Podcasts about Google Brain

Explore how the latest advancements in AI are shifting from traditional training to inference-focused efficiencies, and how companies like Adaptation Labs are pioneering adaptive, full-stack AI solutions that democratize control across industries.Key topics:The evolution from compute-heavy training models to efficient inference layersHow inference costs are changing despite increasing AI demandThe role of adaptive, gradient-free learning in democratizing AI customizationChallenges with the last 5% reliability gap and continuous learningThe importance of full-stack optimization—from data to interfaces in AI systemsFuture trends: decentralized AI, edge computing, and ongoing innovationTimestamps:00:00 - Introduction to AI trends: scaling vs inference efficiencies01:01 - Sudip's background: Google Brain, DeepMind, and inference infrastructure01:34 - The rapid growth of foundation and large language models02:36 - Comparing traditional ML project timelines to large foundation models04:20 - The transformative potential of foundation models in enterprise and underserved communities05:33 - The shift from task-specific models to general-purpose foundation models07:07 - How inference costs have evolved: the rising demand vs falling per-token costs08:37 - The challenge of inference in trillion-parameter models and the move towards smaller, verticalized models10:14 - Factors driving high inference costs: model size, reasoning, agentic workloads12:13 - The probabilistic nature of inference and API pricing complexities13:07 - Variability in inference costs and demand in real-world scenarios14:14 - The autoregressive, sequential nature of LLM inference and system challenges16:45 - Cost implications of autoregressive inference and the move to more efficient, localized models18:18 - The motivation behind Adaptation Labs: democratizing AI control and customization19:47 - Adaptive, gradient-free continual learning and environment interaction21:26 - Co-optimizing full-stack AI: systems, interfaces, and models22:34 - How interface design impacts AI adoption and continuous learning23:55 - The evolution of techniques: from foundational training to open-source innovations26:18 - Handling the ‘last 5%' reliability challenge in enterprise AI deployments28:02 - The importance of system feedback and adaptive learning in coding and decision-making31:12 - Adaptive Data and AutoScientist: seamless data transformation and model co-optimization32:55 - Use cases: finance, low-resource languages, long context data34:13 - The role of inference techniques and creating high-quality data for customization36:10 - Future of adaptive, task-specific interfaces and continuous, real-time learning38:49 - Full-stack AI: data, models, interfaces, and their iterative feedback loops41:18 - The competition between fine-tuning and adaptive inference techniques43:29 - The origin of new inference techniques: industry labs, open source, and innovation hubs45:27 - The “last 5%” reliability gap: why it's critical and how dynamic learning can help48:27 - Hardware vs software optimization in AI systems and the future of systemic efficiency51:25 - Growing AI demand, hardware constraints, and the opportunity for systemic innovation52:48 - The shift from training to inference and decentralized AI models at the edge54:12 - Final thoughts: the evolving landscape and long-term AI innovationConnect with Sudip:LinkedInConnect with Nataraj:⁠LinkedIn⁠

AI can write code, pass exams, and summarize the web, but ask it to reason through a real-world image, and the magic often breaks. Andrew Dai, co-founder and CEO of Elorian, joins The Neuron to explain why visual reasoning may be one of the biggest unsolved problems in AI.Andrew spent years at Google Brain and DeepMind, including work connected to Gemini and sparse mixture-of-experts systems. Now, he's building Elorian around a simple but powerful idea: if AI is going to understand the physical world, it needs more than text-based reasoning layered on top of images.In this episode, Corey and Grant talk with Andrew about why frontier models struggle with counting, navigation, design, engineering, charts, and physical reasoning; why scaling language models hasn't solved vision; what a “visual chain of thought” might look like; and how better visual reasoning could accelerate robotics, satellite analysis, product design, and mechanical engineering.Sponsored by Dell Technologies and NVIDIA. Learn more at techrepublic.com/hubs/the-enterprise-guide-to-scalable-ai/.Sponsored by Outshift: Visit https://outshift.cisco.com/?utm_campaign=fy26q3_outshift_ww_paid-media_ioc-neuronai-outshift_podcast&utm_channel=podcast&utm_source=podcast to learn more about the Internet of Cognition.Subscribe to The Neuron for more conversations with the people building the future of AI.

“Could we really reduce your conscious mind to a set of underlying processes that, when composed, create the feeling of you, the view of right now?” This is what my guest questions and proposes. David Sussillo is a world-renowned neuroscientist, an adjunct professor at Stanford University and has been a scientist at the Google Brain group and Meta Reality Labs. In his professional pursuits, David researches brain-machine interfaces to develop the next generation of computers. He works to understand the ghost in the machine – how cells in our brain collectively give rise to the computations that determine behavior. But David is not just a researcher. He's his own test subject. He had a difficult childhood, to put it mildly. He spent five years living in the Albuquerque Christian Children's Home. A home for children who were basically abandoned. They had unfit parents, but weren't up for adoption. This was near to my heart, as my family and I served at a similar children's home in Gallup, NM, and I understand much of the heartbreak associated with such a place. My core interest was how David came from such a traumatic childhood, to be the high achieving adult he is today. His sister, who experienced much of the same lifestyle, killed herself. So again, what was different about David? And the point here is not David and his story. But you and me and our stories, and understanding how we imprison, and free ourselves. David discusses his journey in his new book, EMERGENCE: A Memoir of Boyhood, Computation, and the Mysteries of Mind. Sign up for your $1/month trial period at shopify.com/kevin Go to shipstation.com and use code KEVIN to start your free trial. Learn more about your ad choices. Visit megaphone.fm/adchoices

This episode with Lukasz Kaiser, co-author of the seminal "Attention Is All You Need" transformer paper and former researcher at both Google Brain and OpenAI, is a wide-ranging conversation about the fundamental limits of current AI architectures and whether transformers will continue to dominate or eventually give way to something new. Lukasz brings a rare dual perspective: deep belief in how far the current paradigm has taken us (he's an enthusiastic daily Codex user who's seen 10x productivity gains in his own research), while maintaining genuine intellectual humility about whether transformers can truly generalize the way humans do. The episode weaves together questions about data efficiency, the non-verifiable RL frontier, the coding agent revolution, the open vs. closed source gap, and what the next architectural leap might look like: all filtered through the lens of someone who helped build the foundation the entire field is standing on.   (0:00) Intro (1:12) Transformers vs. Human Learning (8:37) How Do We Get Physical World Generalization? (10:52) What Comes After Transformers (13:59) How Much Have Agents Improved Lukasz's AI Research Productivity? (17:21) How Close Is an AI Research Intern? (26:06) RL Beyond Verifiable Tasks (35:38) App Companies: Build Models or Lean on Labs? (46:21) Multimodal Is Still Missing Something (49:46) OpenAI's Bet on Reasoning (55:26) The AI Coding Wars (59:26) Focus vs. Keeping Embers Burning (1:02:09) Open Source vs. Closed Source Gap (1:05:15) Quickfire With your host: @jacobeffron - Managing Director at Redpoint

Illia Polosukhin, founder of NEAR and co-author of 'Attention Is All You Need,' on why confidentiality will let crypto become daily commerce — plus, some Near lore. ======================================================== Thank you to our sponsors! ⁠⁠Multichain Advisors⁠⁠: Get help navigating TGEs, go‑to‑market, BD and partnerships, capital markets advisory, PR, media placements, KOL activations and more at ⁠⁠multichainadv.com⁠⁠. ⁠⁠⁠⁠⁠Coinbase One⁠⁠⁠⁠⁠: Get 20% off the first year of your Coinbase One annual plan at ⁠⁠⁠⁠⁠coinbase.com/unchained⁠⁠⁠⁠⁠. ======================================================== Before co-founding NEAR Protocol, Illia Polosukhin was on the eight-person Google Brain team that wrote the transformer paper — the architecture behind every large language model running today. He never mentioned it. When Kain Warwick found out two weeks ago, via a crypto AI chatbot, his reaction was: you have to be kidding me. That backstory sets the tone for a conversation that moves from how transformers actually came together, to why confidentiality is what unlocks on-chain commerce for real businesses, and what NEAR is doing to keep criminals off its network without becoming a surveillance layer. The hosts also get into the Ethereum Foundation's identity crisis, why Illia thinks decentralization is a tool and not a goal, and what the economy looks like when AI handles execution and blockchain handles coordination. Host: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Kain Warwick⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Founder of Infinex and Synthetix ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Taylor Monahan⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Security Expert ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Luca Netz⁠⁠⁠⁠⁠⁠⁠, CEO of Pudgy Penguins Guest: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Illia Polosukhin — Co-Founder, NEAR Protocol - https://x.com/ilblackdragon Learn more about your ad choices. Visit megaphone.fm/adchoices

Illia Polosukhin, founder of NEAR and co-author of 'Attention Is All You Need,' on why confidentiality will let crypto become daily commerce — plus, some Near lore. ======================================================== Thank you to our sponsors! ⁠⁠Multichain Advisors⁠⁠: Get help navigating TGEs, go‑to‑market, BD and partnerships, capital markets advisory, PR, media placements, KOL activations and more at ⁠⁠multichainadv.com⁠⁠. ⁠⁠⁠⁠⁠Coinbase One⁠⁠⁠⁠⁠: Get 20% off the first year of your Coinbase One annual plan at ⁠⁠⁠⁠⁠coinbase.com/unchained⁠⁠⁠⁠⁠. ======================================================== Before co-founding NEAR Protocol, Illia Polosukhin was on the eight-person Google Brain team that wrote the transformer paper — the architecture behind every large language model running today. He never mentioned it. When Kain Warwick found out two weeks ago, via a crypto AI chatbot, his reaction was: you have to be kidding me. That backstory sets the tone for a conversation that moves from how transformers actually came together, to why confidentiality is what unlocks on-chain commerce for real businesses, and what NEAR is doing to keep criminals off its network without becoming a surveillance layer. The hosts also get into the Ethereum Foundation's identity crisis, why Illia thinks decentralization is a tool and not a goal, and what the economy looks like when AI handles execution and blockchain handles coordination. Host: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Kain Warwick⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Founder of Infinex and Synthetix ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Taylor Monahan⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Security Expert ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Luca Netz⁠⁠⁠⁠⁠⁠⁠, CEO of Pudgy Penguins Guest: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Illia Polosukhin — Co-Founder, NEAR Protocol - https://x.com/ilblackdragon Timestamps

Demis Hassabis, Co-Founder and CEO of Google DeepMind, refused to leave London, challenged Google on AI safety and helped lead DeepMind back into the AI race.Sebastian Mallaby, author of The Infinity Machine and The Power Law, joins Andreas Munk Holm to discuss the founder psychology of Demis, the story behind DeepMind and why Europe may be entering a new era in technology.The conversation explores DeepMind's fundraising journey, the Google acquisition, the merger with Google Brain, AI safety, sovereign technology and why Demis remains sceptical of parts of Silicon Valley culture despite operating at the centre of it.Timestamps(00:00) Why Demis Hassabis matters(01:12) Why DeepMind could not raise from European VCs(07:35) The Peter Thiel chess story(11:00) What DeepMind reveals about European venture(14:42) Why Europe's tech ecosystem is accelerating(18:20) European sovereignty, defence tech and AI(21:20) DeepMind's sale to Google and tensions over AI safety(29:40) The founder psychology of Demis(41:35) Google's ChatGPT moment and Gemini's comeback(45:05) Demis' critique of Silicon Valley(50:45) Europe's AI sovereignty problem(54:05) Final thoughts and Sebastian's new bookSubscribe to EUVC, the home of European tech, for more insights.

On this episode of Fostering Change, Rob Scheer is joined by David Sussillo, a neuroscientist, author, and former youth who experienced a childhood marked by instability, poverty, and time in group homes.His story begins in environments many children in foster care and group settings know all too well — uncertainty, trauma, and systems that don't always provide the support they should. But his story doesn't end there.Through a combination of resilience, critical intervention, and moments where someone stepped in, David found a path forward. Today, he is a leading neuroscientist who has worked at Stanford, Google, and Meta, studying the very thing that shaped his life: the human brain.His memoir, Emergence, is not just a story of survival — it is a powerful reminder of what can happen when even one opportunity changes the trajectory of a child's life.This conversation challenges us to ask a difficult but necessary question: how many children are out there right now, just one moment away from a different future?Episode HighlightsGrowing up in instability, poverty, and group home environmentsHow trauma shapes memory, identity, and developmentThe role of mentors, teachers, and small interventionsFrom survival to success in neuroscience and researchReflecting on resilience, loss, and the paths not takenAbout the GuestDavid Sussillo is a neuroscientist, author, and adjunct professor at Stanford University. After a childhood marked by instability and time in group homes, he earned a PhD in computational neuroscience from Columbia University and has worked at leading institutions, including Google Brain and Meta.His memoir, Emergence: A Memoir of Boyhood, Computation, and the Mysteries of Mind, tells the story of his journey from trauma to transformation.Key Questions from This EpisodeWhat led you to write Emergence now?What was it like to revisit your childhood experiences through writing?How did you navigate growing up in group homes and unstable environments?Who were the people who helped change your path?What role did small moments or opportunities play in your journey?How do you reflect on your success alongside those who didn't have the same outcome?What would you say to a young person facing similar challenges today?Closing ThoughtSometimes it doesn't take everything changing — it takes one moment, one person, one opportunity.And for a child navigating instability, that can be the difference between surviving and becoming something far beyond anyone's expectations.Connect with David

