23:16]: They're the Chinese version of you. Exactly.William [00:23:18]: Well, there's a lot of similarities. Yes. Yes. I have a great affinity for companies which are like, um, founder led, customer obsessed and just try and build something great. And I think what deep seek have achieved. There's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cash significantly. And then by being able to do that, they're able to significantly reduce their inference costs. And I think with kind of with AI, people get really focused on like the kind of the foundation model or like the model itself. And they sort of don't pay much attention to the inference. To give you an example with Chai, let's say a typical user session is 90 minutes, which is like, you know, is very, very long for comparison. Let's say the average session length on TikTok is 70 minutes. So people are spending a lot of time. And in that time they're able to send say 150 messages. That's a lot of completions, right? It's quite different from an open AI scenario where people might come in, they'll have a particular question in mind. And they'll ask like one question. And a few follow up questions, right? So because they're consuming, say 30 times as many requests for a chat, or a conversational experience, you've got to figure out how to how to get the right balance between the cost of that and the quality. And so, you know, I think with AI, it's always been the case that if you want a better experience, you can throw compute at the problem, right? So if you want a better model, you can just make it bigger. If you want it to remember better, give it a longer context. And now, what open AI is doing to great fanfare is with projection sampling, you can generate many candidates, right? And then with some sort of reward model or some sort of scoring system, you can serve the most promising of these many candidates. And so that's kind of scaling up on the inference time compute side of things. And so for us, it doesn't make sense to think of AI is just the absolute performance. So. But what we're seeing, it's like the MML you score or the, you know, any of these benchmarks that people like to look at, if you just get that score, it doesn't really tell tell you anything. Because it's really like progress is made by improving the performance per dollar. And so I think that's an area where deep seek have been able to form very, very well, surprisingly so. And so I'm very interested in what Lama four is going to look like. And if they're able to sort of match what deep seek have been able to achieve with this performance per dollar gain.Alessio [00:25:59]: Before we go into the inference, some of the deeper stuff, can you give people an overview of like some of the numbers? So I think last I checked, you have like 1.4 million daily active now. It's like over 22 million of revenue. So it's quite a business.William [00:26:12]: Yeah, I think we grew by a factor of, you know, users grew by a factor of three last year. Revenue over doubled. You know, it's very exciting. We're competing with some really big, really well funded companies. Character AI got this, I think it was almost a $3 billion valuation. And they have 5 million DAU is a number that I last heard. Torquay, which is a Chinese built app owned by a company called Minimax. They're incredibly well funded. And these companies didn't grow by a factor of three last year. Right. And so when you've got this company and this team that's able to keep building something that gets users excited, and they want to tell their friend about it, and then they want to come and they want to stick on the platform. I think that's very special. And so last year was a great year for the team. And yeah, I think the numbers reflect the hard work that we put in. And then fundamentally, the quality of the app, the quality of the content, the quality of the content, the quality of the content, the quality of the content, the quality of the content. AI is the quality of the experience that you have. You actually published your DAU growth chart, which is unusual. And I see some inflections. Like, it's not just a straight line. There's some things that actually inflect. Yes. What were the big ones? Cool. That's a great, great, great question. Let me think of a good answer. I'm basically looking to annotate this chart, which doesn't have annotations on it. Cool. The first thing I would say is this is, I think the most important thing to know about success is that success is born out of failures. Right? Through failures that we learn. You know, if you think something's a good idea, and you do and it works, great, but you didn't actually learn anything, because everything went exactly as you imagined. But if you have an idea, you think it's going to be good, you try it, and it fails. There's a gap between the reality and expectation. And that's an opportunity to learn. The flat periods, that's