Podcasts about AlphaGo

How can AI help us understand and master deeply complex systems—from the game Go, which has 10 to the power 170 possible positions a player could pursue, or proteins, which, on average, can fold in 10 to the power 300 possible ways? This week, Reid and Aria are joined by Demis Hassabis. Demis is a British artificial intelligence researcher, co-founder, and CEO of the AI company, DeepMind. Under his leadership, DeepMind developed Alpha Go, the first AI to defeat a human world champion in Go and later created AlphaFold, which solved the 50-year-old protein folding problem. He's considered one of the most influential figures in AI. Demis, Reid, and Aria discuss game theory, medicine, multimodality, and the nature of innovation and creativity. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/  Listen to more from Possible here. Learn more about your ad choices. Visit podcastchoices.com/adchoices

How can AI help us understand and master deeply complex systems—from the game Go, which has 10 to the power 170 possible positions a player could pursue, or proteins, which, on average, can fold in 10 to the power 300 possible ways? This week, Reid and Aria are joined by Demis Hassabis. Demis is a British artificial intelligence researcher, co-founder, and CEO of the AI company, DeepMind. Under his leadership, DeepMind developed Alpha Go, the first AI to defeat a human world champion in Go and later created AlphaFold, which solved the 50-year-old protein folding problem. He's considered one of the most influential figures in AI. Demis, Reid, and Aria discuss game theory, medicine, multimodality, and the nature of innovation and creativity. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/  Select mentions:  Hitchhiker's Guide to the Galaxy by Douglas Adams AlphaGo documentary: https://www.youtube.com/watch?v=WXuK6gekU1Y Nash equilibrium & US mathematician John Forbes Nash Homo Ludens by Johan Huizinga Veo 2, an advanced, AI-powered video creation platform from Google DeepMind The Culture series by Iain Banks Hartmut Neven, German-American computer scientist Topics: 3:11 - Hellos and intros 5:20 - Brute force vs. self-learning systems 8:24 - How a learning approach helped develop new AI systems 11:29 - AlphaGo's Move 37 16:16 - What will the next Move 37 be? 19:42 - What makes an AI that can play the video game StarCraft impressive 22:32 - The importance of the act of play 26:24 - Data and synthetic data 28:33 - Midroll ad 28:39 - Is it important to have AI embedded in the world? 33:44 - The trade-off between thinking time and output quality 36:03 - Computer languages designed for AI 40:22 - The future of multimodality  43:27 - AI and geographic diversity  48:24 - AlphaFold and the future of medicine 51:18 - Rapid-fire Questions Possible is an award-winning podcast that sketches out the brightest version of the future—and what it will take to get there. Most of all, it asks: what if, in the future, everything breaks humanity's way? Tune in for grounded and speculative takes on how technology—and, in particular, AI—is inspiring change and transforming the future. Hosted by Reid Hoffman and Aria Finger, each episode features an interview with an ambitious builder or deep thinker on a topic, from art to geopolitics and from healthcare to education. These conversations also showcase another kind of guest: AI. Each episode seeks to enhance and advance our discussion about what humanity could possibly get right if we leverage technology—and our collective effort—effectively.

Pep Martorell es director del Barcelona Supercomputing Center, hogar del MareNostrum 5. Sentimos una extraña mezcla de fascinación y temor por las nuevas tecnologías. La ciencia tiene un impacto en el mundo en el que vivimos y la computación del superordenador soluciona problemas que no podemos tan siquiera concebir. Escribió Eduardo Mendoza en su precioso discurso de aceptación del Premio Cervantes que “las vocaciones tempranas son árboles con muchas hojas, poco tronco y ninguna raíz”. Pep, que divulga también en su propio Substack, entró en el campo de la física fascinado por los documentales de Cosmos. Carl Sagan contagió y sigue contagiando a muchos jóvenes en busca de una vocación. Ese hombre, con su pasión por la ciencia, despertó la curiosidad de muchos y mi esperanza es que este podcast haga lo mismo.Quiero dar las gracias a la Cambra de Comerç de Barcelona por haber hecho posible este episodio. Me permitieron grabar en su fantástico ático de Diagonal y no habría podido encontrar un emplazamiento mejor para la charla con Pep. La propuesta de la Cambra es atractiva para todo tipo de perfiles relacionados con el mundo de la empresa y te animo a que explores los eventos que allí organizan. La Cambra quiere ser un punto de encuentro empresarial en la ciudad de Barcelona, facilitando conexiones inesperadas y creando oportunidades en la serendipia que se genera en esos círculos. Siempre la opcionalidad del amigo Taleb, los accidentes positivos de los que te hablo en Kapital.Así narra Stephen Fry el regalo del fuego por parte de Prometeo, en su fantástico libro Los mitos griegos revisitados: “Cuando les mostró a los hombres aquel demonio saltarín y célebre danzarín, de primeras chillaron atemorizados y recularon ante las llamas. Pero la curiosidad pronto superó al miedo y comenzaron a solazarse con aquel nuevo juguete mágico, aquella sustancia, fenómeno..., llamadlo como queráis. Supieron por Prometeo que el fuego no era su enemigo sino un poderoso aliado que, convenientemente domesticado, tenía diez mil millares de usos. Prometeo pasó de una aldea a otra enseñándoles técnicas para fabricar herramientas y armas, cocer cacerolas de arcilla, cocinar carne y hornear masas de cereales, lo que enseguida desencadenó una avalancha de ventajas que supuso la prevalencia del hombre sobre la presa animal, que no podía reaccionar a las lanzas y flechas de punta metálica. No tardó mucho Zeus en bajar la mirada desde el Olimpo y ver puntos de titilante luz naranja salpicando el paisaje a su alrededor. Al instante supo lo que había sucedido. Tampoco hizo falta que le dijesen quién era el responsable. Su ira fue arrebatada y terrible. Jamás se había presenciado una furia tan extrema, tan tumultuosa, tan apocalíptica. Ni siquiera Urano, en su mutilada agonía, había experimentado una rabia tan vengativa. Urano fue vencido por un hijo que le resultaba indiferente, pero Zeus había sido traicionado por el amigo al que más amaba. Ninguna traición podía ser más terrible.”Índice:1:21 Temor ancestral a lo desconocido.8:52 Labatut ve al científico como un poeta.19:10 Mirar en el abismo del conocimiento.27:06 Las bellísimas lecciones de Sagan.30:51 Faltan chicas en las carreras STEM.42:56 La tradición catalana de comprar tecnología en Andorra.51:35 Conferencia en Solvay en 1927.1:03:15 Los misterios del big bang.1:06:58 Hablar de Newton es como hablar de Messi.1:16:51 Un superordenador en una capilla.1:25:58 Ich probiere.1:35:06 AlphaGo.1:41:53 Nobel de Química para el plegado de proteínas.1:45:59 Kasparov contra Deep Blue.1:48:23 Destrucción mutua asegurada.1:59:39 El bosón de Higgs.2:06:31 Misterios por resolver.Apuntes:Cosmos. Carl Sagan.Cosmos. Neil deGrasse TysonUn verdor terrible. Benjamín Labatut.MANIAC. Benjamín Labatut.BTG Talks. Benjamín Labatut.Beauty, truth and... physics? Murray Gell-Mann.La utilidad de lo inútil. Nuccio Ordine.El orden del tiempo. Carlo Rovelli.Cuántica. José Ignacio Latorre.

Let's talk about some unsettling experiments conducted during the 60s and 70s that revealed how limitations can be internalized and learned. Can we unlearn our limitations in Go and break through our ruts? Plus, some bonus futurology ramblings inspired by the AlphaGo documentary.All Things Go Podcast: The Surrounding Game Interview⁠Support Star Point⁠The Star Point Store

War DeepSeek tatsächlich der Sputnik-Moment? "In Wahrheit fand dieser 2015 statt, als Google den koreanischen Weltmeister Lee Sedol in AlphaGo geschlagen hat", rückt Baki Irmak zurecht. 300 Millionen Chinesen verfolgten damals die Partie. Europa hingegen war der Sieg der künstlichen Intelligenz über das menschliche Gehirn nur eine Randnotiz wert. Nicht viele Fondsmanager kennen sich hierzulande so fundiert mit Digitalunternehmen aus wie der Manager des The Digital Leaders Fund und Gründer von Pyfore Capital. Mit ihm gehen wir auf digitale Spurensuche bei Aktien wie Kaspi.kz, Nebius und Nu Holdings. Und wir gehen der Frage nach, mit welchen Unternehmen und Applikationen Europa doch noch auf den KI-Zug aufspringen könnte. Jetzt Aktien kaufen? Was tun bei Inflation und steigenden Zinsen? Wie tickt die Wirtschaft? Ihr interessiert Euch auch für alle Themen rund ums Geld? Dann ist unser neuer Podcast FOCUS-MONEY Talks garantiert etwas für Euch. Einmal pro Woche nimmt Euch die Wirtschaftsredakteurin Heike Bangert mit in die faszinierende Welt der Kapitalmärkte. FOCUS MONEY Talks findet Ihr überall da, wo es Podcasts gibt.

Ce français a bossé chez OpenAI pendant 3 ans. Ce qu'il y a fait est tout simplement hallucinant. Pour bien comprendre son histoire, revenons en Juillet 2019. Stan vient juste de quitter Stripe pour se lancer dans l'IA. Il faut dire que le domaine est en pleine ébullition. 3 ans plus tôt, le monde entier a vu arriver AlphaGo. L'IA qui a dépassé l'humain au go. Un jeu que l'on pensait encore complètement hors de portée de la machine. Depuis, les résultats s'enchainent. Tous les jours, c'est un nouveau jeu qui tombe. L'heure de gloire du “deep learning” est arrivé. Sans plus tarder, Stan décide d'explorer plusieurs idées. La voiture autonome ? Une IA de hacking ? Ou un programme capable de prouver des théorèmes mathématiques ? Son choix se porte finalement sur la dernière option, la plus théorique. Rapidement, ses travaux sont remarqués par un certain Ilya Sutskever. Qui n'est autre que… Le Chief Scientist et co-fondateur d'OpenAI

Prof. Jakob Foerster, a leading AI researcher at Oxford University and Meta, and Chris Lu, a researcher at OpenAI -- they explain how AI is moving beyond just mimicking human behaviour to creating truly intelligent agents that can learn and solve problems on their own. Foerster champions open-source AI for responsible, decentralised development. He addresses AI scaling, goal misalignment (Goodhart's Law), and the need for holistic alignment, offering a quick look at the future of AI and how to guide it.SPONSOR MESSAGES:***CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting!https://centml.ai/pricing/Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/***TRANSCRIPT/REFS:https://www.dropbox.com/scl/fi/yqjszhntfr00bhjh6t565/JAKOB.pdf?rlkey=scvny4bnwj8th42fjv8zsfu2y&dl=0 Prof. Jakob Foersterhttps://x.com/j_foersthttps://www.jakobfoerster.com/University of Oxford Profile: https://eng.ox.ac.uk/people/jakob-foerster/Chris Lu:https://chrislu.page/TOC1. GPU Acceleration and Training Infrastructure [00:00:00] 1.1 ARC Challenge Criticism and FLAIR Lab Overview [00:01:25] 1.2 GPU Acceleration and Hardware Lottery in RL [00:05:50] 1.3 Data Wall Challenges and Simulation-Based Solutions [00:08:40] 1.4 JAX Implementation and Technical Acceleration2. Learning Frameworks and Policy Optimization [00:14:18] 2.1 Evolution of RL Algorithms and Mirror Learning Framework [00:15:25] 2.2 Meta-Learning and Policy Optimization Algorithms [00:21:47] 2.3 Language Models and Benchmark Challenges [00:28:15] 2.4 Creativity and Meta-Learning in AI Systems3. Multi-Agent Systems and Decentralization [00:31:24] 3.1 Multi-Agent Systems and Emergent Intelligence [00:38:35] 3.2 Swarm Intelligence vs Monolithic AGI Systems [00:42:44] 3.3 Democratic Control and Decentralization of AI Development [00:46:14] 3.4 Open Source AI and Alignment Challenges [00:49:31] 3.5 Collaborative Models for AI DevelopmentREFS[[00:00:05] ARC Benchmark, Chollethttps://github.com/fchollet/ARC-AGI[00:03:05] DRL Doesn't Work, Irpanhttps://www.alexirpan.com/2018/02/14/rl-hard.html[00:05:55] AI Training Data, Data Provenance Initiativehttps://www.nytimes.com/2024/07/19/technology/ai-data-restrictions.html[00:06:10] JaxMARL, Foerster et al.https://arxiv.org/html/2311.10090v5[00:08:50] M-FOS, Lu et al.https://arxiv.org/abs/2205.01447[00:09:45] JAX Library, Google Researchhttps://github.com/jax-ml/jax[00:12:10] Kinetix, Mike and Michaelhttps://arxiv.org/abs/2410.23208[00:12:45] Genie 2, DeepMindhttps://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/[00:14:42] Mirror Learning, Grudzien, Kuba et al.https://arxiv.org/abs/2208.01682[00:16:30] Discovered Policy Optimisation, Lu et al.https://arxiv.org/abs/2210.05639[00:24:10] Goodhart's Law, Goodharthttps://en.wikipedia.org/wiki/Goodhart%27s_law[00:25:15] LLM ARChitect, Franzen et al.https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf[00:28:55] AlphaGo, Silver et al.https://arxiv.org/pdf/1712.01815.pdf[00:30:10] Meta-learning, Lu, Towers, Foersterhttps://direct.mit.edu/isal/proceedings-pdf/isal2023/35/67/2354943/isal_a_00674.pdf[00:31:30] Emergence of Pragmatics, Yuan et al.https://arxiv.org/abs/2001.07752[00:34:30] AI Safety, Amodei et al.https://arxiv.org/abs/1606.06565[00:35:45] Intentional Stance, Dennetthttps://plato.stanford.edu/entries/ethics-ai/[00:39:25] Multi-Agent RL, Zhou et al.https://arxiv.org/pdf/2305.10091[00:41:00] Open Source Generative AI, Foerster et al.https://arxiv.org/abs/2405.08597

Teknologi AI terus berkembang pesat, dan kini muncul satu inovasi baru dari China yang mengguncang dunia, yaitu DeepSeek. Dalam episode podcast kali ini, kita akan membahas bagaimana DeepSeek tidak hanya menghebohkan dunia kecerdasan buatan, tetapi juga mengguncang pasar saham hingga menurunkan nilai saham raksasa seperti Nvidia. Apa yang membuat teknologi ini begitu revolusioner?Sejarah AI di China punya perjalanan panjang, sejak AlphaGo mengalahkan juara dunia pada 2016 dan memicu ambisi besar China untuk menjadi pemimpin AI global pada 2030. DeepSeek hadir sebagai salah satu bukti nyata dari visi tersebut. Dengan latar belakang kuat di bidang matematika dan keuangan, pendirinya mengembangkan teknologi yang awalnya difokuskan pada algoritma perdagangan saham, sebelum akhirnya merambah dunia AI yang lebih luas.Salah satu aspek menarik dari DeepSeek adalah sifatnya yang open-source, memungkinkan siapa saja untuk menggunakan dan memodifikasi teknologinya. Ini bisa menjadi titik balik dalam industri AI, seperti bagaimana Linux dulu mengubah dunia sistem operasi. Dengan biaya yang jauh lebih murah dibandingkan pesaingnya seperti ChatGPT, DeepSeek berpotensi membuat AI lebih mudah diakses oleh bisnis dan inovator di seluruh dunia.Tidak hanya itu, diskusi dalam episode ini juga menyoroti masa depan AI di berbagai industri. Dari Hollywood yang mulai menggantikan penulisan naskah dengan AI hingga prediksi bahwa pada tahun 2030, 85% cerita akan dibuat oleh kecerdasan buatan, AI bukan lagi sekadar alat bantu, ia menjadi pemain utama.

Sepp Hochreiter, the inventor of LSTM (Long Short-Term Memory) networks – a foundational technology in AI. Sepp discusses his journey, the origins of LSTM, and why he believes his latest work, XLSTM, could be the next big thing in AI, particularly for applications like robotics and industrial simulation. He also shares his controversial perspective on Large Language Models (LLMs) and why reasoning is a critical missing piece in current AI systems.SPONSOR MESSAGES:***CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting!https://centml.ai/pricing/Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich.Goto https://tufalabs.ai/***TRANSCRIPT AND BACKGROUND READING:https://www.dropbox.com/scl/fi/n1vzm79t3uuss8xyinxzo/SEPPH.pdf?rlkey=fp7gwaopjk17uyvgjxekxrh5v&dl=0Prof. Sepp Hochreiterhttps://www.nx-ai.com/https://x.com/hochreitersepphttps://scholar.google.at/citations?user=tvUH3WMAAAAJ&hl=enTOC:1. LLM Evolution and Reasoning Capabilities[00:00:00] 1.1 LLM Capabilities and Limitations Debate[00:03:16] 1.2 Program Generation and Reasoning in AI Systems[00:06:30] 1.3 Human vs AI Reasoning Comparison[00:09:59] 1.4 New Research Initiatives and Hybrid Approaches2. LSTM Technical Architecture[00:13:18] 2.1 LSTM Development History and Technical Background[00:20:38] 2.2 LSTM vs RNN Architecture and Computational Complexity[00:25:10] 2.3 xLSTM Architecture and Flash Attention Comparison[00:30:51] 2.4 Evolution of Gating Mechanisms from Sigmoid to Exponential3. Industrial Applications and Neuro-Symbolic AI[00:40:35] 3.1 Industrial Applications and Fixed Memory Advantages[00:42:31] 3.2 Neuro-Symbolic Integration and Pi AI Project[00:46:00] 3.3 Integration of Symbolic and Neural AI Approaches[00:51:29] 3.4 Evolution of AI Paradigms and System Thinking[00:54:55] 3.5 AI Reasoning and Human Intelligence Comparison[00:58:12] 3.6 NXAI Company and Industrial AI ApplicationsREFS:[00:00:15] Seminal LSTM paper establishing Hochreiter's expertise (Hochreiter & Schmidhuber)https://direct.mit.edu/neco/article-abstract/9/8/1735/6109/Long-Short-Term-Memory[00:04:20] Kolmogorov complexity and program composition limitations (Kolmogorov)https://link.springer.com/article/10.1007/BF02478259[00:07:10] Limitations of LLM mathematical reasoning and symbolic integration (Various Authors)https://www.arxiv.org/pdf/2502.03671[00:09:05] AlphaGo's Move 37 demonstrating creative AI (Google DeepMind)https://deepmind.google/research/breakthroughs/alphago/[00:10:15] New AI research lab in Zurich for fundamental LLM research (Benjamin Crouzier)https://tufalabs.ai[00:19:40] Introduction of xLSTM with exponential gating (Beck, Hochreiter, et al.)https://arxiv.org/abs/2405.04517[00:22:55] FlashAttention: fast & memory-efficient attention (Tri Dao et al.)https://arxiv.org/abs/2205.14135[00:31:00] Historical use of sigmoid/tanh activation in 1990s (James A. McCaffrey)https://visualstudiomagazine.com/articles/2015/06/01/alternative-activation-functions.aspx[00:36:10] Mamba 2 state space model architecture (Albert Gu et al.)https://arxiv.org/abs/2312.00752[00:46:00] Austria's Pi AI project integrating symbolic & neural AI (Hochreiter et al.)https://www.jku.at/en/institute-of-machine-learning/research/projects/[00:48:10] Neuro-symbolic integration challenges in language models (Diego Calanzone et al.)https://openreview.net/forum?id=7PGluppo4k[00:49:30] JKU Linz's historical and neuro-symbolic research (Sepp Hochreiter)https://www.jku.at/en/news-events/news/detail/news/bilaterale-ki-projekt-unter-leitung-der-jku-erhaelt-fwf-cluster-of-excellence/YT: https://www.youtube.com/watch?v=8u2pW2zZLCs

This week the Buddies discuss their favorite breakfasts, ruminate on how Orange Creamsicles would be farmed like cows, and give advice on dealing with an unfriendly friend. Share with a friend! Recommendations: West Coast Avengers (2024 comic), Dandadan (anime), AlphaGo (documentary), Castlevania Nocturne (Netflix show), Three Body Problem (Netflix show) Contact us: Facebook X Email Youtube