Rajat Monga, CVP AI Frameworks @ Microsoft, joins the podcast to discuss his leadership and founder journey, from Google Brain / Tensorflow to inference.io and back to Microsoft. He dissects what it means to refound vs. start from scratch, the value of the open source community, and strategies for discovering what problem to solve when going the startup route. We also cover how to determine your users' hidden incentives and what that means for both product development & marketing, along with navigating the balance between a product's usefulness and consumers' willingness to pay for it. Additionally, Rajat shares about what he's currently up to at Microsoft and the emerging ML / AI technologies he's most excited about.   ABOUT RAJAT MONGA Rajat Monga is responsible for enabling an efficient AI stack at Microsoft from cloud to the edge. Before joining Microsoft, Rajat was founder and CEO of Inference.io, a smart analytics platform powered by AI. During his decade-long tenure at Google, he co-founded and led TensorFlow, and was a founding member of Google Brain. He's built out and led many engineering teams, and designed large scale distributed systems including web scale crawling and eBay's search engine. Rajat is a graduate of IIT Delhi.   Unblocked: The context engine your coding agents are missing. Give your coding agents the context your best engineers have. Your agents can read code, but they don't know how your team works. Rules and MCPs give access to information but not understanding. That's why you still have to tell them where to look and what to look for. Unblocked gives your agents the history, conventions, and decisions behind your code so they generate mergeable output without the back and forth. It automatically surfaces the right context for every task, so agents stay on track without the set up tax or the correction loops. getunblocked.com/elc   SHOW NOTES: Rajat's journey with Google Brain: Scaling deep learning from single PCs to thousands of machines with Jeff Dean & Andrew Ng (2:57) Moving from Google Brain to TensorFlow: Why new hardware and architectures required a total system rebuild (6:02) The "refounding" question: Choosing between starting from scratch or evolving an existing system (8:33) Why Google open-sourced TensorFlow to set industry standards and avoid supporting external copies (10:16) How open-source enabled global innovation, from Japanese cucumber sorting to African plant health (12:02) Transitioning as a leader: Why Rajat left Google during the height of TensorFlow to found a company (13:57) The discovery phase at inference.io: Navigating the pivot from IoT into solving data analytics gaps (15:31) Lessons on PMF: Moving beyond a "useful" product to one that solves a truly critical customer pain point (16:52) Why habits are harder to change than technology and the challenge of competing with established workflows (21:02) Marketing strategies: Tailoring personas for top-down prestige versus bottom-up personal efficiency (23:19) Deciding when to stop: A founder's framework for re-evaluating bets based on current knowledge (24:57) Rajat's new role at Microsoft: Overseeing Edge infrastructure and large-scale Cloud AI inference (27:46) Dissecting ML edge strategy: Using ONNX Runtime to unify AI performance across Windows, iOS, and Android (30:02) Edge AI trends: Shifting from experimental models to production models optimized for cost and privacy (31:20) The future of Edge: How on-device processing will power AI in robotics, smart glasses, and wearables (33:23) Scaling inference: Treating multi-GPU clusters like a distributed operating system for AI models (34:25) Rapid fire questions (37:45)   LINKS AND RESOURCES Epic Disruptions: 11 Innovations That Shaped Our Modern World - Innovation expert Scott Anthony masterfully weaves together the fascinating stories behind history's most transformative disruptions—from ninth-century China to twenty-first-century Silicon Valley. Through eleven pivotal innovations, including the printing press, mass-produced automobiles, the McDonald's revolutionary food system, and the iPhone, Anthony reveals the hidden patterns behind world-changing breakthroughs.   This episode wouldn't have been possible without the help of our incredible production team: Patrick Gallagher - Producer & Co-Host Jerry Li - Co-Host Noah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/ Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/ Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dans cet épisode solo, je reviens sur une position que j'ai longtemps défendue, celle de tempérer face au catastrophisme ambiant sur l'IA, et j'explique pourquoi les preuves qui s'accumulent depuis quelques mois m'obligent à regarder les choses autrement. Pas pour rejoindre la panique, mais parce qu'une position qui ne s'interroge jamais devient une posture, pas une analyse.Dans cet épisode, nous parlons de la contradiction structurelle au cœur du capitalisme numérique : l'IA générative détruit les emplois cognitifs de niveau intermédiaire, précisément ceux qui constituent la base de consommation sur laquelle repose l'économie. J'ai questionné les travaux de Nick Dyer-Witheford, Karen Hao, Emad Mostaque et Anis Rahman sur ce que ça signifie concrètement, au-delà des chiffres de Goldman Sachs et des fuites internes d'Anthropic. Et parce que je déteste laisser les gens dans un état d'impuissance intellectuelle pire qu'avant la lecture, je finis sur des exemples concrets, locaux, qui montrent qu'une autre IA est possible même si les rapports de forces sont pour l'instant très déséquilibrés. Le tout pour vous redonner envie du futur bien sur :)CITATIONS MARQUANTES"Il y a un mot pour décrire un système qui détruit méthodiquement sa propre base de clients. Ce mot n'est pas 'innovation' mais 'suicide'.""C'est la boîte qui construit les outils qui sonne elle-même l'alarme sur leur impact. Ce n'est pas un philosophe marxiste.""Ils ont entraîné leurs propres remplaçants." (sur les travailleurs d'annotation de Nairobi, Manille, Lahore)"Regarde qui te chuchote à l'oreille chaque jour, et demande-toi de qui c'est l'intérêt." (Emad Mostaque)"Une position qui ne s'interroge jamais elle-même, c'est une posture, pas une analyse."IDÉES CENTRALES 1. Le contrat de Ford est rompu, par design Henry Ford payait ses ouvriers pour qu'ils puissent acheter ses voitures : le capital paie le travail, le travail consomme, la production nourrit le capital. L'IA générative rompt ce cercle en rendant le capital structurellement indépendant du travail humain. Ce n'est pas un bug du système, c'est une conséquence logique de sa propre optimisation poussée à l'extrême. C'est important parce que cela remet en cause le mécanisme de stabilisation automatique sur lequel les démocraties libérales se sont appuyées depuis Keynes.2. L'IA s'attaque précisément aux emplois qui étaient censés être la solution Contrairement aux révolutions industrielles précédentes qui frappaient d'abord les peu qualifiés, l'IA générative cible le travail cognitif intermédiaire : analyse, rédaction, code, diagnostic, comptabilité, marketing. Ces emplois constituaient la colonne vertébrale des classes moyennes éduquées. Ce sont eux qui avaient fait les études recommandées pour s'adapter. Si eux ne peuvent pas, qui peut ?3. La disruption du mécanisme de relance économique Quand les banques centrales baissent les taux pour relancer l'emploi, les entreprises recrutent désormais des agents IA, pas des travailleurs humains. Le lien entre capital et emploi se rompt pour la première fois depuis deux siècles. Et contrairement à toutes les crises précédentes, l'IA ne devient pas moins intelligente après une récession.4. La broligarchy et la capture réglementaire Les "Magnificent Seven" contrôlent 90,2% des modèles d'IA notables mondiaux. En 2024, les entreprises privées ont investi 109 milliards de dollars dans l'IA, contre 5,3 milliards d'investissement public. Sam Altman se pose en défenseur de la régulation en public et fait du lobby pour l'affaiblir en coulisses. L'administration Trump a inclus un moratoire de dix ans sur toute régulation étatique de l'IA. C'est une capture de la démocratie, pas seulement une concentration de marché.5. L'IA coloniale et la souveraineté cognitive L'IA ne transmet pas seulement des informations, elle transmet les valeurs et le cadre moral de ceux qui l'ont construite. Quand 90% des modèles viennent de Silicon Valley, la question de la souveraineté cognitive devient aussi urgente que la souveraineté économique. Et le "colonialisme par l'IA" s'exerce aussi dans le sud global, où des travailleurs ont littéralement entraîné les outils qui ont ensuite concurrencé leur propre travail.6. L'IA-vélo contre l'IA-fusée Karen Hao propose une distinction utile : l'IA-fusée, paradigme dominant à des centaines de milliards de paramètres visant l'AGI, et l'IA-vélo, des outils à échelle humaine pour des besoins spécifiques. Les architectures techniques sont les mêmes. Ce qui diffère, c'est le principe directeur. Des exemples comme Te Hiku Media en Nouvelle-Zélande, Chattanooga dans le Tennessee ou le modèle S1 développé pour 70 dollars prouvent que le choix existe.7. La destruction créatrice a un problème de rythme L'argument de Schumpeter tient sur le fond : chaque vague technologique crée plus qu'elle ne détruit. Mais il bute sur le rythme. La machine à vapeur s'est étalée sur des décennies. L'IA générative frappe en années. Si le pouvoir d'achat des classes moyennes disparaît avant que de nouveaux emplois émergent, qui consomme les produits que les entreprises continuent de produire ?QUESTIONS DE L'ÉPISODEEst-ce que ma position rassurante sur l'IA reflétait une lecture lucide, ou était-elle aussi une façon d'éviter une conclusion que je n'avais pas envie de regarder en face ?Le capitalisme peut-il fonctionner sans consommateurs, et les consommateurs peuvent-ils exister sans travailleurs ?Qu'est-ce qui différencie fondamentalement l'IA générative des révolutions industrielles précédentes en termes d'impact sur l'emploi ?Pourquoi l'argument de la "destruction créatrice" de Schumpeter bute-t-il cette fois sur quelque chose de structurellement différent ?Comment fonctionne concrètement la capture réglementaire par les grandes entreprises tech, et qu'est-ce que l'exemple de Sam Altman révèle sur ce phénomène ?Qu'est-ce que le sort des travailleurs d'annotation du sud global dit de la nature systémique de l'IA capitaliste ?Pourquoi le mécanisme de relance économique des banques centrales risque-t-il de ne plus fonctionner dans un monde d'IA générative ?Qu'est-ce que la distinction entre "IA-fusée" et "IA-vélo" change concrètement à la façon dont on peut construire et déployer ces technologies ?Comment des initiatives locales comme Te Hiku Media ou Chattanooga incarnent-elles une alternative crédible au paradigme dominant ?Quelle est votre part personnelle dans cette reconfiguration, en tant qu'individu, professionnel, citoyen ?RÉFÉRENCES CITÉESLivres et rapportsInhuman Power : Artificial Intelligence and the Future of Capitalism de Nick Dyer-Witheford (2019, + Cybernetic Circulation Complex, 2026, Verso). Thèse centrale : l'IA comme instrument par lequel le capital se rend indépendant du travail humain. Référence tout au long du texte.The Last Economy d'Emad Mostaque (août 2025, disponible gratuitement). Fondateur de Stability AI, ex-gérant de fonds. Concept de "transition de phase" et des "mille jours". Utilisé sur la chute des coûts de l'IA et la fin du mécanisme de relance keynésien.Empire of AI : Dreams and Nightmares in Sam Altman's OpenAI de Karen Hao (2025). Journaliste, ex-MIT Technology Review. Travailleurs d'annotation, double discours sur l'AGI, distinction IA-fusée vs IA-vélo.Is Another AI Possible ? d'Anis Rahman (rapport, Annenberg School / Media Inequality & Change Center, Université de Washington, disponible gratuitement). Concentration des modèles, investissements publics vs privés, initiatives alternatives.AI Snake Oil de Narayanan et Kapoor (Princeton University Press). Cité comme référence pour "démêler le réel du fantasme dans le discours tech".Personnes et institutions citéesHenry Ford : intuition du salaire comme condition de la consommation (1914, 5 dollars/jour).Karl Marx : concept de "sujet automatique" dans les Grundrisse (vers 1850).Joseph Schumpeter : concept de "destruction créatrice".Andrew Ng (ex-Baidu, ex-Google Brain, Stanford) : formule "l'IA est la nouvelle électricité".Dario Amodei (Anthropic) : projection de 10 à 20% de chômage dans certaines catégories professionnelles sur 5 ans.Goldman Sachs : estimation de 300 millions d'emplois à plein temps à risque.FMI : 89% des emplois de services externalisés aux Philippines à haut risque d'automatisation.PwC : l'IA ajoutera 15 700 milliards de dollars au PIB mondial, 70% ira aux États-Unis et à la Chine.Amy Webb et Sam Jordan (Future Today Institute) : concept de "crédit de contribution".Les Magnificent Seven : Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, Tesla (90,2% des modèles d'IA notables).Initiatives et exemplesTe Hiku Media (radio Maori, Nouvelle-Zélande) : développement souverain d'outils IA en langue Maori, principe "kia tangata whenua".Chattanooga, Tennessee : réseau haut débit municipal, 900 communautés américaines ayant suivi.Modèle S1 (Stanford / Université de Washington) : modèle de raisonnement comparable à OpenAI pour 70 dollars de frais cloud.xAI d'Elon Musk à Memphis, Tennessee : data center dans quartier majoritairement noir, dégradation de qualité de l'air signalée.TIMESTAMPS CLÉS Note : il s'agit d'une newsletter sans timestamps réels. Les repères ci-dessous sont structurés par section éditoriale et peuvent servir de chapitres si l'épisode est enregistré.00:00 Introduction : pourquoi j'ai changé de position sur l'IA Pendant dix ans j'ai tempéré le catastrophisme. Quelque chose a changé. Des gens autour de moi perdent des contrats qu'ils avaient depuis dix ans. Je reviens sur ma posture et j'explique ce qui m'a forcé à regarder les choses autrement.06:00 La contradiction centrale : le capitalisme peut-il se passer de consommateurs ? L'intuition de Ford et pourquoi elle s'effondre. Pas de travail, pas de salaires, pas de consommation, pas de capitalisme. La vraie question n'est peut-être pas "l'IA va-t-elle tuer des emplois ?" mais "l'IA va-t-elle tuer le système qui l'a créée ?"12:00 Ce que les chiffres disent vraiment Goldman Sachs, Dario Amodei, les fuites internes d'Anthropic. Un "white-collar bloodbath" annoncé par la boîte qui construit les outils. La nature de cette vague est différente des précédentes : elle frappe d'abord les cols blancs qualifiés.20:00 Nick Dyer-Witheford et le capital qui se libère du travail "Inhuman Power" et la thèse centrale : l'IA comme instrument par lequel le capital pourrait se rendre structurellement indépendant du travail humain. Marx avait formulé ça comme une crainte théorique. On s'en approche.28:00 La fin du mécanisme keynésien de relance Quand les banques centrales baissent les taux, les entreprises recrutent des agents IA, pas des humains. Ce mécanisme qui a fonctionné pendant deux siècles risque de ne plus fonctionner du tout. Personne ne le formule clairement dans le débat public.36:00 Le sud global et l'extraction coloniale Les Philippines, le Bangladesh, les travailleurs d'annotation de Nairobi et Manille. Ils ont entraîné leurs propres remplaçants. Karen Hao et la dimension coloniale de ce modèle économique.44:00 La broligarchy et la capture réglementaire 109 milliards d'investissement privé contre 5,3 milliards publics. Sam Altman défenseur de la régulation en public, lobbyiste pour l'affaiblir en coulisses. Le moratoire de dix ans de l'administration Trump. Ce n'est pas qu'une question de marché.52:00 L'argument de Schumpeter est réel, mais il a un problème de rythme La destruction créatrice a toujours fonctionné. Mais sur des décennies, pas des années. Si le pouvoir d'achat s'effondre avant que de nouveaux emplois émergent, qui consomme la production ?60:00 L'IA-vélo contre l'IA-fusée : une autre IA est possible Te Hiku Media, Chattanooga, le modèle S1 à 70 dollars. La distinction de Karen Hao entre l'IA construite pour la performance commerciale et l'IA construite à échelle humaine pour des usages définis. Ce sont les mêmes architectures techniques.70:00 Ce que vous pouvez faire maintenant : individu, collectif, citoyen Trois niveaux d'action concrets. Parce que je déteste les textes qui laissent dans l'impuissance. Les décisions se prennent maintenant, pas dans dix ans.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

What happens when you apply the scaling laws of large language models to the physical work of atoms? Elad Gil sits down with Liam Fedus, co-founder at Periodic Labs, which is pioneering an AI foundation lab for atoms. Liam discusses how he pivoted from dark matter physics research to the front lines of artificial intelligence, including stints at Google Brain and working on ChatGPT at OpenAI. He talks about how Periodic is connecting massive language models to the physical world to overcome data bottlenecks in material science. Liam also shares how they use language models as an orchestration layer operating alongside specialized neural nets to run closed-loop physical experiments. They also explore the future of AGI and ASI, as well as the role of robotics in lab automation. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @LiamFedus | @periodiclabs Chapters: 00:00 – Cold Open 00:05 – Liam Fedus Introduction 00:39 – Liam's Background at Google Brain, OpenAI 05:14 – From ChatGPT to Materials and Atoms 06:34 – Training Data in the Physical World 09:52 – Generalization Across Domains 11:31 – Models as an Orchestration Layer 12:48 – Commercialization and Business Model 16:10 – How Periodic's Success May Shape the Future  17:45 – Multidisciplinary Scaling 19:41 – Capital and Compute 21:12 – Hiring at Periodic 21:44 – Thoughts on AGI and ASI 23:30 – Timeline for Machine-Directed Self-Improvement 25:39 – Automation and Data Generation 27:59 – Why Liam is Excited About the Future of Robotics 29:25 – Conclusion

How do you build a system for turning wild ideas into world-changing innovations? Astro Teller, Captain of Moonshots at X, The Moonshot Factory, has spent over 15 years leading Google's audacious innovation lab—the birthplace of Waymo, Google Brain, and other breakthrough projects.In this special episode, recorded live in Austin at last year's SXSW, Astro shares the playbook to create a moonshot factory.(I'm at this year's SXSW right now and you'll hear all about it soon. If you are here, drop me a line and let's meet up!)What You'll Learn in This Episode:The “Train the Monkey First” approach to innovationWhy audacity, humility, and intellectual honesty are key to moonshotsHow your org can get more 10x (not +10%) outcomes — and how to avoid the “innovator's dilemma”Why you should “greenlight everything” and then redlight most projects quickly, following kill criteria you've agreed to in advanceWhere X is placing bets today, including climate-tech, modernizing the electric grid and bioengineering---Future Around & Find Out newsletter and podcast: https://www.futurearound.com

Nuestros amigos Audible: audible.com/exitomx En diciembre de 2022, Sundar Pichai declaró “código rojo” dentro de Google.