us learning. And then the up periods is that's us reaping the rewards of that. So I think the big, of the growth shot of just 2024, I think the first thing that really kind of put a dent in our growth was our backend. So we just reached this scale. So we'd, from day one, we'd built on top of Google's GCP, which is Google's cloud platform. And they were fantastic. We used them when we had one daily active user, and they worked pretty good all the way up till we had about 500,000. It was never the cheapest, but from an engineering perspective, man, that thing scaled insanely good. Like, not Vertex? Not Vertex. Like GKE, that kind of stuff? We use Firebase. So we use Firebase. I'm pretty sure we're the biggest user ever on Firebase. That's expensive. Yeah, we had calls with engineers, and they're like, we wouldn't recommend using this product beyond this point, and you're 3x over that. So we pushed Google to their absolute limits. You know, it was fantastic for us, because we could focus on the AI. We could focus on just adding as much value as possible. But then what happened was, after 500,000, just the thing, the way we were using it, and it would just, it wouldn't scale any further. And so we had a really, really painful, at least three-month period, as we kind of migrated between different services, figuring out, like, what requests do we want to keep on Firebase, and what ones do we want to move on to something else? And then, you know, making mistakes. And learning things the hard way. And then after about three months, we got that right. So that, we would then be able to scale to the 1.5 million DAE without any further issues from the GCP. But what happens is, if you have an outage, new users who go on your app experience a dysfunctional app, and then they're going to exit. And so your next day, the key metrics that the app stores track are going to be something like retention rates. And so your next day, the key metrics that the app stores track are going to be something like retention rates. Money spent, and the star, like, the rating that they give you. In the app store. In the app store, yeah. Tyranny. So if you're ranked top 50 in entertainment, you're going to acquire a certain rate of users organically. If you go in and have a bad experience, it's going to tank where you're positioned in the algorithm. And then it can take a long time to kind of earn your way back up, at least if you wanted to do it organically. If you throw money at it, you can jump to the top. And I could talk about that. But broadly speaking, if we look at 2024, the first kink in the graph was outages due to hitting 500k DAU. The backend didn't want to scale past that. So then we just had to do the engineering and build through it. Okay, so we built through that, and then we get a little bit of growth. And so, okay, that's feeling a little bit good. I think the next thing, I think it's, I'm not going to lie, I have a feeling that when Character AI got... I was thinking. I think so. I think... So the Character AI team fundamentally got acquired by Google. And I don't know what they changed in their business. I don't know if they dialed down that ad spend. Products don't change, right? Products just what it is. I don't think so. Yeah, I think the product is what it is. It's like maintenance mode. Yes. I think the issue that people, you know, some people may think this is an obvious fact, but running a business can be very competitive, right? Because other businesses can see what you're doing, and they can imitate you. And then there's this... There's this question of, if you've got one company that's spending $100,000 a day on advertising, and you've got another company that's spending zero, if you consider market share, and if you're considering new users which are entering the market, the guy that's spending $100,000 a day is going to be getting 90% of those new users. And so I have a suspicion that when the founders of Character AI left, they dialed down their spending on user acquisition. And I think that kind of gave oxygen to like the other apps. And so Chai was able to then start growing again in a really healthy fashion. I think that's kind of like the second thing. I think a third thing is we've really built a great data flywheel. Like the AI team sort of perfected their flywheel, I would say, in end of Q2. And I could speak about that at length. But fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours. And when we did that, we could really, really, really perfect techniques like DPO, fine tuning, prompt engineering, blending, rejection sampling, training a reward model, right, really successfully, like boom, boom, boom, boom, boom. And so I think in Q3 and Q4, we got, the amount of AI improvements we got was like astounding. It was getting to the point, I thought like how much more, how much more edge is there to be had here? But the team just could keep going and