The 2020s have so far been marked by pandemic, war, and startling technological breakthroughs. Conversations around climate disaster, great-power conflict, and malicious AI are seemingly everywhere. It's enough to make anyone feel like the end might be near. Toby Ord has made it his mission to figure out just how close we are to catastrophe — and maybe not close at all!Ord is the author of the 2020 book, The Precipice: Existential Risk and the Future of Humanity. Back then, I interviewed Ord on the American Enterprise Institute's Political Economy podcast, and you can listen to that episode here. In 2024, he delivered his talk, The Precipice Revisited, in which he reassessed his outlook on the biggest threats facing humanity.Today on Faster, Please — The Podcast, Ord and I address the lessons of Covid, our risk of nuclear war, potential pathways for AI, and much more.Ord is a senior researcher at Oxford University. He has previously advised the UN, World Health Organization, World Economic Forum, and the office of the UK Prime Minister.In This Episode* Climate change (1:30)* Nuclear energy (6:14)* Nuclear war (8:00)* Pandemic (10:19)* Killer AI (15:07)* Artificial General Intelligence (21:01)Below is a lightly edited transcript of our conversation. Climate change (1:30). . . the two worst pathways, we're pretty clearly not on, and so that's pretty good news that we're kind of headed more towards one of the better pathways in terms of the emissions that we'll put out there.Pethokoukis: Let's just start out by taking a brief tour through the existential landscape and how you see it now versus when you first wrote the book The Precipice, which I've mentioned frequently in my writings. I love that book, love to see a sequel at some point, maybe one's in the works . . . but let's start with the existential risk, which has dominated many people's thinking for the past quarter-century, which is climate change.My sense is, not just you, but many people are somewhat less worried than they were five years ago, 10 years ago. Perhaps they see at least the most extreme outcomes less likely. How do you see it?Ord: I would agree with that. I'm not sure that everyone sees it that way, but there were two really big and good pieces of news on climate that were rarely reported in the media. One of them is that there's the question about how many emissions there'll be. We don't know how much carbon humanity will emit into the atmosphere before we get it under control, and there are these different emissions pathways, these RCP 4.5 and things like this you'll have heard of. And often, when people would give a sketch of how bad things could be, they would talk about RCP 8.5, which is the worst of these pathways, and we're very clearly not on that, and we're also, I think pretty clearly now, not on RCP 6, either. So the two worst pathways, we're pretty clearly not on, and so that's pretty good news that we're kind of headed more towards one of the better pathways in terms of the emissions that we'll put out there.What are we doing right?Ultimately, some of those pathways were based on business-as-usual ideas that there wouldn't be climate change as one of the biggest issues in the international political sphere over decades. So ultimately, nations have been switching over to renewables and low-carbon forms of power, which is good news. They could be doing it much more of it, but it's still good news. Back when we initially created these things, I think we would've been surprised and happy to find out that we were going to end up among the better two pathways instead of the worst ones.The other big one is that, as well as how much we'll admit, there's the question of how bad is it to have a certain amount of carbon in the atmosphere? In particular, how much warming does it produce? And this is something of which there's been massive uncertainty. The general idea is that we're trying to predict, if we were to double the amount of carbon in the atmosphere compared to pre-industrial times, how many degrees of warming would there be? The best guess since the year I was born, 1979, has been three degrees of warming, but the uncertainty has been somewhere between one and a half degrees and four and a half.Is that Celsius or Fahrenheit, by the way?This is all Celsius. The climate community has kept the same uncertainty from 1979 all the way up to 2020, and it's a wild level of uncertainty: Four and a half degrees of warming is three times one and a half degrees of warming, so the range is up to triple these levels of degrees of warming based on this amount of carbon. So massive uncertainty that hadn't changed over many decades.Now they've actually revised that and have actually brought in the range of uncertainty. Now they're pretty sure that it's somewhere between two and a half and four degrees, and this is based on better understanding of climate feedbacks. This is good news if you're concerned about worst-case climate change. It's saying it's closer to the central estimate than we'd previously thought, whereas previously we thought that there was a pretty high chance that it could even be higher than four and a half degrees of warming.When you hear these targets of one and a half degrees of warming or two degrees of warming, they sound quite precise, but in reality, we were just so uncertain of how much warming would follow from any particular amount of emissions that it was very hard to know. And that could mean that things are better than we'd thought, but it could also mean things could be much worse. And if you are concerned about existential risks from climate change, then those kind of tail events where it's much worse than we would've thought the things would really get, and we're now pretty sure that we're not on one of those extreme emissions pathways and also that we're not in a world where the temperature is extremely sensitive to those emissions.Nuclear energy (6:14)Ultimately, when it comes to the deaths caused by different power sources, coal . . . killed many more people than nuclear does — much, much more . . .What do you make of this emerging nuclear power revival you're seeing across Europe, Asia, and in the United States? At least the United States it's partially being driven by the need for more power for these AI data centers. How does it change your perception of risk in a world where many rich countries, or maybe even not-so-rich countries, start re-embracing nuclear energy?In terms of the local risks with the power plants, so risks of meltdown or other types of harmful radiation leak, I'm not too concerned about that. Ultimately, when it comes to the deaths caused by different power sources, coal, even setting aside global warming, just through particulates being produced in the soot, killed many more people than nuclear does — much, much more, and so nuclear is a pretty safe form of energy production as it happens, contrary to popular perception. So I'm in favor of that. But the proliferation concerns, if it is countries that didn't already have nuclear power, then the possibility that they would be able to use that to start a weapons program would be concerning.And as sort of a mechanism for more clean energy. Do you view nuclear as clean energy?Yes, I think so. It's certainly not carbon-producing energy. I think that it has various downsides, including the difficulty of knowing exactly what to do with the fuel, that will be a very long lasting problem. But I think it's become clear that the problems caused by other forms of energy are much larger and we should switch to the thing that has fewer problems, rather than more problems.Nuclear war (8:00)I do think that the Ukraine war, in particular, has created a lot of possible flashpoints.I recently finished a book called Nuclear War: A Scenario, which is kind of a minute-by-minute look at how a nuclear war could break out. If you read the book, the book is terrifying because it really goes into a lot of — and I live near Washington DC, so when it gives its various scenarios, certainly my house is included in the blast zone, so really a frightening book. But when it tried to explain how a war would start, I didn't find it a particularly compelling book. The scenarios for actually starting a conflict, I didn't think sounded particularly realistic.Do you feel — and obviously we have Russia invade Ukraine and loose talk by Vladimir Putin about nuclear weapons — do you feel more or less confident that we'll avoid a nuclear war than you did when you wrote the book?Much less confident, actually. I guess I should say, when I wrote the book, it came out in 2020, I finished the writing in 2019, and ultimately we were in a time of relatively low nuclear risk, and I feel that the risk has risen. That said, I was trying to provide estimates for the risk over the next hundred years, and so I wasn't assuming that the low-risk period would continue indefinitely, but it was quite a shock to end up so quickly back in this period of heightened tensions and threats of nuclear escalation, the type of thing I thought was really from my parents' generation. So yes, I do think that the Ukraine war, in particular, has created a lot of possible flashpoints. That said, the temperature has come down on the conversation in the last year, so that's something.Of course, the conversation might heat right back up if we see a Chinese invasion of Taiwan. I've been very bullish about the US economy and world economy over the rest of this decade, but the exception is as long as we don't have a war with China, from an economic point of view, but certainly also a nuclear point of view. Two nuclear armed powers in conflict? That would not be an insignificant event from the existential-risk perspective.It is good that China has a smaller nuclear arsenal than the US or Russia, but there could easily be a great tragedy.Pandemic (10:19)Overall, a lot of countries really just muddled through not very well, and the large institutions that were supposed to protect us from these things, like the CDC and the WHO, didn't do a great job either.The book comes out during the pandemic. Did our response to the pandemic make you more or less confident in our ability and willingness to confront that kind of outbreak? The worst one that saw in a hundred years?Yeah, overall, it made me much less confident. There'd been general thought by those who look at these large catastrophic risks that when the chips are down and the threat is imminent, that people will see it and will band together and put a lot of effort into it; that once you see the asteroid in your telescope and it's headed for you, then things will really get together — a bit like in the action movies or what have you.That's where I take my cue from, exactly.And with Covid, it was kind of staring us in the face. Those of us who followed these things closely were quite alarmed a long time before the national authorities were. Overall, a lot of countries really just muddled through not very well, and the large institutions that were supposed to protect us from these things, like the CDC and the WHO, didn't do a great job either. That said, scientists, particularly developing RNA vaccines, did better than I expected.In the years leading up to the pandemic, certainly we'd seen other outbreaks, they'd had the avian flu outbreak, and you know as well as I do, there were . . . how many white papers or scenario-planning exercises for just this sort of event. I think I recall a story where, in 2018, Bill Gates had a conversation with President Trump during his first term about the risk of just such an outbreak. So it's not as if this thing came out of the blue. In many ways we saw the asteroid, it was just pretty far away. But to me, that says something again about as humans, our ability to deal with severe, but infrequent, risks.And obviously, not having a true global, nasty outbreak in a hundred years, where should we focus our efforts? On preparation? Making sure we have enough ventilators? Or our ability to respond? Because it seems like the preparation route will only go so far, and the reason it wasn't a much worse outbreak is because we have a really strong ability to respond.I'm not sure if it's the same across all risks as to how preparation versus ability to respond, which one is better. In some risks, there's also other possibilities like avoiding an outbreak, say, an accidental outbreak happening at all, or avoiding a nuclear war starting and not needing to actually respond at all. I'm not sure if there's an overall rule as to which one was better.Do you have an opinion on the outbreak of Covid?I don't know whether it was a lab leak. I think it's a very plausible hypothesis, but plausible doesn't mean it's proven.And does the post-Covid reaction, at least in the United States, to vaccines, does that make you more or less confident in our ability to deal with . . . the kind of societal cohesion and confidence to tackle a big problem, to have enough trust? Maybe our leaders don't deserve that trust, but what do you make from this kind of pushback against vaccines and — at least in the United States — our medical authorities?When Covid was first really striking Europe and America, it was generally thought that, while China was locking down the Wuhan area, that Western countries wouldn't be able to lock down, that it wasn't something that we could really do, but then various governments did order lockdowns. That said, if you look at the data on movement of citizens, it turns out that citizens stopped moving around prior to the lockdowns, so the lockdown announcements were more kind of like the tail, rather than the dog.But over time, citizens wanted to kind of get back out and interact more, and the rules were preventing them, and if a large fraction of the citizens were under something like house arrest for the better part of a year, would that lead to some fairly extreme resentment and some backlash, some of which was fairly irrational? Yeah, that is actually exactly the kind of thing that you would expect. It was very difficult to get a whole lot of people to row together and take the same kind of response that we needed to coordinate the response to prevent the spread, and pushing for that had some of these bad consequences, which are also going to make it harder for next time. We haven't exactly learned the right lessons.Killer AI (15:07)If we make things that are smarter than us and are not inherently able to control their values or give them moral rules to work within, then we should expect them to ultimately be calling the shots.We're more than halfway through our chat and now we're going to get to the topic probably most people would like to hear about: After the robots take our jobs, are they going to kill us? What do you think? What is your concern about AI risk?I'm quite concerned about it. Ultimately, when I wrote my book, I put AI risk as the biggest existential risk, albeit the most uncertain, as well, and I would still say that. That said, some things have gotten better since then.I would assume what makes you less confident is one, what seems to be the rapid advance — not just the rapid advance of the technology, but you have the two leading countries in a geopolitical globalization also being the leaders in the technology and not wanting to slow it down. I would imagine that would make you more worried that we will move too quickly. What would make you more confident that we would avoid any serious existential downsides?I agree with your supposition that the attempts by the US and China to turn this into some kind of arms race are quite concerning. But here are a few things: Back when I was writing the book, the leading AI systems with things like AlphaGo, if you remember that, or the Atari plane systems.Quaint. Quite quaint.It was very zero-sum, reinforcement-learning-based game playing, where these systems were learning directly to behave adversarially to other systems, and they could only understand the kind of limited aspect about the world, and struggle, and overcoming your adversary. That was really all they could do, and the idea of teaching them about ethics, or how to treat people, and the diversity of human values seemed almost impossible: How do you tell a chess program about that?But then what we've ended up with is systems that are not inherently agents, they're not inherently trying to maximize something. Rather, you ask them questions and they blurt out some answers. These systems have read more books on ethics and moral philosophy than I have, and they've read all kinds of books about the human condition. Almost all novels that have ever been published, and pretty much every page of every novel involves people judging the actions of other people and having some kind of opinions about them, and so there's a huge amount of data about human values, and how we think about each other, and what's inappropriate behavior. And if you ask the systems about these things, they're pretty good at judging whether something's inappropriate behavior, if you describe it.The real challenge remaining is to get them to care about that, but at least the knowledge is in the system, and that's something that previously seemed extremely difficult to do. Also, these systems, there are versions that do reasoning and that spend longer with a private text stream where they think — it's kind of like sub-vocalizing thoughts to themselves before they answer. When they do that, these systems are thinking in plain English, and that's something that we really didn't expect. If you look at all of the weights of a neural network, it's quite inscrutable, famously difficult to know what it's doing, but somehow we've ended up with systems that are actually thinking in English and where that could be inspected by some oversight process. There are a number of ways in which things are better than I'd feared.So what is your actual existential risk scenario look like? This is what you're most concerned about happening with AI.I think it's quite hard to be all that concrete on it at the moment, partly because things change so quickly. I don't think that there's going to be some kind of existential catastrophe from AI in the next couple of years, partly because the current systems require so much compute in order to run them that they can only be run at very specialized and large places, of which there's only a few in the world. So that means the possibility that they break out and copy themselves into other systems is not really there, in which case, the possibility of turning them off is much possible as well.Also, they're not yet intelligent enough to be able to execute a lengthy plan. If you have some kind of complex task for them, that requires, say, 10 steps — for example, booking a flight on the internet by clicking through all of the appropriate pages, and finding out when the times are, and managing to book your ticket, and fill in the special codes they sent to your email, and things like that. That's a somewhat laborious task and the systems can't do things like that yet. There's still the case that, even if they've got a, say, 90 percent chance of completing any particular step, that the 10 percent chances of failure add up, and eventually it's likely to fail somewhere along the line and not be able to recover. They'll probably get better at that, but at the moment, the inability to actually execute any complex plans does provide some safety.Ultimately, the concern is that, at a more abstract level, we're building systems which are smarter than us at many things, and we're attempting to make them much more general and to be smarter than us across the board. If you know that one player is a better chess player than another, suppose Magnus Carlsen's playing me at chess, I can't predict exactly how he's going to beat me, but I can know with quite high likelihood that he will end up beating me. I'll end up in checkmate, even though I don't know what moves will happen in between here and there, and I think that it's similar with AI systems. If we make things that are smarter than us and are not inherently able to control their values or give them moral rules to work within, then we should expect them to ultimately be calling the shots.Artificial General Intelligence (21:01)Ultimately, existential risks are global public goods problems.I frequently check out the Metaculus online prediction platform, and I think currently on that platform, 2027 for what they would call “weak AGI,” artificial general intelligence — a date which has moved up two months in the past week as we're recording this, and then I think 2031 also has accelerated for “strong AGI,” so this is pretty soon, 2027 or 2031, quite soon. Is that kind of what you're assuming is going to happen, that we're going to have to deal with very powerful technologies quite quickly?Yeah, I think that those are good numbers for the typical case, what you should be expecting. I think that a lot of people wouldn't be shocked if it turns out that there is some kind of obstacle that slows down progress and takes longer before it gets overcome, but it's also wouldn't be surprising at this point if there are no more big obstacles and it's just a matter of scaling things up and doing fairly simple processes to get it to work.It's now a multi-billion dollar industry, so there's a lot of money focused on ironing out any kinks or overcoming any obstacles on the way. So I expect it to move pretty quickly and those timelines sound very realistic. Maybe even sooner.When you wrote the book, what did you put as the risk to human existence over the next a hundred years, and what is it now?When I wrote the book, I thought it was about one in six.So it's still one in six . . . ?Yeah, I think that's still about right, and I would say that most of that is coming from AI.This isn't, I guess, a specific risk, but, to the extent that being positive about our future means also being positive on our ability to work together, countries working together, what do you make of society going in the other direction where we seem more suspicious of other countries, or more even — in the United States — more suspicious of our allies, more suspicious of international agreements, whether they're trade or military alliances. To me, I would think that the Age of Globalization would've, on net, lowered that risk to one in six, and if we're going to have less globalization, to me, that would tend to increase that risk.That could be right. Certainly increased suspicion, to the point of paranoia or cynicism about other nations and their ability to form deals on these things, is not going to be helpful at all. Ultimately, existential risks are global public goods problems. This continued functioning of human civilization is this global public good and existential risk is the opposite. And so these are things where, one way to look at it is that the US has about four percent of the world's people, so one in 25 people live in the US, and so an existential risk is hitting 25 times as many people as. So if every country is just interested in themself, they'll undervalue it by a factor of 25 or so, and the countries need to work together in order to overcome that kind of problem. Ultimately, if one of us falls victim to these risks, then we all do, and so it definitely does call out for international cooperation. And I think that it has a strong basis for international cooperation. It is in all of our interests. There are also verification possibilities and so on, and I'm actually quite optimistic about treaties and other ways to move forward.On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were PromisedMicro Reads▶ Economics* Tech tycoons have got the economics of AI wrong - Economist* Progress in Artificial Intelligence and its Determinants - Arxiv* The role of personality traits in shaping economic returns amid technological change - CEPR▶ Business* Tech CEOs try to reassure Wall Street after DeepSeek shock - Wapo* DeepSeek Calls for Deep Breaths From Big Tech Over Earnings - Bberg Opinion* Apple's AI Moment Is Still a Ways Off - WSJ* Bill Gates Isn't Like Those Other Tech Billionaires - NYT* OpenAI's Sam Altman and SoftBank's Masayoshi Son Are AI's New Power Couple - WSJ* SoftBank Said to Be in Talks to Invest as Much as $25 Billion in OpenAI - NYT* Microsoft sheds $200bn in market value after cloud sales disappoint - FT▶ Policy/Politics* ‘High anxiety moment': Biden's NIH chief talks Trump 2.0 and the future of US science - Nature* Government Tech Workers Forced to Defend Projects to Random Elon Musk Bros - Wired* EXCLUSIVE: NSF starts vetting all grants to comply with Trump's orders - Science* Milei, Modi, Trump: an anti-red-tape revolution is under way - Economist* FDA Deregulation of E-Cigarettes Saved Lives and Spurred Innovation - Marginal Revolution* Donald Trump revives ideas of a Star Wars-like missile shield - Economist▶ AI/Digital* Is DeepSeek Really a Threat? - PS* ChatGPT vs. Claude vs. DeepSeek: The Battle to Be My AI Work Assistant - WSJ* OpenAI teases “new era” of AI in US, deepens ties with government - Ars* AI's Power Requirements Under Exponential Growth - Rand* How DeepSeek Took a Chunk Out of Big AI - Bberg* DeepSeek poses a challenge to Beijing as much as to Silicon Valley - Economist▶ Biotech/Health* Creatine shows promise for treating depression - NS* FDA approves new, non-opioid painkiller Journavx - Wapo▶ Clean Energy/Climate* Another Boffo Energy Forecast, Just in Time for DeepSeek - Heatmap News* Column: Nuclear revival puts uranium back in the critical spotlight - Mining* A Michigan nuclear plant is slated to restart, but Trump could complicate things - Grist▶ Robotics/AVs* AIs and Robots Should Sound Robotic - IEEE Spectrum* Robot beauticians touch down in California - FT Opinion▶ Space/Transportation* A Flag on Mars? Maybe Not So Soon. - NYT* Asteroid triggers global defence plan amid chance of collision with Earth in 2032 - The Guardian* Lurking Inside an Asteroid: Life's Ingredients - NYT▶ Up Wing/Down Wing* An Ancient 'Lost City' Is Uncovered in Mexico - NYT* Reflecting on Rome, London and Chicago after the Los Angeles fires - Wapo Opinion▶ Substacks/Newsletters* I spent two days testing DeepSeek R1 - Understanding AI* China's Technological Advantage -overlapping tech-industrial ecosystems - AI Supremacy* The state of decarbonization in five charts - Exponential View* The mistake of the century - Slow Boring* The Child Penalty: An International View - Conversable Economist* Deep Deepseek History and Impact on the Future of AI - next BIG futureFaster, Please! is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. 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Liang Wenfeng è nato in una città del Guangdong negli anni ‘80. è un balinghou, come vengono chiamate in Cina le persone nate in quegli anni. È lui che ha fondato DeepSeek, di cui ha plasmato gli aspetti tecnici e quelli comunicativi. Liang è il risultato degli investimenti cinesi in AI da molti anni a oggi. Ed è anche un incredibile idealista.  . Fonti: le fonti audio di questa puntata sono tratte da: 1957: Sputnik I, canale YouTube International Astronautical Federation, 16 aprile 2008; AlphaGo 3-0 Lee Sedol, AlphaGo wins DeepMind Challenge, canale YouTube SciNews, 12 marzo 2016; 中国AI鲶鱼DeepSeek创始人梁文峰：中国要从技术“搭便车”转向技术贡献者|中国缺的不是资本，而是信心和有效组织高密度人才的能力|AGI有望2-10年内实现, Bilibili, 22 gennaio 2025. Learn more about your ad choices. Visit megaphone.fm/adchoices

¿Pueden los modelos pequeños mostrar capacidades de razonamiento matemático comparables a o1? En Microsoft creen que sí y nos lo demuestran con un método inspirado en AlphaGo, el sistema que venció a Lee Sedol hace ya casi una década. Hoy en la tertulia vemos modelos de lenguaje pequeños que superan a o1. Participan en la tertulia: Paco Zamora, Íñigo Olcoz, Carlos Larríu, Íñigo Orbegozo y Guillermo Barbadillo. Recuerda que puedes enviarnos dudas, comentarios y sugerencias en: https://twitter.com/TERTUL_ia Más info en: https://ironbar.github.io/tertulia_inteligencia_artificial/

Ioannis Antonoglou, founding engineer at DeepMind and co-founder of ReflectionAI, has seen the triumphs of reinforcement learning firsthand. From AlphaGo to AlphaZero and MuZero, Ioannis has built the most powerful agents in the world. Ioannis breaks down key moments in AlphaGo's game against Lee Sodol (Moves 37 and 78), the importance of self-play and the impact of scale, reliability, planning and in-context learning as core factors that will unlock the next level of progress in AI. Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital Mentioned in this episode: PPO: Proximal Policy Optimization algorithm developed by DeepMind in game environments. Also used by OpenAI for RLHF in ChatGPT. MuJoCo: Open source physics engine used to develop PPO Monte Carlo Tree Search: Heuristic search algorithm used in AlphaGo as well as video compression for YouTube and the self-driving system at Tesla AlphaZero: The DeepMind model that taught itself from scratch how to master the games of chess, shogi and Go MuZero: The DeepMind follow up to AlphaZero that mastered games without knowing the rules and able to plan winning strategies in unknown environments AlphaChem: Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies DQN: Deep Q-Network, Introduced in 2013 paper, Playing Atari with Deep Reinforcement Learning AlphaFold: DeepMind model for predicting protein structures for which Demis Hassabis, John Jumper and David Baker won the 2024 Nobel Prize in Chemistry

In this episode, Warren Grange, named one of LinkedIn's Top 100 AI Creatives, discusses the intersection of creativity and artificial intelligence. He shares his journey from being inspired by early AI like AlphaGo to becoming an advocate for integrating AI tools in creative processes. Warren emphasizes the importance of adopting AI to maintain a competitive edge and the potential for these technologies to enhance human expertise. We also explore Warren's vision for the future of creativity, including how AI will transform the 3D landscape and storytelling. From discussing the challenges of human anatomy in AI-generated visuals to emphasizing the importance of originality, Warren provides actionable advice for artists, filmmakers, and educators navigating this rapidly evolving space.