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“I can point to things. But is that a systemic explanation? I think there the answer is a little less clear. I mean, surely people need love and all of that, but then there's this risk of just devolving into platitude.” — David SussilloDavid Sussillo is a big time neural reverse engineer. The Stanford brain scientist worked at Google Brain with Geoffrey Hinton, and now is at Meta Reality Labs. What distinguishes Sussillo, however, is not his Silicon Valley good luck, but the bad luck of his origins. In his memoir, Emergent: A Memoir of Boyhood, Computation, and the Mysteries of the Mind, Sussillo begins at the Albuquerque Christian Children's Home — a modern-day orphanage — and the Milton Hershey School, the boarding school endowed by the chocolate magnate for kids with nowhere else to go. Both his parents were addicts. His mom died young. His dad spent his life as an untrained preacher ministering to homeless people on the streets of Albuquerque while managing a lifelong heroin habit.The book's thesis borrows from the science he studies: “emergence” — simple things interacting to produce complex behaviour that none of them could produce alone. His life is both proof of and a challenge to this concept. He made it out. Most of the kids he grew up with didn't. He can point to moments — a gifted-and-talented test in third grade, an aunt and uncle's intervention at nine, a first love in college — but he can't build an explanatory system from these haphazard events. The Sussillo quilt doesn't have an innate pattern. It just has patches.What makes Sussillo unusual as a memoirist is his refusal to sentimentalise. Twenty years of psychotherapy, he confesses, has taught him something most authors never learn: that understanding your own story doesn't mean you've explained it. His science can't explain his childhood either. “The big dirty secret of neuroscience,” he says, “is that we don't really understand much in the ways that people would love us to understand.” The man who reverse-engineers neural networks can't reverse-engineer himself.I asked him whether having children would have been harder than writing the book. Yes, he said. With the book, you can take a break. With kids, you relive things through a very specific way of relating. He and his wife chose not to. His mentors all told him he'd have been great at it. He's not so sure. That honesty — the willingness to say “I don't know” and mean it — runs through everything Sussillo does. He says he's happy, claiming to have found peace with his past. But he still carries the baggage. Who wouldn't? He's just learned to manage it. Emergent, not emerged. Five Takeaways•       From Orphanage to Google Brain: Both parents were heroin addicts. Sussillo grew up in a modern-day orphanage in Albuquerque and then the Milton Hershey School. He went on to work at Google Brain with Geoffrey Hinton, now works at Meta Reality Labs, teaches at Stanford. Most of the kids he grew up with didn't make it.•       Emergence as Autobiography: The book's thesis borrows from the science he studies: simple pieces combining into complicated outcomes. His life is the proof of concept and the counter-example simultaneously. The quilt doesn't have a pattern. It just has patches.•       The Dirty Secret of Neuroscience: The man who reverse-engineers neural networks can't reverse-engineer himself. “We don't really understand much in the ways that people would love us to understand.” Twenty years of therapy taught him more than the science.•       Would Kids Have Been Harder Than the Book? Yes. With the book, you can take a break. With kids, you relive trauma through a very specific way of relating. He and his wife chose not to have children. His mentors told him he'd have been great at it. He's not so sure.•       Emergent, Not Emerged: Sussillo has found peace with his past. He's happy. He still carries the baggage from his childhood. He's just learned how to manage it. The emergence is ongoing. About the GuestDavid Sussillo is a research scientist at Meta Reality Labs and a consulting professor at Stanford University. He previously worked at Google Brain. His memoir is Emergent: A Memoir of Boyhood, Computation, and the Mysteries of the Mind. He grew up in the Albuquerque Christian Children's Home and the Milton Hershey School. He lives in New Mexico.References:•       Emergent: A Memoir of Boyhood, Computation, and the Mysteries of the Mind by David Sussillo — the book under discussion.•       The Albuquerque Christian Children's Home — the group home where Sussillo spent five years of his childhood.•       The Milton Hershey School — founded in 1906 by the Hershey chocolate magnate for children with nowhere else to go. Sussillo spent four years there.•       Google Brain — the lab where Sussillo worked alongside Geoffrey Hinton on the neural network research that became the foundation of modern AI.•       John Conway's Game of Life — the cellular automaton simulation Sussillo cites as an early example of emergence: complicated outcomes from simple rules.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:00) - Introduction (01:30) - The Albuquerque Christian Children's Home and Milton Hershey School (03:30) - Why write a memoir? Five years and twenty years of therapy (05:00) - Heroin-addicted parents: the origin story (08:00) - A father as untrained preacher on the streets of Albuquerque (10:00) - Which parent had more impact? (12:00) - The gifted-and-talented test that changed everything (15:00) - From Milton Hershey to Carnegie Mellon: the jump (18:00) - Life falls apart at 23: panic attacks and psychotherapy (21:00) - Neural networks, Google Brain, and the dirty secret of neuroscience (25:00) - Would having kids have been harder than writing the book? (28:00) - The Albanian friend and the beach: what America gets right (31:00) - Silicon...

This episode is sponsored by tastytrade.  Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature.   Learn more at https://tastytrade.com/ In this episode of Eye on AI, Nick Frosst, Co-Founder of Cohere and former Google Brain researcher, explains why Cohere is betting on enterprise AI instead of chasing AGI.   While much of the AI industry is focused on artificial general intelligence, Cohere is building practical, capital-efficient large language models designed for real-world enterprise deployment. Nick breaks down why scaling transformers does not equal AGI, why inference cost and ROI matter, and how enterprise AI differs from consumer AI hype.   We discuss enterprise LLM deployment, private data, regulated industries like banking and healthcare, agentic systems, evaluation benchmarks, and why AI will likely become embedded infrastructure rather than a headline breakthrough.   If you care about enterprise AI, AGI debates, large language models, and the future of AI in business, this conversation delivers a grounded perspective from inside one of the leading AI companies.   Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI   (00:00) From Google Brain to Cohere (03:54) Discovering Transformers (06:39) The Transformer Dominance (09:44) What AGI Actually Means (12:26) Planes vs Birds: The AI Analogy (14:08) Why Cohere Isn't Chasing AGI (18:38) Distillation & Model Efficiency (21:42) What Enterprise AI Really Does (25:20) Private Data & Secure Deployment (26:59) Enterprise Use Cases (RBC Example) (32:22) Why AI Benchmarks Mislead (34:55) Why Most AI Stays in Demo (38:23) What "Agents" Actually Are (43:32) The Problem With AGI Fear (49:15) Scaling Enterprise AI (53:24) Why AI Will Get "Boring"  

Jeremy Nixon is a prominent AI researcher, entrepreneur, and the co-founder of AGI House, a leading "hacker house" community for artificial intelligence developers in Silicon Valley. He studied Applied Math, Computer Science, and Economics at Harvard and was previously a researcher at Google Brain.This footage was shot for a documentary project, "Dreamers and Doomers," about the SF Bay Area and the dawn of AGI.(00:00) - Dreamers and Doomers: Jeremy Nixon at AGI House – #105 (01:47) - Introduction and Welcome (05:56) - Jeremy Nixon's biography (08:48) - AGI House and collectives (43:59) - AI and Scientific Research (45:52) - Existential Risks and Doom (54:14) - AI and Human Progress (01:26:42) - Job Automation and Society (01:31:35) - Future of AI and Technology –Steve Hsu is Professor of Theoretical Physics and of Computational Mathematics, Science, and Engineering at Michigan State University. Previously, he was Senior Vice President for Research and Innovation at MSU and Director of the Institute of Theoretical Science at the University of Oregon. Hsu is a startup founder (SuperFocus.ai, SafeWeb, Genomic Prediction, Othram) and advisor to venture capital and other investment firms. He was educated at Caltech and Berkeley, was a Harvard Junior Fellow, and has held faculty positions at Yale, the University of Oregon, and MSU. Please send any questions or suggestions to manifold1podcast@gmail.com or Steve on X @hsu_steve.

Show Notes Tarek Matar, founder of Scalar AI, explains the tool's purpose. He describes Scalar AI as an AI engine designed for consultants to build McKinsey level, end-to-end slides and presentations. The tool is differentiated from general AI tools like ChatGPT and GPT-3 by focusing on consulting-grade presentations. The founders include a research scientist from Google Brain and two other experienced professionals. Features and Functionality of Scalar AI Scalar AI automates the entire research, analysis, structure, and visualization process for consultants. The tool can create single slides or entire decks based on user prompts.It offers various modes: AI generation, text to slide, and sketch to slide, allowing flexibility in input methods. The tool includes a custom brand identity feature, allowing users to upload and customize their firm's PowerPoint templates. A Scalar.AI Demonstration Tarek demonstrates the tool by creating a slide and a deck.  Adding Prompts   Adding custom brand identity Tarek creates a waterfall slide showing the top five countries by international tourist arrivals.  Detailed data and insights The tool generates a visually appealing slide with detailed data and insights. Tarek explains the process of editing and refining the generated slides to meet specific needs. The Text to Slide Mode Tarek demonstrates the text to slide mode by pasting a long text about key success factors for post-merger integration in banking.  Data generation  The tool summarizes the text into a concise slide with bullet points and icons. They also show the sketch to slide mode by uploading a hand-drawn image, which the tool converts into a PowerPoint slide. The tool supports various image formats, including JPEG, PNG, and PDF. The Custom Brand Identity Feature Tarek explains the custom brand identity feature, which allows users to upload their firm's PowerPoint templates. The tool can save and apply custom colors, fonts, and slide masters. A prompting guide and video tutorials are available to help users effectively use the tool. Tarek mentions the importance of proper prompting to get the best results from the AI. Pricing and Subscription Details Tarek talks about the pricing and mentions discounts available for annual subscriptions and partnerships. The tool is designed for B2B clients, including consulting firms and independent consultants. Tarek discusses the possibility of working with freelancers and organizations like Umbrex to offer special pricing. The tool is integrated with PowerPoint, making it easy for users to access and use. Security and Data Privacy Tarek addresses concerns about data security and privacy when using Scalar AI. The tool uses enterprise LLMs and follows strict data retention policies, ensuring data is encrypted and anonymized. The tool generates slides on the user's device, not on Scalar AI's servers, maintaining data privacy. Tarek mentions that the tool is compliant with GDPR and can meet the security requirements of government entities. The Genesis Story of Scalar.AI Tarek shares the background of Scalar AI, including his experience as a consultant and his co-founders' technical expertise. The idea for the tool came from the need to automate workflows and create professional slides for consulting clients. The founders spent a significant amount of time in stealth mode, refining and testing the product. The tool is now entering the commercialization stage, with plans to expand its user base and features. Scalar.AI and the Consulting Industry Tarek discusses the potential impact of Scalar AI on the consulting industry. Tarek emphasizes the tool's ability to save time and improve productivity for consultants. They plan to continue refining the tool and exploring partnerships with organizations like Umbrex. Timestamps: 02:21: Features and Functionality of Scalar AI  02:37: Demonstration of Scalar AI's Capabilities  04:11: Text to Slide and Sketch to Slide Modes  22:15: Custom Brand Identity and Prompting Guide  22:36: Pricing and Subscription Details 31:08: Security and Data Privacy  36:14: Backstory and Development of Scalar AI  Links: Website: getscalar.ai  This episode on Umbrex: https://umbrex.com/wp-admin/post-new.php?post_type=unleashed#:~:text=https%3A//umbrex.com/unleashed/240677/   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com. *AI generated timestamps and show notes.  

Medicus Pharma Chief Medical Officer Dr. Faisel Mehmud joined Steve Darling from Proactive to announce that the company has entered into a non-binding letter of intent with Reliant AI, a decision-intelligence company specializing in generative artificial intelligence for the life sciences. The proposed collaboration is focused on developing an AI-powered data analytics platform designed to enhance clinical trial execution through advanced, data-driven insights. Reliant AI is a privately held company founded by former DeepMind and Google Brain researchers Karl Moritz Hermann and Marc Bellemare, alongside life sciences expert Richard Schlegel. The company combines state-of-the-art machine learning techniques with deep biomedical expertise to automate data-intensive workflows across the life sciences, ranging from systematic literature reviews to commercial success prediction. Its platform is designed to enable biopharma teams to make faster, more informed, evidence-based decisions throughout the research and development lifecycle. Dr. Mehmud explained that the proposed platform would integrate Reliant AI's proprietary generative AI technology with Medicus' clinical, operational, and internal datasets. The goal is to strengthen data-driven decision-making across Medicus' clinical pipeline, with a particular focus on improving trial efficiency. Key anticipated capabilities include dynamic clinical-site selection, enhanced patient stratification, and more accurate enrollment forecasting. The initial phase of the collaboration is expected to focus on dynamic site selection supported by targeted patient-stratification analyses for an upcoming Teverelix clinical study, which is planned to begin in 2026. Subject to the execution of definitive agreements, Medicus expects the data analytics platform to be deployed initially in support of a Medicus-sponsored study during the Q2 to Q4 2026 period. There is also potential for the platform to be expanded to support a larger, late-stage clinical study planned for 2028, potentially in collaboration with a development or commercial partner. Medicus Pharma is currently advancing its SKNJCT-003 Phase 2 clinical study, which is being conducted across nine clinical sites in the United States. The study, which began randomizing patients in August 2024, is a randomized, double-blind, placebo-controlled, triple-arm proof-of-concept trial evaluating a non-invasive treatment for basal cell carcinoma of the skin. The study uses Medicus' novel, patent-protected, dissolvable doxorubicin-containing microneedle array (D-MNA) technology, highlighting the company's broader commitment to innovation in both therapeutic development and clinical execution. #proactiveinvestors #nasdaq #mdcx #tsxv #mdcx #pharma #Biotech #CancerTreatment #ClinicalTrials #FDAApproval #SkinCancer #HealthcareInnovation #Investing #MedicalResearch #SkinCancer #BasalCellCarcinoma #BiotechNews #CancerResearch #GorlinSyndrome #BasalCellCarcinoma #CompassionateUse #FDAApproval #RareDiseaseTreatment #NoninvasiveTherapy #BiotechNews