going and going. That was like number three for the inflection point.swyx [00:34:53]: There's a fourth?William [00:34:54]: The important thing about the third one is if you go on our Reddit or you talk to users of AI, there's like a clear date. It's like somewhere in October or something. The users, they flipped. Before October, the users... The users would say character AI is better than you, for the most part. Then from October onwards, they would say, wow, you guys are better than character AI. And that was like a really clear positive signal that we'd sort of done it. And I think people, you can't cheat consumers. You can't trick them. You can't b******t them. They know, right? If you're going to spend 90 minutes on a platform, and with apps, there's the barriers to switching is pretty low. Like you can try character AI, you can't cheat consumers. You can't cheat them. You can't cheat them. You can't cheat AI for a day. If you get bored, you can try Chai. If you get bored of Chai, you can go back to character. So the users, the loyalty is not strong, right? What keeps them on the app is the experience. If you deliver a better experience, they're going to stay and they can tell. So that was the fourth one was we were fortunate enough to get this hire. He was hired one really talented engineer. And then they said, oh, at my last company, we had a head of growth. He was really, really good. And he was the head of growth for ByteDance for two years. Would you like to speak to him? And I was like, yes. Yes, I think I would. And so I spoke to him. And he just blew me away with what he knew about user acquisition. You know, it was like a 3D chessswyx [00:36:21]: sort of thing. You know, as much as, as I know about AI. Like ByteDance as in TikTok US. Yes.William [00:36:26]: Not ByteDance as other stuff. Yep. He was interviewing us as we were interviewing him. Right. And so pick up options. Yeah, exactly. And so he was kind of looking at our metrics. And he was like, I saw him get really excited when he said, guys, you've got a million daily active users and you've done no advertising. I said, correct. And he was like, that's unheard of. He's like, I've never heard of anyone doing that. And then he started looking at our metrics. And he was like, if you've got all of this organically, if you start spending money, this is going to be very exciting. I was like, let's give it a go. So then he came in, we've just started ramping up the user acquisition. So that looks like spending, you know, let's say we're spending, we started spending $20,000 a day, it looked very promising than 20,000. Right now we're spending $40,000 a day on user acquisition. That's still only half of what like character AI or talkie may be spending. But from that, it's sort of, we were growing at a rate of maybe say, 2x a year. And that got us growing at a rate of 3x a year. So I'm growing, I'm evolving more and more to like a Silicon Valley style hyper growth, like, you know, you build something decent, and then you canswyx [00:37:33]: slap on a huge... You did the important thing, you did the product first.William [00:37:36]: Of course, but then you can slap on like, like the rocket or the jet engine or something, which is just this cash in, you pour in as much cash, you buy a lot of ads, and your growth is faster.swyx [00:37:48]: Not to, you know, I'm just kind of curious what's working right now versus what surprisinglyWilliam [00:37:52]: doesn't work. Oh, there's a long, long list of surprising stuff that doesn't work. Yeah. The surprising thing, like the most surprising thing, what doesn't work is almost everything doesn't work. That's what's surprising. And I'll give you an example. So like a year and a half ago, I was working at a company, we were super excited by audio. I was like, audio is going to be the next killer feature, we have to get in the app. And I want to be the first. So everything Chai does, I want us to be the first. We may not be the company that's strongest at execution, but we can always be theswyx [00:38:22]: most innovative. Interesting. Right? So we can... You're pretty strong at execution.William [00:38:26]: We're much stronger, we're much stronger. A lot of the reason we're here is because we were first. If we launched today, it'd be so hard to get the traction. Because it's like to get the flywheel, to get the users, to build a product people are excited about. If you're first, people are naturally excited about it. But if you're fifth or 10th, man, you've got to beswyx [00:38:46]: insanely good at execution. So you were first with voice? We were first. We were first. I only knowWilliam [00:38:51]: when character launched voice. They launched it, I think they launched it at least nine months after us. Okay. Okay. But the team worked so hard for it. At the time we did it, latency is a huge problem. Cost is a huge problem. Getting the right quality of the voice is a