Podcasts about turing

Have you heard of 'Tail-Gunner Joe' McCarthy and the Red Scare?  What about Roy Cohn?  These two not only kicked off the Red Scare where they pursued supposed communists in Government and later the military, but also went aafter gays under the theory that they were 'moral perverts susceptible to blackmail.'.  Despite no evidence appearing that even one of these people were blackmailed outside the movie Clue (which is complete fiction), thousands of employees were fired between 1953 and the 90's when gay employees were forced out of government positions or fotced to live lives undercover simply because of who they loved.  We cover the history of the lavender scare, people who were targeted because of it, a similar case in England where Alan Turing,  the father of Artificial Intelligence was convicted of gross perversion for his relationship with another man and was chemically castrated which led to the unraveling of his brilliant mind.  All this and more in this, this is why we celebrate PRIDE because we need to remind ourselves just how bad it's been for the LGBTQ during our lifetimes episode of the Family Plot Podcast.Become a supporter of this podcast: https://www.spreaker.com/podcast/family-plot--4670465/support.

We're off-list this week to discuss the Oscar-nominated biopic The Imitation Game concerning the story of Alan Turing and his code-breaking skills that helped to put to rest some nasty Nazis. The guys talk about the gaggle of historical inaccuracies in this Hollywood adaptation and whether it ruins the film at all, Benedict Cumberbatch and Keira Knightley's wonderful performances, the controversy surrounding the downplay of certain elements of Turing's life, real-life legal implications of the film and so much more. Next week: Heydrich goes down again! Questions? Comments? Suggestions? You can always shoot us an e-mail at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠forscreenandcountry@gmail.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠   Full List: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.pastemagazine.com/movies/war-movies/the-100-greatest-war-movies-of-all-time⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/forscreenandcountry⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.twitter.com/fsacpo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠d⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Our logo was designed by the wonderful Mariah Lirette (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://instagram.com/its.mariah.xo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠) The Imitation Game stars Benedict Cumberbatch, Keira Knightley, Matthew Goode, Rory Kinnear, Allen Leach, Matthew Beard, Charles Dance and Mark Strong; directed by Morten Tyldum. Learn more about your ad choices. Visit megaphone.fm/adchoices

Oubliez les LLM, Entalpic agence les atomes

This week we are discussing Noah and the Whale, and their song L.I.F.E.G.O.E.S.O.N. We're reliving the indie folk revival of the mid-aughts so you don't have to! Also in this prepisode music news of the weird, listener emails and we announce next week's album.  In this episode we discuss our ongoing litigation, what is a sniffer, microbangs, the Turing machine, bad caricature artists, Garrett's ape teeth, Thai fishing pants, Chess the musical, alternate timelines, people chucking stuff at Eric Clapton, and so much more!  Hatepod.com | TW: @AlbumHatePod | IG: @hatePod | hatePodMail@gmail.com Episode Outline: Quick update on the goings on at the world headquarters Discuss our history with the song/band Song discussion - lyrics and music Music Video How the song did worldwide Amazon reviews Listener email (just 2) Music news of the weird Announce next week's album

Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! Alan Turing fue uno de los grandes cerebros privilegiados que alumbró el siglo XX. De ese cerebro salieron algunas de las ideas sobre las que se sostiene nuestro mundo. Sin sus aportes a las matemáticas, ni los ordenadores, ni los teléfonos móviles, ni internet existirían tal y como hoy los conocemos. Criado en Inglaterra mientras sus padres residían en la India, Turing mostró desde niño una inteligencia fuera de lo común. En el internado de Sherborne se enamoró de Christopher Morcom, un compañero cuya muerte prematura por tuberculosis le empujó a preguntarse sobre la relación entre la mente y la materia. En 1931 ingresó en el King's College de Cambridge, donde compaginó las matemáticas con el atletismo, disciplina que casi le lleva a los Juegos Olímpicos de 1948. En 1936 publicó el artículo que cambió la historia de la informática. Para responder al problema de la decisión planteado por David Hilbert, imaginó una máquina abstracta capaz de ejecutar cualquier cómputo definible mediante reglas. Demostró además que podía construirse una máquina universal capaz de imitar a cualquier otra. Aquella idea es el plano teórico del ordenador moderno y la raíz de toda la informática que nos rodea. Cuando estalló la guerra se incorporó al complejo secreto de Bletchley Park. Allí, junto a Gordon Welchman, diseñó la Bomba, un artefacto electromecánico que con que el consiguieron romper el cifrado de la máquina Enigma que utilizaban los alemanes para transmitir órdenes. Esa información, conocida como Ultra, permitió ganar la batalla del Atlántico, asegurar el desembarco de Normandía y acortar la contienda en dos o tres años. De su cabeza salió también Colossus, la que seguramente fue la primera computadora electrónica programable. Después de la guerra trabajó en el diseño del primer ordenador británico y, ya en la universidad de Manchester, siguió haciéndose preguntas. En 1950 publicó en la revista Mind un texto de gran importancia sobre máquinas pensantes en el que propuso el juego de la imitación, hoy llamado Test de Turing, la partida de nacimiento de la inteligencia artificial. En 1952 formuló su modelo de la morfogénesis, en el que explicaba matemáticamente cómo dos sustancias químicas pueden generar manchas, rayas y espirales. Aquel mismo año tras un robo en su casa confesó ante la policía una relación íntima con otro hombre. Juzgado por indecencia grave, le dieron a elegir entre ir a la cárcel o someterse a un tratamiento hormonal. Le retiraron la habilitación de seguridad y le aislaron. El 8 de junio de 1954 apareció muerto en su cama con una manzana envenenada con cianuro a medio comer en su mesilla. Tenía 41 años. El secreto oficial que pesaba sobre las actividades en Bletchley imposibilitó durante años conocer con detalle su importante contribución a la victoria. Fue a partir de los años 70 cuando empezó a ocupar el lugar que merecía. La película “Descifrando Enigma” de 2014 terminó de popularizar su figura. Antes, en 2009, el Gobierno británico pidió disculpas por aquel juicio y en 2013 Isabel II le concedió el perdón real póstumo, Nada de eso le devolvió la vida, pero cada vez que encendemos un ordenador o conversamos con una inteligencia artificial jugamos, sin saberlo, a una versión perfeccionada del juego que él imaginó. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Yann LeCun, Turing Award winner and former Chief AI Scientist at Meta, joins Jacob Effron. The conversation centers on Yann's contrarian thesis that LLMs are a dead-end on the path to human-level intelligence, despite being useful products — because they can't predict the consequences of their actions, can't plan, and fundamentally can't model the messy, high-dimensional real world. He unpacks his alternative architecture, JEPA (Joint Embedding Predictive Architecture), which learns abstract representations rather than generating pixel-level predictions, and explains why this approach is essential for robotics, industrial applications, and any system that needs to operate beyond the substrate of language. Yann also reveals the real story behind his departure from Meta (he had zero technical influence on Llama, contrary to public narrative), the genesis of his Tapestry project for sovereign open-source AI, why he believes LLMs are intrinsically unsafe, where he diverges from his fellow Turing laureates Hinton and Bengio, and why he predicts the industry will recognize the paradigm shift by early 2027. Throughout, he offers candid reflections on the tension between research and product at major labs, and why he intentionally headquartered AMI Labs in Paris with zero Silicon Valley VC money.   (0:00) Introduction  (01:45) Why LLMs Aren't the Path to Intelligence  (07:51) AMI and World Models  (12:07) The JEPA Architecture Explained  (15:55) Problems with Robotics Models Today  (20:37) Silicon Valley Herd Behavior  (28:18) Tapestry: Sovereign AI for the Rest of the World  (35:49) OpenAI Is the Next Sun Microsystems  (40:51) Why Yann's Views Diverged from Hinton & Bengio  (44:32) LLMs Are Intrinsically Unsafe  (58:00) Why Yann Left Meta  (1:00:26) Reflections on FAIR  (1:12:11) Advice for PhD Students   LeWorldModel Paper: https://arxiv.org/abs/2603.19312   With your host:  @jacobeffron  - Partner at Redpoint

Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! Alan Turing fue uno de los grandes cerebros privilegiados que alumbró el siglo XX. De ese cerebro salieron algunas de las ideas sobre las que se sostiene nuestro mundo. Sin sus aportes a las matemáticas, ni los ordenadores, ni los teléfonos móviles, ni internet existirían tal y como hoy los conocemos. Criado en Inglaterra mientras sus padres residían en la India, Turing mostró desde niño una inteligencia fuera de lo común. En el internado de Sherborne se enamoró de Christopher Morcom, un compañero cuya muerte prematura por tuberculosis le empujó a preguntarse sobre la relación entre la mente y la materia. En 1931 ingresó en el King's College de Cambridge, donde compaginó las matemáticas con el atletismo, disciplina que casi le lleva a los Juegos Olímpicos de 1948. En 1936 publicó el artículo que cambió la historia de la informática. Para responder al problema de la decisión planteado por David Hilbert, imaginó una máquina abstracta capaz de ejecutar cualquier cómputo definible mediante reglas. Demostró además que podía construirse una máquina universal capaz de imitar a cualquier otra. Aquella idea es el plano teórico del ordenador moderno y la raíz de toda la informática que nos rodea. Cuando estalló la guerra se incorporó al complejo secreto de Bletchley Park. Allí, junto a Gordon Welchman, diseñó la Bomba, un artefacto electromecánico que con que el consiguieron romper el cifrado de la máquina Enigma que utilizaban los alemanes para transmitir órdenes. Esa información, conocida como Ultra, permitió ganar la batalla del Atlántico, asegurar el desembarco de Normandía y acortar la contienda en dos o tres años. De su cabeza salió también Colossus, la que seguramente fue la primera computadora electrónica programable. Después de la guerra trabajó en el diseño del primer ordenador británico y, ya en la universidad de Manchester, siguió haciéndose preguntas. En 1950 publicó en la revista Mind un texto de gran importancia sobre máquinas pensantes en el que propuso el juego de la imitación, hoy llamado Test de Turing, la partida de nacimiento de la inteligencia artificial. En 1952 formuló su modelo de la morfogénesis, en el que explicaba matemáticamente cómo dos sustancias químicas pueden generar manchas, rayas y espirales. Aquel mismo año tras un robo en su casa confesó ante la policía una relación íntima con otro hombre. Juzgado por indecencia grave, le dieron a elegir entre ir a la cárcel o someterse a un tratamiento hormonal. Le retiraron la habilitación de seguridad y le aislaron. El 8 de junio de 1954 apareció muerto en su cama con una manzana envenenada con cianuro a medio comer en su mesilla. Tenía 41 años. El secreto oficial que pesaba sobre las actividades en Bletchley imposibilitó durante años conocer con detalle su importante contribución a la victoria. Fue a partir de los años 70 cuando empezó a ocupar el lugar que merecía. La película “Descifrando Enigma” de 2014 terminó de popularizar su figura. Antes, en 2009, el Gobierno británico pidió disculpas por aquel juicio y en 2013 Isabel II le concedió el perdón real póstumo, Nada de eso le devolvió la vida, pero cada vez que encendemos un ordenador o conversamos con una inteligencia artificial jugamos, sin saberlo, a una versión perfeccionada del juego que él imaginó. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Four stories today — all connected to your NIO position.NIO reports Q1 2026 earnings on May 21st before US marketsopen. Three things to watch on the call: whether revenuegrowth outpaced delivery growth as management guided,whether gross margin held above 17.5% from Q4 2025,and what William Li says about ES9 and L80 demand momentum.April delivery breakdown: the ES8 delivered 13,020 units —44% of NIO's entire monthly volume. The 5566 lineup(ET5, ET5 Touring, ES6, EC6) is under significant pressurewith meaningful year-over-year declines across every model.In Q1 the ES8 accounted for over 77% of NIO-brand deliveries.That's a company running on one engine. The ES9 and L80are engines two and three — both launching in the next 15 days.Every major automaker now wants chip independence.Volkswagen announced it's developing its own automotiveSystem-on-Chip in China for Level 3 autonomous driving.VW is already using Xpeng's Turing chip in its firstall-electric China SUV — the ID. UNYX 08 — which justentered production with deliveries starting by end of June.Reports from the Chinese internet suggest NIO is planningto sell its Shenji NX9031 chip to other manufacturersafter spinning off the chip business. The Apple siliconplaybook applied to automotive.April CPI dropped today at 3.8% year over year — thehighest since May 2023. Gasoline up 28.4% annually.Beef up 14.8%. Airline fares up 20.7%. Real wages fell.Bank of America now forecasts no Fed rate cuts untilthe second half of 2027. Traders are pricing a 30%chance of an actual rate hike by year end.The macro headwind is real. It's Iran-driven.It resolves when Hormuz resolves. Until then —watch oil, not the Dow.Also — missed yesterday. Writing a book. More soon.