We're told that AI progress is slowing down, that pre-training has hit a wall, that scaling laws are running out of road. Yet we're releasing this episode in the middle of a wild couple of weeks that saw GPT-5.1, GPT-5.1 Codex Max, fresh reasoning modes and long-running agents ship from OpenAI — on top of a flood of new frontier models elsewhere. To make sense of what's actually happening at the edge of the field, I sat down with someone who has literally helped define both of the major AI paradigms of our time.Łukasz Kaiser is one of the co-authors of “Attention Is All You Need,” the paper that introduced the Transformer architecture behind modern LLMs, and is now a leading research scientist at OpenAI working on reasoning models like those behind GPT-5.1. In this conversation, he explains why AI progress still looks like a smooth exponential curve from inside the labs, why pre-training is very much alive even as reinforcement-learning-based reasoning models take over the spotlight, how chain-of-thought actually works under the hood, and what it really means to “train the thinking process” with RL on verifiable domains like math, code and science. We talk about the messy reality of low-hanging fruit in engineering and data, the economics of GPUs and distillation, interpretability work on circuits and sparsity, and why the best frontier models can still be stumped by a logic puzzle from his five-year-old's math book.We also go deep into Łukasz's personal journey — from logic and games in Poland and France, to Ray Kurzweil's team, Google Brain and the inside story of the Transformer, to joining OpenAI and helping drive the shift from chatbots to genuine reasoning engines. Along the way we cover GPT-4 → GPT-5 → GPT-5.1, post-training and tone, GPT-5.1 Codex Max and long-running coding agents with compaction, alternative architectures beyond Transformers, whether foundation models will “eat” most agents and applications, what the translation industry can teach us about trust and human-in-the-loop, and why he thinks generalization, multimodal reasoning and robots in the home are where some of the most interesting challenges still lie.OpenAIWebsite - https://openai.comX/Twitter - https://x.com/OpenAIŁukasz KaiserLinkedIn - https://www.linkedin.com/in/lukaszkaiser/X/Twitter - https://x.com/lukaszkaiserFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)Blog - https://mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) – Cold open and intro(01:29) – “AI slowdown” vs a wild week of new frontier models(08:03) – Low-hanging fruit: infra, RL training and better data(11:39) – What is a reasoning model, in plain language?(17:02) – Chain-of-thought and training the thinking process with RL(21:39) – Łukasz's path: from logic and France to Google and Kurzweil(24:20) – Inside the Transformer story and what “attention” really means(28:42) – From Google Brain to OpenAI: culture, scale and GPUs(32:49) – What's next for pre-training, GPUs and distillation(37:29) – Can we still understand these models? Circuits, sparsity and black boxes(39:42) – GPT-4 → GPT-5 → GPT-5.1: what actually changed(42:40) – Post-training, safety and teaching GPT-5.1 different tones(46:16) – How long should GPT-5.1 think? Reasoning tokens and jagged abilities(47:43) – The five-year-old's dot puzzle that still breaks frontier models(52:22) – Generalization, child-like learning and whether reasoning is enough(53:48) – Beyond Transformers: ARC, LeCun's ideas and multimodal bottlenecks(56:10) – GPT-5.1 Codex Max, long-running agents and compaction(1:00:06) – Will foundation models eat most apps? The translation analogy and trust(1:02:34) – What still needs to be solved, and where AI might go next

Zach sits down with Harvey cofounder Gabe Pereyra to talk about what changed after “Attention Is All You Need,” why partners - not juniors - are driving Harvey's adoption, and how AI is teaching law firms to learn faster than ever.     In this episode: The origin story of Harvey and how it became the “Dallas Cowboys of Legal Tech” Lessons from the early days of AI at Google Brain and DeepMind Why the real opportunity isn't automation — it's amplification How top law firms are already using AI for high-level strategic work The emerging hybrid model of AI + human legal reasoning Why firm size and structure could shift dramatically in the next decade Rethinking ROI: morale, retention, and client trust as key performance drivers How Harvey is helping firms pitch, collaborate, and win new business The future of AI-first law firms — from pricing models to client partnerships Learn More: Zach - https://www.legallydisrupted.com/ Gabe - https://www.harvey.ai/blog/author/gabe-pereyra   Follow Along: Zach - linkedin.com/in/zachabramowitz Gabe - https://www.linkedin.com/in/gabepereyra

After endless Silicon Valley takes on how generative AI would radically change scientific discovery, the founders decided that the pieces were finally in place to make this a reality. Or at least to found a startup that attempted it. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Google faces the greatest innovator's dilemma in history. They invented the Transformer — the breakthrough technology powering every modern AI system from ChatGPT to Claude (and, of course, Gemini). They employed nearly all the top AI talent: Ilya Sutskever, Geoff Hinton, Demis Hassabis, Dario Amodei — more or less everyone who leads modern AI worked at Google circa 2014. They built the best dedicated AI infrastructure (TPUs!) and deployed AI at massive scale years before anyone else. And yet... the launch of ChatGPT in November 2022 caught them completely flat-footed. How on earth did the greatest business in history wind up playing catch-up to a nonprofit-turned-startup?Today we tell the complete story of Google's 20+ year AI journey: from their first tiny language model in 2001 through the creation Google Brain, the birth of the transformer, the talent exodus to OpenAI (sparked by Elon Musk's fury over Google's DeepMind acquisition), and their current all-hands-on-deck response with Gemini. And oh yeah — a little business called Waymo that went from crazy moonshot idea to doing more rides than Lyft in San Francisco, potentially building another Google-sized business within Google. This is the story of how the world's greatest business faces its greatest test: can they disrupt themselves without losing their $140B annual profit-generating machine in Search?Sponsors:Many thanks to our fantastic Fall ‘25 Season partners:J.P. Morgan PaymentsSentryWorkOSShopifyAcquired's 10th Anniversary Celebration!When: October 20th, 4:00 PM PTWho: All of you!Where: https://us02web.zoom.us/j/84061500817?pwd=opmlJrbtOAen4YOTGmPlNbrOMLI8oo.1Links:Sign up for email updates and vote on future episodes!Geoff Hinton's 2007 Tech Talk at GoogleOur recent ACQ2 episode with Tobi LutkeWorldly Partners' Multi-Decade Alphabet StudyIn the PlexSupremecyGenius MakersAll episode sourcesCarve Outs:We're hosting the Super Bowl Innovation Summit!F1: The MovieTravelpro suitcasesGlue Guys PodcastSea of StarsStepchange PodcastMore Acquired:Get email updates and vote on future episodes!Join the SlackSubscribe to ACQ2Check out the latest swag in the ACQ Merch Store!‍Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.

Periodic Labs was founded by Ekin Dogus Cubuk and Liam Fedus. Cubuk led the materials and chemistry team at Google Brain and DeepMind, where one of his projects was, for instance, an AI tool called GNoME. Researchers say that tool discovered over 2 million new crystals in 2023, materials that could one day be used to power new generations of technology.  Whoop Advanced Labs offers health-screening blood tests from Quest Diagnostics that cover a variety of markers, from calcium to white blood cells. The platform integrates those results with the band's continuous monitoring of activity, sleep, respiratory rate, and blood pressure to offer more personalized wellness advice. Learn more about your ad choices. Visit podcastchoices.com/adchoices

This week, we talk with Gabe Pereyra, President and co-founder at Harvey, about his path from DeepMind and Google Brain to launching Harvey with Winston Weinberg; how a roommate's real-world legal workflows met early GPT-4 access and OpenAI backing; why legal emerged as the right domain for large models; and how personal ties to the profession plus a desire to tackle big societal problems shaped a mission to apply advanced AI where language and law intersect.Gabe's core thesis lands hard, “the models are the product.” Rather than narrow tools for single tasks, Harvey opted for a broad assistant approach. Lawyers live in text and email, so dialog becomes the control surface, an “AI associate” supporting partners and teams. Early demos showed useful output across many tasks, which reinforced a generalist design, then productized connections into Outlook and Word, plus a no-code Workflow Builder.Go-to-market strategy flipped the usual script. Instead of starting small, Harvey partnered early with Allen & Overy and leaders like David Wakeling. Large firms supplied layered review, which reduced risk from model errors and increased learning velocity. From there the build list grew, security and data privacy, dedicated capacity, links to firm systems, case law, DMS, data rooms, and eDiscovery. A matter workspace sits at the center. Adoption rises with surface area, with daily activity approaching seventy percent where four or more product surfaces see regular use. ROI work now includes analysis of write-offs and specialized workflows co-built with firms and clients, for example Orrick, A&O, and PwC.Talent, training, and experience value come next. Firms worry about job paths, and Gabe does not duck that concern. Models handle complex work, which raises anxiety, yet also shortens learning curves. Harvey collaborates on curricula using past deals, plus partnerships with law schools. Return on experience shows up in recruiting, PwC reports stronger appeal among early-career talent, and quality-of-life gains matter. On litigation use cases, chronology builders require firm expertise and guardrails, with evaluation methods that mirror how senior associates review junior output. Frequent use builds a mental model for where errors tend to appear.Partnerships round out the strategy. Research content from LexisNexis and Wolters Kluwer, work product in iManage and NetDocuments, CLM workflows via Ironclad, with plans for data rooms, eDiscovery, and billing. Vision extends to a complete matter management service, emails, documents, prior work, evaluation, billing links, and strict ethical walls, all organized by client-matter. Global requirements drive multi-region storage and controls, including Australia's residency rules. The forward look centers on differentiation through customization, firms encode expertise into models, workflows, and agents, then deliver outcomes faster and at software margins. “The value sits in your people,” Gabe says, and firms that convert know-how into systems will lead the pack.Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.] ⁠⁠⁠Email: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠ Transcript

Nick Frosst is a Canadian AI researcher and entrepreneur, best known as co-founder of Cohere, the enterprise-focused LLM. Cohere has raised over $900 million, most recently a $500 million round, bringing its valuation to $6.8 billion. Under his leadership, Cohere hit $100M in ARR. Prior to founding Cohere, Nick was a researcher at Google Brain and a protégé of Geoffrey Hinton. AGENDA:  00:00 – Biggest lessons from Geoff Hinton at Google Brain? 02:10 – Did Google completely sleep at the wheel and miss ChatGPT? 05:45 – Is data or compute the real bottleneck in AI's future? 07:20 – Does GPT5 Prove That Scaling Laws are BS? 13:30 – Are AI benchmarks just total BS? 17:00 – Would Cohere spend $5M on a single AI researcher? 19:40 – What is nonsense in AI that everyone is talking about? 25:30 – What is no one talking about in AI that everyone should be talking about? 33:00 – How do Cohere compete with OpenAI and Anthropic's billions? 44:30 – Why does being American actually hurt tech companies today? 45:10 – Should countries fund their own models? Is model sovereignty the future? 52:00 – Why has Sam Altman actually done a disservice to AI?  

A Note from JamesWhat does it take to make a discovery that changes the world? Think about landing on the moon — a true moonshot. Along the way, countless technologies were invented that reshaped life back on Earth.My guest today, Astro Teller, has been part of that same kind of world-changing work. At X — Alphabet's Moonshot Factory — he's led projects that gave us self-driving cars, Google Brain, drone delivery, augmented reality with Google Glass, and much more. We even talk about quantum computing, drones that bring your groceries to your backyard, and the mindset it takes to believe in something that once sounded like science fiction.Astro and I first crossed paths when I visited Google X back in 2012 or 2013. He was on this podcast in 2015, and now, ten years later, he's back to talk about his own show — The Moonshot Podcast — and the latest bold projects that could shape our future.Episode DescriptionAstro Teller, Captain of Moonshots at Alphabet's X, joins James to share how impossible-sounding ideas become real. From Waymo's self-driving cars to Wing's drones, from the birth of Google Brain to breakthroughs in quantum networking and modernizing electric grids, Astro explains the engineering mindset that drives innovation.This episode goes beyond technology — it's about how to think like a moonshot maker. You'll hear how X chooses projects, why systems engineering often matters more than pure science, and how to break down massive problems into solvable steps.What You'll LearnThe three elements that define a true moonshot at X.Why self-driving cars succeeded not because of new science, but because of paradigm-shifting systems engineering.How Google Brain kickstarted the modern AI revolution by betting on scale when neural nets were out of fashion.Why Wing's drone delivery service may soon feel as ordinary as rideshare apps.How Project Tapestry is mapping and optimizing the electric grid to cut connection times from years to days.The promise (and risks) of quantum networking, quantum sensing, and the looming “Q-Day” when current cryptography could break.Why empathy is crucial for workers displaced by new technologies.Timestamped Chapters[01:00] A Note from James[04:00] Inside Alphabet's Moonshot Factory (X)[06:00] Defining moonshots: problem, radical solution, breakthrough tech[08:00] Waymo and the hidden challenges of self-driving cars[13:00] Safety, comfort, and the “body language” of cars[17:00] Google Brain and the rebirth of neural networks[20:00] Cats, YouTube, and AI's first big proof point[23:00] Wing: drones delivering groceries like magic[29:00] Moonshot mindset vs. the Apollo mission[31:00] How X evaluates and selects moonshots[34:00] Breakthroughs behind Waymo and simulation at scale[39:00] What if every car was autonomous?[40:00] Project Tapestry: modernizing the electric grid[45:00] Mapping PJM and national-scale grids[46:00] Lessons from Google Glass: too early, or misframed?[48:00] The future of AR glasses and AI assistants[51:00] Why X left longevity research to Calico and Verily[52:00] Quantum computing, networking, and sensing explained[57:00] The coming “Q-Day” and what it means for security[59:00] AI, jobs, and the importance of empathy[61:00] Closing thoughts and Astro's Moonshot PodcastAdditional ResourcesThe Moonshot Podcast with Astro Teller (YouTube)X, the Moonshot FactoryWaymo (Self-Driving Cars)Wing (Drone Delivery)Google BrainProject Tapestry – Grid ModernizationPJM Interconnection (Eastern US Grid)Calico (Alphabet's Longevity Research)Verily Life SciencesSandbox AQ (Quantum & AI)Carnegie Mellon University School of Computer ScienceSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Andrew Ng has always been at the bleeding edge of fast-evolving AI technologies, founding companies and projects like Google Brain, AI Fund, and DeepLearning.AI. So he knows better than anyone that founders who operate the same way in 2025 as they did in 2022 are doing it wrong. Sarah Guo and Elad Gil sit down with Andrew Ng, the godfather of the AI revolution, to discuss the rise of agentic AI, and how the technology has changed everything from what makes a successful founder to the value of small teams. They talk about where future capability growth may come from, the potential for models to bootstrap themselves, and why Andrew doesn't like the term “vibe coding.” Also, Andrew makes the case for why everybody in an organization—not just the engineers—should learn to code.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @AndrewYNg Chapters: 00:00 – Andrew Ng Introduction 00:32 – The Next Frontier for Capability Growth 01:29 – Andrew's Definition of Agentic AI 02:44 – Obstacles to Building True Agents 06:09 – The Bleeding Edge of Agentic AI 08:12 – Will Models Bootstrap Themselves? 09:05 – Vibe Coding vs. AI Assisted Coding 09:56 – Is Vibe Coding Changing the Nature of Startups? 11:35 – Speeding Up Project Management 12:55 – The Evolution of the Successful Founder Profile 19:23 – Finding Great Product People 21:14 – Building for One User Profile vs. Many 22:47 – Requisites for Leaders and Teams in the AI Age 28:21 – The Value of Keeping Teams Small 32:13 – The Next Industry Transformations 34:04 – Future of Automation in Investing Firms and Incubators 37:39 – Technical People as First Time Founders 41:08– Broad Impact of AI Over the Next 5 Years 41:49 – Conclusion