huge problem. Right? Then there's this user interface and getting the right user experience. Because you don't just want it to start blurting out. Right? You want to kind of activate it. But then you don't have to keep pressing a button every single time. There's a lot that goes into getting a really smooth audio experience. So we went ahead, we invested the three months, we built it all. And then when we did the A-B test, there was like, no change in any of the numbers. And I was like, this can't be right, there must be a bug. And we spent like a week just checking everything, checking again, checking again. And it was like, the users just did not care. And it was something like only 10 or 15% of users even click the button to like, they wanted to engage the audio. And they would only use it for 10 or 15% of the time. So if you do the math, if it's just like something that one in seven people use it for one seventh of their time. You've changed like 2% of the experience. So even if that that 2% of the time is like insanely good, it doesn't translate much when you look at the retention, when you look at the engagement, and when you look at the monetization rates. So audio did not have a big impact. I'm pretty big on audio. But yeah, I like it too. But it's, you know, so a lot of the stuff which I do, I'm a big, you can have a theory. And you resist. Yeah. Exactly, exactly. So I think if you want to make audio work, it has to be a unique, compelling, exciting experience that they can't have anywhere else.swyx [00:40:37]: It could be your models, which just weren't good enough.William [00:40:39]: No, no, no, they were great. Oh, yeah, they were very good. it was like, it was kind of like just the, you know, if you listen to like an audible or Kindle, or something like, you just hear this voice. And it's like, you don't go like, wow, this is this is special, right? It's like a convenience thing. But the idea is that if you can, if Chai is the only platform, like, let's say you have a Mr. Beast, and YouTube is the only platform you can use to make audio work, then you can watch a Mr. Beast video. And it's the most engaging, fun video that you want to watch, you'll go to a YouTube. And so it's like for audio, you can't just put the audio on there. And people go, oh, yeah, it's like 2% better. Or like, 5% of users think it's 20% better, right? It has to be something that the majority of people, for the majority of the experience, go like, wow, this is a big deal. That's the features you need to be shipping. If it's not going to appeal to the majority of people, for the majority of the experience, and it's not a big deal, it's not going to move you. Cool. So you killed it. I don't see it anymore. Yep. So I love this. The longer, it's kind of cheesy, I guess, but the longer I've been working at Chai, and I think the team agrees with this, all the platitudes, at least I thought they were platitudes, that you would get from like the Steve Jobs, which is like, build something insanely great, right? Or be maniacally focused, or, you know, the most important thing is saying no to, not to work on. All of these sort of lessons, they just are like painfully true. They're painfully true. So now I'm just like, everything I say, I'm either quoting Steve Jobs or Zuckerberg. I'm like, guys, move fast and break free.swyx [00:42:10]: You've jumped the Apollo to cool it now.William [00:42:12]: Yeah, it's just so, everything they said is so, so true. The turtle neck. Yeah, yeah, yeah. Everything is so true.swyx [00:42:18]: This last question on my side, and I want to pass this to Alessio, is on just, just multi-modality in general. This actually comes from Justine Moore from A16Z, who's a friend of ours. And a lot of people are trying to do voice image video for AI companions. Yes. You just said voice didn't work. Yep. What would make you revisit?William [00:42:36]: So Steve Jobs, he was very, listen, he was very, very clear on this. There's a habit of engineers who, once they've got some cool technology, they want to find a way to package up the cool technology and sell it to consumers, right? That does not work. So you're free to try and build a startup where you've got your cool tech and you want to find someone to sell it to. That's not what we do at Chai. At Chai, we start with the consumer. What does the consumer want? What is their problem? And how do we solve it? So right now, the number one problems for the users, it's not the audio. That's not the number one problem. It's not the image generation either. That's not their problem either. The number one problem for users in AI is this. All the AI is being generated by middle-aged men in Silicon Valley, right? That's all the content. You're interacting with this AI. You're speaking to it for 90 minutes on average. It's being trained by middle-aged men. The guys out there, they're out there. They're talking to you. They're talking