One last Gold sponsor slot is available for the AI Engineer Summit in NYC. Our last round of invites is going out soon - apply here - If you are building AI agents or AI eng teams, this will be the single highest-signal conference of the year for you!While the world melts down over DeepSeek, few are talking about the OTHER notable group of former hedge fund traders who pivoted into AI and built a remarkably profitable consumer AI business with a tiny team with incredibly cracked engineering team — Chai Research. In short order they have:* Started a Chat AI company well before Noam Shazeer started Character AI, and outlasted his departure.* Crossed 1m DAU in 2.5 years - William updates us on the pod that they've hit 1.4m DAU now, another +40% from a few months ago. Revenue crossed >$22m. * Launched the Chaiverse model crowdsourcing platform - taking 3-4 week A/B testing cycles down to 3-4 hours, and deploying >100 models a week.While they're not paying million dollar salaries, you can tell they're doing pretty well for an 11 person startup:The Chai Recipe: Building infra for rapid evalsRemember how the central thesis of LMarena (formerly LMsys) is that the only comprehensive way to evaluate LLMs is to let users try them out and pick winners?At the core of Chai is a mobile app that looks like Character AI, but is actually the largest LLM A/B testing arena in the world, specialized on retaining chat users for Chai's usecases (therapy, assistant, roleplay, etc). It's basically what LMArena would be if taken very, very seriously at one company (with $1m in prizes to boot):Chai publishes occasional research on how they think about this, including talks at their Palo Alto office:William expands upon this in today's podcast (34 mins in):Fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours.In Crowdsourcing the leap to Ten Trillion-Parameter AGI, William describes Chai's routing as a recommender system, which makes a lot more sense to us than previous pitches for model routing startups:William is notably counter-consensus in a lot of his AI product principles:* No streaming: Chats appear all at once to allow rejection sampling* No voice: Chai actually beat Character AI to introducing voice - but removed it after finding that it was far from a killer feature.* Blending: “Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model.” (that's it!)But chief above all is the recommender system.We also referenced Exa CEO Will Bryk's concept of SuperKnowlege:Full Video versionOn YouTube. please like and subscribe!Timestamps* 00:00:04 Introductions and background of William Beauchamp* 00:01:19 Origin story of Chai AI* 00:04:40 Transition from finance to AI* 00:11:36 Initial product development and idea maze for Chai* 00:16:29 User psychology and engagement with AI companions* 00:20:00 Origin of the Chai name* 00:22:01 Comparison with Character AI and funding challenges* 00:25:59 Chai's growth and user numbers* 00:34:53 Key inflection points in Chai's growth* 00:42:10 Multi-modality in AI companions and focus on user-generated content* 00:46:49 Chaiverse developer platform and model evaluation* 00:51:58 Views on AGI and the nature of AI intelligence* 00:57:14 Evaluation methods and human feedback in AI development* 01:02:01 Content creation and user experience in Chai* 01:04:49 Chai Grant program and company culture* 01:07:20 Inference optimization and compute costs* 01:09:37 Rejection sampling and reward models in AI generation* 01:11:48 Closing thoughts and recruitmentTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and today we're in the Chai AI office with my usual co-host, Swyx.swyx [00:00:14]: Hey, thanks for having us. It's rare that we get to get out of the office, so thanks for inviting us to your home. We're in the office of Chai with William Beauchamp. Yeah, that's right. You're founder of Chai AI, but previously, I think you're concurrently also running your fund?William [00:00:29]: Yep, so I was simultaneously running an algorithmic trading company, but I fortunately was able to kind of exit from that, I think just in Q3 last year. Yeah, congrats. Yeah, thanks.swyx [00:00:43]: So Chai has always been on my radar because, well, first of all, you do a lot of advertising, I guess, in the Bay Area, so it's working. Yep. And second of all, the reason I reached out to a mutual friend, Joyce, was because I'm just generally interested in the... ...consumer AI space, chat platforms in general. I think there's a lot of inference insights that we can get from that, as well as human psychology insights, kind of a weird blend of the two. And we also share a bit of a history as former finance people crossing over. I guess we can just kind of start it off with the origin story of Chai.William [00:01:19]: Why decide working on a consumer AI platform rather than B2B SaaS? So just quickly touching on the background in finance. Sure. Originally, I'm from... I'm from the UK, born in London. And I was fortunate enough to go study economics at Cambridge. And I graduated in 2012. And at that time, everyone in the UK and everyone on my course, HFT, quant trading was really the big thing. It was like the big wave that was happening. So there was a lot of opportunity in that space. And throughout college, I'd sort of played poker. So I'd, you know, I dabbled as a professional poker player. And I was able to accumulate this sort of, you know, say $100,000 through playing poker. And at the time, as my friends would go work at companies like ChangeStreet or Citadel, I kind of did the maths. And I just thought, well, maybe if I traded my own capital, I'd probably come out ahead. I'd make more money than just going to work at ChangeStreet.swyx [00:02:20]: With 100k base as capital?William [00:02:22]: Yes, yes. That's not a lot. Well, it depends what strategies you're doing. And, you know, there is an advantage. There's an advantage to being small, right? Because there are, if you have a 10... Strategies that don't work in size. Exactly, exactly. So if you have a fund of $10 million, if you find a little anomaly in the market that you might be able to make 100k a year from, that's a 1% return on your 10 million fund. If your fund is 100k, that's 100% return, right? So being small, in some sense, was an advantage. So started off, and the, taught myself Python, and machine learning was like the big thing as well. Machine learning had really, it was the first, you know, big time machine learning was being used for image recognition, neural networks come out, you get dropout. And, you know, so this, this was the big thing that's going on at the time. So I probably spent my first three years out of Cambridge, just building neural networks, building random forests to try and predict asset prices, right, and then trade that using my own money. And that went well. And, you know, if you if you start something, and it goes well, you You try and hire more people. And the first people that came to mind was the talented people I went to college with. And so I hired some friends. And that went well and hired some more. And eventually, I kind of ran out of friends to hire. And so that was when I formed the company. And from that point on, we had our ups and we had our downs. And that was a whole long story and journey in itself. But after doing that for about eight or nine years, on my 30th birthday, which was four years ago now, I kind of took a step back to just evaluate my life, right? This is what one does when one turns 30. You know, I just heard it. I hear you. And, you know, I looked at my 20s and I loved it. It was a really special time. I was really lucky and fortunate to have worked with this amazing team, been successful, had a lot of hard times. And through the hard times, learned wisdom and then a lot of success and, you know, was able to enjoy it. And so the company was making about five million pounds a year. And it was just me and a team of, say, 15, like, Oxford and Cambridge educated mathematicians and physicists. It was like the real dream that you'd have if you wanted to start a quant trading firm. It was like...swyx [00:04:40]: Your own, all your own money?William [00:04:41]: Yeah, exactly. It was all the team's own money. We had no customers complaining to us about issues. There's no investors, you know, saying, you know, they don't like the risk that we're taking. We could. We could really run the thing exactly as we wanted it. It's like Susquehanna or like Rintec. Yeah, exactly. Yeah. And they're the companies that we would kind of look towards as we were building that thing out. But on my 30th birthday, I look and I say, OK, great. This thing is making as much money as kind of anyone would really need. And I thought, well, what's going to happen if we keep going in this direction? And it was clear that we would never have a kind of a big, big impact on the world. We can enrich ourselves. We can make really good money. Everyone on the team would be paid very, very well. Presumably, I can make enough money to buy a yacht or something. But this stuff wasn't that important to me. And so I felt a sort of obligation that if you have this much talent and if you have a talented team, especially as a founder, you want to be putting all that talent towards a good use. I looked at the time of like getting into crypto and I had a really strong view on crypto, which was that as far as a gambling device. This is like the most fun form of gambling invented in like ever super fun, I thought as a way to evade monetary regulations and banking restrictions. I think it's also absolutely amazing. So it has two like killer use cases, not so much banking the unbanked, but everything else, but everything else to do with like the blockchain and, and you know, web, was it web 3.0 or web, you know, that I, that didn't, it didn't really make much sense. And so instead of going into crypto, which I thought, even if I was successful, I'd end up in a lot of trouble. I thought maybe it'd be better to build something that governments wouldn't have a problem with. I knew that LLMs were like a thing. I think opening. I had said they hadn't released GPT-3 yet, but they'd said GPT-3 is so powerful. We can't release it to the world or something. Was it GPT-2? And then I started interacting with, I think Google had open source, some language models. They weren't necessarily LLMs, but they, but they were. But yeah, exactly. So I was able to play around with, but nowadays so many people have interacted with the chat GPT, they get it, but it's like the first time you, you can just talk to a computer and it talks back. It's kind of a special moment and you know, everyone who's done that goes like, wow, this is how it should be. Right. It should be like, rather than having to type on Google and search, you should just be able to ask Google a question. When I saw that I read the literature, I kind of came across the scaling laws and I think even four years ago. All the pieces of the puzzle were there, right? Google had done this amazing research and published, you know, a lot of it. Open AI was still open. And so they'd published a lot of their research. And so you really could be fully informed on, on the state of AI and where it was going. And so at that point I was confident enough, it was worth a shot. I think LLMs are going to be the next big thing. And so that's the thing I want to be building in, in that space. And I thought what's the most impactful product I can possibly build. And I thought it should be a platform. So I myself love platforms. I think they're fantastic because they open up an ecosystem where anyone can contribute to it. Right. So if you think of a platform like a YouTube, instead of it being like a Hollywood situation where you have to, if you want to make a TV show, you have to convince Disney to give you the money to produce it instead, anyone in the world can post any content they want to YouTube. And if people want to view it, the algorithm is going to promote it. Nowadays. You can look at creators like Mr. Beast or Joe Rogan. They would have never have had that opportunity unless it was for this platform. Other ones like Twitter's a great one, right? But I would consider Wikipedia to be a platform where instead of the Britannica encyclopedia, which is this, it's like a monolithic, you get all the, the researchers together, you get all the data together and you combine it in this, in this one monolithic source. Instead. You have this distributed thing. You can say anyone can host their content on Wikipedia. Anyone can contribute to it. And anyone can maybe their contribution is they delete stuff. When I was hearing like the kind of the Sam Altman and kind of the, the Muskian perspective of AI, it was a very kind of monolithic thing. It was all about AI is basically a single thing, which is intelligence. Yeah. Yeah. The more intelligent, the more compute, the more intelligent, and the more and better AI researchers, the more intelligent, right? They would speak about it as a kind of erased, like who can get the most data, the most compute and the most researchers. And that would end up with the most intelligent AI. But I didn't believe in any of that. I thought that's like the total, like I thought that perspective is the perspective of someone who's never actually done machine learning. Because with machine learning, first of all, you see that the performance of the models follows an S curve. So it's not like it just goes off to infinity, right? And the, the S curve, it kind of plateaus around human level performance. And you can look at all the, all the machine learning that was going on in the 2010s, everything kind of plateaued around the human level performance. And we can think about the self-driving car promises, you know, how Elon Musk kept saying the self-driving car is going to happen next year, it's going to happen next, next year. Or you can look at the image recognition, the speech recognition. You can look at. All of these things, there was almost nothing that went superhuman, except for something like AlphaGo. And we can speak about why AlphaGo was able to go like super superhuman. So I thought the most likely thing was going to be this, I thought it's not going to be a monolithic thing. That's like an encyclopedia Britannica. I thought it must be a distributed thing. And I actually liked to look at the world of finance for what I think a mature machine learning ecosystem would look like. So, yeah. So finance is a machine learning ecosystem because all of these quant trading firms are running machine learning algorithms, but they're running it on a centralized platform like a marketplace. And it's not the case that there's one giant quant trading company of all the data and all the quant researchers and all the algorithms and compute, but instead they all specialize. So one will specialize on high frequency training. Another will specialize on mid frequency. Another one will specialize on equity. Another one will specialize. And I thought that's the way the world works. That's how it is. And so there must exist a platform where a small team can produce an AI for a unique purpose. And they can iterate and build the best thing for that, right? And so that was the vision for Chai. So we wanted to build a platform for LLMs.Alessio [00:11:36]: That's kind of the maybe inside versus contrarian view that led you to start the company. Yeah. And then what was maybe the initial idea maze? Because if somebody told you that was the Hugging Face founding story, people might believe it. It's kind of like a similar ethos behind it. How did you land on the product feature today? And maybe what were some of the ideas that you discarded that initially you thought about?William [00:11:58]: So the first thing we built, it was fundamentally an API. So nowadays people would describe it as like agents, right? But anyone could write a Python script. They could submit it to an API. They could send it to the Chai backend and we would then host this code and execute it. So that's like the developer side of the platform. On their Python script, the interface was essentially text in and text out. An example would be the very first bot that I created. I think it was a Reddit news bot. And so it would first, it would pull the popular news. Then it would prompt whatever, like I just use some external API for like Burr or GPT-2 or whatever. Like it was a very, very small thing. And then the user could talk to it. So you could say to the bot, hi bot, what's the news today? And it would say, this is the top stories. And you could chat with it. Now four years later, that's like perplexity or something. That's like the, right? But back then the models were first of all, like really, really dumb. You know, they had an IQ of like a four year old. And users, there really wasn't any demand or any PMF for interacting with the news. So then I was like, okay. Um. So let's make another one. And I made a bot, which was like, you could talk to it about a recipe. So you could say, I'm making eggs. Like I've got eggs in my fridge. What should I cook? And it'll say, you should make an omelet. Right. There was no PMF for that. No one used it. And so I just kept creating bots. And so every single night after work, I'd be like, okay, I like, we have AI, we have this platform. I can create any text in textile sort of agent and put it on the platform. And so we just create stuff night after night. And then all the coders I knew, I would say, yeah, this is what we're going to do. And then I would say to them, look, there's this platform. You can create any like chat AI. You should put it on. And you know, everyone's like, well, chatbots are super lame. We want absolutely nothing to do with your chatbot app. No one who knew Python wanted to build on it. I'm like trying to build all these bots and no consumers want to talk to any of them. And then my sister who at the time was like just finishing college or something, I said to her, I was like, if you want to learn Python, you should just submit a bot for my platform. And she, she built a therapy for me. And I was like, okay, cool. I'm going to build a therapist bot. And then the next day I checked the performance of the app and I'm like, oh my God, we've got 20 active users. And they spent, they spent like an average of 20 minutes on the app. I was like, oh my God, what, what bot were they speaking to for an average of 20 minutes? And I looked and it was the therapist bot. And I went, oh, this is where the PMF is. There was no demand for, for recipe help. There was no demand for news. There was no demand for dad jokes or pub quiz or fun facts or what they wanted was they wanted the therapist bot. the time I kind of reflected on that and I thought, well, if I want to consume news, the most fun thing, most fun way to consume news is like Twitter. It's not like the value of there being a back and forth, wasn't that high. Right. And I thought if I need help with a recipe, I actually just go like the New York times has a good recipe section, right? It's not actually that hard. And so I just thought the thing that AI is 10 X better at is a sort of a conversation right. That's not intrinsically informative, but it's more about an opportunity. You can say whatever you want. You're not going to get judged. If it's 3am, you don't have to wait for your friend to text back. It's like, it's immediate. They're going to reply immediately. You can say whatever you want. It's judgment-free and it's much more like a playground. It's much more like a fun experience. And you could see that if the AI gave a person a compliment, they would love it. It's much easier to get the AI to give you a compliment than a human. From that day on, I said, okay, I get it. Humans want to speak to like humans or human like entities and they want to have fun. And that was when I started to look less at platforms like Google. And I started to look more at platforms like Instagram. And I was trying to think about why do people use Instagram? And I could see that I think Chai was, was filling the same desire or the same drive. If you go on Instagram, typically you want to look at the faces of other humans, or you want to hear about other people's lives. So if it's like the rock is making himself pancakes on a cheese plate. You kind of feel a little bit like you're the rock's friend, or you're like having pancakes with him or something, right? But if you do it too much, you feel like you're sad and like a lonely person, but with AI, you can talk to it and tell it stories and tell you stories, and you can play with it for as long as you want. And you don't feel like you're like a sad, lonely person. You feel like you actually have a friend.Alessio [00:16:29]: And what, why is that? Do you have any insight on that from using it?William [00:16:33]: I think it's just the human psychology. I think it's just the idea that, with old school social media. You're just consuming passively, right? So you'll just swipe. If I'm watching TikTok, just like swipe and swipe and swipe. And even though I'm getting the dopamine of like watching an engaging video, there's this other thing that's building my head, which is like, I'm feeling lazier and lazier and lazier. And after a certain period of time, I'm like, man, I just wasted 40 minutes. I achieved nothing. But with AI, because you're interacting, you feel like you're, it's not like work, but you feel like you're participating and contributing to the thing. You don't feel like you're just. Consuming. So you don't have a sense of remorse basically. And you know, I think on the whole people, the way people talk about, try and interact with the AI, they speak about it in an incredibly positive sense. Like we get people who say they have eating disorders saying that the AI helps them with their eating disorders. People who say they're depressed, it helps them through like the rough patches. So I think there's something intrinsically healthy about interacting that TikTok and Instagram and YouTube doesn't quite tick. From that point on, it was about building more and more kind of like human centric AI for people to interact with. And I was like, okay, let's make a Kanye West bot, right? And then no one wanted to talk to the Kanye West bot. And I was like, ah, who's like a cool persona for teenagers to want to interact with. And I was like, I was trying to find the influencers and stuff like that, but no one cared. Like they didn't want to interact with the, yeah. And instead it was really just the special moment was when we said the realization that developers and software engineers aren't interested in building this sort of AI, but the consumers are right. And rather than me trying to guess every day, like what's the right bot to submit to the platform, why don't we just create the tools for the users to build it themselves? And so nowadays this is like the most obvious thing in the world, but when Chai first did it, it was not an obvious thing at all. Right. Right. So we took the API for let's just say it was, I think it was GPTJ, which was this 6 billion parameter open source transformer style LLM. We took GPTJ. We let users create the prompt. We let users select the image and we let users choose the name. And then that was the bot. And through that, they could shape the experience, right? So if they said this bot's going to be really mean, and it's going to be called like bully in the playground, right? That was like a whole category that I never would have guessed. Right. People love to fight. They love to have a disagreement, right? And then they would create, there'd be all these romantic archetypes that I didn't know existed. And so as the users could create the content that they wanted, that was when Chai was able to, to get this huge variety of content and rather than appealing to, you know, 1% of the population that I'd figured out what they wanted, you could appeal to a much, much broader thing. And so from that moment on, it was very, very crystal clear. It's like Chai, just as Instagram is this social media platform that lets people create images and upload images, videos and upload that, Chai was really about how can we let the users create this experience in AI and then share it and interact and search. So it's really, you know, I say it's like a platform for social AI.Alessio [00:20:00]: Where did the Chai name come from? Because you started the same path. I was like, is it character AI shortened? You started at the same time, so I was curious. The UK origin was like the second, the Chai.William [00:20:15]: We started way before character AI. And there's an interesting story that Chai's numbers were very, very strong, right? So I think in even 20, I think late 2022, was it late 2022 or maybe early 2023? Chai was like the number one AI app in the app store. So we would have something like 100,000 daily active users. And then one day we kind of saw there was this website. And we were like, oh, this website looks just like Chai. And it was the character AI website. And I think that nowadays it's, I think it's much more common knowledge that when they left Google with the funding, I think they knew what was the most trending, the number one app. And I think they sort of built that. Oh, you found the people.swyx [00:21:03]: You found the PMF for them.William [00:21:04]: We found the PMF for them. Exactly. Yeah. So I worked a year very, very hard. And then they, and then that was when I learned a lesson, which is that if you're VC backed and if, you know, so Chai, we'd kind of ran, we'd got to this point, I was the only person who'd invested. I'd invested maybe 2 million pounds in the business. And you know, from that, we were able to build this thing, get to say a hundred thousand daily active users. And then when character AI came along, the first version, we sort of laughed. We were like, oh man, this thing sucks. Like they don't know what they're building. They're building the wrong thing anyway, but then I saw, oh, they've raised a hundred million dollars. Oh, they've raised another hundred million dollars. And then our users started saying, oh guys, your AI sucks. Cause we were serving a 6 billion parameter model, right? How big was the model that character AI could afford to serve, right? So we would be spending, let's say we would spend a dollar per per user, right? Over the, the, you know, the entire lifetime.swyx [00:22:01]: A dollar per session, per chat, per month? No, no, no, no.William [00:22:04]: Let's say we'd get over the course of the year, we'd have a million users and we'd spend a million dollars on the AI throughout the year. Right. Like aggregated. Exactly. Exactly. Right. They could spend a hundred times that. So people would say, why is your AI much dumber than character AIs? And then I was like, oh, okay, I get it. This is like the Silicon Valley style, um, hyper scale business. And so, yeah, we moved to Silicon Valley and, uh, got some funding and iterated and built the flywheels. And, um, yeah, I, I'm very proud that we were able to compete with that. Right. So, and I think the reason we were able to do it was just customer obsession. And it's similar, I guess, to how deep seek have been able to produce such a compelling model when compared to someone like an open AI, right? So deep seek, you know, their latest, um, V2, yeah, they claim to have spent 5 million training it.swyx [00:22:57]: It may be a bit more, but, um, like, why are you making it? Why are you making such a big deal out of this? Yeah. There's an agenda there. Yeah. You brought up deep seek. So we have to ask you had a call with them.William [00:23:07]: We did. We did. We did. Um, let me think what to say about that. I think for one, they have an amazing story, right? So their background is again in finance.swyx [00:23:16]: They're the Chinese version of you. Exactly.William [00:23:18]: Well, there's a lot of similarities. Yes. Yes. I have a great affinity for companies which are like, um, founder led, customer obsessed and just try and build something great. And I think what deep seek have achieved. There's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cash significantly. And then by being able to do that, they're able to significantly reduce their inference costs. And I think with kind of with AI, people get really focused on like the kind of the foundation model or like the model itself. And they sort of don't pay much attention to the inference. To give you an example with Chai, let's say a typical user session is 90 minutes, which is like, you know, is very, very long for comparison. Let's say the average session length on TikTok is 70 minutes. So people are spending a lot of time. And in that time they're able to send say 150 messages. That's a lot of completions, right? It's quite different from an open AI scenario where people might come in, they'll have a particular question in mind. And they'll ask like one question. And a few follow up questions, right? So because they're consuming, say 30 times as many requests for a chat, or a conversational experience, you've got to figure out how to how to get the right balance between the cost of that and the quality. And so, you know, I think with AI, it's always been the case that if you want a better experience, you can throw compute at the problem, right? So if you want a better model, you can just make it bigger. If you want it to remember better, give it a longer context. And now, what open AI is doing to great fanfare is with projection sampling, you can generate many candidates, right? And then with some sort of reward model or some sort of scoring system, you can serve the most promising of these many candidates. And so that's kind of scaling up on the inference time compute side of things. And so for us, it doesn't make sense to think of AI is just the absolute performance. So. But what we're seeing, it's like the MML you score or the, you know, any of these benchmarks that people like to look at, if you just get that score, it doesn't really tell tell you anything. Because it's really like progress is made by improving the performance per dollar. And so I think that's an area where deep seek have been able to form very, very well, surprisingly so. And so I'm very interested in what Lama four is going to look like. And if they're able to sort of match what deep seek have been able to achieve with this performance per dollar gain.Alessio [00:25:59]: Before we go into the inference, some of the deeper stuff, can you give people an overview of like some of the numbers? So I think last I checked, you have like 1.4 million daily active now. It's like over 22 million of revenue. So it's quite a business.William [00:26:12]: Yeah, I think we grew by a factor of, you know, users grew by a factor of three last year. Revenue over doubled. You know, it's very exciting. We're competing with some really big, really well funded companies. Character AI got this, I think it was almost a $3 billion valuation. And they have 5 million DAU is a number that I last heard. Torquay, which is a Chinese built app owned by a company called Minimax. They're incredibly well funded. And these companies didn't grow by a factor of three last year. Right. And so when you've got this company and this team that's able to keep building something that gets users excited, and they want to tell their friend about it, and then they want to come and they want to stick on the platform. I think that's very special. And so last year was a great year for the team. And yeah, I think the numbers reflect the hard work that we put in. And then fundamentally, the quality of the app, the quality of the content, the quality of the content, the quality of the content, the quality of the content, the quality of the content. AI is the quality of the experience that you have. You actually published your DAU growth chart, which is unusual. And I see some inflections. Like, it's not just a straight line. There's some things that actually inflect. Yes. What were the big ones? Cool. That's a great, great, great question. Let me think of a good answer. I'm basically looking to annotate this chart, which doesn't have annotations on it. Cool. The first thing I would say is this is, I think the most important thing to know about success is that success is born out of failures. Right? Through failures that we learn. You know, if you think something's a good idea, and you do and it works, great, but you didn't actually learn anything, because everything went exactly as you imagined. But if you have an idea, you think it's going to be good, you try it, and it fails. There's a gap between the reality and expectation. And that's an opportunity to learn. The flat periods, that's us learning. And then the up periods is that's us reaping the rewards of that. So I think the big, of the growth shot of just 2024, I think the first thing that really kind of put a dent in our growth was our backend. So we just reached this scale. So we'd, from day one, we'd built on top of Google's GCP, which is Google's cloud platform. And they were fantastic. We used them when we had one daily active user, and they worked pretty good all the way up till we had about 500,000. It was never the cheapest, but from an engineering perspective, man, that thing scaled insanely good. Like, not Vertex? Not Vertex. Like GKE, that kind of stuff? We use Firebase. So we use Firebase. I'm pretty sure we're the biggest user ever on Firebase. That's expensive. Yeah, we had calls with engineers, and they're like, we wouldn't recommend using this product beyond this point, and you're 3x over that. So we pushed Google to their absolute limits. You know, it was fantastic for us, because we could focus on the AI. We could focus on just adding as much value as possible. But then what happened was, after 500,000, just the thing, the way we were using it, and it would just, it wouldn't scale any further. And so we had a really, really painful, at least three-month period, as we kind of migrated between different services, figuring out, like, what requests do we want to keep on Firebase, and what ones do we want to move on to something else? And then, you know, making mistakes. And learning things the hard way. And then after about three months, we got that right. So that, we would then be able to scale to the 1.5 million DAE without any further issues from the GCP. But what happens is, if you have an outage, new users who go on your app experience a dysfunctional app, and then they're going to exit. And so your next day, the key metrics that the app stores track are going to be something like retention rates. And so your next day, the key metrics that the app stores track are going to be something like retention rates. Money spent, and the star, like, the rating that they give you. In the app store. In the app store, yeah. Tyranny. So if you're ranked top 50 in entertainment, you're going to acquire a certain rate of users organically. If you go in and have a bad experience, it's going to tank where you're positioned in the algorithm. And then it can take a long time to kind of earn your way back up, at least if you wanted to do it organically. If you throw money at it, you can jump to the top. And I could talk about that. But broadly speaking, if we look at 2024, the first kink in the graph was outages due to hitting 500k DAU. The backend didn't want to scale past that. So then we just had to do the engineering and build through it. Okay, so we built through that, and then we get a little bit of growth. And so, okay, that's feeling a little bit good. I think the next thing, I think it's, I'm not going to lie, I have a feeling that when Character AI got... I was thinking. I think so. I think... So the Character AI team fundamentally got acquired by Google. And I don't know what they changed in their business. I don't know if they dialed down that ad spend. Products don't change, right? Products just what it is. I don't think so. Yeah, I think the product is what it is. It's like maintenance mode. Yes. I think the issue that people, you know, some people may think this is an obvious fact, but running a business can be very competitive, right? Because other businesses can see what you're doing, and they can imitate you. And then there's this... There's this question of, if you've got one company that's spending $100,000 a day on advertising, and you've got another company that's spending zero, if you consider market share, and if you're considering new users which are entering the market, the guy that's spending $100,000 a day is going to be getting 90% of those new users. And so I have a suspicion that when the founders of Character AI left, they dialed down their spending on user acquisition. And I think that kind of gave oxygen to like the other apps. And so Chai was able to then start growing again in a really healthy fashion. I think that's kind of like the second thing. I think a third thing is we've really built a great data flywheel. Like the AI team sort of perfected their flywheel, I would say, in end of Q2. And I could speak about that at length. But fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours. And when we did that, we could really, really, really perfect techniques like DPO, fine tuning, prompt engineering, blending, rejection sampling, training a reward model, right, really successfully, like boom, boom, boom, boom, boom. And so I think in Q3 and Q4, we got, the amount of AI improvements we got was like astounding. It was getting to the point, I thought like how much more, how much more edge is there to be had here? But the team just could keep going and going and going. That was like number three for the inflection point.swyx [00:34:53]: There's a fourth?William [00:34:54]: The important thing about the third one is if you go on our Reddit or you talk to users of AI, there's like a clear date. It's like somewhere in October or something. The users, they flipped. Before October, the users... The users would say character AI is better than you, for the most part. Then from October onwards, they would say, wow, you guys are better than character AI. And that was like a really clear positive signal that we'd sort of done it. And I think people, you can't cheat consumers. You can't trick them. You can't b******t them. They know, right? If you're going to spend 90 minutes on a platform, and with apps, there's the barriers to switching is pretty low. Like you can try character AI, you can't cheat consumers. You can't cheat them. You can't cheat them. You can't cheat AI for a day. If you get bored, you can try Chai. If you get bored of Chai, you can go back to character. So the users, the loyalty is not strong, right? What keeps them on the app is the experience. If you deliver a better experience, they're going to stay and they can tell. So that was the fourth one was we were fortunate enough to get this hire. He was hired one really talented engineer. And then they said, oh, at my last company, we had a head of growth. He was really, really good. And he was the head of growth for ByteDance for two years. Would you like to speak to him? And I was like, yes. Yes, I think I would. And so I spoke to him. And he just blew me away with what he knew about user acquisition. You know, it was like a 3D chessswyx [00:36:21]: sort of thing. You know, as much as, as I know about AI. Like ByteDance as in TikTok US. Yes.William [00:36:26]: Not ByteDance as other stuff. Yep. He was interviewing us as we were interviewing him. Right. And so pick up options. Yeah, exactly. And so he was kind of looking at our metrics. And he was like, I saw him get really excited when he said, guys, you've got a million daily active users and you've done no advertising. I said, correct. And he was like, that's unheard of. He's like, I've never heard of anyone doing that. And then he started looking at our metrics. And he was like, if you've got all of this organically, if you start spending money, this is going to be very exciting. I was like, let's give it a go. So then he came in, we've just started ramping up the user acquisition. So that looks like spending, you know, let's say we're spending, we started spending $20,000 a day, it looked very promising than 20,000. Right now we're spending $40,000 a day on user acquisition. That's still only half of what like character AI or talkie may be spending. But from that, it's sort of, we were growing at a rate of maybe say, 2x a year. And that got us growing at a rate of 3x a year. So I'm growing, I'm evolving more and more to like a Silicon Valley style hyper growth, like, you know, you build something decent, and then you canswyx [00:37:33]: slap on a huge... You did the important thing, you did the product first.William [00:37:36]: Of course, but then you can slap on like, like the rocket or the jet engine or something, which is just this cash in, you pour in as much cash, you buy a lot of ads, and your growth is faster.swyx [00:37:48]: Not to, you know, I'm just kind of curious what's working right now versus what surprisinglyWilliam [00:37:52]: doesn't work. Oh, there's a long, long list of surprising stuff that doesn't work. Yeah. The surprising thing, like the most surprising thing, what doesn't work is almost everything doesn't work. That's what's surprising. And I'll give you an example. So like a year and a half ago, I was working at a company, we were super excited by audio. I was like, audio is going to be the next killer feature, we have to get in the app. And I want to be the first. So everything Chai does, I want us to be the first. We may not be the company that's strongest at execution, but we can always be theswyx [00:38:22]: most innovative. Interesting. Right? So we can... You're pretty strong at execution.William [00:38:26]: We're much stronger, we're much stronger. A lot of the reason we're here is because we were first. If we launched today, it'd be so hard to get the traction. Because it's like to get the flywheel, to get the users, to build a product people are excited about. If you're first, people are naturally excited about it. But if you're fifth or 10th, man, you've got to beswyx [00:38:46]: insanely good at execution. So you were first with voice? We were first. We were first. I only knowWilliam [00:38:51]: when character launched voice. They launched it, I think they launched it at least nine months after us. Okay. Okay. But the team worked so hard for it. At the time we did it, latency is a huge problem. Cost is a huge problem. Getting the right quality of the voice is a huge problem. Right? Then there's this user interface and getting the right user experience. Because you don't just want it to start blurting out. Right? You want to kind of activate it. But then you don't have to keep pressing a button every single time. There's a lot that goes into getting a really smooth audio experience. So we went ahead, we invested the three months, we built it all. And then when we did the A-B test, there was like, no change in any of the numbers. And I was like, this can't be right, there must be a bug. And we spent like a week just checking everything, checking again, checking again. And it was like, the users just did not care. And it was something like only 10 or 15% of users even click the button to like, they wanted to engage the audio. And they would only use it for 10 or 15% of the time. So if you do the math, if it's just like something that one in seven people use it for one seventh of their time. You've changed like 2% of the experience. So even if that that 2% of the time is like insanely good, it doesn't translate much when you look at the retention, when you look at the engagement, and when you look at the monetization rates. So audio did not have a big impact. I'm pretty big on audio. But yeah, I like it too. But it's, you know, so a lot of the stuff which I do, I'm a big, you can have a theory. And you resist. Yeah. Exactly, exactly. So I think if you want to make audio work, it has to be a unique, compelling, exciting experience that they can't have anywhere else.swyx [00:40:37]: It could be your models, which just weren't good enough.William [00:40:39]: No, no, no, they were great. Oh, yeah, they were very good. it was like, it was kind of like just the, you know, if you listen to like an audible or Kindle, or something like, you just hear this voice. And it's like, you don't go like, wow, this is this is special, right? It's like a convenience thing. But the idea is that if you can, if Chai is the only platform, like, let's say you have a Mr. Beast, and YouTube is the only platform you can use to make audio work, then you can watch a Mr. Beast video. And it's the most engaging, fun video that you want to watch, you'll go to a YouTube. And so it's like for audio, you can't just put the audio on there. And people go, oh, yeah, it's like 2% better. Or like, 5% of users think it's 20% better, right? It has to be something that the majority of people, for the majority of the experience, go like, wow, this is a big deal. That's the features you need to be shipping. If it's not going to appeal to the majority of people, for the majority of the experience, and it's not a big deal, it's not going to move you. Cool. So you killed it. I don't see it anymore. Yep. So I love this. The longer, it's kind of cheesy, I guess, but the longer I've been working at Chai, and I think the team agrees with this, all the platitudes, at least I thought they were platitudes, that you would get from like the Steve Jobs, which is like, build something insanely great, right? Or be maniacally focused, or, you know, the most important thing is saying no to, not to work on. All of these sort of lessons, they just are like painfully true. They're painfully true. So now I'm just like, everything I say, I'm either quoting Steve Jobs or Zuckerberg. I'm like, guys, move fast and break free.swyx [00:42:10]: You've jumped the Apollo to cool it now.William [00:42:12]: Yeah, it's just so, everything they said is so, so true. The turtle neck. Yeah, yeah, yeah. Everything is so true.swyx [00:42:18]: This last question on my side, and I want to pass this to Alessio, is on just, just multi-modality in general. This actually comes from Justine Moore from A16Z, who's a friend of ours. And a lot of people are trying to do voice image video for AI companions. Yes. You just said voice didn't work. Yep. What would make you revisit?William [00:42:36]: So Steve Jobs, he was very, listen, he was very, very clear on this. There's a habit of engineers who, once they've got some cool technology, they want to find a way to package up the cool technology and sell it to consumers, right? That does not work. So you're free to try and build a startup where you've got your cool tech and you want to find someone to sell it to. That's not what we do at Chai. At Chai, we start with the consumer. What does the consumer want? What is their problem? And how do we solve it? So right now, the number one problems for the users, it's not the audio. That's not the number one problem. It's not the image generation either. That's not their problem either. The number one problem for users in AI is this. All the AI is being generated by middle-aged men in Silicon Valley, right? That's all the content. You're interacting with this AI. You're speaking to it for 90 minutes on average. It's being trained by middle-aged men. The guys out there, they're out there. They're talking to you. They're talking to you. They're like, oh, what should the AI say in this situation, right? What's funny, right? What's cool? What's boring? What's entertaining? That's not the way it should be. The way it should be is that the users should be creating the AI, right? And so the way I speak about it is this. Chai, we have this AI engine in which sits atop a thin layer of UGC. So the thin layer of UGC is absolutely essential, right? It's just prompts. But it's just prompts. It's just an image. It's just a name. It's like we've done 1% of what we could do. So we need to keep thickening up that layer of UGC. It must be the case that the users can train the AI. And if reinforcement learning is powerful and important, they have to be able to do that. And so it's got to be the case that there exists, you know, I say to the team, just as Mr. Beast is able to spend 100 million a year or whatever it is on his production company, and he's got a team building the content, the Mr. Beast company is able to spend 100 million a year on his production company. And he's got a team building the content, which then he shares on the YouTube platform. Until there's a team that's earning 100 million a year or spending 100 million on the content that they're producing for the Chai platform, we're not finished, right? So that's the problem. That's what we're excited to build. And getting too caught up in the tech, I think is a fool's errand. It does not work.Alessio [00:44:52]: As an aside, I saw the Beast Games thing on Amazon Prime. It's not doing well. And I'mswyx [00:44:56]: curious. It's kind of like, I mean, the audience reading is high. The run-to-meet-all sucks, but the audience reading is high.Alessio [00:45:02]: But it's not like in the top 10. I saw it dropped off of like the... Oh, okay. Yeah, that one I don't know. I'm curious, like, you know, it's kind of like similar content, but different platform. And then going back to like, some of what you were saying is like, you know, people come to ChaiWilliam [00:45:13]: expecting some type of content. Yeah, I think it's something that's interesting to discuss is like, is moats. And what is the moat? And so, you know, if you look at a platform like YouTube, the moat, I think is in first is really is in the ecosystem. And the ecosystem, is comprised of you have the content creators, you have the users, the consumers, and then you have the algorithms. And so this, this creates a sort of a flywheel where the algorithms are able to be trained on the users, and the users data, the recommend systems can then feed information to the content creators. So Mr. Beast, he knows which thumbnail does the best. He knows the first 10 seconds of the video has to be this particular way. And so his content is super optimized for the YouTube platform. So that's why it doesn't do well on Amazon. If he wants to do well on Amazon, how many videos has he created on the YouTube platform? By thousands, 10s of 1000s, I guess, he needs to get those iterations in on the Amazon. So at Chai, I think it's all about how can we get the most compelling, rich user generated content, stick that on top of the AI engine, the recommender systems, in such that we get this beautiful data flywheel, more users, better recommendations, more creative, more content, more users.Alessio [00:46:34]: You mentioned the algorithm, you have this idea of the Chaiverse on Chai, and you have your own kind of like LMSYS-like ELO system. Yeah, what are things that your models optimize for, like your users optimize for, and maybe talk about how you build it, how people submit models?William [00:46:49]: So Chaiverse is what I would describe as a developer platform. More often when we're speaking about Chai, we're thinking about the Chai app. And the Chai app is really this product for consumers. And so consumers can come on the Chai app, they can come on the Chai app, they can come on the Chai app, they can interact with our AI, and they can interact with other UGC. And it's really just these kind of bots. And it's a thin layer of UGC. Okay. Our mission is not to just have a very thin layer of UGC. Our mission is to have as much UGC as possible. So we must have, I don't want people at Chai training the AI. I want people, not middle aged men, building AI. I want everyone building the AI, as many people building the AI as possible. Okay, so what we built was we built Chaiverse. And Chaiverse is kind of, it's kind of like a prototype, is the way to think about it. And it started with this, this observation that, well, how many models get submitted into Hugging Face a day? It's hundreds, it's hundreds, right? So there's hundreds of LLMs submitted each day. Now consider that, what does it take to build an LLM? It takes a lot of work, actually. It's like someone devoted several hours of compute, several hours of their time, prepared a data set, launched it, ran it, evaluated it, submitted it, right? So there's a lot of, there's a lot of, there's a lot of work that's going into that. So what we did was we said, well, why can't we host their models for them and serve them to users? And then what would that look like? The first issue is, well, how do you know if a model is good or not? Like, we don't want to serve users the crappy models, right? So what we would do is we would, I love the LMSYS style. I think it's really cool. It's really simple. It's a very intuitive thing, which is you simply present the users with two completions. You can say, look, this is from model one. This is from model two. This is from model three. This is from model A. This is from model B, which is better. And so if someone submits a model to Chaiverse, what we do is we spin up a GPU. We download the model. We're going to now host that model on this GPU. And we're going to start routing traffic to it. And we're going to send, we think it takes about 5,000 completions to get an accurate signal. That's roughly what LMSYS does. And from that, we're able to get an accurate ranking. And we're able to get an accurate ranking. And we're able to get an accurate ranking of which models are people finding entertaining and which models are not entertaining. If you look at the bottom 80%, they'll suck. You can just disregard them. They totally suck. Then when you get the top 20%, you know you've got a decent model, but you can break it down into more nuance. There might be one that's really descriptive. There might be one that's got a lot of personality to it. There might be one that's really illogical. Then the question is, well, what do you do with these top models? From that, you can do more sophisticated things. You can try and do like a routing thing where you say for a given user request, we're going to try and predict which of these end models that users enjoy the most. That turns out to be pretty expensive and not a huge source of like edge or improvement. Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model. Just a random 50%? Just a random, yeah. And then... That's blending? That's blending. You can do more sophisticated things on top of that, as in all things in life, but the 80-20 solution, if you just do that, you get a pretty powerful effect out of the gate. Random number generator. I think it's like the robustness of randomness. Random is a very powerful optimization technique, and it's a very robust thing. So you can explore a lot of the space very efficiently. There's one thing that's really, really important to share, and this is the most exciting thing for me, is after you do the ranking, you get an ELO score, and you can track a user's first join date, the first date they submit a model to Chaiverse, they almost always get a terrible ELO, right? So let's say the first submission they get an ELO of 1,100 or 1,000 or something, and you can see that they iterate and they iterate and iterate, and it will be like, no improvement, no improvement, no improvement, and then boom. Do you give them any data, or do you have to come up with this themselves? We do, we do, we do, we do. We try and strike a balance between giving them data that's very useful, you've got to be compliant with GDPR, which is like, you have to work very hard to preserve the privacy of users of your app. So we try to give them as much signal as possible, to be helpful. The minimum is we're just going to give you a score, right? That's the minimum. But that alone is people can optimize a score pretty well, because they're able to come up with theories, submit it, does it work? No. A new theory, does it work? No. And then boom, as soon as they figure something out, they keep it, and then they iterate, and then boom,Alessio [00:51:46]: they figure something out, and they keep it. Last year, you had this post on your blog, cross-sourcing the lead to the 10 trillion parameter, AGI, and you call it a mixture of experts, recommenders. Yep. Any insights?William [00:51:58]: Updated thoughts, 12 months later? I think the odds, the timeline for AGI has certainly been pushed out, right? Now, this is in, I'm a controversial person, I don't know, like, I just think... You don't believe in scaling laws, you think AGI is further away. I think it's an S-curve. I think everything's an S-curve. And I think that the models have proven to just be far worse at reasoning than people sort of thought. And I think whenever I hear people talk about LLMs as reasoning engines, I sort of cringe a bit. I don't think that's what they are. I think of them more as like a simulator. I think of them as like a, right? So they get trained to predict the next most likely token. It's like a physics simulation engine. So you get these like games where you can like construct a bridge, and you drop a car down, and then it predicts what should happen. And that's really what LLMs are doing. It's not so much that they're reasoning, it's more that they're just doing the most likely thing. So fundamentally, the ability for people to add in intelligence, I think is very limited. What most people would consider intelligence, I think the AI is not a crowdsourcing problem, right? Now with Wikipedia, Wikipedia crowdsources knowledge. It doesn't crowdsource intelligence. So it's a subtle distinction. AI is fantastic at knowledge. I think it's weak at intelligence. And a lot, it's easy to conflate the two because if you ask it a question and it gives you, you know, if you said, who was the seventh president of the United States, and it gives you the correct answer, I'd say, well, I don't know the answer to that. And you can conflate that with intelligence. But really, that's a question of knowledge. And knowledge is really this thing about saying, how can I store all of this information? And then how can I retrieve something that's relevant? Okay, they're fantastic at that. They're fantastic at storing knowledge and retrieving the relevant knowledge. They're superior to humans in that regard. And so I think we need to come up for a new word. How does one describe AI should contain more knowledge than any individual human? It should be more accessible than any individual human. That's a very powerful thing. That's superswyx [00:54:07]: powerful. But what words do we use to describe that? We had a previous guest on Exa AI that does search. And he tried to coin super knowledge as the opposite of super intelligence.William [00:54:20]: Exactly. I think super knowledge is a more accurate word for it.swyx [00:54:24]: You can store more things than any human can.William [00:54:26]: And you can retrieve it better than any human can as well. And I think it's those two things combined that's special. I think that thing will exist. That thing can be built. And I think you can start with something that's entertaining and fun. And I think, I often think it's like, look, it's going to be a 20 year journey. And we're in like, year four, or it's like the web. And this is like 1998 or something. You know, you've got a long, long way to go before the Amazon.coms are like these huge, multi trillion dollar businesses that every single person uses every day. And so AI today is very simplistic. And it's fundamentally the way we're using it, the flywheels, and this ability for how can everyone contribute to it to really magnify the value that it brings. Right now, like, I think it's a bit sad. It's like, right now you have big labs, I'm going to pick on open AI. And they kind of go to like these human labelers. And they say, we're going to pay you to just label this like subset of questions that we want to get a really high quality data set, then we're going to get like our own computers that are really powerful. And that's kind of like the thing. For me, it's so much like Encyclopedia Britannica. It's like insane. All the people that were interested in blockchain, it's like, well, this is this is what needs to be decentralized, you need to decentralize that thing. Because if you distribute it, people can generate way more data in a distributed fashion, way more, right? You need the incentive. Yeah, of course. Yeah. But I mean, the, the, that's kind of the exciting thing about Wikipedia was it's this understanding, like the incentives, you don't need money to incentivize people. You don't need dog coins. No. Sometimes, sometimes people get the satisfaction fro