Thanks to MLflow for supporting this episode — the platform helping teams track, manage, and deploy ML and GenAI projects with ease. Try it free at mlflow.org.What if AI could build and maintain your software—like a co-worker who never forgets state? In this episode, Jiquan Ngiam chats with Demetrios about agents that actually do the work: parsing emails, updating spreadsheets, and reshaping how we design software itself. Less hype, more hands-on AI—tune in for a glimpse at the future of truly personalized computing.// BioJiquan Ngiam is the Co-Founder and CEO of Lutra AI, with deep expertise in artificial intelligence and machine learning. He was previously at Google Brain, Coursera, and in the Stanford CS Ph.D. program advised by Andrew Ng. He helped develop the first online courses in Machine Learning, and is now building agentic AI systems that can complete tasks for us.// Related Linkshttps://www.youtube.com/@LutraAI#api #llm #lutra #costefficiency #latentspace ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Jiquan on LinkedIn: /jngiam/Timestamps:[00:00] Agents That Actually Do Work[08:21] Building Tables With AI Help[12:54] Guardrails for Smarter Code[16:35 - 18:00] MLFlow Ad[18:30] What's Next for MCP?[23:23] AI as Your Data Conductor[31:13] Rethinking AI + Data Stacks[32:10] Sandbox Security, Real Risks[40:48] Smarter Reviews, Powered by Use[46:08] Cost vs. Quality in AI[52:00] Podcast Editing Gets Creative[56:27] Transparent UIs, Powered by AI[01:00:28] Can AI Learn Good Taste?[01:04:45] Peeking Into Wild AI Futures

What really happened inside Google Brain when the “Attention is All You Need” paper was born? In this episode, Aidan Gomez — one of the eight co-authors of the Transformers paper and now CEO of Cohere — reveals the behind-the-scenes story of how a cold email and a lucky administrative mistake landed him at the center of the AI revolution.Aidan shares how a group of researchers, given total academic freedom, accidentally stumbled into one of the most important breakthroughs in AI history — and why the architecture they created still powers everything from ChatGPT to Google Search today.We dig into why synthetic data is now the secret sauce behind the world's best AI models, and how Cohere is using it to build enterprise AI that's more secure, private, and customizable than anything else on the market. Aidan explains why he's not interested in “building God” or chasing AGI hype, and why he believes the real impact of AI will be in making work more productive, not replacing humans.You'll also get a candid look at the realities of building an AI company for the enterprise: from deploying models on-prem and air-gapped for banks and telecoms, to the surprising demand for multimodal and multilingual AI in Japan and Korea, to the practical challenges of helping customers identify and execute on hundreds of use cases.CohereWebsite - https://cohere.comX/Twitter - https://x.com/cohereAidan GomezLinkedIn - https://ca.linkedin.com/in/aidangomezX/Twitter - https://x.com/aidangomezFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (02:00) The Story Behind the Transformers Paper (03:09) How a Cold Email Landed Aidan at Google Brain (10:39) The Initial Reception to the Transformers Breakthrough (11:13) Google's Response to the Transformer Architecture (12:16) The Staying Power of Transformers in AI (13:55) Emerging Alternatives to Transformer Architectures (15:45) The Significance of Reasoning in Modern AI (18:09) The Untapped Potential of Reasoning Models (24:04) Aidan's Path After the Transformers Paper and the Founding of Cohere (25:16) Choosing Enterprise AI Over AGI Labs (26:55) Aidan's Perspective on AGI and Superintelligence (28:37) The Trajectory Toward Human-Level AI (30:58) Transitioning from Researcher to CEO (33:27) Cohere's Product and Platform Architecture (37:16) The Role of Synthetic Data in AI (39:32) Custom vs. General AI Models at Cohere (42:23) The AYA Models and Cohere Labs Explained (44:11) Enterprise Demand for Multimodal AI (49:20) On-Prem vs. Cloud (50:31) Cohere's North Platform (54:25) How Enterprises Identify and Implement AI Use Cases (57:49) The Competitive Edge of Early AI Adoption (01:00:08) Aidan's Concerns About AI and Society (01:01:30) Cohere's Vision for Success in the Next 3–5 Years

If we want AI systems that actually work in production, we need better infrastructure—not just better models. In this episode, Hugo talks with Akshay Agrawal (Marimo, ex-Google Brain, Netflix, Stanford) about why data and AI pipelines still break down at scale, and how we can fix the fundamentals: reproducibility, composability, and reliable execution. They discuss:

Have you ever spent an afternoon wrestling with a Jupyter notebook, hoping that you ran the cells in just the right order, only to realize your outputs were completely out of sync? Today's guest has a fresh take on solving that exact problem. Akshay Agrawal is here to introduce Marimo, a reactive Python notebook that ensures your code and outputs always stay in lockstep. And that's just the start! We'll also dig into Akshay's background at Google Brain and Stanford, what it's like to work on the cutting edge of AI, and how Marimo is uniting the best of data science exploration and real software engineering. Episode sponsors Worth Search Talk Python Courses Links from the show Akshay Agrawal: akshayagrawal.com YouTube: youtube.com Source: github.com Docs: marimo.io Marimo: marimo.io Discord: marimo.io WASM playground: marimo.new Experimental generate notebooks with AI: marimo.app Pluto.jl: plutojl.org Observable JS: observablehq.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

How do you build a system for turning wild ideas into world-changing innovations? Astro Teller, Captain of Moonshots at X, The Moonshot Factory, has spent over 15 years leading Google's audacious innovation lab—the birthplace of Waymo, Google Brain, and other breakthrough projects.In this special episode, recorded live in Austin at SXSW, Astro shares the playbook to create a moonshot factory. You'll Learn:

Astro Teller is Alphabet’s Captain of Moonshots. He oversees projects at X – the moonshot factory behind innovations like Waymo and Google Brain. To celebrate X’s 15 years of pushing boundaries, Astro Teller decided to take listeners inside the factory. On The Moonshot Podcast, inventors and entrepreneurs behind breakthrough technologies reflect on their projects, both the highs and the lows. Teller sits down with Oz to discuss the process of experimentation, the importance of accepting failure and the future of innovation at Alphabet’s X.See omnystudio.com/listener for privacy information.

David is an OG in AI who has been at the forefront of many of the major breakthroughs of the past decade. His resume: VP of Engineering at OpenAI, a key contributor to Google Brain, co-founder of Adept, and now leading Amazon's SF AGI Lab. In this episode we focused on how far test-time compute gets us, the real implications of DeepSeek, what agents milestones he's looking for and more.[0:00] Intro[1:14] DeepSeek Reactions and Market Implications[2:44] AI Models and Efficiency[4:11] Challenges in Building AGI[7:58] Research Problems in AI Development[11:17] The Future of AI Agents[15:12] Engineering Challenges and Innovations[19:45] The Path to Reliable AI Agents[21:48] Defining AGI and Its Impact[22:47] Challenges and Gating Factors[24:05] Future Human-Computer Interaction[25:00] Specialized Models and Policy[25:58] Technical Challenges and Model Evaluation[28:36] Amazon's Role in AGI Development[30:33] Data Labeling and Team Building[36:37] Reflections on OpenAI[42:12] Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint

Join JJ as he delves into AI Agents with the CEO of Lutra.ai, Jiquan Ngiam. Discover how their no-code AI platform is revolutionizing business automation, enhancing workflow efficiency, and empowering users with AI-driven superpowers. Learn about Jiquan's journey from Coursera and Google Brain to founding Lutra.ai, and get insights into the future of AI, self-driving cars, and the dynamic tech industry. Tune in for a live demo and explore how Lutra.ai makes complex tasks simpler and more streamlined. Available on all platforms: Apple, Spotify, and YouTube.Lutra:Learn more about Lutra.ai:https://lutra.ai/Grab thisLutra playbookFollow Jiquan:https://www.linkedin.com/in/jngiam/View JJ'sAI Agent Course: 00:00 Introduction to Lutra.ai00:27 Founder's Journey: From Coursera to Google Brain02:04 The Evolution of AI: Key Milestones09:20 The Future of AI and Self-Driving Cars13:20 Introducing Lutra.ai: Vision and Challenges19:59 Lutra.ai Demo: Automating Email Management24:31 Navigating AI Downtime24:43 AI in Document Processing25:25 Step-by-Step AI Task Management26:15 Advanced AI Capabilities27:12 Creating and Using Playbooks28:23 Integrations and Practical Applications30:33 Complex Workflows and Hierarchical Playbooks31:34 AI in Social Listening32:42 Challenges and Future of AI Adoption43:32 Tips for Professionals Embracing AI45:57 Conclusion and How to Get Started with LutraWant to learnhow to build AI Agents?

In this special guest episode of the Effortless Podcast, Amit Prakash sits down with Rajat Monga, the creator of TensorFlow and current Corporate Vice President of Engineering at Microsoft. With a career spanning Google Brain, founding Inference, and leading AI inferencing at Microsoft, Rajat offers a unique perspective on the evolution of AI. The conversation dives into TensorFlow's revolutionary impact, the challenges of building startups, the rise of PyTorch, the future of inferencing, and how transformative tools like GPT-4 and OpenAI's Gemini are reshaping the AI landscape.Key Topics and Chapter Markers:Introduction to Rajat Monga & TensorFlow Legacy [0:00]The inflection points in AI: TensorFlow's role and challenges [6:00]PyTorch vs. TensorFlow: A tale of shifting paradigms [16:00]The startup journey: Building Inference and lessons learned [27:00]Exploring O1 and advancements in reasoning frameworks [54:00]AI inference: Cost optimizations and hardware innovations [57:00]Agents, trust, and validation: AI in decision-making workflows [1:05:00]Rajat's personal journey: Tools for resilience and finding balance [1:20:00] Host:Amit Prakash: Co-founder and CTO at ThoughtSpot, formerly at Google AdSense and Bing, and a PhD in Computer Engineering. Amit has a strong track record in analytics, machine learning, and large-scale systems. Follow Amit on:LinkedIn - https://www.linkedin.com/in/amit-prakash-50719a2/  X (Twitter) - https://x.com/amitp42 Guest:Rajat Monga: He is a pioneer in the AI industry, best known as the co-creator of TensorFlow. He has held senior roles at Google Brain and Microsoft, shaping the foundational tools that power today's AI systems. Rajat also co-founded Inference, a startup focused on anomaly detection in data analytics. At Microsoft, he leads AI software engineering, advancing inferencing infrastructure for the next generation of AI applications. He holds a Btech Degree from IIT, Delhi. Follow Rajat on:LinkedIn - https://www.linkedin.com/in/rajatmonga/  X (Twitter) - https://twitter.com/rajatmonga Share Your Thoughts: Have questions or comments? Drop us a mail at EffortlessPodcastHQ@gmail.com Email: EffortlessPodcastHQ@gmail.com

Dr. Mike Schuster is the head of the AI Core team at Two Sigma, where he leads engineers and quantitative researchers in advancing AI technologies across the firm's investment strategies and internal efficiencies. With over 25 years of expertise in machine learning and deep learning, Mike has been at the forefront of AI trends in tech and finance. Prior to Two Sigma, he spent 12 years at Google, contributing to transformative projects like Google Translate as part of the Google Brain team. Dr. Schuster holds a PhD in Electrical Engineering from the Nara Institute of Science and Technology in Japan and is recognized as a pioneer whose work has significantly shaped the AI landscape.In this conversation, we discuss:The challenges and importance of building collaborative teams for complex AI systems in finance.Key differences between developing AI technologies in tech companies like Google versus finance firms like Two Sigma.The evolution of neural networks and their transformative impact on applications like Google Translate.The ethical considerations and risks of using AI in finance compared to other industries.Insights into data quality challenges and strategies for addressing bias in financial modeling.Predictions for the future of AI, focusing on efficiency, data quality, and practical advancements over the next five years.Resources:Subscribe to the AI & The Future of Work NewsletterConnect with Mike SchusterAI fun fact articleEpisode on how AI is diagnosing and treating sleep disorders  

Jiquan Ngiam, Co-Founder and CEO of Lutra AI, discusses his career journey Stanford University to eventually founding Lutra. He shares how Lutra helps streamline workflows by assisting with data prospecting, lead enrichment, and automating repetitive tasks. Jiquan also explores the balance between AI and human creativity in marketing, highlights his vision for making Lutra user-friendly for non-technical users, and encourages listeners to explore its potential for automating their workflows.   About Lutra AI Lutra aims to revolutionise automation and allow users to easily create AI-driven workflows. The platform simplifies complex processes, helping automate tasks and optimise work effortlessly. Whether you're managing data, streamlining operations, or integrating apps, Lutra makes automation accessible to everyone. Since its launch, Lutra has been empowering businesses to boost productivity and focus on what matters, eliminating the barriers of traditional workflow tools and delivering a seamless automation experience.   About Jiquan Ngiam Jiquan Niam is the CEO and Co-Founder of Lutra, an innovative automation platform. Before founding Lutra, Jiquan was a key contributor at Google Brain and studied at Stanford University where he achieved a PHD in Computer Science. Jiquan Niam is a driving force behind AI-driven automation and is passionate about making advanced technology accessible to all.   Time Stamps [00:00:18] - Jiquan provides some background to his career and why he founded Lutra. 00:02:44] - Overview of Lutra's Purpose and Functionality [00:09:36] - Enhancing Marketing Efforts with Timely Data [00:15:16] - User-Friendly Interface and Accessibility [00:20:23] - Marketing Strategy: Product-Led Growth Approach [00:23:27] - The Future of Marketing Roles with AI [00:26:19] - Advice for Young Marketers: Embrace Technology [00:28:30]- How to Get Started with Lutra   Quotes “I felt like education was this new superpower that I could give people.” Jiquan Ngiam, co-founder and CEO of Lutra "AI will not replace you, but a person who's using AI really well is going to do a lot more than you." Jiquan Ngiam, co-founder and CEO of Lutra "Help the team understand, investing into understanding this technology and using it... It's going to be potentially very game-changing." Jiquan Ngiam, co-founder and CEO of Lutra   Follow Jiquan: Jiquan Ngiam on LinkedIn: https://www.linkedin.com/in/jngiam/ Lutra AI website: https://lutra.ai/ Lutra AI on LinkedIn: https://www.linkedin.com/company/lutra-ai/   Follow Mike: Mike Maynard on LinkedIn: https://www.linkedin.com/in/mikemaynard/ Napier website: https://www.napierb2b.com/ Napier LinkedIn: https://www.linkedin.com/company/napier-partnership-limited/   If you enjoyed this episode, be sure to subscribe to our podcast for more discussions about the latest in Marketing B2B Tech and connect with us on social media to stay updated on upcoming episodes. We'd also appreciate it if you could leave us a review on your favourite podcast platform. Want more? Check out Napier's other podcast - The Marketing Automation Moment: https://podcasts.apple.com/ua/podcast/the-marketing-automation-moment-podcast/id1659211547