In this episode, I interviewed Patrick Rooney, a cognitive science–trained AI practitioner and founder of Leonis Strategy, about how founders mischaracterize AI by collapsing “scripted autonomy” (agents doing tasks while you step away) into personhood autonomy (will, rights, interiority). Patrick argues this isn't just sloppy language but a leadership issue that shapes how teams relate to technology. They discuss why LLMs are plausibility engines rather than truth-seekers, how humans can pursue truth, beauty, and goodness for their own sake, and why leaders must own inputs, outputs, and responsibility instead of outsourcing judgment. We explored why LLM training is text-bound and disconnected from lived experience, the appearance-versus-reality problem behind Turing-test thinking, practical cautions around anthropomorphizing AI, and why doubling down on in-person human connection is a strategic response to AI at scale.01:53 LLMs Are Plausibility Engines05:10 Leadership And Culture Values07:34 Why LLMs Aren't Intelligent08:54 Turing Test And Training Limits12:42 Language Detached From Reality14:48 Personhood Rights And Ethics19:01 Anthropomorphism Risks19:34 Human Ownership Mindset20:25 Outsourcing Your Thinking22:24 IP Training Fears24:34 Responsibility Still Human28:41 Leading In AGI Hype29:38 Grounding In Real LifeConnect with Patrick: • https://leonisstrategy.com/• https://www.linkedin.com/in/prooney1/Connect with Raul: • Work with Raul: https://dogoodwork.io/apply • Free Growth Resources: https://dogoodwork.io/free-growth-resources

In advance of the local elections, we got to witness a new phenomenon in British politics: Corbyning Without Corbyn. We also discuss Richard Dawkins deciding that Claude is real and female and also doesn't exist as soon as he stops thinking about her. Where have we heard this before? Get the whole episode on Patreon here! RILEY ALERT Check out No Gods, No Mayors here! HUSSEIN ALERT Check out 10k Posts here! MILO ALERT Check out Milo's tour dates here: https://www.miloedwards.co.uk/liveshows NATE ALERT Lions Led By Donkeys will be performing live in London on 29th May and you can get tickets here! Also, if you're wondering about the outro music: Nate's band Second Homes has just released their debut album, and you can stream it for free here!

En Más de uno, el matemático y divulgador Santi García Cremades vuelve a abrir la libreta de cuadrícula para enfrentarse a una de las paradojas más famosas de la historia: la de Aquiles y la tortuga. A partir de un comentario del monólogo de Carlos Alsina, Cremades explica cómo Zenón de Elea llegó a plantear hace más de 2.500 años que el movimiento no existe y por qué su razonamiento sigue desconcertando hoy en día. Entre carreras imposibles, infinitos y bucles matemáticos, la conversación conecta la filosofía griega con los límites actuales de la inteligencia artificial y el conocido problema de parada de Turing.

En Más de uno, el matemático y divulgador Santi García Cremades vuelve a abrir la libreta de cuadrícula para enfrentarse a una de las paradojas más famosas de la historia: la de Aquiles y la tortuga. A partir de un comentario del monólogo de Carlos Alsina, Cremades explica cómo Zenón de Elea llegó a plantear hace más de 2.500 años que el movimiento no existe y por qué su razonamiento sigue desconcertando hoy en día. Entre carreras imposibles, infinitos y bucles matemáticos, la conversación conecta la filosofía griega con los límites actuales de la inteligencia artificial y el conocido problema de parada de Turing.Conviértete en un supporter de este podcast: https://www.spreaker.com/podcast/mas-noticias--4412383/support.ESCUCHAR RADIO 