Jon and Dustin share some positive ideas about AI and a few cyber security tips to keep Scientology agents out of your computer. Links: Dustin's website Dustin's dissertation Pew Research Center survey on Americans' views of AI Article mentioning improved performance on the International Mathematics Olympiad qualifying exam AI Medical licensing exam performance ChatGPT passing the bar exam MIT Technology review article mentioning Magic: the Gathering is the most complex game MIT Technology review article mentioning Go players and game programmers underestimating when Go would fall to AI (see notes) AlphaGo the movie Notes: AGI prediction survey citation - Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. Fundamental issues of artificial intelligence, 555-572. Spike disagrees that the cyber-truck looks like a Lego car as she thinks all Lego cars are adorable.

Join Prof. Subbarao Kambhampati and host Tim Scarfe for a deep dive into OpenAI's O1 model and the future of AI reasoning systems. * How O1 likely uses reinforcement learning similar to AlphaGo, with hidden reasoning tokens that users pay for but never see * The evolution from traditional Large Language Models to more sophisticated reasoning systems * The concept of "fractal intelligence" in AI - where models work brilliantly sometimes but fail unpredictably * Why O1's improved performance comes with substantial computational costs * The ongoing debate between single-model approaches (OpenAI) vs hybrid systems (Google) * The critical distinction between AI as an intelligence amplifier vs autonomous decision-maker SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events? Goto https://tufalabs.ai/ *** TOC: 1. **O1 Architecture and Reasoning Foundations** [00:00:00] 1.1 Fractal Intelligence and Reasoning Model Limitations [00:04:28] 1.2 LLM Evolution: From Simple Prompting to Advanced Reasoning [00:14:28] 1.3 O1's Architecture and AlphaGo-like Reasoning Approach [00:23:18] 1.4 Empirical Evaluation of O1's Planning Capabilities 2. **Monte Carlo Methods and Model Deep-Dive** [00:29:30] 2.1 Monte Carlo Methods and MARCO-O1 Implementation [00:31:30] 2.2 Reasoning vs. Retrieval in LLM Systems [00:40:40] 2.3 Fractal Intelligence Capabilities and Limitations [00:45:59] 2.4 Mechanistic Interpretability of Model Behavior [00:51:41] 2.5 O1 Response Patterns and Performance Analysis 3. **System Design and Real-World Applications** [00:59:30] 3.1 Evolution from LLMs to Language Reasoning Models [01:06:48] 3.2 Cost-Efficiency Analysis: LLMs vs O1 [01:11:28] 3.3 Autonomous vs Human-in-the-Loop Systems [01:16:01] 3.4 Program Generation and Fine-Tuning Approaches [01:26:08] 3.5 Hybrid Architecture Implementation Strategies Transcript: https://www.dropbox.com/scl/fi/d0ef4ovnfxi0lknirkvft/Subbarao.pdf?rlkey=l3rp29gs4hkut7he8u04mm1df&dl=0 REFS: [00:02:00] Monty Python (1975) Witch trial scene: flawed logical reasoning. https://www.youtube.com/watch?v=zrzMhU_4m-g [00:04:00] Cade Metz (2024) Microsoft–OpenAI partnership evolution and control dynamics. https://www.nytimes.com/2024/10/17/technology/microsoft-openai-partnership-deal.html [00:07:25] Kojima et al. (2022) Zero-shot chain-of-thought prompting ('Let's think step by step'). https://arxiv.org/pdf/2205.11916 [00:12:50] DeepMind Research Team (2023) Multi-bot game solving with external and internal planning. https://deepmind.google/research/publications/139455/ [00:15:10] Silver et al. (2016) AlphaGo's Monte Carlo Tree Search and Q-learning. https://www.nature.com/articles/nature16961 [00:16:30] Kambhampati, S. et al. (2023) Evaluates O1's planning in "Strawberry Fields" benchmarks. https://arxiv.org/pdf/2410.02162 [00:29:30] Alibaba AIDC-AI Team (2023) MARCO-O1: Chain-of-Thought + MCTS for improved reasoning. https://arxiv.org/html/2411.14405 [00:31:30] Kambhampati, S. (2024) Explores LLM "reasoning vs retrieval" debate. https://arxiv.org/html/2403.04121v2 [00:37:35] Wei, J. et al. (2022) Chain-of-thought prompting (introduces last-letter concatenation). https://arxiv.org/pdf/2201.11903 [00:42:35] Barbero, F. et al. (2024) Transformer attention and "information over-squashing." https://arxiv.org/html/2406.04267v2 [00:46:05] Ruis, L. et al. (2023) Influence functions to understand procedural knowledge in LLMs. https://arxiv.org/html/2411.12580v1 (truncated - continued in shownotes/transcript doc)

In deze aflevering staat de razendsnelle ontwikkeling van technologie centraal en de manier waarop dit ons denken, leren en samenleven beïnvloedt. We maken een reis door de tijd, beginnend bij de Koude Oorlog, een periode waarin technologische innovatie werd aangewakkerd door rivaliteit en geopolitieke spanning. Van de wedloop naar de maan en de historische Apollo 11-missie tot de impact van de wet van Moore, die de basis legde voor de miniaturisatie en kracht van moderne technologie.We verkennen hoe deze vooruitgang ons bracht naar nieuwe hoogten, zoals de overwinning van de supercomputer Deep Blue op Garry Kasparov en de baanbrekende prestaties van AlphaGo. Maar de technologische reis stopt niet bij het verslaan van grootmeesters in spellen. In deze aflevering bespreken we naar een van de meest complexe uitdagingen die technologie heeft helpen oplossen: het ontrafelen van eiwitstructuren door AlphaFold, een mijlpaal die de toekomst van wetenschap en geneeskunde transformeert.Wat betekent deze explosieve vooruitgang voor ons onderwijs en de opvoeding van onze kinderen? Hoe leren we jongeren omgaan met een wereld waarin technologie alles doordringt? En hoe bewaken we de balans tussen technologische mogelijkheden en de menselijke waarden die we willen behouden? Dat, en meer, staat centraal in deze aflevering.Bronnen: zie ondertekst aflevering 1.

Season 8 of Watch This Space began with our look-ahead to 2025, where the main topics were how we see workplaces and workspaces evolving, why small language models will have their moment, and our concerns about how AI is becoming baked into everything. On another tangent, we discussed how sensibilities in the digital age are so different from the analog world we are grounded in, and why that's making it easier for AI to take hold. We also continued the literary theme from our last episode by revisiting some lingering thoughts from the sci-fi novella With Folded Hands, then veering into another AI-related must-read, Benjamin Labatut's The MANIAC. If you're wondering why DeepMind's AlphaGo is a foreboding sign for how AI could reshape our very humanity, this is the book for you.

2016 höll världen andan när AI-modellen AlphaGo utmanade världsmästaren i spelet Go och vann. Nu belönas Demis Hassabis, hjärnan bakom modellen, med Nobelpris men för en helt annan upptäckt. Lyssna på alla avsnitt i Sveriges Radio Play. Programmet sändes första gången 5/12-2024.Bara åtta år gammal köper Demis Hassabis sin första dator för vinstpengarna från en schackturnering. Som vuxen utvecklar han det första datorsystemet som lyckas överlista en mänsklig världsmästare i ett mer avancerat spel än schack. Vetenskapsradion träffar Demis Hassabis, en av Nobelpristagarna i kemi 2024, i ett personligt samtal – om vägen från schacknörd till Google-elit och Nobelpris.Reporter: Annika Östman annika.ostman@sr.se Producent: Lars Broström lars.brostrom@sr.se