to you. They're like, oh, what should the AI say in this situation, right? What's funny, right? What's cool? What's boring? What's entertaining? That's not the way it should be. The way it should be is that the users should be creating the AI, right? And so the way I speak about it is this. Chai, we have this AI engine in which sits atop a thin layer of UGC. So the thin layer of UGC is absolutely essential, right? It's just prompts. But it's just prompts. It's just an image. It's just a name. It's like we've done 1% of what we could do. So we need to keep thickening up that layer of UGC. It must be the case that the users can train the AI. And if reinforcement learning is powerful and important, they have to be able to do that. And so it's got to be the case that there exists, you know, I say to the team, just as Mr. Beast is able to spend 100 million a year or whatever it is on his production company, and he's got a team building the content, the Mr. Beast company is able to spend 100 million a year on his production company. And he's got a team building the content, which then he shares on the YouTube platform. Until there's a team that's earning 100 million a year or spending 100 million on the content that they're producing for the Chai platform, we're not finished, right? So that's the problem. That's what we're excited to build. And getting too caught up in the tech, I think is a fool's errand. It does not work.Alessio [00:44:52]: As an aside, I saw the Beast Games thing on Amazon Prime. It's not doing well. And I'mswyx [00:44:56]: curious. It's kind of like, I mean, the audience reading is high. The run-to-meet-all sucks, but the audience reading is high.Alessio [00:45:02]: But it's not like in the top 10. I saw it dropped off of like the... Oh, okay. Yeah, that one I don't know. I'm curious, like, you know, it's kind of like similar content, but different platform. And then going back to like, some of what you were saying is like, you know, people come to ChaiWilliam [00:45:13]: expecting some type of content. Yeah, I think it's something that's interesting to discuss is like, is moats. And what is the moat? And so, you know, if you look at a platform like YouTube, the moat, I think is in first is really is in the ecosystem. And the ecosystem, is comprised of you have the content creators, you have the users, the consumers, and then you have the algorithms. And so this, this creates a sort of a flywheel where the algorithms are able to be trained on the users, and the users data, the recommend systems can then feed information to the content creators. So Mr. Beast, he knows which thumbnail does the best. He knows the first 10 seconds of the video has to be this particular way. And so his content is super optimized for the YouTube platform. So that's why it doesn't do well on Amazon. If he wants to do well on Amazon, how many videos has he created on the YouTube platform? By thousands, 10s of 1000s, I guess, he needs to get those iterations in on the Amazon. So at Chai, I think it's all about how can we get the most compelling, rich user generated content, stick that on top of the AI engine, the recommender systems, in such that we get this beautiful data flywheel, more users, better recommendations, more creative, more content, more users.Alessio [00:46:34]: You mentioned the algorithm, you have this idea of the Chaiverse on Chai, and you have your own kind of like LMSYS-like ELO system. Yeah, what are things that your models optimize for, like your users optimize for, and maybe talk about how you build it, how people submit models?William [00:46:49]: So Chaiverse is what I would describe as a developer platform. More often when we're speaking about Chai, we're thinking about the Chai app. And the Chai app is really this product for consumers. And so consumers can come on the Chai app, they can come on the Chai app, they can come on the Chai app, they can interact with our AI, and they can interact with other UGC. And it's really just these kind of bots. And it's a thin layer of UGC. Okay. Our mission is not to just have a very thin layer of UGC. Our mission is to have as much UGC as possible. So we must have, I don't want people at Chai training the AI. I want people, not middle aged men, building AI. I want everyone building the AI, as many people building the AI as possible. Okay, so what we built was we built Chaiverse. And Chaiverse is kind of, it's kind of like a prototype, is the way to think about it. And it started with this, this observation that, well, how many models get submitted into Hugging Face a day? It's hundreds, it's hundreds, right? So there's hundreds of LLMs submitted each day. Now consider that, what does it take to build an LLM? It takes a lot of work, actually. It's like someone devoted several hours of compute, several hours of their time, prepared a data set, launched it, ran it, evaluated it, submitted it, right? So there's