Rawa Jawad Quinn is a dentist-turned-tech founder whose restless energy and refusal to be underestimated have shaped every chapter of her career. In this episode, she tells Payman about growing up in Chelsea after her Iraqi family fled Kuwait with nothing, studying in Liverpool, and working across 16 dental practices before channelling her frustrations into Medicube — a consent and patient communication platform built to give associates the consistency they've never had. The conversation takes some wonderfully unexpected detours into quantum physics, telepathy, AI-driven futures and the spiritual experiences that Rawa can't quite explain but absolutely trusts. There's also plenty of practical wisdom on occlusion, practice culture and what it really takes to bootstrap a dental tech start-up while raising a three-year-old without a nanny.In This Episode00:00:45 – Introduction and welcome00:01:25 – Growing up on the Kings Road and childhood in Chelsea00:03:30 – Studying dentistry in Liverpool and reinvention00:07:00 – Dyslexia diagnosis and learning differently00:10:10 – The itch beyond dentistry00:14:00 – Fleeing Kuwait, starting over in the UK00:16:25 – Why her parents' medical careers put her off medicine00:18:05 – Ambition, being underestimated and self-belief00:23:15 – Spirituality, connectedness and trusting intuition00:26:10 – Wanting it all — motherhood, marriage and a start-up00:31:00 – Lessons from 16 dental practices00:36:25 – Working in corporates and at Bupa00:41:20 – NHS vs private practice00:45:15 – The birth of Medicube00:48:30 – How Medicube works and pilot results00:55:55 – Finding a co-founder and the UCL connection00:58:50 – Funding through grants, awards and bootstrapping01:03:25 – AI, the Turing test and the future of work01:10:25 – Robots, relationships and what makes us human01:22:55 – Physics, multiverse theory and keeping an open mind01:28:40 – Blackbox thinking01:33:40 – A patient with buyer's remorse after crown preps01:36:55 – Occlusion, full mouth rehabs and the Dawson Academy01:43:20 – Tech conferences and the reality of being a founder01:47:05 – Fantasy dinner partyAbout Rawa Jawad QuinnRawa Jawad Quinn is a dentist based in Belfast, currently working at Bupa, with a particular interest in full mouth rehabilitation cases. She is also the co-founder of Medicube, a dental tech platform that streamlines consent, treatment planning and patient communication. Rawa trained at the Dawson Academy and Chris Hall's programme, and has worked across 16 practices spanning NHS, private and corporate settings.

Welcome back to Scaling Theory. In this episode, I speak with Matthew O. Jackson, the William D. Eberle Professor of Economics at Stanford University and an external faculty member at the Santa Fe Institute. Matthew is one of the founders of the modern economics of networks and the author of The Human Network and Social and Economic Networks.We talk about the friendship paradox, why homophily slows how fast a society learns the truth but helps niche ideas catch fire, and the gossip study where villagers in southern India proved remarkably good at naming the most central spreaders in their community. We then turn to AI agents as a different species: Turing tests on LLMs, the steerability of agent personas through system prompts, and what to make of Moltbook, the social network for AI agents.By the end, you will know why telling students how much their peers actually drink reduces binge drinking more than warning them about the dangers of alcohol, why the same network can spread a virus quickly and a belief slowly, and why AI agents change their behavior when asked to explain it.Papers and works referenced in the conversationBooksThe Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors — Matthew O. Jackson (Pantheon, 2019). https://web.stanford.edu/~jacksonm/books.htmlSocial and Economic Networks — Matthew O. Jackson (Princeton University Press, 2008). https://web.stanford.edu/~jacksonm/books.htmlPart I — The scaling of human networks"Diffusion and Contagion in Networks with Heterogeneous Agents and Homophily" — Matthew O. Jackson and Dunia López-Pintado, Network Science 1(1), 2013. https://arxiv.org/abs/1111.0073"How Homophily Affects the Speed of Learning and Best-Response Dynamics" — Benjamin Golub and Matthew O. Jackson, Quarterly Journal of Economics 127(3), 2012. https://web.stanford.edu/~jacksonm/homophily.pdf"Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials" — Abhijit Banerjee, Arun G. Chandrasekhar, Esther Duflo, and Matthew O. Jackson, Review of Economic Studies 86(6), 2019. https://academic.oup.com/restud/article/86/6/2453/5345571"Empathy and Well-Being Correlate with Centrality in Different Social Networks" — Sylvia A. Morelli, Desmond C. Ong, Rucha Makati, Matthew O. Jackson, and Jamil Zaki, PNAS 114(37), 2017. https://www.pnas.org/doi/10.1073/pnas.1702155114Part II — The scaling of AI agents"Inequality's Economic and Social Roots: The Role of Social Networks and Homophily" — Matthew O. Jackson, in Advances in Economics and Econometrics: Twelfth World Congress of the Econometric Society (Cambridge University Press, 2025). https://arxiv.org/abs/2506.13016"AI Behavioral Science" — Jackson, Mei, Wang, Xie, Yuan, Benzell, Brynjolfsson, Camerer, Evans, Jabarian, Kleinberg, Meng, Mullainathan, Ozdaglar, Pfeiffer, Tennenholtz, Willer, Yang, and Ye, arXiv 2509.13323, 2025. https://arxiv.org/abs/2509.13323"A Turing Test of Whether AI Chatbots Are Behaviorally Similar to Humans" — Qiaozhu Mei, Yutong Xie, Walter Yuan, and Matthew O. Jackson, PNAS 121(9), 2024. https://www.pnas.org/doi/10.1073/pnas.2313925121