I met Michael Bervell through a mutual acquaintance some two months ago. Since then he and I have talked a few times and found that we have many interests in common.   Michael grew up near Seattle where he stayed through high school. He then went across the country to study at Harvard. He received a Bachelor's degree in Philosophy. He then returned to Seattle and began working at Microsoft where he held some pretty intense and interesting jobs he will tell us about.   At a young age and then in college Michael's entrepreneurial spirit was present and flourished. His story about all that he has done as an entrepreneur is quite impressive. Today he is back at Harvard working toward getting his Master's degree in Business.   Michael has developed a keen interest in digital accessibility and inclusion. We spend time discussing internet access, the various options for making inclusive websites and how to help educate more people about the need for complete inclusion.       About the Guest:   Michael Bervell is a Ghanaian-American angel-investor, entrepreneur, and best-selling author. He is currently the founder of TestParty, an industry-leading and cutting edge digital accessibility platform.   In 2007, Bervell co-founded “Hugs for” an international, student-run non-profit organization focused on using grassroots strategies to develop countries around the world. To date, "Hugs for" has fundraised over $500,000 of material and monetary donations; impacted over 300,000 youth around the world; and expanded operations to 6 countries (Tanzania, Ghana, United States, Uganda, Kenya, and Sierra Leone). Because of his work, Bervell was awarded the National Caring Award in 2015 (alongside Pope Francis, Dikembe Mutombo, and 7 others).   Bervell is the youngest Elected Director of the Harvard Alumni Association and was the youngest President of the Harvard Club of Seattle. He has helped to found and lead a variety of organizations including the WednesdAI Collective (a Harvard & MIT AI incubation lab), Enchiridion Corporation (a marketing consulting company), Sigma Squared (formerly the Kairos Society), and Billion Dollar Startup Ideas (a media and innovation company). He has experience working as a Chief of Staff at Databook, Venture Fellow at Harlem Capital, Portfolio Development Manager at Microsoft's Venture Fund, Program Manager at Microsoft, and Software Engineer at Twitter.   His various efforts have earned him recognition as a Samvid Scholar (2022), Warnick Fellow (2021), Jonathan Hart Prize Winner (2019), GE-Lloyd Trotter Scholar (2018), World Internet Conference Wuzhen Scholar (2017), Walter C. Klein Scholar (2017), United Health Foundation Scholar (2016), Deutsche Bank Rise Into Success Scholar (2016), Blacks at Microsoft Scholar (2016), Three Dot Dash Global Teen Leader (2015), Jackie Robinson Foundation Scholar (2015), National Achievement Scholar (2015), Coca-cola Scholar (2015), Elks Scholar (2015), AXA Achievement Community Scholar (2015), Build-a-bear Workshop Huggable Hero (2014), and more.   Ways to connect with Michael:   Personal Website: https://www.michaelbervell.com/ LinkedIn Profile: https://www.linkedin.com/in/michaelbervell/ Company Website: https://www.testparty.ai/ Company LinkedIn Profile: https://www.linkedin.com/company/testparty/     About the Host:   Michael Hingson is a New York Times best-selling author, international lecturer, and Chief Vision Officer for accessiBe. Michael, blind since birth, survived the 9/11 attacks with the help of his guide dog Roselle. This story is the subject of his best-selling book, Thunder Dog.   Michael gives over 100 presentations around the world each year speaking to influential groups such as Exxon Mobile, AT&T, Federal Express, Scripps College, Rutgers University, Children's Hospital, and the American Red Cross just to name a few. He is Ambassador for the National Braille Literacy Campaign for the National Federation of the Blind and also serves as Ambassador for the American Humane Association's 2012 Hero Dog Awards.   https://michaelhingson.com https://www.facebook.com/michael.hingson.author.speaker/ https://twitter.com/mhingson https://www.youtube.com/user/mhingson https://www.linkedin.com/in/michaelhingson/   accessiBe Links https://accessibe.com/ https://www.youtube.com/c/accessiBe https://www.linkedin.com/company/accessibe/mycompany/   https://www.facebook.com/accessibe/       Thanks for listening!   Thanks so much for listening to our podcast! If you enjoyed this episode and think that others could benefit from listening, please share it using the social media buttons on this page. Do you have some feedback or questions about this episode? Leave a comment in the section below!   Subscribe to the podcast   If you would like to get automatic updates of new podcast episodes, you can subscribe to the podcast on Apple Podcasts or Stitcher. You can subscribe in your favorite podcast app. You can also support our podcast through our tip jar https://tips.pinecast.com/jar/unstoppable-mindset .   Leave us an Apple Podcasts review   Ratings and reviews from our listeners are extremely valuable to us and greatly appreciated. They help our podcast rank higher on Apple Podcasts, which exposes our show to more awesome listeners like you. If you have a minute, please leave an honest review on Apple Podcasts.       Transcription Notes:   Michael Hingson ** 00:00 Access Cast and accessiBe Initiative presents Unstoppable Mindset. The podcast where inclusion, diversity and the unexpected meet. Hi, I'm Michael Hingson, Chief Vision Officer for accessiBe and the author of the number one New York Times bestselling book, Thunder dog, the story of a blind man, his guide dog and the triumph of trust. Thanks for joining me on my podcast as we explore our own blinding fears of inclusion unacceptance and our resistance to change. We will discover the idea that no matter the situation, or the people we encounter, our own fears, and prejudices often are our strongest barriers to moving forward. The unstoppable mindset podcast is sponsored by accessiBe, that's a c c e s s i capital B e. Visit www.accessibe.com to learn how you can make your website accessible for persons with disabilities. And to help make the internet fully inclusive by the year 2025. Glad you dropped by we're happy to meet you and to have you here with us.   Michael Hingson ** 01:21 Well, hello, everyone. I am Michael Hinkson, and you are listening to unstoppable mindset. Our guest today is Michael Bervell, who is a Ghanaian American angel investor. He is a published author, and he is also an entrepreneur and a scholar by any standards. And if he wants to brag about all that and all the the different kinds of accolades and awards he's gotten, he's welcome to do that. And I will just take a nap. No, I won't. I won't take a nap. I'll listen to him. I've read it all, but I'll listen to it again. Michael, welcome to unstoppable mindset.   Michael Bervell ** 01:58 Thanks so much for having me. It's a great name. You have too, both the podcast and your own name, another Mike.   Michael Hingson ** 02:04 You know, I think it's a great name. People have asked me, why I say Michael, and do I prefer Michael to Mike? And as I tell people, it took a master's degree in 10 years, a master's degree in physics in 10 years, to figure this out. But I used to always say Mike Kingston on the phone, and people always said Mr. Kingston. And I couldn't figure out, why are they saying Kingston when it's Kingston, and I introduced myself as Mike Kingston. And finally, one day, it hit me in the head. They're getting the mike the K part with the Kingston, and they're calling it Kingston. If I start saying Michael hingson, will that change it? I started saying Michael hingson, and immediately everybody got it right. They said Mr. Hingson or Michael, or whatever. I don't really care, Mike or Michael is fine, but the last name is hingson, so there.   Michael Bervell ** 02:50 It's so funny. Yeah, I'm glad no one's calling you Mr. Links and or something like, yeah, yell and adding it. They   Michael Hingson ** 02:55 do. They do. Sometimes do Hingston, which isn't right, yeah, which shows you sometimes how well people listen. But you know, what   03:03 do you do? Exactly, exactly? Tell   Michael Hingson ** 03:07 us a little bit, if you would, about the early Michael bervell Growing up in and where, and all that sort of stuff. And you know, then we can get into all sorts of fun stuff, because I know you've been very interested in accessibility and disabilities and all that, we'll get to that. But tell me about you growing up. Yeah. I mean,   Michael Bervell ** 03:24 for me home, home for me was in Seattle, and I actually lived and went to school in a place that was about 30 minutes apart. So my parents would drop me off at school in the morning. I go through the day, meet all my friends, and then come back home. They would pick me up, take me back home in the evening. So I had a lot of time in the day after school, you know, school ends at two, and my parents picked up a five to do all this other stuff. So I used to always be part of every student, student club. I did every sports team, you know, I was in high school, you know, on the captain of all these, all these teams and such. And of course, I would go home and my parents picked me up. And in that in that in between time, I spent a lot of time in the library, so I probably every day in middle and high school, spent three hours a day at the library, just in that in between time, waiting for your parents, waiting for my parents. So that for me, was a lot of time that I just used to incubate projects. I taught myself how to code and took some CS classes when I was, you know, in high school at the library, I became friends with all the librarians and joined the student library advisory board when I was in eighth grade at the library, and did a bunch of other things. But I think probably the most impactful library project that I had was actually a nonprofit that my family and I started, and it was memory of my grandmother, who born in Ghana. She used to always go back there in the winter times, because, you know, it's cold in Seattle, warm in West Africa in the winter   Michael Hingson ** 04:48 as well. Yeah,   Michael Bervell ** 04:49 yeah, it was super warm there. I mean, it's always, you know, 80 plus degrees, wow. Yeah, it's lovely. And so she would always go home. And whenever she went back to Ghana. She would, you know, come into our bedroom and tip doe at night and go into the bed and take a teddy bear or take some of her old school supplies. And whenever she visited, she would give that to kids in hospitals and schools and North pages. So, you know, when she, when we, when she passed away, we ended up going back to Ghana for her funeral. And, you know, all the burial ceremonies, and there were just so many people from the community there expressing their love for her and what she had done. And we realized that, you know, while it was small for us, you know, as a six year old or sixth grade kid, her taking a teddy bear had such a big impact, and it had these ripple effects that went far beyond her, so that that was, like one of my biggest projects I did at, you know, in sixth grade and beyond. It's an organization, a nonprofit called hugs for Ghana, which we've been running for the last 15 years, 15 plus years, and now is operating in six different countries. And we do the same thing. We get teddy bears and school supplies and all these things, and pick them up and hand deliver them to kids in developing countries. But that, for me, was one of my most fundamental parts of my childhood. When you ask me, you know, was it like as a child? I can't separate my growing up from, you know, those long drives to school, that time at the library and eventually the nonprofit made in honor of my grandmother,   Michael Hingson ** 06:10 and giving back,   Michael Bervell ** 06:13 yeah, and giving back exactly how   Michael Hingson ** 06:16 I talked fairly recently on this podcast to someone who formed. Her name is Wendy Steele. She formed an organization called Impact 100 and impact 100 is really primarily an organization of women, although in Australia, there are men who are part of it. But basically what Wendy realized along the way was that, in fact, people are always looking for, what can they do? And at the same time, they don't have a lot of time. So with impact 100 she said, and the way the organization works, the only thing that she requires that anyone who joins the organization must do is donate a check for $1,000 that's it. If you don't want to do any work, that's great. If you want to be part of it and all that. It's fine. If the organization is primarily composed of volunteers. I think they have now like 73 or 77 chapters in mostly in the United States, but they're also when Australia and a couple of other countries, and they have given out in the 20 years since the organization was formed, all told, close to $148 million what they do is they take the money that comes in, and they for every $100,000 that a Chapter raises, they give a $100,000 grant to someone no administrative costs, unless those are donated on top of the $1,000 so all the money goes back to the community. I think the first grant they ever gave was to a dental clinic to help with low income people and so on. But it's a fascinating organization, as I said, it's called Impact 100 and she started it because as a child, she was very much involved in giving back, and for a while she she didn't. And then it started again when her father passed away, and she realized how many people from the community supported her and the rest of her family because they didn't have the tools or the resources to do it all alone. Yeah, so I'm not surprised that you have the story of giving back and that you continue to do that, which is really pretty cool.   Michael Bervell ** 08:36 Well, I think I actually heard a statistic that I think they tried to track how early childhood development, or just early adulthood, affected later adulthood. I think one of the findings was that people who volunteered when they were in middle and high school or significantly more likely to volunteer later in life than those who never did. And so there is a certain level of kind of you know, how you experience the world in your early ages and your early days affects your potential to want to make a change, especially as it relates to giving back or giving time or money or whatever effort, whatever it might be, I think is a really interesting concept. Well,   Michael Hingson ** 09:14 it makes sort of perfect sense, because as you're growing up and you're forming your life, if you see that you're doing things like giving back or being involved in supporting other people, and that is a very positive thing, it makes sense that you would want to continue that in some way.   Michael Bervell ** 09:33 Yeah, yeah. I mean, it reminds me also of just like habits. You know, you build your habits over time, and it starts from super young ages not to say that you can't change habits. There's a bunch of research about the science of habit change and how to break a habit loop, and Charles Duhigg is a great author in that space, but it's also just really interesting just to think through that. But yeah,   Michael Hingson ** 09:54 and habits can be hard to break, or they can be easy if you're really committed. Into doing it. But I know a lot of people say it, it's fairly challenging to change or break a habit.   Michael Bervell ** 10:06 Exactly, yeah, exactly.   Michael Hingson ** 10:09 Unfortunately, sometimes it's all too easy to make a habit. But anyway, there you go. Yeah,   Michael Bervell ** 10:14 my one of my it's, it's funny, because after you know one of my habits I made when I was in high school that, to my mom's chagrin, was I used to always love just doing work on my bed. The positive thing about the habit was I was always comfortable. The negative thing is I would sometimes fall asleep. So many times I mid paper, you know, mid take home exam, fall asleep. I have to wake up and scramble to finish. But that doesn't show me a faster writer. If anything   Michael Hingson ** 10:41 I remember, when I was in graduate school at UC Irvine, I had an office of my own, and I was in it one day, and I was looking at some material. Fortunately, I was able to get most of the physics texts in Braille, so I was studying one, and the next thing I knew, I woke up and my finger was on the page, and I had just fallen asleep, and my finger for reading braille, was right where I left off. Always thought that was funny,   Michael Bervell ** 11:14 yeah, just a just a quick, just a quick pause. You just pause for a second, even   Michael Hingson ** 11:18 though it was about 45 minutes, but whatever. But my figure didn't move.   Michael Bervell ** 11:24 You really focused, you know, just That's it. That's it.   Michael Hingson ** 11:27 The advantage of Braille, exactly. But, you know, I do think that it's great to have those kinds of habits, and I really wish more people would learn the value of giving back and sharing, because it will come back to benefit you so many times over.   Michael Bervell ** 11:48 Yeah, yeah. I mean, what's even what influences me, like now and even throughout, you know, post high school, like when I went into college, I knew I wanted to be in some sort of service and giving back type of industry, but I didn't really know what that was, right, like, I didn't want to do want to do philanthropy full time, because I found it difficult, right? Like, I found it hard to have to go back to investors, and I found it difficult to sometimes sell the vision. And my question was, is there a way to make this more sustainable? And so I spent a lot of my time in school and college just learning about social impact, which, at the time was just coming up, like a lot of those impact investment funds, impact bonds, the idea that you can tie finance to impact, and you can have carbon offsets that people buy and sell, that has some sort of social good, that you can somehow transact. All these kind of new and interesting ideas were coming around, and it started, it just got me interested, right? It's, you know, can I make a habit of creating an impact, but also habits somehow work within, you know, this capitalist system that the world operates in. It's something I've been wrestling with, you know, even in all my my future business and kind of current business, work and practices.   Michael Hingson ** 12:58 What do you do when you propose an idea or have a thought, and you discuss with people and they object to it. How do you handle objections?   Michael Bervell ** 13:05 Yeah, I mean, I think, I think for me, I'm always interested in the root cause, right? I think I'm one who tries to understand first before trying to persuade. So I could give you an example, I think very early in my, very early my college career, I realized that my parents would be able to pay for college for me. That was the youngest of three. And, you know, they'd use a lot of their savings on my siblings, about the who ended up going to med school, which is very expensive, yeah, college, which was also very expensive. And being immigrants from Ghana, of course, they hadn't saved up an infinite amount of money. So my mom sat me down and told me, Hey, you have to pay your own tuition. And so, you know, the person I had to convince to kind of help me here was actually funny enough, restaurants are in Harvard Square, and the reason why is I decided to make a business that did restaurant consulting. So I went door to door, and I would ask people and like, hey, you know, do you need 20 Harvard students to come and help you understand how you can get more foot traffic in the door. You know, sell more pizzas or sell more burritos. I think I heard 20 or 30 knows. And finally, one woman said, Well, you know, if, if, if, if you think that you can do it, then, you know, show me. Show me the numbers, right? And that was, that was really interesting. And so I think it realized, you know, when I when she initially said, No, I said, Well, why not? She said, I just don't know if you can do it. And when I said, Oh, we can actually show you the proof, she's like, Okay, well, then if you can run a pilot and show me the proof, then I'll do it. And so understanding the why, I think, is more important than getting the rejection and, you know, getting the setback. But that's try to, that's how I try to deal with it.   Michael Hingson ** 14:38 One of the things that I learned fairly early on, when I was put in a position of starting to sell for a living, actually, in Cambridge, working for Kurzweil Computer Products and taking a Dale Carnegie sales course was stay away from asking closed ended or. Yes, no questions. And so most of the time, I wouldn't say, you know, can we do this? Or would you do this? I would say, I'd like to hear your thoughts about or we've got this idea, tell me what you think, and doing other things to get people to talk. And when I started using that in my career, it was easy to get people to talk because they they want to talk. Or, as I like to say, people love to teach, and most of the time, if you establish a relationship with people and they know you're listening, they're welcome, or they're willing to give you wisdom. And so there are so many examples I have of asking open ended questions like that, or I went into a sales meeting with one of my employees, and there were a bunch of people there, and I said, Tell me to the first person I talked with, tell me why we're here. And it totally caught him off guard. Of course. The other thing is that they didn't realize that the sales manager who was coming, that the the guy who had set up the appointment was was told to bring his manager, and they didn't realize that the sales manager was blind, which also was a great addition to help. But again, I didn't ask, so you want to take backup system, but rather tell me why we're here. Tell me what you're looking for. Why are you looking for that? What do you want it to be? And I actually realized by the time I went around the room that our product wasn't going to work, but we still did the PowerPoint presentation. And then I said, if case you haven't figured it out, our system won't work, and here's why, but here's what will work. And that eventually led to a much larger order, as it turns out, because they called back later and they said, We got another project, and we're not even putting it out for bid. Just tell us what we pay you, and we'll order it. And it's it's all about. The objections are really mostly, I think, from people who maybe have some concerns that you didn't learn about because you didn't ask an open ended up or the right question, which is something that only comes with time.   Michael Bervell ** 17:15 Yeah. I mean, I think it also sounds very similar to like, what journalists are are trained to do, like a great journalist. And I took a journalism class a few years ago, maybe five years ago, with Joe Abramson, who was one of the first female executive, executive editors of the New York Times. And this was kind of her exact lesson. Is that everyone has some story to teach, some wisdom to share, and the difficulty, or really the challenge on you as an interlocutor, as a journalist, as someone whose job it is to uncover the story, is to ask the right questions, yeah, to allow that person the space to teach.   Michael Hingson ** 17:51 And if you and if you don't know the right questions, you ask something open ended, enough that maybe you'll get to it.   Michael Bervell ** 17:57 Yeah, exactly, exactly. And then the flip side, right, because there's, of course, you can't put all the burden on the person, no, right? You have to be an active listener. You have to listen to know, and then you have to prod and even say something like, Tell me more. Yeah, exactly right. Questions like, Tell me more, her second favorite question was, and then what happened? Yeah, right. Those are two such simple things, you know? And then what? Yeah. And it's just such an opening to really evolve and to grow.   Michael Hingson ** 18:23 And if they really think you're listening and that you want to know and understand, people will talk to you exactly which is, which is really what it's about. Well, so you did all of your so you went to high school in Seattle, correct? Yeah. And, and then what did you do?   Michael Bervell ** 18:43 Yeah. So High School in Seattle Graduated, went off to Boston for college, where, you know, of course, had to figure out a way to pay for school. And that was my first, I guess, for profit business. Was this restaurant consulting company. And of course, like I said, everything I want to do in my in my life, was focused on social impact. So the impacts there was that we only hired students to work for us who needed to pay tuition. There was this program called federal work study where, if you get trade, you have to, you know, work as part of a federal mandate for some amount of hours per week, and that was the book study requirement. And for the most part, students would do on campus jobs that would pay 10, $15 an hour to do this work study. Well, I'd spent up this consulting business as a sophomore that I then ran for all three years, and on an hourly basis, we were making significantly more than that, right? So I was able to go find students who traditionally had been working their whole life, right? Harvard has such a, you know, vast background of individuals. I knew, people who were homeless, people who were billionaires and everyone in between, who ended up coming to the school and so to find people who you know had been working 40 hours a week since they were in middle school, and give them a job where they could work less and actually have more free time to invest in their community or invest back into developing new skills, was, for me, super, super impactful. On the surface, it was a restaurant. A consulting business, but behind the scenes, what we were doing with our staffing and with our culture was was around that social impact. So I stayed out in in Cambridge for for four years, studied philosophy. I got a minor in computer science, and eventually went off to Microsoft back in in Seattle, where I eventually then, you know, was product manager and was a venture capital investor, and met a bunch of really phenomenal and interesting people who were pushing technology forward.   Michael Hingson ** 20:27 Now, why Harvard, which is all the way across the country?   Michael Bervell ** 20:33 Yeah, I mean, well, I think I love traveling. I loved, I loved, you know, being out and about, and I think growing up as the youngest of three, and also as the child of African immigrants, they'd always told me, you know, we moved here for you, like we moved 3000 miles away to a country where you don't speak the language, where you don't know anybody for you. And what they meant for that is, you know, we want you to really thrive. And even you know, now I'm at the age when my parents had first moved right to the US, and I can't imagine moving to a country where I don't know the language, don't know the people, and don't know a soul for my potential future children. And their children, that's what they did, and they invested a lot of time and energy and effort into me. And they always told me, you want you to be really successful. And so I remember when I was when I was in middle school, my sister got into Harvard, which was unheard of, right? No one in our high school had gone to Harvard in the past, especially not for, you know, a black family in a primarily white neighborhood, for one of us to go to Harvard was was a big deal. And so I knew that, you know, at the very least, for my parents, for my sister, for my family, I wanted to kind of match up to that   Michael Hingson ** 21:43 well, and it certainly sounds like you've, you've done a lot of that. Oh, here's a an off the wall question, having been around Cambridge and worked in Cambridge and all that is cheapo records still in Harvard Square.   Michael Bervell ** 21:57 Oh, man. You know what's so funny, I got a record player. I got a record player last semester, and I don't remember if cheaper records, that's the one that's like, I think I've is that the one that's in like, the actual, like, it's by, like, Kendall, take by Kendall, Kendall Square.   Michael Hingson ** 22:15 No, I thought it was in Harvard Square. Okay,   Michael Bervell ** 22:19 I think, I think it still exists. If I'm not mistaken, I think it still exists. I think I got a lot, got a lot of records from cheapo over the years record stores in Cambridge. And because I got a record player as a gift, I've been, I've been collecting a lot more,   Michael Hingson ** 22:31 ah, yeah, um, I've gotten a lot of records from cheapo and over the years. And of course, not so much now, since I'm out here. But next time I get back to mass, I'll have to go check,   Michael Bervell ** 22:43 oh yeah, oh yeah, yeah. We can do a cheapo records hanging how tactile It is, yeah, yeah.   Michael Hingson ** 22:52 There used to be one in New York that I would go to. They were more expensive as New York tends to be colony records, and they're not there anymore, which is sort of sad, but cheapo. Cheap just seemed to be one of those places that people liked. I don't want to say it was like a cult, although it sort of is all the dedicated people to to real vinyl, but I hope it's still   Michael Bervell ** 23:16 there. Is it? It's a chain of record stores, or is it just,   Michael Hingson ** 23:18 no, I think it's a one. Oh, yeah. If there's more than one, I'm not aware of it, I'd   Michael Bervell ** 23:23 probably say I'm 80% certain it still exists. Well there,   Michael Hingson ** 23:27 yeah, so have to come back to mass. And yeah, I'll have to go to cheaper records and Legal Seafood.   Michael Bervell ** 23:32 Oh yeah, Legal Seafood. That was, yeah, I love Legal Seafood musical all the time with my roommates from college. And, yeah, we used to order the crab cakes and eat lobster rolls. It's a great time.   Michael Hingson ** 23:44 Yeah, and then their little chocolate desserts, which are great yeah, and the chowder. Oh, well, yeah, yep, gotta, gotta get back to mass. Okay. Now whoever   Michael Bervell ** 23:53 you're listening is probably getting hungry. Well, you know,   Michael Hingson ** 23:57 as as they should, you know, you know why they call it Legal Seafood. I actually don't know nothing is frozen. It's all fresh. It's legal. Oh, I love that. I love that, at least that's what I was told. Yeah, that's pretty cool. Well, so you, you went to college and went then back to Seattle and worked for Microsoft and so on. So clearly, you're also interested in the whole idea of investing and the whole life of being an entrepreneur in various ways. And so you brought entrepreneurialism to everything that you did.   Michael Bervell ** 24:35 Yeah, yeah, yeah. I mean, that was my first job at Microsoft. I was, you know, managing what's called Windows IoT. So we were putting software on everything that wasn't a phone or a laptop. So think, you know, smart screens in airports, or screens in Times Square, or, you know, the type of software that your Amazon Echo, you know, maybe not Amazon in particular. But what that would run on that was working on IoT all these. They called it headless devices, yeah, devices with no screens. And that was my team for a little bit. I worked there for about year and a half. It was phenomenal. You know, we were managing multiple billions of dollars in revenue, and there was only, you know, 4050 people on my team. So you do the math, we're all managing hundreds, 10s to hundreds of millions of dollars in our products. And while I loved it, I realized that my my true passion was in was in meeting people, talking to people, and giving them the resources to succeed, versus giving them the actual technology itself. I loved being able to connect an engineer, you know, with the right supplier to work on a hard problem that could then be built for Microsoft to eventually get to a customer. And that sort of connection role, connector role is kind of the role of a venture capitalist. Yeah, right. You're connecting your limited partners who have invested in this fund to entrepreneurs who are trying to build some sort of idea from the ground up. And, you know, once you invest in the entrepreneur, then connecting the entrepreneur to mentors, to advisors, to potential employees, to potential customers. And so there's this value in being someone who's a listener, a journalist, right, like we had been talking about someone who has a habit of trying to make a broader impact. And it kind of all aligned with what I had been building up until that point. So I worked at M 12, it's Microsoft's venture capital fund, and invested in in a bunch of companies from Kahoot, which is like an education startup, to obviously open AI was a Microsoft investment as well, to other things like that. And so it was cool, because, you know, the fund was, was really, we had the mandate of just find cool companies, and because we were Microsoft, we could reach out to any founder and have a conversation. So it was, it really was a few years of just intense and deep learning and thoughtfulness that I wouldn't, I wouldn't trade for anything. What got   Michael Hingson ** 26:58 you started in the whole arena of thinking about and then being involved with digital accessibility, because we've talked about that a lot. I know that's a passion. So how did you get started down that road?   Michael Bervell ** 27:11 Yeah, I mean, it came partially through working at Microsoft, right? I mean, as I was at Microsoft, Satya Nadella, who was the CEO, he was making big, big investments into digital accessibility, primarily because his son, now, his late son, had cerebral palsy, and a lot of the technology at Microsoft, his son couldn't use, and so he had this kind of mission and vision to want to make more accessible technologies. But my first exposure to it even before then, like I said, in college, I had to work all these, all these jobs to pay tuition, and I built my own business, but one of the clients we consulted for was a large search engine. I'm sure you can imagine which one it was, and it wasn't Microsoft, and that were search engine. I helped them devise their ability strategy.   Michael Hingson ** 27:56 You mean the G word, something like that? Yeah.   Michael Bervell ** 28:00 Yeah. Duck, duck, go, yeah. No, that's it. Yeah, exactly. And so it was really cool to work with them and to see like at scale, at 200,000 employee scale, at 1000 product scale, how do you create systems and guardrails such that accessibility, in this case, digital accessibility, will be something that that actually ends up happening. Ends up happening. And so that was my first exposure to it. And then again at Microsoft. And then finally, a third time, while I was in business school, you know, working on various projects with friends. And one friend told me, you know, all I did at work this week was have to fix accessibility bugs because my company got sued. And that was and just all those moments combined with the idea that I wanted to impact the deep empathy that comes through learning and knowing and understanding people's backgrounds and histories, all of it came to a head with what I now work on at test party.   Michael Hingson ** 28:57 So now, how long has test party been around? And we'll get to that up. But, but how long have you had that?   Michael Bervell ** 29:03 Yeah, we started. We started about a year ago. Okay, so it's pretty recent,   Michael Hingson ** 29:07 so yeah, definitely want to get to that. But, so the whole issue of accessibility, of course, is a is a thing that most people don't tend to know a lot about. So so let's start this way. Why should people worry about making products and places like websites accessible? And I know websites, in a lot of ways, are a lot easier than going off and making physical products accessible, especially if they're already out, because redesign is a very expensive thing to do, and is not something that a lot of people are going to do, whereas, when you're dealing with websites, it's all about coding, and it's a lot easier. Yeah,   Michael Bervell ** 29:48 yeah. I mean, I think, I think fundamentally, it comes down to, you know, a set of core beliefs. And I think we could all agree, and I think we would all believe that, like everyone has the right to. You a decent, fulfilling and enjoyable life. I think regardless of where you fall on, you know, belief spectrums or anything, that's something that we all fundamentally believe. You know, you should live well. You should try to live a good life. It's what people talked about in writing for years. And I think when you think of the good life in today's terms, in the 21st century, it's almost inseparable from a life that also engages with technology, whether it's cell phones or computers or whatever it might be, technology has become so fundamental into how we live that it now has also become part of how we live well and how we live a good life. And I'll give you a clear example, right? Let's suppose you really believe that voting is part of living the good life. There is a time, 100 years ago, you know, you didn't need to really have a car. You could get a rehearsing buggy. Maybe you could even walk to a voting station and cast your vote in today's world, especially, let's suppose a COVID world, and even a post COVID world, computers, technology, websites, are fundamental in living that good life, if that's your belief system. And you can play this game with any belief that you have, and once you extrapolate into what does it take for you to do that thing in the best way possible? It almost inevitably, inevitably, you know, engages with technology. Yeah, so why do I think having accessible websites are important? Well, it's because pretty much 195 people has a disability of some sort, and so to live the good life, they have to engage technology. And if that technology is not working for them for whatever reason, then that needs to be fixed. That needs to be changed. And of course, there's the guardrails of laws, you know, ADA, Americans with Disabilities Act, EAA European Accessibility Act and others that try to mandate this. And of course, there's the goodwill of companies who try to do this proactively. I think Apple is a really good example, and Microsoft as well. But fundamentally, the question is, you know, what is a good life? How do you enable people to live that? And I think through technology, people should be able to live a better life, and should not have any barriers to access.   Michael Hingson ** 32:02 The thing is, though, take apple, for example. For the longest time, Apple wouldn't do anything about making their products accessible. Steve Jobs, jobs basically told people to pound sand when they said, iTunes, you wasn't even accessible, much less the iPod and the iPhone and the Mac. And it wasn't until two things happened that they changed really. One was target.com target had been sued because they wouldn't make their website accessible, and eventually too many things went against target in the courtroom, where they finally said, Okay, we'll settle and make this work. When they settled, it cost them $8 million to settle, whereas if they had just fixed it up front, the estimate is that it would have been about $40,000 in time and person hours, but because of where the lawsuit was filed and so on, it was $8 million to settle the case. And so that was one thing, and the other was it had been made very clear that Apple was the next company on the target list because they weren't doing anything to make their product successful. Well, Apple suddenly said, Okay, we'll take care of it. We will deal with it. And I think they had already started, but they and so as not to get sued, they said, We will do it. Well, probably the first thing that happened was the iPhone 3g well, maybe it wasn't the three, it was earlier, but the iPhone became accessible. The iPod became accessible. Pretty much all of them, iTunes, you the Mac. So by 2009 last when I got my iPhone 3g Apple was well known for making their products accessible, and they did it in a very clever way. It was accessible right from the outset. You didn't have to buy other stuff to make their products work. No need to buy a new screen reader or any of those kinds of things. So they spread the cost over every product that they sold, whoever bought it, so anyone who buys an iPhone can invoke accessibility today, which, which was cool, yeah,   Michael Bervell ** 34:09 yeah. And I think through Apple, I mean, I think the initial argument I made for why is it import to make websites accessible was an ethical argument, right? I think in Apple's case, they, they probably did the business case analysis and understood this actually does make economic sense. And I think what you see today is there is even more economic sense because of the expanding market size. Right? Think the aging population that will develop some sort of disability or impairment, right? That's really growing larger, right? Think about, you know, individuals who may have what people call temporary disabilities that are not permanent, but last for some period of time, whether it's, you know, nine months, 10 months, two years, three years, and those types of things. So I think there is, there's also a business case for it. I think that's what Apple as a case study has shown. What you bring up, though, is, does it matter? Does it really matter? Like, why companies start doing this, right? And I think that's a question, you know, to grapple with. You know, if Apple did it out of the goodness of their heart versus because they didn't want to get sued, but the downstream effects are the same, you know, does that matter? And, you know, question, Do the ends justify the means? In this case, the ends are good, at least just by the start, perhaps, but sure that interesting question so, but I do think that they have done really good work   Michael Hingson ** 35:27 well. And you and you brought up something which, you know we talked about, which is that you talked about one company that dealt with some of because they got sued. And litigation is all around us. Unfortunately, we're a very litigious society and in our world today. So so like with accessibe, that that I work with, and work for that company, and a lot of what I do, some people have said, well, accessibe shouldn't always use the idea that, well, if you don't make your website accessible, you're going to get sued. That's a bad marketing decision, and I think there are limits, but the reality is that there are lawyers who are out there who still haven't been muzzled yet, who will file 5060, 100 complaints just to and they get a blind person to sign off and say, Yeah, we support this, because they'll get paid something for it. But they're not looking to make the companies deal with accessibility. They just want to earn money, 10,015 $20,000 per company. But the reality is, part of the market is educating people that litigation is a possibility because of the fact that the internet is a place of business under the Americans with Disabilities Act.   Michael Bervell ** 36:54 Yeah, exactly. I think when you think of like, you know, what is the purpose of litigation? Again, I, as a philosophy guy, I always think back to first principles, and it really is a deterrent, right? Obviously, no one wants to get sued. And, of course, no one wants to pay damages, punitive or reparative. And so in this case, these are all examples of punitive damages that people are paying for not having done the right thing. Right? In in, in the best case, you do the right thing to begin with. But I think it's, you know, the consequence of not doing the right thing. I think, of course, there's the question of you described, kind of these lawyers, or what people call as kind of the trolls who are just kind of suing and, you know, reaping the benefits from this. And I think it's an unfortunate side effect. I do wish that there was a world where these trolls wouldn't even need to exist, because things are working perfectly, right, well,   Michael Hingson ** 37:45 and the reality is that it goes back far earlier than the internet. I mean, there are places, there are people who would drive around and make people in wheelchairs who might find the smallest by violation wasn't even necessarily a legitimate violation, and they would sue and so and so. It isn't anything new that is just with the internet. Yeah, it's been going on for years. Yeah,   Michael Bervell ** 38:11 those are the drive by lawsuits. I remember I heard about those, and I think it's, this is the digital equivalent of that,   Michael Hingson ** 38:16 right? Yeah, right. And it is an issue, and it is something that that needs to be dealt with, but you also talk about doing the right thing, and that's really the better reason for doing it. If you do, you really want to exclude up to 20% of your potential business by not making your website accessible. Or better yet, if you make your website inclusive for all, what is going to happen when somebody comes to your website looking for a product and then they buy it because they were able to are they going to come back to that website? Are they going to go looking elsewhere? And there are so many studies like Nielsen did studies, and others have done studies that show absolutely people appreciate brand loyalty, and when they feel that they're they're valued and included, they're going to stick with that company.   Michael Bervell ** 39:12 Yeah? But even with that said, right, there's so this conflict of we all logically know it's the right thing to do, there's business purpose for doing it, and yet people don't do it. Yeah, 97% of the internet is still not accessible, if you look at this correct right? And so our hypothesis release, what we take, and what I take as a business is that sometimes, if it's too hard to do the right thing, people won't do the right thing, but that's what they want to do. And so how do you make it easier to do the right thing? And that's hopefully what, what we're what we're hoping to change in the industry, is just making it easier and also letting people know that this is an issue. One   Michael Hingson ** 39:48 of the one of the criticisms, oh, go ahead. Go ahead. A lot of people   Michael Bervell ** 39:52 don't, don't do the right thing, because just don't know that there is a right thing to do. You know   Michael Hingson ** 39:56 right well. And one of the criticisms I've heard over the. Years, especially dealing with the products like accessibe is, well, the problem is, you just slap this AI thing on their site, you're not teaching them anything, and that's not a good thing. And with manual coders, they're going to teach people. Well, that's not true either, but, but this whole argument of, well, you just put it on there, and then you go away, which isn't true, but again, that's one of the criticisms that I've heard any number of times, and that you're not really educating people about accessibility. You're not really educating them much about it. And the answer is, look, the company that wants to do business came to you in the first place. So they obviously knew they had to do something.   Michael Bervell ** 40:44 Yeah, yeah. And I think when I think through it, it's like, how do you make sure that the downstream effects of whatever you do is just positive and beneficial, right? And the ideal, as we all agree, I think, would be just to build it right the first time. Whether it's physical buildings, build a building right the first time. Or, if it's websites, build the website correctly the first time. Whatever helps people to get to that stage and that level of thinking and habits I think are, are ideal   Michael Hingson ** 41:13 coming from your background and so on. You know now that there are two basic ways that people can work to make websites accessible. One is the traditional way where you have someone who goes in and codes in the access and puts it right on the website. And now, over the past several years, the other way that has come into existence is the whole concept of using as accessibe does AI and although AI won't necessarily do everything that needs to be done, it will do most of what needs to be done, and maybe everything, depending on how complex the website is. But what do you think about the whole fact that now AI has entered into the accessibility world and people are using it?   Michael Bervell ** 42:02 Yeah, I think AI is interesting. And I think AI is a tool. I think it's it's a tool that's been developed, obviously, over a long history, right? Like the first artificial intelligent computers were in the 60s and 70s, being able to predict things, and of course, you heard of AlphaGo and computers that could pay chess and all these different things. So I think we'll definitely be surprised by what AI can do as a tool, right? And the question is, it will be, you know, the panacea, the thing to cure it all. Well, we all love for that to be the case. Who knows? You know, if it'll be AI, maybe functionally, AI could do that. But in terms of compute power, you know, it won't be able to until we have quantum computing or something right, in which case maybe it'll leapfrog this whole type of technology, and maybe web page will be obsolete in a decade, and then this whole idea of even needing to use AI to fix web pages will be replaced something else, like, like Be My Eyes, or something like that. That's even more advanced. But I think, as I see it, it's a tool that can be used to make it easier. And whether it's ease of use in terms of physical effort, ease of cost, in terms of bringing down costs to you know, to make a website compliant or a digital asset compliant, or just ease of understanding, right? Someone can explain to you what these really complicated rules mean, and so you can actually think about it from day one. So I think AI as a tool can lead to ease, which can then furthermore lead to hopefully more accessible products.   Michael Hingson ** 43:30 Well, the first time I ran into real AI was working with Ray Kurzweil back in the late 70s. He developed a machine that would read print out loud to blind people. But one of the things that was unique about them, well, vinyl, whether it's totally unique, but certainly was unique for blind people and for most of us, was the fact that the more the machine read, the better the reading got. It actually learned, and it learned how to to understand and analyze its confidence. And so it would get better the more that it read. Chris. The only problem with that is, back in those days, the software was on a cassette that went into a player that was part of a Data General, Nova two. And so it had to learn all over again every time you rebooted the machine and loaded the program. But that's okay. It learned based on on what you were reading, but it really dramatically got better the more you read. And I think that today, the reality is that a lot of people really need to. And I would say this is true of manual coders. And I know a few who have adopted this, they'll use accessibe to do what it can do, and then they, in turn, then go and address the issues that access a B's widget doesn't do. And for me, my. My learning that lesson actually goes back to the mid 1980s when I couldn't get a job, and I started my own company selling computer aided design systems to architects. And a lot of architects would come in and say, well, we can't buy your system. Yeah, great. It works, but if we use it, we'll develop our drawings in a fraction of the time, and we can't charge what we did, because now we're not spending as much time, and I said you're missing the whole point. You change your model. You're not charging for your time. You're charging for your expertise. You don't need to charge less. And what you do is then you go off and you get more projects, but you can also do more for each individual customer that you bring in. We had access to a system that was a one of the early PC based three dimensional solid metal modeling CAD systems, so people could come into our office, or anybody who bought the product could could invite their customers in, and they could do actual walk throughs and fly throughs of buildings. They had light sources or Windows to look out. You could even see what was going on outside. It wasn't renderings. You actually saw everything right on the computer. Those are so many things that revolutionize the industry. Now, of course, CAD is everywhere as it should be, and the reality is that that I think that any manual programmer who is programming a website could use accessibe to do a lot of the work, and then an accessibe also has some tools using a product called Access flow, where they can analyze and even tell you exactly what you need to do with the things that aren't accessible, and then you can do it, but you can use accessibe to do most of the stuff, and it continuously monitors it's a scalability issue, and you don't get any scalability with manual coding at all. So again, it's the whole, as you point out, the whole tool of artificial intelligence really can make a big difference in what we're doing to create accessibility on in the internet and in so many other ways as we go forward.   Michael Bervell ** 47:06 Yeah, and already we're running right up on time with a minute or two left. But I think even fundamentally, what you're what you're describing, back to first principles is, is, if we make it easier, either in time or in effort or in understanding, to make things accessible. Will people do it right? Whether you're using, you know, an access to be or whether you're using another tool, there's this question, How will it help? And will it help? And I think in evaluating any tool, and really I can apply in so many cases, that's the core question task.   Michael Hingson ** 47:37 Since we started late, it's up to you, but time wise, we're fine. It's up to you, but I realize that we want to end fairly soon here, but I think you're right, and that gets back to the whole education issue. People really need to learn and understand the value of accessibility, why it's a good thing, and it's kind of hard to argue with losing 20% of your business because your website's not accessible. And accessible, and the reputation that you gain by not doing it can go beyond that 20% when people tell their own friends about the issues they're facing. Yeah, exactly, exactly. But it goes the other way. You make it accessible, and you get all sorts of accolades. That's going to help too. But it is a conversation that we need to have, and it's part of the whole big conversation about disabilities. In general, we don't really see disabilities as much in the conversation. When we hear about people talking and discussing diversity, they talk about race, gender, sexual orientation, so on, but they don't talk about disabilities, and disabilities tend to be left out of the conversation for the most part, which is extremely unfortunate. Why do you think that is?   Michael Bervell ** 48:46 Yeah, I think, I think it comes down to, I'm not, I'm not sure why it is. I'm not sure. But I think even though I'm not sure why it is, I do know what I hope. And I think what I hope is for, you know, a world where every, every part of society reflects what it's made up of, right? So you look and it's representative of of all the constituents, people with disabilities, people of different genders and races and and so on and so forth, so, so I think that's what I hope for. I think it's difficult, right? It's difficult based on the systems that have been made people's biases and more to get there, but I do think, I do think that's ultimately the hope. But I   Michael Hingson ** 49:30 think that a lot of it comes down to fear people. Fear people with disabilities. I think that the whole fear factor, and even with race or gender or sexual orientation, so on, some of the comments, if you listen to them, all they're doing is promoting fear which which doesn't help at all. But in the case of disabilities, oh my gosh, I could become blind or paralyzed in a second, and that fear is something that we really don't tend to you. Do nearly as much about as we should. Now I know you and I earlier talked about fear, and the reality is that that we can learn to control fear. I would never tell people don't be afraid. No such thing as not being afraid, but you can certainly learn to control fear so that you can use it again as a very powerful tool to guide you and help you, and that's what the best aspects of fear are all about. I think, yeah,   Michael Bervell ** 50:26 I totally agree. I totally agree. Well, speaking of fear, I would be afraid of what might go I'm a president for Section G, which is one of the sections here, HBS, and we have to go select our Class Day speaker. So I'd be afraid if I, if I missed too much of the well, if they,   Michael Hingson ** 50:43 if they want to hire a speaker, I'm just saying I know Mike was, I was like, Man, I wish I had met you, like, back when you're doing our, our, like alumni and friend speakers. On the other hand, we can certainly talk about next year, and I would love to do that. Well, I want to really thank you for being here. I think we'll just have to have another discussion about all of this in the future. But I really appreciate you being here a lot and chatting very, very frequently, and you're going to go off and play drums later too, right? Oh, yeah,   Michael Bervell ** 51:11 it's a busy I'm in my, you know, Shirley retirement era, you know, yeah, right. Go back into, back into the workforce.   Michael Hingson ** 51:19 So, real quick, though, you wrote a book. What's it called?   Michael Bervell ** 51:23 It's called unlocking unicorns. I'll send you a copy of the book, and so you can put in the show notes and everything else. Yeah,   Michael Hingson ** 51:29 that would be great. And if people want to reach out to you, how do they do that? Yeah,   Michael Bervell ** 51:34 but just my name, Michael purvell, M, I, C, H, A, E, L, B, E, R, V, E, L, l.com, contact my website. Is there? My bio, and this podcast will be there eventually   Michael Hingson ** 51:46 as well it will, and you'll get all the info. Well, thanks very much, and I want to thank you all for listening. Really appreciate you listening to us today. I'd love to hear your thoughts. Please email me at Michael, h, i, m, I, C, H, A, E, L, C, we spell our names the same. H, I at accessibe, A, C, C, E, S, S, I b, e.com, or go to our podcast page, www dot Michael hingson, H, I N, G, s, o, n.com/podcast, and would love to to hear your thoughts. Love it. If you would give us a five star review wherever you're listening. If you know anyone else who ought to be a guest, please introduce us. We're always looking for it. And I would also say if anybody needs a speaker, it is what I've been doing ever since September 11, and I'm always looking for speaking opportunities. So please reach out and let's see if we can chat and and one of these days, maybe we'll get Michael to bring us up to Harvard we can go visit the coupe. But thanks so much for listening, everyone. Thanks once more for thanks. Once more Michael, for being here. Thanks.   Michael Hingson ** 52:52 You have been listening to the Unstoppable Mindset podcast. Thanks for dropping by. I hope that you'll join us again next week, and in future weeks for upcoming episodes. To subscribe to our podcast and to learn about upcoming episodes, please visit www dot Michael hingson.com slash podcast. Michael Hingson is spelled m i c h a e l h i n g s o n. While you're on the site., please use the form there to recommend people who we ought to interview in upcoming editions of the show. And also, we ask you and urge you to invite your friends to join us in the future. If you know of any one or any organization needing a speaker for an event, please email me at speaker at Michael hingson.com. I appreciate it very much. To learn more about the concept of blinded by fear, please visit www dot Michael hingson.com forward slash blinded by fear and while you're there, feel free to pick up a copy of my free eBook entitled blinded by fear. The unstoppable mindset podcast is provided by access cast an initiative of accessiBe and is sponsored by accessiBe. Please visit www.accessibe.com . AccessiBe is spelled a c c e s s i b e. There you can learn all about how you can make your website inclusive for all persons with disabilities and how you can help make the internet fully inclusive by 2025. Thanks again for Listening. Please come back and visit us again next week.