a lot of, there's a lot of, there's a lot of work that's going into that. So what we did was we said, well, why can't we host their models for them and serve them to users? And then what would that look like? The first issue is, well, how do you know if a model is good or not? Like, we don't want to serve users the crappy models, right? So what we would do is we would, I love the LMSYS style. I think it's really cool. It's really simple. It's a very intuitive thing, which is you simply present the users with two completions. You can say, look, this is from model one. This is from model two. This is from model three. This is from model A. This is from model B, which is better. And so if someone submits a model to Chaiverse, what we do is we spin up a GPU. We download the model. We're going to now host that model on this GPU. And we're going to start routing traffic to it. And we're going to send, we think it takes about 5,000 completions to get an accurate signal. That's roughly what LMSYS does. And from that, we're able to get an accurate ranking. And we're able to get an accurate ranking. And we're able to get an accurate ranking of which models are people finding entertaining and which models are not entertaining. If you look at the bottom 80%, they'll suck. You can just disregard them. They totally suck. Then when you get the top 20%, you know you've got a decent model, but you can break it down into more nuance. There might be one that's really descriptive. There might be one that's got a lot of personality to it. There might be one that's really illogical. Then the question is, well, what do you do with these top models? From that, you can do more sophisticated things. You can try and do like a routing thing where you say for a given user request, we're going to try and predict which of these end models that users enjoy the most. That turns out to be pretty expensive and not a huge source of like edge or improvement. Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model. Just a random 50%? Just a random, yeah. And then... That's blending? That's blending. You can do more sophisticated things on top of that, as in all things in life, but the 80-20 solution, if you just do that, you get a pretty powerful effect out of the gate. Random number generator. I think it's like the robustness of randomness. Random is a very powerful optimization technique, and it's a very robust thing. So you can explore a lot of the space very efficiently. There's one thing that's really, really important to share, and this is the most exciting thing for me, is after you do the ranking, you get an ELO score, and you can track a user's first join date, the first date they submit a model to Chaiverse, they almost always get a terrible ELO, right? So let's say the first submission they get an ELO of 1,100 or 1,000 or something, and you can see that they iterate and they iterate and iterate, and it will be like, no improvement, no improvement, no improvement, and then boom. Do you give them any data, or do you have to come up with this themselves? We do, we do, we do, we do. We try and strike a balance between giving them data that's very useful, you've got to be compliant with GDPR, which is like, you have to work very hard to preserve the privacy of users of your app. So we try to give them as much signal as possible, to be helpful. The minimum is we're just going to give you a score, right? That's the minimum. But that alone is people can optimize a score pretty well, because they're able to come up with theories, submit it, does it work? No. A new theory, does it work? No. And then boom, as soon as they figure something out, they keep it, and then they iterate, and then boom,Alessio [00:51:46]: they figure something out, and they keep it. Last year, you had this post on your blog, cross-sourcing the lead to the 10 trillion parameter, AGI, and you call it a mixture of experts, recommenders. Yep. Any insights?William [00:51:58]: Updated thoughts, 12 months later? I think the odds, the timeline for AGI has certainly been pushed out, right? Now, this is in, I'm a controversial person, I don't know, like, I just think... You don't believe in scaling laws, you think AGI is further away. I think it's an S-curve. I think everything's an S-curve. And I think that the models have proven to just be far worse at reasoning than people sort of thought. And I think whenever I hear people talk about LLMs as reasoning engines, I sort of cringe a bit. I don't think that's what they are. I think of them more as like a simulator. I think of them as like a, right? So they get trained to predict the next most likely token. It's like a physics simulation engine. So you get these like games where you can like construct a bridge, and you drop a car down, and then it predicts what should happen. And that's really what LLMs are doing. It's not so much that they're reasoning, it's more that they're just