Startup Project Podcast: Building AI Agents for Knowledge Workers with Lutra AI Jiquan Ngiam joins Nataraj to discuss the future of AI, from the rise of deep learning to the potential of AI agents for knowledge workers. They delve into [Guest Name]'s experiences working with Andrew Ng at Coursera and Google Brain, where he witnessed the power of scaling up compute and data in pushing the boundaries of AI. Timestamps: * **0:00 - Introduction:** Nataraj welcomes [Guest Name] to the show and introduces his impressive background. * **2:28 - Working with Andrew Ng:** [Guest Name] shares his experience working with Andrew Ng, emphasizing Ng's foresight and focus on scaling up neural networks. * **6:15 - The Importance of Data and Compute:** [Guest Name] highlights how data and compute became key drivers in the success of AI, using the example of AlexNet's breakthrough in 2012. * **12:25 - Democratizing Education with Coursera:** [Guest Name] discusses the early days of Coursera and the team's vision for democratizing access to education, especially in fields like machine learning. * **17:55 - Google Brain and the Rise of Transformers:** [Guest Name] reflects on his time at Google Brain, where he witnessed the emergence of transformers and their potential for generalizing across modalities. * **21:24 - The Limits of Scaling:** [Guest Name] questions the future of AI scaling, suggesting that we may be approaching a point of diminishing returns due to data limitations and the difficulty of creating truly effective synthetic data. * **28:13 - The Need for Data on Physical Tasks:** [Guest Name] proposes a bold idea: collecting real-world data on mundane tasks to train AI agents for robotics and other applications that require replicating human behavior. * **34:23 - Lutrei.ai: AI Agents for Knowledge Work:** [Guest Name] introduces Lutrei.ai, an AI agent designed to assist knowledge workers with tasks like research, data manipulation, and automation. * **42:49 - Different Approaches to AI Agents:** [Guest Name] compares Lutrei's approach to building AI agents with other common methods, highlighting the importance of separating data and logic for reliable and scalable solutions. * **45:38 - Choosing the Right Models:** [Guest Name] discusses the diverse landscape of AI models and how Lutrei leverages different models for different tasks, from small models for summarization to larger models for reasoning and planning. * **52:04 - AI Code Generation: Cursor vs. GitHub Copilot:** [Guest Name] shares his experience using Cursor, a code generation tool, and compares it to GitHub Copilot, highlighting the potential for AI to empower average developers. * **1:00:16 - The Future of AI Code Generation:** [Guest Name] predicts that AI code generation capabilities will become ubiquitous, and the key innovations will be in user experience and interaction design. * **1:05:43 - Consuming Information:** [Guest Name] shares his favorite sources of information, including podcasts, books, and news outlets. * **1:08:44 - Mentorship and Learning:** [Guest Name] reflects on the key mentors in his career, including Andrew Ng, Daphne Koller, and John Chen. * **1:12:34 - Advice for Early Career Professionals:** [Guest Name] advises young professionals to be voracious learners and prioritize gaining diverse experiences early in their careers. * **1:16:21 - The Motivation Behind Lutrei:** [Guest Name] explains his passion for pushing the boundaries of AI while simultaneously making it accessible and impactful for a wider audience. * **1:18:33 - Closing Thoughts:** Nataraj thanks [Guest Name] for sharing his insights and expresses his excitement for the future of Lutrei.ai. **Don't miss this episode to learn more about the exciting things happening in gen AI and how it's poised to revolutionize the way we work!**

Before Elon Musk rebranded Twitter, X was already in use — at Google. Google X was Google's secret research lab, where Google's most imaginative ideas came to life. As CEO and co-founder, Astro Teller's job is to harness X's wildest, most futuristic technology to solve the world's hardest problems. The same "moonshot factory" that created Google Brain and Waymo self-driving cars is also working on carbon capture, laser-beam Internet, delivery drones, and more. I sat down with Astro to discuss how to build a culture of radical innovation. He shares some deep wisdom about unlearning what we know and why it's the counterintuitive approach that allows us to land a moonshot. This...is A Bit of Optimism. To learn more about Astro and his work, check out: X, the moonshot factorySee omnystudio.com/listener for privacy information.