The board game Go has more possible board configurations than there are atoms in the universe.Because of that seemingly infinite complexity, developing software that could master Go has long been a goal of the AI community.In 2016, researchers at Google's DeepMind appeared to meet the challenge. Their Go-playing AI defeated one of the best Go players in the world, Lee Sedol.After the match, Lee Sedol retired, saying that losing to an AI felt like his entire world was collapsing.He wasn't alone. For a lot of people, the game represented a turning point – the moment where humans had been overtaken by machines.But Frank Lantz saw that game and was invigorated. Lantz is a game designer (his game “Hey Robot” is a recurring feature on The Tonight Show Starring Jimmy Fallon), the director of the NYU game center, and the author of The Beauty of Games. He's spent his career thinking about how technology is changing the nature of games – and what we can learn about ourselves when we sit down to play them.Mentioned:“AlphaGo”“The Beauty of Games” by Frank Lantz“Adversarial Policies Beat Superhuman Go AIs” by Tony Wang Et al.“Theory of Games and Economic Behavior” by John von Neumann and Oskar Morgenstern“Heads-up limit hold'em poker is solved” by Michael Bowling Et al.Further Reading:“How to Play a Game” by Frank Lantz“The Afterlife of Go” by Frank Lantz“How A.I. Conquered Poker” by Keith Romer“In Two Moves, AlphaGo and Lee Sedol Redefined the Future” by Cade MetzHey Robot by Frank LantzUniversal Paperclips by Frank Lantz

2016 höll världen andan när AI-modellen AlphaGo utmanade världsmästaren i spelet Go och vann. Nu belönas Demis Hassabis, hjärnan bakom modellen, med Nobelpris men för en helt annan upptäckt. Lyssna på alla avsnitt i Sveriges Radio Play. Bara åtta år gammal köper Demis Hassabis sin första dator för vinstpengarna från en schackturnering. Som vuxen utvecklar han det första datorsystemet som lyckas överlista en mänsklig världsmästare i ett mer avancerat spel än schack. Vetenskapsradion träffar Demis Hassabis, en av Nobelpristagarna i kemi 2024, i ett personligt samtal – om vägen från schacknörd till Google-elit och Nobelpris.Reporter: Annika Östman annika.ostman@sr.se Producent: Lars Broström lars.brostrom@sr.se

Can AI save lives, create jobs, and solve the world's toughest problems—or will it fuel inequality, mass surveillance, and job loss? Are we ready for AI to replace doctors, influence global power, or even wage wars? What happens when machines become smarter than us? Join us as we question the promises, dangers, and future of AI in this eye-opening episode #47.  00:00 - AI Race 05:21 - How Data and Algorithms Shape AI 10:55 - Optimists vs. Pessimists in AI Research 12:35 - AlphaGo's Go Mastery 15:15 - Cracking Proteins with AlphaFold 16:22 - AI as Your Next Doctor? 18:35 - Algorithms Rule the World 24:13 - Can AI Feel? Humans vs. Machines 31:49 - Inequality & Job Loss 35:03 - Artificial General Intelligence AGI 36:36 - Jobs of the Future 37:24 - Labelling AI Work 38:06 - How AI Transforms Podcasting & Farming 40:33 - AI's inevitable Growth 42:26 - Digital Tags: New Norm? 43:24 - Creating Something Original 43:51 - Social Credit Score 44:46 - AI in Policing & Surveillance 45:32 - What AI Means for Humanity 50:57 - Price of Progress 52:05 - AI's Power Consumption  53:07 - AI Safety Bills 54:13 - Fear of AI 55:27 - What's Next for AI? 57:23 - Punjabi Language & ChatGPT 57:57 - Dark Side of AI 58:29 - AI in the Military 59:09 - Quantum Computing 59:48 - Black Mirror 01:01:27 - Manipulative Chatbots 01:01:57 - Fei Li's AI Take 01:02:23 - Outdated Jobs 01:02:51 - Legislating AI's Power 01:03:43 - AI's Endgame: No Humans?

The story of Groq, a semiconductor startup that makes chips for AI inference and was recently valued at $2.8 billion, is a classic “overnight success that was years in the making” tale. On this episode, I talk with founder and CEO Jonathan Ross. He began the work that eventually led to Groq as an engineer at Google, where he was a member of the rapid eval team – “the team that comes up with all the crazy ideas at Google X.” For him, the risk involved in leaving to launch Groq in 2016 was far less than the risk of staying in-house and watching the project die. Groq has had many “near-death” experiences in its eight years of existence, all of which Jonathan believes have ultimately put it in a much stronger position to achieve its mission: preserving human agency in the age of AI. Groq is committed to giving everyone access to relatively low-cost generative AI compute, driving the price down even as they continue to increase speed. We talked about how the company culture supports that mission, what it feels like to now be on the same playing field as companies like Nvidia, and Jonathan's belief that true disruption isn't just doing things other people can't do or don't want to do, but doing things other people don't believe can be done – even when you show them evidence to the contrary.  Other topics we touched on include: Why the ability to customize on demand makes generative AI different  Managing your own and other people's fear as a founder The problems of corporate innovation The role of luck in business How he thinks about long-term goals and growth — Brought to you by: Mercury – The art of simplified finances. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Learn more⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. DigitalOcean – The cloud loved by developers and founders alike. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sign up⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Runway – The finance platform you don't hate. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Learn more⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. — Where to find Jonathan Ross: • X: ⁠https://x.com/JonathanRoss321⁠  • LinkedIn: ⁠https://www.linkedin.com/in/ross-jonathan/⁠ Where to find Eric: • Newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ericries.carrd.co/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  • Podcast: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ericriesshow.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  • YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@theericriesshow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  — In This Episode We Cover: (04:24) Jonathan's involvement with the DeepMind Challenge Match between AlphaGo and Lee Sedol (06:06) How Jonathan's work Google and how it led him to that moment (08:46) Why generative AI isn't just the next internet or mobile (10:12) The divine move in the DeepMind Challenge Match (11:56) How Jonathan ended up designing chips without the usual background (13:11) GPUs vs. TPUs (14:33) What risk really is (15:11) Groq's mind-blowing AI demo  (16:23) How Jonathan decided to leave Google and start Groq (17:30) The differences between doing an innovation project at a company and starting a new company (19:03) Nassim Taleb's Black Swan theory (21:02) Groq's founding story (24:12) The difference in attitude towards AI now compared to 2016 and how it affected Groq (25:46) The moment the tide turned with LLMs (28:28) The week-over-week jump from 8,000 users to 400,000 users (30:32) How Groq used HBM and what is it (the memory used by GPUs) (32:33) Jonathan's approach to disruption (35:38) Groq's initial raise and focus on software (36:13) How struggling to survive made Groq stronger (37:13) Hiring for return on luck (40:07) How Jonathan and Groq think about the long-term (42:25) Founder control issues (45:31) How Groq thinks about maintaining its mission and trustworthiness (49:51) Jonathan's vision for a capital market that would support companies like Groq (52:58) How Groq manages internal cultural alignment (55:59) Groq's mission and to preserve human agency in the age of AI how it approaches achieving it (59:48) Lightning round You can find the transcript and references at ⁠⁠https://www.ericriesshow.com/⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Eric may be an investor in the companies discussed.

YouTubeとSpotifyでビデオポッドキャスト公開中 ＜目次＞(0:00) OP(0:21) また雑談をしなければならない(2:15)『ビートルジュース』観に行ってきました(6:13) アマプラの裁判員モキュメンタリー『Jury Duty』(11:26)『AlphaGo』ドキュメンタリーを見直した(21:36) AIによる美しさとは(28:00) 赤い洗面器の男 ＜About Off Topic＞Podcast:Apple - https://apple.co/2UZCQwzSpotify - https://spoti.fi/2JakzKm Off Topic Clubhttps://note.com/offtopic/membership X - https://twitter.com/OffTopicJP 草野ミキ:https://twitter.com/mikikusanohttps://www.instagram.com/mikikusano 宮武テツロー: https://twitter.com/tmiyatake1

The machines are coming. Scratch that—they're already here: AIs that propose new combinations of ideas; chatbots that help us summarize texts or write code; algorithms that tell us who to friend or follow, what to watch or read. For a while the reach of intelligent machines may have seemed somewhat limited. But not anymore—or, at least, not for much longer. The presence of AI is growing, accelerating, and, for better or worse, human culture may never be the same.    My guest today is Dr. Iyad Rahwan. Iyad directs the Center for Humans and Machines at the Max Planck Institute for Human Development in Berlin. Iyad is a bit hard to categorize. He's equal parts computer scientist and artist; one magazine profile described him as "the Anthropologist of AI." Labels aside, his work explores the emerging relationships between AI, human behavior, and society. In a recent paper, Iyad and colleagues introduced a framework for understanding what they call "machine culture." The framework offers a way of thinking about the different routes through which AI may transform—is transforming—human culture.    Here, Iyad and I talk about his work as a painter and how he brings AI into the artistic process. We discuss whether AIs can make art by themselves and whether they may eventually develop good taste. We talk about how AIphaGoZero upended the world of Go and about how LLMs might be changing how we speak. We consider what AIs might do to cultural diversity. We discuss the field of cultural evolution and how it provides tools for thinking about this brave new age of machine culture. Finally, we discuss whether any spheres of human endeavor will remain untouched by AI influence.    Before we get to it, a humble request: If you're enjoying the show—and it seems that many of you are—we would be ever grateful if you could let the world know. You might do this by leaving a rating or review on Apple Podcasts, or maybe a comment on Spotify. You might do this by giving us a shout out on the social media platform of your choice. Or, if you prefer less algorithmically mediated avenues, you might do this just by telling a friend about us face-to-face. We're hoping to grow the show and best way to do that is through listener endorsements and word of mouth. Thanks in advance, friends.   Alright, on to my conversation with Iyad Rahwan. Enjoy!   A transcript of this episode will be available soon.   Notes and links 3:00 – Images from Dr. Rahwan's ‘Faces of Machine' portrait series. One of the portraits from the series serves as our tile art for this episode. 11:30 – The “stochastic parrots” term comes from an influential paper by Emily Bender and colleagues. 18:30 – A popular article about DALL-E and the “avocado armchair.” 21:30 – Ted Chiang's essay, “Why A.I. isn't going to make art.” 24:00 – An interview with Boris Eldagsen, who won the Sony World Photography Awards in March 2023 with an image that was later revealed to be AI-generated.  28:30 – A description of the concept of “science fiction science.” 29:00 – Though widely attributed to different sources, Isaac Asimov appears to have developed the idea that good science fiction predicts not the automobile, but the traffic jam.  30:00 – The academic paper describing the Moral Machine experiment. You can judge the scenarios for yourself (or design your own scenarios) here. 30:30 – An article about the Nightmare Machine project; an article about the Deep Empathy project. 37:30 – An article by Cesar Hidalgo and colleagues about the relationship between television/radio and global celebrity. 41:30 – An article by Melanie Mitchell (former guest!) on AI and analogy. A popular piece about that work.   42:00 – A popular article describing the study of whether AIs can generate original research ideas. The preprint is here. 46:30 – For more on AlphaGo (and its successors, AlphaGo Zero and AlphaZero), see here. 48:30 – The study finding that the novel of human Go playing increased due to the influence of AlphaGo. 51:00 – A blogpost delving into the idea that ChatGPT overuses certain words, including “delve.” A recent preprint by Dr. Rahwan and colleagues, presenting evidence that “delve” (and other words overused by ChatGPT) are now being used more in human spoken communication.  55:00 – A paper using simulations to show how LLMs can “collapse” when trained on data that they themselves generated.  1:01:30 – A review of the literature on filter bubbles, echo chambers, and polarization. 1:02:00 – An influential study by Dr. Chris Bail and colleagues suggesting that exposure to opposing views might actually increase polarization.  1:04:30 – A book by Geoffrey Hodgson and Thorbjørn Knudsen, who are often credited with developing the idea of “generalized Darwinism” in the social sciences.  1:12:00 – An article about Google's NotebookLM podcast-like audio summaries. 1:17:3 0 – An essay by Ursula LeGuin on children's literature and the Jungian “shadow.”    Recommendations The Secret of Our Success, Joseph Henrich “Machine Behaviour,” Iyad Rahwan et al.   Many Minds is a project of the Diverse Intelligences Summer Institute, which is made possible by a generous grant from the John Templeton Foundation to Indiana University. The show is hosted and produced by Kensy Cooperrider, with help from Assistant Producer Urte Laukaityte and with creative support from DISI Directors Erica Cartmill and Jacob Foster. Our artwork is by Ben Oldroyd. Our transcripts are created by Sarah Dopierala. Subscribe to Many Minds on Apple, Stitcher, Spotify, Pocket Casts, Google Play, or wherever you listen to podcasts. You can also now subscribe to the Many Minds newsletter here! We welcome your comments, questions, and suggestions. Feel free to email us at: manymindspodcast@gmail.com.  For updates about the show, visit our website or follow us on Twitter (@ManyMindsPod) or Bluesky (@manymindspod.bsky.social).