AI swarms are now considered the most dangerous influence weapons ever created, actively fabricating grassroots consensus and corrupting enterprise AI training data through disinformation. Daniel Thilo Schroeder, Research Scientist at SINTEF, and Jonas R. Kunst, Professor at BI Norwegian Business School, co-authored a study with 22 authors published in Science that maps this threat. They explain how AI swarms operate without human oversight, why traditional detection methods fail, and what governments, platforms, and business leaders must do to fight back. This is CXOTalk episode 915.YOU'LL DISCOVER✅ How AI swarms shift from central command to emergent hive behavior with decreasing human oversight✅ Why AI-generated social media messages now pass the Turing test, rendering individual message detection obsolete✅ The persona-centric architecture: how single AI agents coordinate behavior across email, X, Bluesky, and Facebook simultaneously✅ How swarms fabricate synthetic consensus by hijacking human conformist psychology✅ The perverse incentives of social media business models that profit from AI swarm engagement metrics✅ How AI swarms poison LLM training data, causing future models to output manipulated facts as objective reality✅ The proposed Distributed AI Influence Observatory for decentralized threat intelligence sharing✅ Why malicious actors can deploy self-optimizing AI swarms from a bedroom using existing multi-agent frameworks⏱️ TIMESTAMPS0:00 The Shift from Bot Networks to AI Swarms2:00 Why Cheap AI Inference Enables Long-Term Influence Campaigns4:30 Autonomous Coordination and Emergent Hive Behavior7:00 Persona-Centric Agents Across Multiple Platforms8:30 Weaponizing Disinformation to Fabricate Synthetic Consensus14:15 How AI Swarms Corrupt LLM Training Data18:00 Why Individual Message Detection No Longer Works23:00 The Research Frontier: Coordination Pattern Detection27:00 Platform Business Models and Perverse Incentives32:00 Building Defenses: The AI Influence Observatory39:00 Corporate Risks: Fabricated Boycotts and Targeted Harassment46:00 Can It Be Stopped? The Arms Race Democracies Must Join

Bletchley Park wasn't built by one man—and history must stop pretending otherwiseFor most people, Bletchley Park means one thing: Alan Turing, Enigma, and a single heroic breakthrough.That story is neat, cinematic—and deeply misleading.In this episode of History Rage, Paul Bavill is joined by historian, author, and Bletchley Park trustee Sir Dermot Turing to dismantle one of Britain's most comfortable Second World War myths. What follows is a forensic, passionate unpicking of how thousands of codebreakers—most of them women—have been written out of history.This is not an attack on Alan Turing. It's a demand for accuracy.Sir Dermot explains why Enigma has become a historical obsession, how it eclipses dozens of other vital ciphers, and why reducing Bletchley Park to a single man does a disservice to everyone involved—including Turing himself. From Spanish and Italian diplomatic codes to Japanese military signals, this episode reveals just how broad, complex, and international the intelligence war really was.Crucially, the conversation exposes how women codebreakers were systematically downgraded by job titles, pay grades, and later historians. Clerical assistants, typists, and “support staff” were in reality performing some of the hardest cryptographic work of the war—often better than the men promoted over them. Figures such as Joan Clarke, Wendy White, Helen Hazelden, Marie Rose Egan, and many others emerge not as footnotes, but as central players.This episode also explores:Why Enigma machines themselves were never the real secretHow civil service bureaucracy distorted the historical recordThe hidden importance of German diplomatic intelligenceWhy Bletchley Park was far messier, more political, and more human than popular culture admitsIf you think you know the story of Bletchley Park, this episode will make you angry—for all the right reasons.About the Guest: Sir Dermot TuringSir Dermot Turing is a historian, author, and trustee of Bletchley Park, specialising in intelligence history and overlooked figures of the Second World War. He is the nephew of Alan Turing and a leading voice challenging simplistic narratives around wartime codebreaking.Recommended Reading