Futurist, Technologist and Author of many titles including the classic “Wealth and Poverty”, George Gilder joins us to discuss supply side economics and the transformative potential of using graphene material in various industries including real estate. We discuss economic growth measured by time prices, showing that private sector progress is faster than GDP estimates. Learn about graphene's properties, including its strength and conductivity, and its potential to transform various industries. Graphene is a single layer of carbon atoms that is 200 times stronger than steel, 1000 times more conductive than copper and the world's thinnest material. Resources: getgilder.com Show Notes: GetRichEducation.com/517 For access to properties or free help with a GRE Investment Coach, start here: GREmarketplace.com Get mortgage loans for investment property: RidgeLendingGroup.com or call 855-74-RIDGE  or e-mail: info@RidgeLendingGroup.com Invest with Freedom Family Investments.  You get paid first: Text FAMILY to 66866 For advertising inquiries, visit: GetRichEducation.com/ad Will you please leave a review for the show? I'd be grateful. Search “how to leave an Apple Podcasts review”  GRE Free Investment Coaching: GREmarketplace.com/Coach Best Financial Education: GetRichEducation.com Get our wealth-building newsletter free— text ‘GRE' to 66866 Our YouTube Channel: www.youtube.com/c/GetRichEducation Follow us on Instagram: @getricheducation Complete episode transcript:   Automatically Transcribed With Otter.ai    Keith Weinhold  00:01 Welcome to GRE. I'm your host. Keith Weinhold. I'm talking about the various economic scare tactics out there, like the BRICS, the FDIC and the housing crash. What lower interest rates mean? How our nation's $35 trillion debt has gone galactic. Then today's guest is a legend. He's a technologist and futurist. It tells us about today's promise of graphene in real estate all today on get rich education.  when you want the best real estate and finance info, the modern Internet experience limits your free articles access, and it's replete with paywalls and you've got pop ups and push notifications and cookies disclaimers. Oh, at no other time in history has it been more vital to place nice, clean, free content in your hands that actually adds no hype value to your life. See, this is the golden age of quality newsletters, and I write every word of ours myself. It's got a dash of humor, and it's to the point to get the letter. It couldn't be more simple text, GRE to 66866, and when you start the free newsletter, you'll also get my one hour fast real estate course, completely free. It's called the Don't quit your Daydream letter, and it wires your mind for wealth. Make sure you read it. Text GRE to 66866, text GRE to 66866.   Corey Coates  01:40 you're listening to the show that has created more financial freedom than nearly any show in the world. This is Get Rich Education.   Keith Weinhold  01:56 Welcome to GRE from Dunedin, Florida to Dunedin, New Zealand and across 188 nations worldwide. I'm Keith Weinhold, and you are listening to get rich education, where real estate investing is our major. That's what we're here for, with minors in real estate economics and wealth mindset. You know, as a consumer of this media type as you are, it's remarkable how often you've probably encountered these de facto scare tactics, like the BRICS are uniting and it will take out the dollar and it's just going to be chaos in the United States. You might know that BRICS, B, R, I, C, S is the acronym for Brazil, Russia, India, China and South Africa. Do you know how hard it is to get off the petro dollar and how hard it is for the BRICS, which is basically more than just those five countries, it's dozens of countries. How hard it is for them to agree on anything with things as various as their different economies, and they'll have different customs and currencies. I mean, sheesh, just for you to get yourself and three friends all to agree to meet at the same coffee shop at the same time, takes, like a Herculean effort, plus a stroke of luck, and all full of you are like minded, so I wouldn't hold your breath on the dollar hyper inflating to worthlessness, although it should slowly debase. What about the scare tactic of the FDIC is going to implode, and this could lead to bank closures and widespread societal panic. Well, the FDIC, which stands for Federal Deposit Insurance Corporation, they're the body that backs all of the US bank deposits, including yours, and it's steered by their systemic resolution Advisory Committee. Well, there are $9 trillion in bank deposits, and is backed by only a few 100 billion in FDIC cash, so there aren't nearly enough dollars to back the deposits. So can you trust your money in the bank? That's a prevalence scare tactic, but my gosh, if nothing else, history has shown that the government will step in to backstop almost any crisis, especially a banking related one, where one failure can have a cascading effect and make other institutions fall. I'm not saying that this is right, but time has proven that the government does and will step in, or the common scare tactic in our core of the world that is the eminent housing price crash. And I define a crash as a loss in value of 20% or more. Do you know how difficult this would be to do anytime soon? Housing demand still outstrips supply. Today's homeowners have loads of protective equity, an all time high of about 300k so they're not walking away from their homes. Inflation has baked higher replacement costs into the real estate cake, and now mortgage rates have fallen one and a half percent from this cycle's highs, and they are poised to fall further, so a housing price crash is super unlikely, and a new scare tactic for media attention seems to be this proposal by a future presidential hopeful about a tax on unrealized gains. Now Tom wheelwright is the tax expert. He's returning to the show with us again soon here, so maybe I'll ask him about it. But a tax on unrealized gains is politically pretty unpopular. It would be a mess to impose, and a lot of others have proposed it in the past as well, and it has not gone anywhere. Plus tax changes need congressional approval, and we have a divided Congress, there's a small chance that attacks on unrealized gains could come to fruition, but it would be tough. It's probably in the category of just another media scare tactic, much like the BRICS and the shaky FDIC banking structure had a housing price crash. I like to keep you informed about these things, and at times we do have guests with a disparate opinion from mine on these things. Good to get a diversity of opinions, but it's best not to go too deep into these scare tactics that are really unlikely to happen any time soon. Well, there was a party going on 10 days ago at what all affectionately dub club fed in Jacksonhole Wyoming, I don't know what the club fed cover charge was, but fortunately, we did not have to watch Janet "Grandma" Yellen dance at Club fed and and share. Jerome Powell, yes, he finally caught a rate cut buzz. He announced that the time has come for interest rate cuts, and as usual, he didn't offer specifics. Total rager. what a party. later this month, he's going to render the long awaited decision, which now seems to be, how much will cut rates by a quarter point or a half point? Did you know that it's been four and a half years since the Fed lowered rates? Yeah, that was March of 2020, at the start of the pandemic. And then we know what happened back in 2022 and 2023 they hiked rates so much that they needed trail mix, a sleeping bag and some Mountain House freeze dried meals to go along with their steady hiking cycle. Interest rates now, though have been untouched for over a year, it's been an interesting year for the Fed and rates many erroneously thought there would be six or more rate cuts this year. And what about Maganomics? Trump recently said that if he becomes president, he should be able to weigh in on fed decisions that would depart from a long time tradition of Fed independence from executive influence. Historically, they've been separated.   Donald Trump  08:26 The Federal Reserve's a very interesting thing, and it's sort of gotten it wrong a lot. And he's tending to be a little bit later on things. He gets a little bit too early and a little bit too late. And, you know, that's very largely a it's a gut feeling. I believe it's really a gut feeling. And I used to have it out with him. I had it out with him a couple of times, very strongly. I fought him very hard. And, you know, we get along fine. We get along fine. But I feel that, I feel the president should have at least say in there. Yeah, I feel that strongly. I think that, in my case, I made a lot of money. Iwas very successful, and I think I have a better instinct than in many cases, people that would be on the Federal Reserve or the chairman.   Keith Weinhold  09:10 Those Trump remarks were just a few weeks ago, and then shortly afterward, he seemed to walk those comments back, but he did say that he would not reappoint. DJ J-pal, to the economic turntables. It's a long standing economic argument as well about whether an outside force like the Fed should set interest rates at all, which is the price of money, rather than allowing the rate to float with the free market as lenders and borrowers negotiate with each other. I mean, no one's out there setting the price of oil or refrigerators or grapes, but it is pretty remarkable that the Fed has signaled that rate cuts are eminent when inflation is still 2.9% well above their 2% target. But let's be mindful about the Fed's twofold mission, what they call their dual mandate. It is stable prices and maximum employment. Well, the Fed's concern is that second one, it's that the labor market has slowed and see the way it works is pretty simple. Lower interest rates boost employment because it's cheaper for businesses to borrow money that encourages them to expand and hire, which is exactly how lower interest rates help the labor market. That's how more people get hired, and this matters because you need a tenant that can pay the rent. So the bottom line here is to expect lower interest rates on savings accounts, HELOCs, credit cards and automobile loans. What this means to real estate investors is that lower mortgage rates are eminent, although the change should be slow. Two years ago, mortgage rates rose faster than they're going to fall. Now, one thing that lower interest rates can do is lower America's own debt. Servicing costs and America's public debt is drastic. Now, between 35 and $36 trillion in fact, to put our debt into perspective, it has gone galactic. And I mean that in an almost literal sense, because look, if you line up dollars, dollar bills, which are about six inches long, if you line those up end to end from Earth, how far do you think that they would reach? How about to the moon? Oh, no, if you line up dollars end to end, they would stretch beyond the moon. Okay, let's see how far we can follow them out through the solar system. They would breeze past Mars, which is 140 million miles away, the next planet out Jupiter. Oh, our trail of dollar bills would extend beyond that. Next up is Saturn and its ring. The dollar bills would reach beyond that. We're getting to the outer planets now, Uranus still going. Neptune, okay, Neptune is about $30 trillion bills away, and we would have to go beyond that then. So our 35 to $36 trillion of national debt would almost reach Pluto that's galactic. That's amazing. That's bad, and it probably means we have to print more dollars in order to pay back the debt, which is, of course, long term inflationary. And I don't know what's stopping us from going from $36 trillion up to say, 100 trillion, gosh. next week here on the show, we're talking about real estate investing in one of the long time best and still hottest real estate investor states, and then later on, we've got brilliant tax wizard Tom wheelwright returning, as we know here at GRE real estate pays five ways, and if you have any Spanish speaking family or friends, I've got a great way for them to consume all five video modules. It's an AI converting my voice to Spanish in these videos, we have a Spanish speaker here on staff at Get Rich Education, and she said the dub is pretty good. Well, the entire package, real estate pays five ways in Espanol is condensed into a powerful one hour total, all five videos a course, all in one wealth building hour. It's free to watch. There's no email address to enter or anything you can tell your Spanish speaking family and friends, or maybe your multilingual and your primary language is Spanish. That is it getricheducation.com/espanolricheducation.com/espanol or a shorter way to get to the same pageis getricheducation.com/espricheducation.com/esp, that's getricheducation.com/esp.richeducation.com/esp.  This week's guest is one of the first people I ever heard discussing the blockchain and cryptocurrency 15 years ago, and then he was early on AI. What got my attention is his education about a promising construction material for building new real estate, though, I expect that our discussion will delve outside of real estate today as well. Let's meet the incomparable George Gilder. This week's guest is the co founder at the Discovery Institute, discovery.org original pillar of supply side economics, former speechwriter to both Presidents Reagan and Nixon. And he's the author of the classic book on economics called Wealth and Poverty. Today he's at the forefront of technological breakthroughs. He's a Harvard grad. He wears a lot of stripes. I've only mentioned a few. Hey, welcome to GRE George Gilder.   George Gilder  15:09 right there better here.   Keith Weinhold  15:11 It's so good to host you, George, in both your writings and your influences on people like President Reagan, you champion supply side economics. And I think of supply side economics as things like lower taxes, less regulation and free trade. We had someone in the Reagan administration here with us a few months ago, David Stockman. He championed a lot of those same things. But go ahead and tell us more about supply side economics and what that means and how that's put into practice.   George Gilder  15:43 Well, it really begins with human creativity in the image of your Creator, essence of supply side economics now super abundant. I mean supply side economics triumphs. We had the whole information technology revolution ignited during the Reagan years and now dominates the world economy and gives the United States seven out of the top 10 companies in market cap. 70% of global corporate market cap is American companies because of supply side economics amazing, and that's why it's distressing to see supply side economics, with its promise of super abundance and prosperity and opportunity, Give way to narrow nationalistic calculations and four tenths of war. I mean, all these Jews are at the forefront. Today, in time, we're going to see human creativity once again prevail in my books, Life After Capitalism is my latest book, my new paradigm is graphene. Graphene is a single layer of carbon atoms, two dimensional layer of carbon atoms that is 200 times stronger than steel, 1000 times more conductive than copper. It switches and the terahertz trillions of times a second, rather than the billions of times a second that our current silicon chips which and you mix it with concrete, the concrete comes 35% stronger, just parts per million of graphene mixed with concrete yields some material that's 35% stronger than ordinary concrete. You mix a parts per million of graphene with asphalt, the roads don't get potholes in the winter. It's radically Abate, but it conducts signals so accurately. If you go on YouTube, you can find a mouse and said it's spinal cord severed completely, injected with graphene, the spinal signals transmitted so accurately that the you see the mouse doing cartwheels by the end of the YouTube measure. I mean, it's material that's going to transform all industries, from real estate to medicine to surgery to electronics. Electronics been kind of the spearhead of our economy, of the transformation and electronics may be more significant than any other domain.   Keith Weinhold  18:49 Well, this is a terrific overview of all the contributions you're making to both the economic world and the technology world with what you told us about right there. And I do want to ask you some more about the graphene and the technology later. But you know, if we bring it back to the economics, it was in your classic book, Wealth and Poverty, which sold over a million copies, where you espouse a lot of the same things that you still espouse today in your more recent books, that is, capitalism begins with giving, we can often think of it that way. As a real estate investor is where we need to give tenants a clean, safe, affordable, functional property before we profit. Capitalism begins with giving.   George Gilder  19:32 Absolutely. That's a crucial debate I had with Ayn Rand The Fountainhead and Atlas Shrugged and I say, capitalism is subsist on altruism. I'm concerned for the interests of others, imaginative anticipation of the needs of others. It's an altruistic, generous system, and from that generosity. Stems the amazing manifestations of super abundance that which I've been writing about recently. And super abundance shows, measured by time prices, how many hours a typical worker has to spend to earn the goods and services that sustain its life. Yeah, that's where the real cost has time. Yeah, time is money. Money is time, tokenized time, and measured by time, economic growth has been 50 to just enormously faster than is estimated by any of the GDP numbers. However, measured by time government services or ordinary GDP assumes that every dollar of government spending is worth what it costs. Prices both show that progress in the private sector has been four or five times faster than is estimated by GDP well government time, price of government dominated goods, including, increasingly, healthcare and education, is way less valuable than the cost. It's value subtracted, and certainly trillions of dollars for windmills and solar panels, trillions of dollars of subsidies is a net subtraction of value in the world economy. So I am with Gale Pooley and Tupy, both who wrote a book called Superabundance that I wrote the introduction to, and William Nordhaus, the Nobel laureate from Yale, who really conceived and developed time prices and showed that economic growth is 1000s of times greater than has been estimated by ordinary economic data. This is a time of abundance. It's not a time of scarcity. It's not a time of the dismal science. It's the time of super abundance.   Keith Weinhold  22:17 Yes, 100% a lot of that is just the government getting out of the way and really let people be givers, be that go giver and lead with giving, because I have never heard of a society that's taxed its way to prosperity.   George Gilder  22:34 Yeah. Well, that's absolutely the case. And I've been talking previously about graphene, which is the great new material that has been discovered of the last a couple decades. It originated, a lot of the science originated in Jim Tour's laboratory. James Tour of Rice University, and he's had scores of companies have emerged from his laboratory, and 18 of them got started in Israel. Israel is really become a leading force in the world economy. And when Israel is in jeopardy, our economy is in jeopardy. We have 100,000 Israeli citizens working in companies in Silicon Valley, 100,000 all the leading American tech companies have outposts in Israel, and now we face what I call the Israel test, which is how you respond to people who are really superior in creativity and accomplishment and intellect, and the appropriate thing to do is emulate them and learn from them. But too many people in the world see success and they want to tear it down, or they think it was stolen from someone else, or it was part of a zero sum game where the riches of one person necessarily come at the expense of someone else, which is the opposite of the truth, the riches proliferate opportunities for others. That's how the economy grows through the creativity and the image of your Creator.   Keith Weinhold  24:25 And when you bring up Israel, they're one of many nations that's made strong contributions to society and the economy, and we think about other nations that's been an increasingly relevant conversation these past few years, a lot of that centers on immigration. I'm not an expert on how many people we should let into this country or any of those sort of policy sorts of things, but here is a real estate investing show. I often think about where and how we're going to house all these immigrants, whether they come from Central America or South America or Israel or. Anywhere else. And I know oftentimes you've touted immigrations economic benefits, so I think it's pretty easy for one to see how in the short term, immigrants could be of economic detriment, but tell us more about those long term economic benefits of immigrants coming to the United States.   George Gilder  25:17 Immigrants come to the United States and become Americans and contribute American opportunity and wealth. We won the second world war because of immigration of Jewish scientists from Europe to the United States, who led by people like John von Neumann and Oppenheimer who forged the Manhattan Project, and that's really how we won the Second World War, was by accepting brilliant immigrants who wanted to serve America. Now there is a threat today where immigrants come to the United States not to contribute to the United States, but to exploit the United States, or even destroy it, not to go givers. They are givers, and so we want immigrants who are inclined to commit to America and create opportunities for the world, but immigrants who want to tear down America and who believe that America owes them something tend to be less productive and less valuable immigrants and immigrants who really want to destroy western civilization, and the jihadists that we know about are actually a threat to America. So the immigration problem isn't simple, but when we had a system where legal immigrants could apply and enter our country and revitalize it, that was a wonderful system, but having boards of illegal immigrants just pour over the border is not an intelligent way to deal with the desire of people around the world to share an American prosperity.   Keith Weinhold  27:13 We've seen several cases in the past year or two where immigrants are given free housing. There are really great case studies about this in Massachusetts and some other places, how they're giving housing before oftentimes, our own Americans, including sometimes retired veterans, are provided with housing. This all comes down to the housing crunch and already having a low housing supply. So what are some more your thoughts about just how much of a layup or a handout should we give new immigrants?   George Gilder  27:42 Housing technology is going to be transformed by the material science revolution that is epitomized by graphene, this miracle material I was describing. I think part of the problem is real estate enterprise is over regulated, and there are too many obstacles to the building of innovative new forms of housing. In 20 years, it'll be hard to recognize many of the structures that emerge as a result of real revolution in material science that is epitomized by this graphene age that I've been describing, and that also will transform electronics as well, and part housing can become a kind of computer platform as Elon Musk is transforming the auto business by seeing Tesla is really a new form of computer platform. I believe there's going to be an Elon Musk of real estate who is going to re envisage housing as a new form of building a computer platform that makes intelligent houses of the future that will be both cheaper and more commodious for human life.   Keith Weinhold  29:12 Real estate is rather old and slow moving when we think about technology in real estate, maybe what comes to mind are smart thermostats, smart doorbells, or 3d printed homes. When we come back, we're going to learn more about graphene and what it can do in real estate in the nanocosm revolution. Our guest is George Gilder. We talked about economics. We're coming back to talk about technology. I'm your host. Keith Weinhold. Keith Weinhold  Hey, you can get your mortgage loans at the same place where I get mine, at Ridge lending group NMLS, 42056, they provided our listeners with more loans than any provider in the entire nation because they specialize in income properties. They help you build a long term plan for growing your real estate empire with less. Ridge you can start your pre qualification and chat with President Caeli Ridge personally. Start now while it's on your mind at ridgelendinggroup.com That's ridgelendinggroup.com. Your bank is getting rich off of you. The national average bank account pays less than 1% on your savings. If your money isn't making 4% you're losing your hard earned cash to inflation. Let the liquidity fund help you put your money to work with minimum risk, your cash generates up to an 8% return with compound interest year in and year out, instead of earning less than 1% sitting in your bank account, the minimum investment is just 25k you keep getting paid until you decide you want your money back. Their decade plus track record proves they've always paid their investors 100% in full and on time. And I would know, because I'm an investor too, earn 8% hundreds of others are text FAMILY to 66866, learn more about freedom. Family Investments Liquidity Fund on your journey to financial freedom through passive income. Text FAMILY to 66866.   Dolf Deroos  31:19 This is the king of commercial real estate. Dolph de Roos, listen to get rich education with Keith Weinhold, and don't quit your Daydream.   Keith Weinhold  31:32 Welcome back to Get Rich Education. We're joined by an illustrious, legendary guest, George Gilder, among being other things, including a prolific writer. He's also the former speechwriter to presidents Reagan and Nixon. He's got a really illustrious and influential career. George, you've been talking about graphene, something that I don't think our audience is very familiar with, and I'm not either. Tell us about graphene promise in real estate.   George Gilder  31:59 Well, back in Manchester, England, in 2004 graphene was first discovered and formulated. It actually was submerged before then, but the Nobel Prizes were awarded to Geim and Novoselov in2010.  So this is a new material that all of us know when we use a lead pencil, a lead is graphite, and graphene is a single layer of graphite. And it turns out, many people imagined if you had a single layer of graphite, it would just break up. It would not be useful.   Keith Weinhold  32:42 We're talking super thin, like an atom.   George Gilder  32:45 Yeah, it's an atom thick, but still, it turns out that it has miraculous properties, that it's 200 times stronger than steel. If you put it in a trampoline, you couldn't see the trampoline, but you could bounce on it without go following through it. It can stop bullets. It means you can have invisible and almost impalpable bulletproof vests, and you mix it with concrete, and the concrete is becomes 35% stronger, even parts per million of graphene can transform the tensile strength of concrete, greatly reduce the amount you need, and enable all sorts of new architectural shapes and capabilities. We really are in the beginning of a new technological age, and all depressionary talk you hear is really going to be eclipsed over coming decades by the emergence of whole an array of new technologies, graphene, for instance, as a perfect film on wafer of silicon carbide and enable what's called terahertz electronics, which is trillions of cycles a second like light rather than billions of cycles a second like or Nvidia or L silicon chips, and it really obviates chips, because you what it allows is what's called wafer scale integration of electronics, and today, it the semiconductor industry, and I've written 10 books on semiconductors over the years, but the semiconductor industry functions by 12 inch wafers that get inscribed with all sorts of complex patterns that are a billionth of a meter in diameter. These big wafers and then the way. First get cut up into 1000s of little pieces that each one gets encapsulated in plastic packages and by some remote Asian islands, and then get implanted on printed circuit boards that arrayed in giant data centers that now can on track to consume half the world's energy over the next 20 years, and these new and all this technology is ultimately going to be displaced by wafer scale integration on The wafer itself. You can have a whole data center on a 12 inch wafer with no chips. It's on the wafer itself. And this has been recently announced in a paper from Georgia Tech by a great scientist named Walter de Heere. And it's thrilling revolution that that render as much as Silicon Valley obsolescent and opens up just huge opportunities in in construction and real estate and architecture and medicine and virtually across the range of contemporary industry.   Keith Weinhold  36:20 You wrote a book about blockchain and how we're moving into the post Google world is what you've called it. So is this graphene technology that you're discussing with us here? Is that part of the next thing, which you're calling the nanocosm revolution?   George Gilder  36:36 The microcosm was an earlier book the quantum revolution and economics and technology. I thought I wrote years ago called microcosm.   Keith Weinhold  36:46 Okay, we're getting smaller than microcosm now in nanocosm.   36:49 that was microns, that was millionths of a meter dimensions of the transistors and devices and silicon chips, the nanocosm is a billionth of the meter. It's 1000 times smaller the features and electronics of the future, and we're moving from the microcosm into the nanocosm. New materials like graphene epitomize this transformation. You know, people think that these giant data centers all around the world, which are amazing structures, but half the energy in these data centers are devoted to removing the heat rather than fueling the computation. And I believe these data centers are represent a kind of IBM mainframe of the current era. When I was coming up, people imagined that a few 100 IBM mainframe computers, each weighing about a ton, would satisfy all the world's needs for computation, and that new artificial minds could be created with these new IBM mainframes. And it's the same thing today, only we're talking about data centers, and I believe that the coming era will allow data centers in your pocket and based on graphene electronics, and wait for scale integration, a whole new paradigm that will make the current data centers look like obsolete, old structures that need to be revitalized.   Keith Weinhold  38:37 Around 2007 Americans and much of the world, they got used to how it feels to have the power of a computer in their pocket with devices like the iPhone. How would it change one's everyday life to have effectively a data center in their pocket?   38:54 This means that we no longer would be governments of a few giant companies hearing a singular model of intelligence. That's what's currently envisaged, that Google Brain or Facebook or these giant data setters would sum up all human intelligence and in a particular definition, but there are now 8 billion human beings on earth, and each of our minds is as densely connected as the entire global internet. And while the global Internet consumes error watts, trillions of watts of power, or brains. Each of these 8 billion human minds functions on 12 to 14 watts, or it's billions of times less than these data center systems. On the internet. I believe that technology works to the extent that it expands human capabilities, not to the extent that it displaces human capabilities. The emergence of distributed databases in all our pockets, distributed knowledge and distributed creativity can revitalize the whole world economy and open new horizons that are hard to imagine today, as long as we don't, all of a sudden decide that we live in a material universe where everything is scarce and successes by one person come at the expense of somebody else, as long as that zero sum model doesn't prevail, right? Human opportunities are really unlimited. Most of economics has been based on a false model of scarcity, the only thing that's really scarce is time. Imagination and creativity are really infinite.   Keith Weinhold  41:10 Yes, well, if someone wants to learn more about graphene in the nanocosm revolution, how can you help them? What should they do?   41:18 They can read my newsletters. I have a company with four newsletters. I write the Gilder Technology Report. Much of the time I write, John Schroeder writes moonshots, which is and I have a Gilder Private Reserve that reaches out with our crowd and Israel, and a lot of those graph gene companies in Israel are part of our Private Reserve. And I do Gilders Guide posts, and those are all available getgilder.com.   Keith Weinhold  41:56 if you'd like to learn more about George and his popular newsletter called the Gilder Technology Report. You can learn more about that at get gilder.com George, it's been an enlightening conversation about economics and where society is moving next. Thanks so much for coming on to the show.   George Gilder  42:16 Thank you, Keith. I really appreciate it.   Keith Weinhold  42:24 yeah, a forward looking discussion with the great George Gilder. Forbes said graphene may be the next multi trillion dollar material. George will tell you that you want to get into graphene now, while the biggest gains are still ahead. If it interests you in at least learning more, check out his video resource. It's free. There's also an opportunity for you to be an investor. You can do all of that and more at getgilder.com again getguilder.com until next week. I'm your host. Keith Weinhold. Don't Quit Your Daydream.   43:04 nothing on this show should be considered specific, personal or professional advice. Please consult an appropriate tax, legal, real estate, financial or business professional for individualized advice. Opinions of guests are their own. Information is not guaranteed. All investment strategies have the potential for profit or loss. The host is operating on behalf of Get Rich Education LLC, exclusively.   Keith Weinhold  43:32 The preceding program was brought to you by your home for wealth building. GetRichEducation.com