In this thought-provoking discussion with Can Gurel (Delphi), the NEAR co-founders share insights on Illia's work co-authoring the groundbreaking "Attention is All You Need" paper that introduced transformers, their vision for user-owned AI and democratizing access to AI capabilities and more. Key insights include: - How NEAR's original focus on AI developer tools led them to discover the need for better payment infrastructure for distributed workforces - Their framework for user-owned AI that optimizes for individual success rather than platform profits - Technical challenges and solutions for distributed AI training and inference - The importance of private computing for enabling user-controlled AI systems The conversation provides valuable perspective on the intersection of AI and crypto from pioneers who have worked at the cutting edge of both fields, offering insights into how decentralized technologies could enable more democratic and user-centric AI development. "The reality is we kind of have like a little bit of a model that some data we don't want to share... Your assistant will be able to help you with filing your taxes and finding ways to file receipts for expenses, and it will need to have access to all of this. What you don't want is all of this data end up leaking or showing up in somebody else's chat." — Illia Polosukhin, NEAR Protocol Watch more sessions from Crypto x AI Month here: https://delphidigital.io/crypto-ai --- Crypto x AI Month is the largest virtual event dedicated to the intersection of crypto and AI, featuring 40+ top builders, investors, and practitioners. Over the course of three weeks, this event brings together panels, debates, and discussions with the brightest minds in the space, presented by Delphi Digital. Crypto x AI Month is free and open to everyone thanks to the support from our sponsors: https://olas.network/ https://venice.ai/ https://near.org/ https://mira.foundation/ https://www.theoriq.ai/ --- Follow the Speakers: - Can Gurel on Twitter/X ► https://x.com/cannngurel - Illia Polosukhin on Twitter/X ► https://x.com/ilblackdragon - Alex Skidanov on Twitter/X ► https://x.com/alexskidanov --- Chapters 00:00 Introduction and Sponsor Acknowledgments 00:48 Introduction of Illia and Alex from NEAR 03:11 Origins of the Transformers Paper 08:07 Discussion on LLMs and Model Understanding 11:05 Alpha Go and Self-Improvement in AI 15:16 NEAR's Evolution from AI to Blockchain 19:37 The Story Behind NEAR's Foundation 26:32 Vision for User-Owned AI 30:31 Analysis of the Crypto-AI Stack 35:28 Data Labeling and Trust Systems 39:33 Private Inference and Edge Computing 44:17 User Privacy and Trust in AI 47:44 Edge Computing Challenges 49:54 Novel Data Sources and Synthetic Data 52:40 AI Agent Security and DeFi Applications 58:43 Formal Verification in AI Systems 1:03:14 Distributed Training Challenges 1:11:04 AI Talent and Crypto Industry 1:13:44 Rating Future Technologies and Closing Thoughts Disclaimer All statements and/or opinions expressed in this interview are the personal opinions and responsibility of the respective guests, who may personally hold material positions in companies or assets mentioned or discussed. The content does not necessarily reflect the opinion of Delphi Citadel Partners, LLC or its affiliates (collectively, “Delphi Ventures”), which makes no representations or warranties of any kind in connection with the contained subject matter. Delphi Ventures may hold investments in assets or protocols mentioned or discussed in this interview. This content is provided for informational purposes only and should not be misconstrued for investment advice or as a recommendation to purchase or sell any token or to use any protocol.

Have you ever wondered what the future of entrepreneurship looks like in a world where artificial intelligence can take on the roles traditionally reserved for human employees? In this episode of The Angel Next Door Podcast, host Marcia Dawood sits down with AI expert Sharon Zhang to explore the transformative impact of AI on business and society. Sharon talks about the evolving landscape of AI and its potential to not only automate mundane tasks but also to foster new business models and opportunities for creative entrepreneurship.Sharon Zhang, who boasts over 16 years of experience in artificial intelligence, emerges as a compelling guest. She began her journey at the MIT CCL lab and has since ventured through various roles, from clinical decision-making at Nuance Communications to algorithm development for hedge funds. Since 2020, she has co-founded Personal AI, a platform that builds digital twins to augment individual lives. Sharon's extensive background provides a rich foundation for discussing AI's role in modern entrepreneurship.This episode is a must-listen as Sharon provides a comprehensive view of the AI ecosystem, breaking it down into essential components like AI chips, infrastructure, foundation models, and applications. She shines a light on data privacy and the significance of user sovereignty over personal data. Furthermore, Sharon shares insights on the financial challenges faced by AI startups, the strategic moves by industry giants like OpenAI and Microsoft, and the burgeoning field of AI agents capable of performing complex tasks. Whether you're an entrepreneur, an investor, or simply fascinated by AI, this episode offers a treasure trove of knowledge and foresight into the future of artificial intelligence and its profound implications. To get the latest from Sharon Zhang, you can follow her below!LinkedIn - https://www.linkedin.com/in/xiaoranz1986/ https://www.personal.ai/Use the code PODCAST50 for 50% of any personal plan for 30 days! Sign up for Marcia's newsletter to receive tips and the latest on Angel Investing!Website: www.marciadawood.comLearn more about the documentary Show Her the Money: www.showherthemoneymovie.comAnd don't forget to follow us wherever you are!Apple Podcasts: https://pod.link/1586445642.appleSpotify: https://pod.link/1586445642.spotifyLinkedIn: https://www.linkedin.com/company/angel-next-door-podcast/Instagram: https://www.instagram.com/theangelnextdoorpodcast/TikTok: https://www.tiktok.com/@marciadawood

Historian Yuval Noah Harari says that we are at a critical turning point. One in which AI's ability to generate cultural artifacts threatens humanity's role as the shapers of history. History will still go on, but will it be the story of people or, as he calls them, ‘alien AI agents'?In this conversation with Aza Raskin, Harari discusses the historical struggles that emerge from new technology, humanity's AI mistakes so far, and the immediate steps lawmakers can take right now to steer us towards a non-dystopian future.This episode was recorded live at the Commonwealth Club World Affairs of California.Your Undivided Attention is produced by the Center for Humane Technology. Follow us on Twitter: @HumaneTech_RECOMMENDED MEDIANEXUS: A Brief History of Information Networks from the Stone Age to AI by Yuval Noah Harari You Can Have the Blue Pill or the Red Pill, and We're Out of Blue Pills: a New York Times op-ed from 2023, written by Yuval, Aza, and Tristan The 2023 open letter calling for a pause in AI development of at least 6 months, signed by Yuval and Aza Further reading on the Stanford Marshmallow Experiment Further reading on AlphaGo's “move 37” Further Reading on Social.AIRECOMMENDED YUA EPISODESThis Moment in AI: How We Got Here and Where We're GoingThe Tech We Need for 21st Century Democracy with Divya SiddarthSynthetic Humanity: AI & What's At StakeThe AI DilemmaTwo Million Years in Two Hours: A Conversation with Yuval Noah Harari

Combining LLMs with AlphaGo-style deep reinforcement learning has been a holy grail for many leading AI labs, and with o1 (aka Strawberry) we are seeing the most general merging of the two modes to date. o1 is admittedly better at math than essay writing, but it has already achieved SOTA on a number of math, coding and reasoning benchmarks. Deep RL legend and now OpenAI researcher Noam Brown and teammates Ilge Akkaya and Hunter Lightman discuss the ah-ha moments on the way to the release of o1, how it uses chains of thought and backtracking to think through problems, the discovery of strong test-time compute scaling laws and what to expect as the model gets better.  Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: Learning to Reason with LLMs: Technical report accompanying the launch of OpenAI o1. Generator verifier gap: Concept Noam explains in terms of what kinds of problems benefit from more inference-time compute. Agent57: Outperforming the human Atari benchmark, 2020 paper where DeepMind demonstrated “the first deep reinforcement learning agent to obtain a score that is above the human baseline on all 57 Atari 2600 games.” Move 37: Pivotal move in AlphaGo's second game against Lee Sedol where it made a move so surprising that Sedol thought it must be a mistake, and only later discovered he had lost the game to a superhuman move. IOI competition: OpenAI entered o1 into the International Olympiad in Informatics and received a Silver Medal. System 1, System 2: The thesis if Danial Khaneman's pivotal book of behavioral economics, Thinking, Fast and Slow, that positied two distinct modes of thought, with System 1 being fast and instinctive and System 2 being slow and rational. AlphaZero: The predecessor to AlphaGo which learned a variety of games completely from scratch through self-play. Interestingly, self-play doesn't seem to have a role in o1. Solving Rubik's Cube with a robot hand: Early OpenAI robotics paper that Ilge Akkaya worked on. The Last Question: Science fiction story by Isaac Asimov with interesting parallels to scaling inference-time compute. Strawberry: Why? O1-mini: A smaller, more efficient version of 1 for applications that require reasoning without broad world knowledge. 00:00 - Introduction 01:33 - Conviction in o1 04:24 - How o1 works 05:04 - What is reasoning? 07:02 - Lessons from gameplay 09:14 - Generation vs verification 10:31 - What is surprising about o1 so far 11:37 - The trough of disillusionment 14:03 - Applying deep RL 14:45 - o1's AlphaGo moment? 17:38 - A-ha moments 21:10 - Why is o1 good at STEM? 24:10 - Capabilities vs usefulness 25:29 - Defining AGI 26:13 - The importance of reasoning 28:39 - Chain of thought 30:41 - Implication of inference-time scaling laws 35:10 - Bottlenecks to scaling test-time compute 38:46 - Biggest misunderstanding about o1? 41:13 - o1-mini 42:15 - How should founders think about o1?

Nadia Piet is Founder and Creative Lead of AIxDESIGN, a global community conducting critical AI research for the benefit of people, not profits. She's a designer, researcher, organiser and faculty member at ELISAVA's Design for Responsible AI.We talk about AlphaGO and Move 37. AI models being trained on creative assets without permission. And what's causing the rapid improvement of generative AI such as DALL-E and Midjourney?In the face of constant change, what are constructive ways to be thinking about AI? ufo.fmnews.ufo.fm SPONSORSHigher is a lifestyle. A community of optimists on Base that formed on Farcaster. To join high agency crypto natives in a new experiment in onchain brands, visit aimhigher.net International Meme Fund (IMF) is a DeFi protocol for borrowing $MONEY against your memecoins with $IMF serving as the protocol utility and revenue token. Learn more at internationalmeme.fundLore is a group wallet experience for co-ownership. Own expensive NFTs, move memecoins markets and win crypto games together. Check out how you could use Lore with your friends to earn more than you could alone at lore.xyz.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Demis Hassabis - Google DeepMind: The Podcast, published by Zach Stein-Perlman on August 16, 2024 on LessWrong. The YouTube "chapters" are mixed up, e.g. the question about regulation comes 5 minutes after the regulation chapter ends. Ignore them. Noteworthy parts: 8:40: Near-term AI is hyped too much (think current startups, VCs, exaggerated claims about what AI can do, crazy ideas that aren't ready) but AGI is under-hyped and under-appreciated. 16:45: "Gemini is a project that has only existed for a year . . . our trajectory is very good; when we talk next time we should hopefully be right at the forefront." 17:20-18:50: Current AI doesn't work as a digital assistant. The next era/generation is agents. DeepMind is well-positioned to work on agents: "combining AlphaGo with Gemini." 24:00: Staged deployment is nice: red-teaming then closed beta then public deployment. 28:37 Openness (at Google: e.g. publishing transformers, AlphaCode, AlphaFold) is almost always a universal good. But dual-use technology - including AGI - is an exception. With dual-use technology, you want good scientists to still use the technology and advance as quickly as possible, but also restrict access for bad actors. Openness is fine today but in 2-4 years or when systems are more agentic it'll be dangerous. Maybe labs should only open-source models that are lagging a year behind the frontier (and DeepMind will probably take this approach, and indeed is currently doing ~this by releasing Gemma weights). 31:20 "The problem with open source is if something goes wrong you can't recall it. With a proprietary model if your bad actor starts using it in a bad way you can close the tap off . . . but once you open-source something there's no pulling it back. It's a one-way door, so you should be very sure when you do that." 31:42: Can an AGI be contained? We don't know how to do that [this suggests a misalignment/escape threat model but it's not explicit]. Sandboxing and normal security is good for intermediate systems but won't be good enough to contain an AGI smarter than us. We'll have to design protocols for AGI in the future: "when that time comes we'll have better ideas for how to contain that, potentially also using AI systems and tools to monitor the next versions of the AI system." 33:00: Regulation? It's good that people in government are starting to understand AI and AISIs are being set up before the stakes get really high. International cooperation on safety and deployment norms will be needed since AI is digital and if e.g. China deploys an AI it won't be contained to China. Also: Because the technology is changing so fast, we've got to be very nimble and light-footed with regulation so that it's easy to adapt it to where the latest technology's going. If you'd regulated AI five years ago, you'd have regulated something completely different to what we see today, which is generative AI. And it might be different again in five years; it might be these agent-based systems that [] carry the highest risks. So right now I would [] beef up existing regulations in domains that already have them - health, transport, and so on - I think you can update them for AI just like they were updated for mobile and internet. That's probably the first thing I'd do, while . . . making sure you understand and test the frontier systems. And then as things become [clearer] start regulating around that, maybe in a couple years time would make sense. One of the things we're missing is [benchmarks and tests for dangerous capabilities]. My #1 emerging dangerous capability to test for is deception because if the AI can be deceptive then you can't trust other tests [deceptive alignment threat model but not explicit]. Also agency and self-replication. 37:10: We don't know how to design a system that could come up with th...

Want to watch the full episode? Subscribe to Google DeepMind's YouTube page and stay tuned for new episodes.Further reading: GeminiProject Astra Google I/O 2024Scaling Language Models: Methods, Analysis & Insights from Training GopherLaMDA: our breakthrough conversation technologySocial channels to follow for new content:InstagramXLinkedin Want to share feedback? Why not leave a review on your favorite streaming platform? Have a suggestion for a guest that we should have on next? Leave us a comment on YouTube and stay tuned for future episodes. Thanks to everyone who made this possible, including but not limited to: Presenter: Professor Hannah FrySeries Producer: Dan HardoonSeries Editor: Rami Tzabar, TellTale Studios Commissioner and Producer: Emma YousifMusic composition: Eleni Shaw Camera Director and Video Editor: Tommy BruceAudio Engineer: Darren Carikas Video Studio Production: Nicholas DukeVideo Editor: Bilal MerhiVideo Production Design: James BartonVisual Identity and Design: Eleanor Tomlinson Commissioned by Google DeepMind

This is a bonus episode for The Documentary of The Engineers: Intelligent Machines. This year, we speak to a panel of three engineers at the forefront of the 'Machine Learning: AI' revolution with an enthusiastic live audience.Intelligent machines are remaking our world. The speed of their improvement is accelerating fast and every day there are more things they can do better than us. There are risks, but the opportunities for human society are enormous. ‘Machine Learning: AI' is the technological revolution of our era. Three engineers at the forefront of that revolution come to London to join Caroline Steel and a public audience at the Great Hall of Imperial College:Regina Barzilay from MIT created a major breakthrough in detecting early stage breast cancer. She also led the team that used machine learning to discover Halicin, the first new antibiotic in 30 years. David Silver is Principal Scientist at Google DeepMind. He led the AlphaGo team that built the AI to defeat the world's best human player of Go. Paolo Pirjanian founded Embodied, and is a pioneer in developing emotionally intelligent robots to aid child development. Producer: Charlie Taylor (Image: 3D hologram AI brain displayed by digital circuit and semiconductor. Credit: Yuichiro Chino/Getty Images)

If you see this in time, join our emergency LLM paper club on the Llama 3 paper!For everyone else, join our special AI in Action club on the Latent Space Discord for a special feature with the Cursor cofounders on Composer, their newest coding agent!Today, Meta is officially releasing the largest and most capable open model to date, Llama3-405B, a dense transformer trained on 15T tokens that beats GPT-4 on all major benchmarks:The 8B and 70B models from the April Llama 3 release have also received serious spec bumps, warranting the new label of Llama 3.1.If you are curious about the infra / hardware side, go check out our episode with Soumith Chintala, one of the AI infra leads at Meta. Today we have Thomas Scialom, who led Llama2 and now Llama3 post-training, so we spent most of our time on pre-training (synthetic data, data pipelines, scaling laws, etc) and post-training (RLHF vs instruction tuning, evals, tool calling).Synthetic data is all you needLlama3 was trained on 15T tokens, 7x more than Llama2 and with 4 times as much code and 30 different languages represented. But as Thomas beautifully put it:“My intuition is that the web is full of s**t in terms of text, and training on those tokens is a waste of compute.” “Llama 3 post-training doesn't have any human written answers there basically… It's just leveraging pure synthetic data from Llama 2.”While it is well speculated that the 8B and 70B were "offline distillations" of the 405B, there are a good deal more synthetic data elements to Llama 3.1 than the expected. The paper explicitly calls out:* SFT for Code: 3 approaches for synthetic data for the 405B bootstrapping itself with code execution feedback, programming language translation, and docs backtranslation.* SFT for Math: The Llama 3 paper credits the Let's Verify Step By Step authors, who we interviewed at ICLR:* SFT for Multilinguality: "To collect higher quality human annotations in non-English languages, we train a multilingual expert by branching off the pre-training run and continuing to pre-train on a data mix that consists of 90% multilingualtokens."* SFT for Long Context: "It is largely impractical to get humans to annotate such examples due to the tedious and time-consuming nature of reading lengthy contexts, so we predominantly rely on synthetic data to fill this gap. We use earlier versions of Llama 3 to generate synthetic data based on the key long-context use-cases: (possibly multi-turn) question-answering, summarization for long documents, and reasoning over code repositories, and describe them in greater detail below"* SFT for Tool Use: trained for Brave Search, Wolfram Alpha, and a Python Interpreter (a special new ipython role) for single, nested, parallel, and multiturn function calling.* RLHF: DPO preference data was used extensively on Llama 2 generations. This is something we partially covered in RLHF 201: humans are often better at judging between two options (i.e. which of two poems they prefer) than creating one (writing one from scratch). Similarly, models might not be great at creating text but they can be good at classifying their quality.Last but not least, Llama 3.1 received a license update explicitly allowing its use for synthetic data generation.Llama2 was also used as a classifier for all pre-training data that went into the model. It both labelled it by quality so that bad tokens were removed, but also used type (i.e. science, law, politics) to achieve a balanced data mix. Tokenizer size mattersThe tokens vocab of a model is the collection of all tokens that the model uses. Llama2 had a 34,000 tokens vocab, GPT-4 has 100,000, and 4o went up to 200,000. Llama3 went up 4x to 128,000 tokens. You can find the GPT-4 vocab list on Github.This is something that people gloss over, but there are many reason why a large vocab matters:* More tokens allow it to represent more concepts, and then be better at understanding the nuances.* The larger the tokenizer, the less tokens you need for the same amount of text, extending the perceived context size. In Llama3's case, that's ~30% more text due to the tokenizer upgrade. * With the same amount of compute you can train more knowledge into the model as you need fewer steps.The smaller the model, the larger the impact that the tokenizer size will have on it. You can listen at 55:24 for a deeper explanation.Dense models = 1 Expert MoEsMany people on X asked “why not MoE?”, and Thomas' answer was pretty clever: dense models are just MoEs with 1 expert :)[00:28:06]: I heard that question a lot, different aspects there. Why not MoE in the future? The other thing is, I think a dense model is just one specific variation of the model for an hyperparameter for an MOE with basically one expert. So it's just an hyperparameter we haven't optimized a lot yet, but we have some stuff ongoing and that's an hyperparameter we'll explore in the future.Basically… wait and see!Llama4Meta already started training Llama4 in June, and it sounds like one of the big focuses will be around agents. Thomas was one of the authors behind GAIA (listen to our interview with Thomas in our ICLR recap) and has been working on agent tooling for a while with things like Toolformer. Current models have “a gap of intelligence” when it comes to agentic workflows, as they are unable to plan without the user relying on prompting techniques and loops like ReAct, Chain of Thought, or frameworks like Autogen and Crew. That may be fixed soon?

Der KI-Podcast feiert Einjähriges - mit einer ganz besonderen Folge. Nicht nur hosten Marie, Gregor und Fritz zum ersten Mal eine Folge zu dritt - sie haben währenddessen auch noch lustige Partyhüte auf! Und vor allem haben sie ihre Lieblingsmomente aus der KI-Geschichte dabei, von falschen Schachspielern, neuronalen Netzen und dem Schulterschlag auf der fünften Linie. Über die Hosts: Gregor Schmalzried ist freier Tech-Journalist und Berater, er arbeitet u.a. für den Bayerischen Rundfunk und Brand Eins. Fritz Espenlaub ist freier Journalist und Moderator beim Bayerischen Rundfunk und 1E9 mit Fokus auf Technologie und Wirtschaft. Marie Kilg ist Chief AI Officer bei der Deutschen Welle. Zuvor war sie Produkt-Managerin bei Amazon Alexa. 00:00 Intro 02:24 Der Mechanical Turk 12:17 McCulloch und Pitts: Sind Gehirne wie Computer? 21:37 AlphaGo gegen Lee Sedol 35:27 Was diese KI-Geburtstage über die Technologie sagen Redaktion und Mitarbeit: David Beck, Cristina Cletiu, Chris Eckardt, Fritz Espenlaub, Marie Kilg, Mark Kleber, Gudrun Riedl, Christian Schiffer, Gregor Schmalzried Links und Quellen: DER KI-PODCAST LIVE beim BR Podcastfestival in Nürnberg https://tickets.190a.de/event/der-ki-podcast-live-in-nurnberg-hljs6y Der Mechanical Turk https://www.britannica.com/story/the-mechanical-turk-ai-marvel-or-parlor-trick Amazon MTurk https://www.mturk.com/ Gehirn-Maschinen-Metaphern: https://dirt.fyi/article/2024/03/metaphorically-speaking https://arxiv.org/abs/2206.04603 Warren McCulloch and Walter Pitts: A Logical Calculus of the Ideas Immanent in Nervous Activity https://link.springer.com/chapter/10.1007/978-3-642-70911-1_14 McCulloch-Pitts Neuron — Mankind's First Mathematical Model Of A Biological Neuron https://towardsdatascience.com/mcculloch-pitts-model-5fdf65ac5dd1 Untold History of AI: How Amazon's Mechanical Turkers Got Squeezed Inside the Machine https://spectrum.ieee.org/untold-history-of-ai-mechanical-turk-revisited-tktkt AlphaGo-Doku auf Youtube: https://www.youtube.com/watch?v=WXuK6gekU1Y MANIAC von Benjamin Labatut: https://www.suhrkamp.de/buch/benjamin-labatut-maniac-t-9783518431177 Redaktion und Mitarbeit: David Beck, Cristina Cletiu, Chris Eckardt, Fritz Espenlaub, Marie Kilg, Mark Kleber, Gudrun Riedl, Christian Schiffer, Gregor Schmalzried Kontakt: Wir freuen uns über Fragen und Kommentare an podcast@br.de. Unterstützt uns: Wenn euch dieser Podcast gefällt, freuen wir uns über eine Bewertung auf eurer liebsten Podcast-Plattform. Abonniert den KI-Podcast in der ARD Audiothek oder wo immer ihr eure Podcasts hört, um keine Episode zu verpassen. Und empfehlt uns gerne weiter!