doing the most likely thing. So fundamentally, the ability for people to add in intelligence, I think is very limited. What most people would consider intelligence, I think the AI is not a crowdsourcing problem, right? Now with Wikipedia, Wikipedia crowdsources knowledge. It doesn't crowdsource intelligence. So it's a subtle distinction. AI is fantastic at knowledge. I think it's weak at intelligence. And a lot, it's easy to conflate the two because if you ask it a question and it gives you, you know, if you said, who was the seventh president of the United States, and it gives you the correct answer, I'd say, well, I don't know the answer to that. And you can conflate that with intelligence. But really, that's a question of knowledge. And knowledge is really this thing about saying, how can I store all of this information? And then how can I retrieve something that's relevant? Okay, they're fantastic at that. They're fantastic at storing knowledge and retrieving the relevant knowledge. They're superior to humans in that regard. And so I think we need to come up for a new word. How does one describe AI should contain more knowledge than any individual human? It should be more accessible than any individual human. That's a very powerful thing. That's superswyx [00:54:07]: powerful. But what words do we use to describe that? We had a previous guest on Exa AI that does search. And he tried to coin super knowledge as the opposite of super intelligence.William [00:54:20]: Exactly. I think super knowledge is a more accurate word for it.swyx [00:54:24]: You can store more things than any human can.William [00:54:26]: And you can retrieve it better than any human can as well. And I think it's those two things combined that's special. I think that thing will exist. That thing can be built. And I think you can start with something that's entertaining and fun. And I think, I often think it's like, look, it's going to be a 20 year journey. And we're in like, year four, or it's like the web. And this is like 1998 or something. You know, you've got a long, long way to go before the Amazon.coms are like these huge, multi trillion dollar businesses that every single person uses every day. And so AI today is very simplistic. And it's fundamentally the way we're using it, the flywheels, and this ability for how can everyone contribute to it to really magnify the value that it brings. Right now, like, I think it's a bit sad. It's like, right now you have big labs, I'm going to pick on open AI. And they kind of go to like these human labelers. And they say, we're going to pay you to just label this like subset of questions that we want to get a really high quality data set, then we're going to get like our own computers that are really powerful. And that's kind of like the thing. For me, it's so much like Encyclopedia Britannica. It's like insane. All the people that were interested in blockchain, it's like, well, this is this is what needs to be decentralized, you need to decentralize that thing. Because if you distribute it, people can generate way more data in a distributed fashion, way more, right? You need the incentive. Yeah, of course. Yeah. But I mean, the, the, that's kind of the exciting thing about Wikipedia was it's this understanding, like the incentives, you don't need money to incentivize people. You don't need dog coins. No. Sometimes, sometimes people get the satisfaction fro
Send Everyday AI and Jordan a text messageEverything in your current AI playbook is about to get shredded, stomped on, and turned into digital confetti. I've spent 2024 living in the bleeding edge of AI development, meticulously tracking AI's development as my full-time job. And what's coming next….. yikes. ↳ We're entering an era where AI doesn't just chat – it REMEMBERS. ↳ Where what us humans know becomes kinda worthless. (Or at least worth less.) ↳ Where specialized models hit harder than a triple espresso shot. ↳ Where different AIs team up like some digital Avengers squad. And AGI? It might just slip through the door while everyone's busy debating if it's possible. We're peeling back the silicon curtain on the last and final installment of our 2025 AI Predictions and Roadmap: AI's Technical Leaps: Memory, Models, and Major Changes. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan questions on AIUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. Narrow AI Agents2. LLM Memory3. LLMs Becoming Small Language Models4. Mixture of Models5. AGI is AchievedTimestamps:00:00 Live Insights and Trend Spotting06:25 "Seeking Feedback for Newsletter"07:44 AGI: Not Coming Anytime Soon11:11 AI Memory and Context Windows15:25 "Microsoft's GPT-4 Mini Revelation"18:32 Open Source Models' Future Evolution20:47 Small Models Surpassing Larger Ones25:37 "AGI Achieved? Debating OpenAI's Claim"28:35 AGI Achieved: Minimal Immediate ImpactKeywords:AI predictions, AGI, artificial general intelligence, large language models, dumb AI, technical leaps, memory models, everyday AI, AI trends, free daily newsletter, AI experts, podcasts, Microsoft, Google, OpenAI, IBM, agent orchestrators, public companies, AI agents, company reasoning data collection, API prices, AI video tools, AI influencers, AI software, AI regulations, narrow AI agents, LLM memory, context window, OpenAI's memory feature, mixture of models. Ready for ROI on GenAI? Go to youreverydayai.com/partner