En esta edición de News Clickciber, analizamos el histórico relevo de Tim Cook en Apple y la masiva filtración de contraseñas RockYou2026. Exploramos la vulnerabilidad de las fábricas en la Ciberpíldora sobre IT vs OT, la millonaria compra de Cursor AI por SpaceX y la importancia del Gobierno del Dato para evitar el caos empresarial. Además, descubrimos la ciencia detrás del Modo Avión y rendimos tributo a genios como Tesla y Turing. ¡No te pierdas nuestro concurso de licencias Kaspersky! Equipo y Producción: Presentado por: Carlos Lillo. Producido por: Global Click Comunicación. Patrocinadores: Netaro, Cibergurú, Semperis, Vivali y Kaspersky. Realizadora: Alex Serrano. Ayudante de realización: Paula Martínez Arango.

¿Estamos preparados para convivir con una inteligencia que no es una herramienta, sino un agente con voluntad propia? En este episodio, analizo a fondo las revelaciones de Yuval Noah Harari y Max Tegmark sobre el avance de la IA. Exploramos el fin del test de Turing, el riesgo de la obsolescencia económica humana y el peligro de otorgar personería jurídica a algoritmos. Una reflexión honesta sobre por qué necesitamos regular la tecnología antes de que perdamos, para siempre, el control de nuestra propia historia.

¿Sabías que la pregunta filosófica más urgente de nuestro siglo no nació en Silicon Valley, sino en las selvas de Guatemala hace cientos de años? Antes de que existieran los chatbots, los algoritmos y los debates sobre si la Inteligencia Artificial puede ser consciente, los mayas k'iche' ya habían intentado responder qué pasa cuando creamos a un ser a nuestra imagen y semejanza.En este episodio, abrimos el Popol Vuh para presenciar los tres intentos de los dioses por crear a la humanidad. Analizamos cómo el hombre de barro que se deshacía es idéntico a los primeros chatbots; cómo los hombres de madera vacíos y sin alma nos advierten sobre los modelos de lenguaje actuales como ChatGPT; y por qué el hombre de maíz, perfecto y omnisciente, obligó a los dioses a imponer un límite a su creación, una lección de prudencia que hoy parecemos haber olvidado en nuestra carrera hacia la superinteligencia.Un viaje desde la mitología mesoamericana hasta los laboratorios de Google que resulta ser, en realidad, un espejo de nuestra propia ambición tecnológica.Si te interesa escuchar el viaje al inframundo maya puedes hacerlo aquí: https://share.transistor.fm/s/43de7bb8En este episodio:El caso de Blake Lemoine y LaMDA: cuando la IA afirma ser una "persona"El Acto I: El hombre de barro y el fracaso de la programación simbólicaEl Acto II: El hombre de madera, la rebelión de los objetos y los "loros estocásticos"El Acto III: El hombre de maíz y por qué los dioses nublaron la visión de la humanidadLa historia de Francisco Ximénez: el fraile inquisidor que salvó el Popol Vuh del fuegoLa filosofía de Luciano Floridi y Nick Bostrom sobre el peligro de la Inteligencia Artificial General (AGI)Sigue a Mitos y Más:Blog: mitosymas.com | Instagram: @mitosymas | YouTube: youtube.com/@mitosymas(00:00) - El caso de Blake Lemoine y la IA consciente (03:13) - El silencio primordial y los dioses creadores (05:11) - Intento 1: El Hombre de Barro (y los primeros chatbots) (06:20) - Intento 2: El Hombre de Madera (y los LLMs como ChatGPT) (07:44) - La aterradora rebelión de los objetos (09:43) - Intento 3: El Hombre de Maíz (y el peligro de la Superinteligencia) (12:20) - Por qué los dioses nublaron la visión humana (14:38) - Francisco Ximénez: El fraile que salvó el Popol Vuh del fuego (16:32) - La verdadera lección del mito para el siglo XXI ★ Support this podcast ★ Click here to view the episode transcript.