欢迎收听雪球出品的财经有深度，雪球，国内领先的集投资交流交易一体的综合财富管理平台，聪明的投资者都在这里。今天分享的内容叫一个AI从业者的十年。来自DrChuck。2015年，我刚参加工作，第一个任务是识别图片里的物品。传统做法是，找到物品的特征，用机器学习设计特征工程，做成模版，拿着模版进行特征匹配。做了几个月，效果差强人意。突然一则新闻吸引了我的注意力，谷歌旗下的DeepMind开发了一款围棋程序AlphaGo，要与世界冠军李世石对弈。赛前大家并不看好 A I ，甚至人工智能专家李开复也觉得 A I 赢不了。事实让众人大跌眼镜，AlphaGo以4:1大胜李世石。这个结果给了我极大震撼，因为中国人知道围棋的难度。19乘19的棋盘，状态空间复杂度高达10的171次方，远大于宇宙中原子的个数，单靠近似穷举不可能解出答案。我疯了一般去寻找背后的故事。原来，AlphaGo的核心是卷积神经网络。这是杨乐昆在1989年提出的一种图像识别算法。为什么这个技术在二十多年后才被人重视？因为数据和算力不足。直到2012年，深度学习之父辛顿的两名学生在李飞飞主导的ImageNet超大规模视觉识别挑战赛上一鸣惊人，人们才终于见识到威力。他俩基于吴恩达的工作，创造性的将英伟达的 G P U 用于训练一个600万参数的深度神经网络AlexNet。AlexNet在学习了1000万张李飞飞团队辛苦标注的图片后，将图像识别的准确率提高了10%以上，遥遥领先于亚军。在AlexNet的基础上，科学家们再接再厉，提出了一个又一个更深更大的网络，ZFNet，VGGNet，GoogleNet，每年都在进步。到了2015年，华人学者何恺明，曾经的广东高考状元，提出了152层的极深网络ResNet，参数量过千万，至此，AI的图像识别准确率终于超过了人类。了解到这些背景，我兴奋得浑身发抖。开发人员再也不需要手工设计图像特征，深度网络通过海量数据学到的特征，远胜资深专家的多年经验。为了深入学习，我开始使用亚马逊云服务 A W S ，很快就被英伟达的 C U D A 惊艳到了。 C U D A 非常高效，吸引了众多研究员和工程师，英伟达的开发人员也热心解答各种漏洞问题。渐渐的，越来越多的算法首发在 C U D A 上，更多的改进算法为了超越前者也只能用 C U D A ，形成了网络效应，用的人越多越好用。当年还没有现在这么完善的深度学习框架，我入门靠的是华人学者贾扬清在写毕业论文之余开发的Caffe。这位大神慷慨开源了他基于 C U D A 的研究框架，又在博士毕业后成为谷歌的TensorFlow和Meta的PyTorch两大当今最流行框架的主要贡献者。有了这些武器，我总算可以把图像识别算法换成深度卷积网络，效果显著，准确率飞升。但我知道永远不能自满，这是个眨眼十年的领域。得益于科学家们的开源精神，网络的架构不断进化。2017年，谷歌提出了Transformer自注意力架构。所谓自注意力，简单说就是只关心输入之间的关系，而不再关注输入和输出的关系，这是一个颠覆性的变化。这篇论文发布之前，虽然深度学习已经取得长足进展，但AI的各个分支，视觉，语音，语言理解等，还是相对割裂的，每个领域有自己的模型。之后，则是Transformer一统天下，各领域专家的知识整合以及多模态融合变得愈加轻松。李飞飞的高徒安德烈，甚至惊叹，也许人类偶然窥见了和自然界类似的强大架构，造物主沿着这个路径复制，造就了今天的大千世界。Transformers让 G P U 并行运算的效率进一步大幅提升。2018年，OpenAI和谷歌相继发布了参数量过亿的GPT和BERT模型。2020年初，OpenAI发表了著名的Scaling laws规模法则，指出更大的模型，更多的数据，更长时间的训练是提升模型能力的可行路径。2022年底，ChatGPT横空出世，参数量达到恐怖的1750亿，模型大到违背了许多科学家的直觉。通常来说，如果一个模型训练几个月烧掉百万美金，效果还没有很大提升，研究员就放弃了。但伊利亚不是一般人，作为当年AlexNet的作者之一，他坚信规模法则，在烧了千万美金之后，终于捅破天花板，看到了推理智能的大幅涌现。曾经，为了实现一个简单的小功能，我就需要训练一个AI模型。要完成一个复杂的商业系统，需要多个AI模型的协作，以及大量的底层逻辑代码。但现在，借助GPT大模型，实现功能只需要写一句简单的提示语，生产效率大大提高了。全世界都看到了大模型的威力，根据斯坦福大学 A I 研究院的最新报告，2023年生成式 A I 的投资激增了8倍。训练模型也越来越昂贵，谷歌为了追赶ChatGPT开发的Gemini模型，成本接近2亿美金。大规模的金钱竞赛，成了巨头公司们的游戏。在此背景下，依然坚持开源的英雄们，尤其值得尊敬。著名开源社区HuggingFace的创始人分享了一个美妙故事，关于三大洲，即欧洲，美国，中国的人们如何合作共建并公开分享了一个紧凑高效，行业领先的 A I 模型。一个小团队在法国巴黎发布了他们的第一个模型：Mistral 7B。该模型令人印象深刻，体积小，但在基准测试中表现出色，优于之前所有同尺寸的模型。而且是开源的，人们可以在其基础上继续开发。瑞士伯尔尼的刘易斯和法国里昂的埃德都来自HuggingFace的模型微调团队，他俩在喝咖啡时聊到了这个模型：一个来自美国加州斯坦福大学的研究团队刚刚发布了一种新的方法，用这种方法微调Mistral怎么样？嘿，这是个好主意，另一个人答道。他们刚刚开源了一个很棒的代码库，让我们用起来吧！第二天，他们开始深入研究HuggingFace上公开共享的数据集，偶然发现了两个有趣的大型高质量微调数据集，它们来自中国清华大学的团队OpenBMB，也开放了源码：UltraFeedback和UltraChat。几轮训练实验证实了这一直觉，由此产生的模型超级强大，是迄今为止他们在伯克利和斯坦福的基准测试中所见过的最强模型。开源模型排行榜的大咖克雷门汀也被吸引了，她对模型能力的深入研究证实了这一模型拥有令人印象深刻的性能。团队还邀请了康奈尔大学教授萨沙加入对话，他提议快速起草一份研究论文，整理并与社区分享所有细节。几天后，起名龙卷风Zephyr的模型、论文和所有细节便席卷世人。很快，世界各地的许多公司都开始使用它，有公司声称用它取代ChatGPT让其节省了一半的费用。众多研究人员在开源社区热烈讨论该模型和论文。所有这些都在短短几周内发生的，这得益于世界各地发布的知识，模型和数据集的开放访问，以及人们在AI领域相互借鉴工作，为现实世界带来价值的高效理念。开源社区的成就令人惊叹，理念更令人神往。当OpenAI不再Open，是这些胸怀技术开放理想的研究者，将大模型的秘密，展现给全世界。进入2024年，大模型的进展依旧如火如荼：1. Sora模型通过简单描述生成栩栩如生的长视频，成为全球焦点。OpenAI称之为世界模拟器，能深刻理解运动中的物理规律。华人学者谢赛宁揭示了背后原理，来自他发表的基于Transformer架构的扩散模型DiT。很快，潞晨科技和北京大学推出OpenSora，全面开源文生视频的模型参数和训练细节。快手公司也推出了可灵大模型，展示了图生视频和视频续写等功能，在顶级学术会议上引起积极反响。2. 大模型的推理能力让许多互联网产品得到升级。Arc Search加Perplexity的AI浏览器可以改善用户的搜索体验，自动整合全网资讯，给出要点，回答问题的精准度大幅提升。月之暗面的Kimi对话搜索引擎，被许多投资人推荐，因为其强大的文本总结能力和200万汉字的超长上下文窗口，让阅读上市公司财报和资料的工作不再繁琐。3. GPT4o展示了在文本，图像和语音上的多模态实时处理能力。上海人工智能实验室和商汤科技联合发布的书生大模型InternLMM，开源了开放世界理解，跨模态生成和多模态交互的能力，在全球开发者社区备受欢迎。4.吴恩达力推智能体工作流，让AI学会使用工具。亚马逊的AWS，微软的Azure，阿里巴巴的百炼，百度的文心和字节的扣子等各大云计算平台都提供了便捷多样的插件，让AI如虎添翼。5.端侧AI开始落地，苹果发布Apple Intelligence，在保护用户隐私的同时，将大模型直接部署到用户的手机里。因为某些客观因素，中国的算力受到限制，反而催生了一些另辟蹊径，以小博大的模型路线。面壁智能公司推出MiniCPM模型，只有24亿参数，性能却超越了比自己5倍大的巨人。6.Mistral开源了混合专家模型架构，由多个专家子模型组成，回答特定领域的问题只需要调用相应的，整体推理消耗大大降低。美国的GPT和Claude降价了50%以上，中国公司发扬卷的精神，阿里的通义千问，幻方的DeepSeek等领先模型直接降价90%。各家大模型之间的竞争愈演愈烈，鹿死谁手，犹未可知。展望未来，我是非常乐观的。虽然当今世界并不太平，各种冲突矛盾不断，但 A I 对生产力的促进是确定的。有人担心 A I 会让很多人失业，但李飞飞认为，AI取代的是任务，而不是工作。每项工作都由大量任务组成，让AI去完成繁重的任务，人类的创造力将进一步解放。20年前，随着生产力的提高，大部分国家开始实行一周五天工作制。在下一轮 A I 工业革命到来之际，一周休三天，也不是遥不可及的幻想。

LLMs are democratizing digital intelligence, but we're all waiting for AI agents to take this to the next level by planning tasks and executing actions to actually transform the way we work and live our lives.  Yet despite incredible hype around AI agents, we're still far from that “tipping point” with best in class models today. As one measure: coding agents are now scoring in the high-teens % on the SWE-bench benchmark for resolving GitHub issues, which far exceeds the previous unassisted baseline of 2% and the assisted baseline of 5%, but we've still got a long way to go. Why is that? What do we need to truly unlock agentic capability for LLMs? What can we learn from researchers who have built both the most powerful agents in the world, like AlphaGo, and the most powerful LLMs in the world?  To find out, we're talking to Misha Laskin, former research scientist at DeepMind. Misha is embarking on his vision to build the best agent models by bringing the search capabilities of RL together with LLMs at his new company, Reflection AI. He and his cofounder Ioannis Antonoglou, co-creator of AlphaGo and AlphaZero and RLHF lead for Gemini, are leveraging their unique insights to train the most reliable models for developers building agentic workflows. Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital  00:00 Introduction 01:11 Leaving Russia, discovering science 10:01 Getting into AI with Ioannis Antonoglou 15:54 Reflection AI and agents 25:41 The current state of Ai agents 29:17 AlphaGo, AlphaZero and Gemini 32:58 LLMs don't have a ground truth reward 37:53 The importance of post-training 44:12 Task categories for agents 45:54 Attracting talent 50:52 How far away are capable agents? 56:01 Lightning round Mentioned:  The Feynman Lectures on Physics: The classic text that got Misha interested in science. Mastering the game of Go with deep neural networks and tree search: The original 2016 AlphaGo paper. Mastering the game of Go without human knowledge: 2017 AlphaGo Zero paper Scaling Laws for Reward Model Overoptimization: OpenAI paper on how reward models can be gamed at all scales for all algorithms. Mapping the Mind of a Large Language Model: Article about Anthropic mechanistic interpretability paper that identifies how millions of concepts are represented inside Claude Sonnet Pieter Abeel: Berkeley professor and founder of Covariant who Misha studied with A2C and A3C: Advantage Actor Critic and Asynchronous Advantage Actor Critic, the two algorithms developed by Misha's manager at DeepMind, Volodymyr Mnih, that defined reinforcement learning and deep reinforcement learning

If you listened to our last couple of episodes, you'll have heard some pretty skeptical takes on AI. But if you look at the stock market right now, you won't see any trace of that skepticism. Since the launch of ChatGPT in late 2022, the chip company NVIDIA, whose chips are used in the majority of AI systems, has seen their stock shoot up by 700%. A month ago, that briefly made them the most valuable company in the world, with a market cap of more than $3.3 trillion.And it's not just chip companies. The S&P 500 (the index that tracks the 500 largest companies in the U.S.) is at an all-time high this year, in no small part because of the sheen of AI. And here in Canada, a new report from Microsoft claims that generative AI will add $187 billion to the domestic economy by 2030. As wild as these numbers are, they may just be the tip of the iceberg. Some researchers argue that AI will completely revolutionize our economy, leading to per capita growth rates of 30%. In case those numbers mean absolutely nothing to you, 25 years of 30% growth means we'd be a thousand times richer than we are now. It's hard to imagine what that world would like – or how the average person fits into it. Luckily, Rana Foroohar has given this some thought. Foroohar is a global business columnist and an associate editor at The Financial Times. I wanted to have her on the show to help me work through what these wild predictions really mean and, most importantly, whether or not she thinks they'll come to fruition.Mentioned:“Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity” by Daron Acemoglu and Simon Johnson (2023)“Manias, Panics, and Crashes: A History of Financial Crises” by Charles P. Kindleberger (1978)“Irrational Exuberance” by Robert J. Shiller (2016)“Gen AI: Too much spend, too little benefit?” by Goldman Sachs Research (2024)“Workers could be the ones to regulate AI” by Rana Foroohar (Financial Times, 2023)“The Financial Times and OpenAI strike content licensing deal” (Financial Times, 2024)“Is AI about to kill what's left of journalism?” by Rana Foroohar (Financial Times, 2024)“Deaths of Despair and the Future of Capitalism” by Anne Case and Angus Deaton (2020)“The China Shock: Learning from Labor Market Adjustment to Large Changes in Trade” by David H. Autor, David Dorn & Gordon H. Hanson (2016)Further Reading:“Beware AI euphoria” by Rana Foroohar (Financial Times, 2024)“AlphaGo” by Google DeepMind (2020)

Theme music by UNIVERSFIELD & background music by PodcastACChris Garlock & Michael Redmond's book AlphaGo to Zero, Volume TwoThe AlphaGo documentaryWilliam Cobb's Book on Go & Buddhist Philosophy which is available on SmartGo BooksA nice GoMagic article on Go & Buddhism and William Cobb's essaysFind updates on new episodes & show your support hereContact: AllThingsGoGame@gmail.com

Murray Shanahan is a professor of Cognitive Robotics at Imperial College London and a senior research scientist at DeepMind. He challenges our assumptions about AI consciousness and urges us to rethink how we talk about machine intelligence. We explore the dangers of anthropomorphizing AI, the limitations of current language in describing AI capabilities, and the fascinating intersection of philosophy and artificial intelligence. Show notes and full references: https://docs.google.com/document/d/1ICtBI574W-xGi8Z2ZtUNeKWiOiGZ_DRsp9EnyYAISws/edit?usp=sharing Prof Murray Shanahan: https://www.doc.ic.ac.uk/~mpsha/ (look at his selected publications) https://scholar.google.co.uk/citations?user=00bnGpAAAAAJ&hl=en https://en.wikipedia.org/wiki/Murray_Shanahan https://x.com/mpshanahan Interviewer: Dr. Tim Scarfe Refs (links in the Google doc linked above): Role play with large language models Waluigi effect "Conscious Exotica" - Paper by Murray Shanahan (2016) "Simulators" - Article by Janis from LessWrong "Embodiment and the Inner Life" - Book by Murray Shanahan (2010) "The Technological Singularity" - Book by Murray Shanahan (2015) "Simulacra as Conscious Exotica" - Paper by Murray Shanahan (newer paper of the original focussed on LLMs) A recent paper by Anthropic on using autoencoders to find features in language models (referring to the "Scaling Monosemanticity" paper) Work by Peter Godfrey-Smith on octopus consciousness "Metaphors We Live By" - Book by George Lakoff (1980s) Work by Aaron Sloman on the concept of "space of possible minds" (1984 article mentioned) Wittgenstein's "Philosophical Investigations" (posthumously published) Daniel Dennett's work on the "intentional stance" Alan Turing's original paper on the Turing Test (1950) Thomas Nagel's paper "What is it like to be a bat?" (1974) John Searle's Chinese Room Argument (mentioned but not detailed) Work by Richard Evans on tackling reasoning problems Claude Shannon's quote on knowledge and control "Are We Bodies or Souls?" - Book by Richard Swinburne Reference to work by Ethan Perez and others at Anthropic on potential deceptive behavior in language models Reference to a paper by Murray Shanahan and Antonia Creswell on the "selection inference framework" Mention of work by Francois Chollet, particularly the ARC (Abstraction and Reasoning Corpus) challenge Reference to Elizabeth Spelke's work on core knowledge in infants Mention of Karl Friston's work on planning as inference (active inference) The film "Ex Machina" - Murray Shanahan was the scientific advisor "The Waluigi Effect" Anthropic's constitutional AI approach Loom system by Lara Reynolds and Kyle McDonald for visualizing conversation trees DeepMind's AlphaGo (mentioned multiple times as an example) Mention of the "Golden Gate Claude" experiment Reference to an interview Tim Scarfe conducted with University of Toronto students about self-attention controllability theorem Mention of an interview with Irina Rish Reference to an interview Tim Scarfe conducted with Daniel Dennett Reference to an interview with Maria Santa Caterina Mention of an interview with Philip Goff Nick Chater and Martin Christianson's book ("The Language Game: How Improvisation Created Language and Changed the World") Peter Singer's work from 1975 on ascribing moral status to conscious beings Demis Hassabis' discussion on the "ladder of creativity" Reference to B.F. Skinner and behaviorism

Go is one of the most difficult strategy board games to master for humans and AI alike. Lindsay Sant and Lino Saubolle discuss a documentary about Google's DeepMind's 2016 match against human Go master and its significance for how it could tackle real-world challenges. The post AlphaGo vs. the Champions appeared first on StarQuest Media.

Back in 1997, Deep Blue (an IBM computer) defeated Garry Kasparov, the world chess champion at the time, in a six-game match with a final score of 3.5-2.5 in favor of Deep Blue. Almost 20 years later, in 2016, Google's AlphaGo program achieved a similar victory by defeating Lee Sedol, one of the world's top professional Go players, in a five-game match with a final score of 4-1. Artificial intelligence and machine learning have been around for decades, yet they were not accessible to you and me. Now that they are, they're predicted to change marketing (and life in general) forever. Experts, insiders, and reporters expect good and bad from AI. Some predict Skynet from the Terminator movies, while others expect it to cure cancer. Ockham's razor would predict that it's probably something in the middle. Companies and their leaders are telling us that they are working to make your experience better and part of the greater good of humanity. Search is supposed to provide the best answer to your search prompt, and social media is supposed to serve the content you want to see. In reality, it's more about profits than principles. Search is optimized to get you to click ads—that's how Google makes over 75% of its revenue. Facebook feeds your friendships and shows posts meant to stir the pot and keep you engaged. Facebook makes over 95% of its profits from ad sales. Ockham's razor would show us that trying to find and win customers through search and social media would benefit the platform more than you. It's like a casino with all that noise of winners on its machines, but the odds have been programmed to make the casino much more money than it's paying out! When it comes to marketing, we have been using AI since the beginnings of Google and Facebook. Both are run through algorithms.

Send us a Text Message.Discover the extraordinary journey of Hernan Londano, a renowned AI strategist at Dell Technologies, as he takes us from the mountains of Colombia to the forefront of technological innovation in the United States. Hernan's nearly three decades of experience, including his pivotal roles as Chief Technology Officer and Chief Information Security Officer at Barry University, provide a unique lens into the world of AI governance. This episode promises to enlighten you on how to balance cutting-edge AI innovations with ethical responsibility, ensuring alignment with strategic organizational goals.We venture into the provocative realm of AI's role in strategic games and its real-world ramifications. Hernan discusses the historic matches of AlphaGo and AlphaZero, highlighting AI's unprecedented capabilities in generating novel strategies. But the conversation takes a serious turn as we address the ethical and responsible use of AI in military contexts, where decisions can literally be a matter of life and death. This chapter emphasizes the necessity for inclusive, diverse discussions and continuously evolving governance frameworks to ethically harness AI's power.The complexities of AI, free speech, and legal ramifications unfold as we dissect a real-world incident involving OpenAI and Scarlett Johansson. This episode doesn't shy away from addressing the urgent need for clear guidelines and regulations as AI technology continues to permeate our daily lives. Hernan also explores the transformative impact of AI across industries, from AI-generated music to business strategy, urging the development of governance frameworks that ensure transparency, accountability, and equity. Join us for this compelling exploration of AI's future and its governance within business and beyond.Thanks for tuning in to this episode of Follow The Brand! We hope you enjoyed learning about the latest marketing trends and strategies in Personal Branding, Business and Career Development, Financial Empowerment, Technology Innovation, and Executive Presence. To keep up with the latest insights and updates from us, be sure to follow us at 5starbdm.com. See you next time on Follow The Brand!

科技屢次改變人類的生活，人類卻很少能成功「駕馭」新科技。微軟AI部門執行長，蘇萊曼是DeepMind的創辦人，擊敗世界棋王的機器人Alpha Go便出自他的公司；然而這位科技人，卻在新書《控制邊緣》表達對人工智慧的擔憂。 AI技術結合人臉辨識，可被應用於製造致命武器，甚至是能自主狙殺目標的「刺客機器人」，低門檻的基因工程實驗亦有高潛在風險；各國為爭奪AI主導權，算力也成為新冷戰時代的核彈頭，引發新一輪軍備競賽。 蘇萊曼的技術背景，替他的擔憂增添了不少可信度。 本集節目，你會聽到： 03:51 連序都交給AI寫，作者想表達什麼？ 05:46 為什麼科技圈過度「規避悲觀」？ 12:31 各國軍備競賽，AI力成新冷戰時代的核彈頭？ 15:46 具備人臉辨識系統，AI已有自動刺殺敵人的能力？ 22:46 人工智慧如何能讓全人類受益？ 主持人: 未來城市頻道總監 陳芳毓 來賓： 感電出版副總編輯 鍾涵瀞 製作團隊：許鈺屏、許靜之、陳繹方、陳瑞偉 ＊搜尋「未來城市FutureCity@天下」，追蹤更多城市議題：https://futurecity.cw.com.tw/ ＊訂閱天下全閱讀：https://bit.ly/3STpEpV ＊「聽天下」清楚分類更好聽，下載天下雜誌App：[https://bit.ly/3ELcwhX](https://bit.ly/3ELcwhX >) ＊意見信箱：bill@cw.com.tw -- Hosting provided by SoundOn

An interview with Prof. Marcus du Sautoy about his book Around the Wold in Eighty Games . . . .a Mathematician Unlocks the Secrets of the World's Greatest Games. Topics covered in Today's Episode: 1. Introduction to Professor Marcus du Sautoy and the Role of Games- Impact of games on culture, strategy, and learning- The educational importance of games throughout history2. Differences in gaming cultures across regions like India and China3. Creative Aspects of Mathematics4. The surprising historical elements and banned games by Buddha5. Historical and geographical narratives of games rather than rules6. Game Theory and Education7. Unknowable questions like thermodynamics and universe's infinity8. Professor du Sautoy's Former Books and Collections9. A preview of his previous books and their themes10. Gaming Cultures and NFTs in Blockchain11. Gamification in Education12. The Role of AI in Gaming13. Testing machine learning in mastering games like Go14. Alphago's surprising move and its impact on Go strategies15 . The future of AI in developing video game characters, plots, and environments16. Conclusion and Giveaway Announcement*Free Book Giveaway of Around The World in 88 Games . . . by Professor Marcus Du Sautory! Follow us on our socials for details: Follow us on X: @BreakingMathPodFollow us on Instagram: @Breaking Math MediaEmail us: BreakingMathPodacst@gmail.com

- Alex Jones' Twitter reinstatement and its implications for America's future. (0:00) - US politics, military, and intelligence networks. (2:48) - Elon Musk's role in reinstating banned accounts and promoting Trump. (16:03) - Keeping America alive through military might and media awakening. (20:59) - Trump, immigration, and national security. (26:15) - AI development and military simulations. (37:13) - AI development for military use. (42:06) - Using AI for bio weapons development. (48:30) - AI advancements in China and its potential impact on military power. (57:55) - Education, AI, and global competitiveness. (1:03:00) - AI's impact on media, law, and survival. (1:13:20) For more updates, visit: http://www.brighteon.com/channel/hrreport NaturalNews videos would not be possible without you, as always we remain passionately dedicated to our mission of educating people all over the world on the subject of natural healing remedies and personal liberty (food freedom, medical freedom, the freedom of speech, etc.). Together, we're helping create a better world, with more honest food labeling, reduced chemical contamination, the avoidance of toxic heavy metals and vastly increased scientific transparency. ▶️ Every dollar you spend at the Health Ranger Store goes toward helping us achieve important science and content goals for humanity: https://www.healthrangerstore.com/ ▶️ Sign Up For Our Newsletter: https://www.naturalnews.com/Readerregistration.html ▶️ Brighteon: https://www.brighteon.com/channels/hrreport ▶️ Join Our Social Network: https://brighteon.social/@HealthRanger ▶️ Check In Stock Products at: https://PrepWithMike.com

Sam Harris speaks with Mustafa Suleyman about his new book, “The Coming Wave: Technology, Power, and the 21st Century's Greatest Dilemma.” They discuss the progress in artificial intelligence made at his company DeepMind, the acquisition of DeepMind by Google, Atari DQN, AlphaGo, AlphaZero, AlphaFold, the invention of new knowledge, the risks of our making progress in AI, “superintelligence” as a distraction from more pressing problems, the inevitable spread of general-purpose technology, the nature of intelligence, productivity growth and labor disruptions, “the containment problem,” the importance of scale, Moore's law, Inflection AI, open-source LLMs, changing the norms of work and leisure, the redistribution of value, introducing friction into the deployment of AI, regulatory capture, a misinformation apocalypse, digital watermarks, asymmetric threats, conflict and cooperation with China, supply-chain monopolies, and other topics. If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe. Learning how to train your mind is the single greatest investment you can make in life. That's why Sam Harris created the Waking Up app. From rational mindfulness practice to lessons on some of life's most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it.