Aidan Gomez is the Co-founder & CEO at Cohere, the leading AI platform for enterprise, having raised over $1BN from some of the best with their last round pricing the company at a whopping $5.5BN. Prior to Cohere, Aidan co-authored the paper “Attention is All You Need,” which introduced the groundbreaking Transformer architecture. He also collaborated with a number of AI luminaries, including Geoffrey Hinton and Jeff Dean, during his time at Google Brain, where the team focused their efforts on large-scale machine learning. In Today's Episode with Aidan Gomez We Discuss: 1. Compute vs Data: What is the Bottleneck: Does Aidan believe that more compute will result in an equal increase in performance?   How much longer do we have before it becomes a case of diminishing returns?   What does Aidan mean when he says "he has changed his mind massively on the role of data"? What did he believe? How has it changed? 2. The Value of the Model: Given the demand for chips, the consumer need for applications, how does Aidan think about the inherent value of models today? Will any value accrue at the model layer? How does Aidan analyze the price dumping that OpenAI are doing? Is it a race to the bottom on price? Why does Aidan believe that "there is no value in last year's model"? Given all of this, is it possible to be an independent model provider without being owned by an incumbent who has a cloud business that acts as a cash cow for the model business? 3. Enterprise AI: It is Changing So Fast: What are the biggest concerns for the world's largest enterprises on adopting AI? Are we still in the experimental budget phase for enterprises? What is causing them to move from experimental budget to core budget today? Are we going to see a mass transition back from Cloud to On Prem with the largest enterprises not willing to let independent companies train with their data in the cloud? What does AI not do today that will be a gamechanger for the enterprise in 3-5 years? 4. The Wider World: Remote Work, Downfall of Europe and Relationships: Given humans spending more and more time talking to models, how does Aidan reflect on the idea of his children spending more time with models than people? Does he want that world? Why does Aidan believe that Europe is challenged immensely? How does the UK differ to Europe? Why does Aidan believe that remote work is just not nearly as productive as in person?  

On this episode of FYI, ARK's Chief Futurist Brett Winton, and Chief Investment Strategist Charlie Roberts sit down with artificial intelligence (Al) luminary Andrew Ng to explore the deployment of artificial intelligence and the evolution of AI education. Andrew shares insights from his extensive career, including his work with Google Brain, Baidu, Coursera, and his current AI fund. We analyze the transformative potential of AI, especially in how large corporations can harness it, the progression toward agentic systems, and the contentious topic of open-source AI. This episode provides a comprehensive overview of AI's current status and future trajectory, offering invaluable insights for technology enthusiasts."For the last 10-15 years, there have constantly been a small number of voices saying AI is hitting a wall. I think that a lot of statements to that effect were all over and over proven to be wrong. I think we're so far from hitting a wall." -Andrew Ng Key Points From This Episode:- Andrew Ng's significant contributions to AI and education through various platforms- Insights into the deployment challenges and future potentials of AI in business- The role of agentic systems in advancing AI applications- The impact of open source on innovation and the AI industry- Distribution and data generation in AI's effectiveness

Crucible Moments will be back shortly with season 2. You'll hear from the founders of YouTube, DoorDash, Reddit, and more. In the meantime, we'd love to introduce you to a new original podcast, Training Data, where Sequoia partners learn from builders, researchers and founders who are defining the technology wave of the future: AI. The following conversation with Harrison Chase of LangChain is all about the future of AI agents—why they're suddenly seeing a step change in performance, and why they're key to the promise of AI.  Follow Training Data wherever you listen to podcasts, and keep an eye out for Season 2 of Crucible Moments, coming soon. LangChain's Harrison Chase on Building the Orchestration Layer for AI Agents Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned:  ReAct: Synergizing Reasoning and Acting in Language Models, the first cognitive architecture for agents SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent from researchers at Princeton Devin, autonomous software engineering from Cognition V0: Generative UI agent from Vercel GPT Researcher, a research agent  Language Model Cascades: 2022 paper by Google Brain and now OpenAI researcher David Dohan that was influential for Harrison in developing LangChain Transcript: https://www.sequoiacap.com/podcast/training-data-harrison-chase/

2024: The Most Important Year in the History of Robotics!Companion podcast #31 to Keynote address at SuperTechFT 3 July 2024 Happy to be with you one and all. I'm Tom Green, your host and companion on this very special journey for 2024. We are only halfway through the year, and already 2024 has shown us that it is the most important year in the history of robotics.This podcast will show you why that is.This podcast is a companion to the live keynote address I will give at SuperTechFT in San Francisco on July 3rd 2024. I want to first thank Dr. Albert Hu, president and director of education at SuperTechFT, and to the staff and patrons of SuperTechFT for inviting me. The title of my keynote: 2024: The Most Important Year in the History of Robotics!What other year can possibly compete for top honors other than 2024?2024 eliminated the barrier to entry for digital programming by eliminating the need to code.As Tesla's former chief of AI, Andrej Karpathy put it: "Welcome to the hottest new programming language...English"2024 opened the door of AI prompt engineering to millions of new jobs and careers in millions of SME industries worldwide.So explains: Andrew Ng, investor and former head of Google Brain and Baidu.2024 converged GenAI with robotics, broadened robot/cobot applications, and freed robots from complexity of operation.So announced NVIDIA's CEO and founder Jensen Huang at the company's March meeting.2024 reinvigorated the liberal arts, creative thinking, expository writing, and language as vital new components in developing robotics applications.So reflects Stephen Wolfram physicist and creator of Mathematica2024 defined the need for the GenAI & the "New Collar" Worker Connection: Vitally needed workers for AI/robot-driven industry worldwide, and just maybe, the revitalization of America's middle class…or the middle class of any nation.Sarah Boisvert technologist, factory owner and wrote the book on the New Collar WorkforceSuddenly in mid-2024, technology has thrown us into a brand-new worldAnd it's only early July of 2024...can you believe it?“Artificial intelligence and robotics could catapult both fields to new heights.”The 4-Year Plight: SMEs in Search of Robots!Tech News May Fade, but Its Stories Are Forever! GenAI & "New Collar" ConnectionDid AI Just Free Humanity from Code?

David Luan is the CEO and Co-Founder at Adept, a company building AI agents for knowledge workers. To date, David has raised over $400M for the company from Greylock, Andrej Karpathy, Scott Belsky, Nvidia, ServiceNow and WorkDay. Previously, he was VP of Engineering at OpenAI, overseeing research on language, supercomputing, RL, safety, and policy and where his teams shipped GPT, CLIP, and DALL-E. He led Google's giant model efforts as a co-lead of Google Brain. In Today's Episode with David Luan We Discuss: 1. The Biggest Lessons from OpenAI and Google Brain: What did OpenAI realise that no one else did that allowed them to steal the show with ChatGPT? Why did it take 6 years post the introduction of transformers for ChatGPT to be released? What are 1-2 of David's biggest lessons from his time leading teams at OpenAI and Google Brain? 2. Foundation Models: The Hard Truths: Why does David strongly disagree that the performance of foundation models is at a stage of diminishing returns? Why does David believe there will only be 5-7 foundation model providers? What will separate those who win vs those who do not? Does David believe we are seeing the commoditization of foundation models? How and when will we solve core problems of both reasoning and memory for foundation models? 3. Bunding vs Unbundling: Why Chips Are Coming for Models: Why does David believe that Jensen and Nvidia have to move into the model layer to sustain their competitive advantage? Why does David believe that the largest model providers have to make their own chips to make their business model sustainable? What does David believe is the future of the chip and infrastructure layer? 4. The Application Layer: Why Everyone Will Have an Agent: What is the difference between traditional RPA vs agents? Why is agents a 1,000x larger business than RPA? In a world where everyone has an agent, what does the future of work look like? Why does David disagree with the notion of "selling the work" and not the tool? What is the business model for the next generation of application layer AI companies?  

Reed Albergotti is the tech editor at Semafor. He joins Big Technology Podcast to break down the week's news. We cover: 1) NVIDIA temporarily becoming the most valuable publicly traded company 2) Is NVIDIA a bubble? 3) What might disrupt NVIDIA? 4) Ilya Sustkever founds Safe Superintelligence Inc. 5) Who's funding Ilya? 6) Will SSI amount to anything? 6) OpenAI might become a public benefit company 7) The state of DeepMind's merger with Google Brain 8) Mustafa Suleyman's entry to Microsoft and how it impacts the relationship with OpenAI 9) Apple Vision Pro hits a speed bump 10) Vision Pro & Apple Intelligence similarities and differences ---- You can subscribe to Big Technology Premium for 25% off at https://bit.ly/bigtechnology Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Questions? Feedback? Write to: bigtechnologypodcast@gmail.com

Welcome to the What's Next! Podcast with Tiffani Bova.    I have a special treat for this show. We have not one, but actually two guests.    The first is Martin Gonzalez. He is the creator of Google's Effective Founders Project, a global research program that decodes the factors that enable startup founders to succeed. He also works closely with Google's engineering and research leaders on org design, leadership, and culture challenges.   Joining him is Josh Yellin, who co-founded Google's first Startup Accelerator and spearheaded its growth, reaching founders in 70 countries. Along with Martin, he co-founded Google's Effective Founders Project, and he recently spent four years as Chief of Staff at Google Brain and is presently an organizational leader at Google DeepMind.    Martin and Josh are the authors of a new book called The Bonfire Moment.    THIS EPISODE IS PERFECT FOR… founders of startups who want to avoid common pitfalls.    TODAY'S MAIN MESSAGE… research shows that 65% of startups will fail due to people issues - not the product development, resourcing, or any of the other challenging parts of being a founder. In this episode, Martin and Josh share how they face the people issues head-on and have trained thousands of founders through Bonfire Moment workshops.    Key takeaways:  Startups thrive when people issues are addressed alongside product and market challenges Structured reflection helps startups solve hidden problems Addressing people issues early on fosters long-term growth and stability Finding a co-founder should be a slow and deliberate decision Self-awareness is a critical character trait for founders to develop   WHAT I LOVE MOST… Josh and Martin's recommendation to not using the “maverick mindset” on the organizational side of things. Lean into innovative ideas for your product of service but rely on best practices when approaching leadership.    Running Time: 30:34   Subscribe on iTunes   Find Tiffani Online: Facebook Twitter LinkedIn   Learn More About Martin and Josh:  The Bonfire Moment Website   Book: The Bonfire Moment  

In this episode of the Crazy Wisdom Podcast, Stewart Alsop talks with John Ballentine, the founder and CEO of Alchemy.ai. With over seven years of experience in machine learning and large language models (LLMs), John shares insights on synthetic data, the evolution of AI from Google's BERT model to OpenAI's GPT-3, and the future of multimodal algorithms. They discuss the significance of synthetic data in reducing costs and energy for training models, the challenges of creating models that understand natural language, and the exciting potential of AI in various fields, including cybersecurity and creative arts. For more information on John and his work, visit Alchemy.ai.  Check out this GPT we trained on the conversation! Timestamps 00:00 - Stewart Alsop introduces Jon Ballentine, founder and CEO of Alchemy.ai, discussing Jon's background in machine learning and LLMs. 05:00 - Jon talks about the beginnings of his work with the BERT model and the development of transformer architecture. 10:00 - Discussion on the capabilities of early AI models and how they evolved, particularly focusing on the Google Brain project and OpenAI's GPT-3. 15:00 - Exploration of synthetic data, its importance, and how it helps in reducing the cost and energy required for training AI models. 20:00 - Jon discusses the impact of synthetic data on the control and quality of AI model outputs, including challenges and limitations. 25:00 - Conversation about the future of AI, multimodal models, and the significance of video data in training models. 30:00 - The potential of AI in creative fields, such as art, and the concept of artists creating personalized AI models. 35:00 - Challenges in the AI field, including cybersecurity risks and the need for better interpretability of models. 40:00 - The role of synthetic data in enhancing AI training and the discussion on novel attention mechanisms and their applications. 45:00 - Stewart and Jon discuss the relationship between AI and mental health, focusing on therapy and support tools for healthcare providers. 50:00 - The importance of clean data and the challenges of reducing bias and toxicity in AI models, as well as potential future developments in AI ethics and governance. 55:00 - Jon shares more about Alchemy.ai and its mission, along with final thoughts on the future of AI and its societal impacts. Key Insights Evolution of AI Models: Jon Ballentine discusses the evolution of AI models, starting from Google's BERT model to OpenAI's GPT-3. He explains how these models expanded on autocomplete algorithms to predict the next token, with GPT-3 scaling up significantly in parameters and compute. This progression highlights the rapid advancements in natural language processing and the increasing capabilities of AI. Importance of Synthetic Data: Synthetic data is a major focus, with Jon emphasizing its potential to reduce the costs and energy associated with training AI models. He explains that synthetic data allows for better control over model outputs, ensuring that models are trained on diverse and comprehensive datasets without the need for massive amounts of real-world data, which can be expensive and time-consuming to collect. Multimodal Models and Video Data: Jon touches on the importance of multimodal models, which integrate multiple types of data such as text, images, and video. He highlights the potential of video data in training AI models, noting that companies like Google and OpenAI are leveraging vast amounts of video data to improve model performance and capabilities. This approach provides models with a richer understanding of the world from different angles and movements. AI in Creative Fields: The conversation delves into the intersection of AI and creativity. Jon envisions a future where artists create personalized AI models that produce content in their unique style, making art more accessible and personalized. This radical idea suggests that AI could become a new medium for artistic expression, blending technology and creativity in unprecedented ways. Challenges in AI Interpretability: Jon highlights the challenges of understanding and interpreting large AI models. He mentions that despite being able to see the parameters, the internal workings of these models remain largely a black box. This lack of interpretability poses significant challenges, especially in ensuring the safety and reliability of AI systems as they become more integrated into various aspects of life. Cybersecurity Risks and AI: The episode covers the potential cybersecurity risks posed by advanced AI models. Jon discusses the dangers of rogue AI systems that could hack and exfiltrate data, creating new types of cyber threats. This underscores the need for robust cybersecurity measures and the development of defensive AI models to counteract these risks. Future of AI and Mental Health: Stewart and Jon explore the potential of AI in the field of mental health, particularly in supporting healthcare providers. While Jon is skeptical about AI replacing human therapists, he sees value in AI tools that enhance the ability of therapists and doctors to access relevant information and provide better care. This highlights a future where AI augments human capabilities, improving the efficiency and effectiveness of mental health care.