In this episode, we dive deep into the world of AI engineering with Chip Huyen, author of the excellent, newly released book "AI Engineering: Building Applications with Foundation Models". We explore the nuances of AI engineering, distinguishing it from traditional machine learning, discuss how foundational models make it possible for anyone to build AI applications and cover many other topics including the challenges of AI evaluation, the intricacies of the generative AI stack, why prompt engineering is underrated, why the rumors of the death of RAG are greatly exaggerated, and the latest progress in AI agents. Book: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ Chip Huyen Website - https://huyenchip.com LinkedIn - https://www.linkedin.com/in/chiphuyen Twitter/X - https://x.com/chipro FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (02:45) What is new about AI engineering? (06:11) The product-first approach to building AI applications (07:38) Are AI engineering and ML engineering two separate professions? (11:00) The Generative AI stack (13:00) Why are language models able to scale? (14:45) Auto-regressive vs. masked models (16:46) Supervised vs. unsupervised vs. self-supervised (18:56) Why does model scale matter? (20:40) Mixture of Experts (24:20) Pre-training vs. post-training (28:43) Sampling (32:14) Evaluation as a key to AI adoption (36:03) Entropy (40:05) Evaluating AI systems (43:21) AI as a judge (46:49) Why prompt engineering is underrated (49:38) In-context learning (51:46) Few-shot learning and zero-shot learning (52:57) Defensive prompt engineering (55:29) User prompt vs. system prompt (57:07) Why RAG is here to stay (01:00:31) Defining AI agents (01:04:04) AI agent planning (01:08:32) Training data as a bottleneck to agent planning
In this episode, Sharon Zhou, Co-Founder and CEO of Lamini AI, shares her expertise in the world of AI, focusing on fine-tuning models for improved performance and reliability.Highlights include:- The integration of determinism and probabilism for handling unstructured data and user queries effectively.- Proprietary techniques like memory tuning and robust evaluation frameworks to mitigate model inaccuracies and hallucinations.- Lessons learned from deploying AI applications, including insights from GitHub Copilot's rollout.Connect with Sharon Zhou and Lamini:https://www.linkedin.com/in/zhousharon/https://x.com/realsharonzhouhttps://www.lamini.ai/
We're experimenting and would love to hear from you!In this episode of Discover Daily, we explore groundbreaking technological and scientific developments shaping our future. MIT's revolutionary DrivAerNet++ database takes center stage, featuring over 8,000 AI-generated electric vehicle designs with comprehensive aerodynamic data, promising to transform automotive development processes and accelerate EV innovation.The show delves into a major medical breakthrough as lenacapavir, Science magazine's 2024 Breakthrough of the Year, emerges as a game-changing HIV prevention drug. This remarkable innovation from Gilead Sciences offers six months of protection with a single injection, demonstrating 96-100% efficacy in clinical trials and holding promise for global HIV prevention efforts.The episode's main focus spotlights DeepSeek-V3, a cutting-edge open-source AI model boasting 671 billion parameters. Using innovative Mixture-of-Experts architecture, this powerful language model activates only 37 billion parameters per token, achieving impressive efficiency while maintaining high performance across various text-based tasks. The discussion explores its capabilities, limitations, and potential impact on the AI landscape.From Perplexity's Discover Feed:https://www.perplexity.ai/page/mit-s-ev-design-database-HW3LeM4gQNO2pa1oYp6AMwhttps://www.perplexity.ai/page/hiv-drug-named-breakthrough-of-kzPk2YAoQPKS.CdzOsNdXAhttps://www.perplexity.ai/page/deepseek-s-new-open-source-ai-YwAwjp_IQKiAJ2l1qFhN9gPerplexity is the fastest and most powerful way to search the web. Perplexity crawls the web and curates the most relevant and up-to-date sources (from academic papers to Reddit threads) to create the perfect response to any question or topic you're interested in. Take the world's knowledge with you anywhere. Available on iOS and Android Join our growing Discord community for the latest updates and exclusive content. Follow us on: Instagram Threads X (Twitter) YouTube Linkedin
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