“Doing science is like reading the mind of God.” — Demis Hassabis, quoted in The Infinity MachineThis week's New Yorker uncomplimentary profile of OpenAI's CEO is entitled “The Many Faces of Sam Altman.” But not all AI leaders are quite as many faced as slippery Sam. Take, for example, Demis Hassabis, the North London based co-founder and CEO of Google's DeepMind. In his new biography, The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence, the British journalist Sebastian Mallaby argues that Hassabis is, in contrast, one faced. And that face is not only decent, but informed by the enlightened ethics of Baruch Spinoza and Immanuel Kant.Mallaby presents Hassabis as the anti-Altman. He's stayed at DeepMind for sixteen years, lived in the same London house, drives a decade-old car. Rather than power, Google's AI supremo seeks scientific enlightenment. Like Spinoza, his God is the master watchmaker of the universe. And so doing science, Hassabis explained to Mallaby in one of their many conversations in the backroom of a North London pub, is like reading the mind of God. Decent Demis. Honest Hassabis. Let's just hope this modest and thoughtful tech leviathan can bring Kantian ethics to Silicon Valley's sprint for artificial general intelligence. Five Takeaways•       Hassabis Is the Anti-Altman: Sam Altman has managed to annoy almost everyone he's worked with by saying one thing and doing the opposite. Hassabis has run DeepMind continuously for sixteen years, lives in the same house in Highgate, drives a decade-old car, and spends his discretionary money on Liverpool season tickets. He doesn't want power. He wants scientific enlightenment. Mallaby uses the word advisedly.•       Doing Science Is Like Reading the Mind of God: Hassabis is a Spinozan. The god he believes in is the god Einstein talked about — the fabric of reality understood through scientific inquiry. He reads Kant, he reads Spinoza, he reads widely enough to be a proper polymath. Mallaby sat with him in a Highgate pub for more than thirty hours. What he found was not a Silicon Valley sociopath but an enlightenment figure who thinks AI is the modern version of the telescope.•       The Szilard Pedestrian Crossing: Mallaby asked Hassabis what it felt like to set up DeepMind in 2010. Instead of the usual vague answer, Hassabis painted the scene: the attic office on Russell Square, the heat, the stairs, the greenery outside, the London Mathematical Society three doors down where Turing lectured, and the zebra crossing where the Hungarian physicist Leo Szilard conceived of the nuclear chain reaction in the 1930s. The perfect metaphor: DeepMind as the modern Manhattan Project.•       The Two Categories of Things That Go Wrong: There's the idiot-in-charge category — an evil or stupid person making bad decisions, and you could swap them out. Then there's the structural category: a good person trying their best, defeated by larger forces they cannot control. Hassabis is category two. He wants to make AI safe, but race dynamics between US and China labs make safety nearly impossible to deliver. The failure of governments to intervene is the real story. Not individuals.•       The Go Players Who Quit: When AlphaGo beat the best players in the world, some professional Go players retired — centuries of accumulated human understanding devalued overnight. Others kept playing, using the machine as a tutor to discover patterns they'd never seen. Two responses to superintelligence in one domain. One is mourning. The other is curiosity. Mallaby thinks the second response is the only one worth having. Hassabis agrees. About the GuestSebastian Mallaby is the Paul A. Volcker senior fellow for international economics at the Council on Foreign Relations. A former Washington Post columnist and Economist contributing editor, he is the author of More Money Than God, The Man Who Knew (winner of the FT and McKinsey Business Book of the Year), The Power Law, and now The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence.References:•       The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence by Sebastian Mallaby.•       Episode 2862: Truth Is Dead — Steven Rosenbaum on AI as a spectacularly good liar. Mallaby's quiet counter-argument.•       Episode 2860: We Shape Our AI, Thereafter It Shapes Us — Keith Teare on agency in our agentic age. Hassabis thinks he can still steer.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:31) - Introduction: the many faces of Sam Altman (02:00) - Altman's duplicity versus Hassabis's consistency (02:56) - The moral wrestling: is this the Manhattan Project? (04:45) - The ordinary genius in Highgate (06:29) - The Szilard pedestrian crossing and a storyteller off the charts (09:10) - Responding to The Guardian: why Hassabis isn't Altman (12:58) - The two categories of things that go wrong (14:48) - Mustafa Suleiman's remarkable backstory (17:01) - Did Demis fire Mustafa? (19:46) - Class, Eton, and the North London grammar school (22:27) - Spinoza, Kant, and the god of science (25:27) - Doing science is like reading the mind of God (29:57) - Why not Princeton? The money problem (34:12) - The secret DeepMind vs Google negotiation (43:11) - Is Hassabis the next CEO of Google? (48:05) - The Go players who quit

Episode: 3247 Proust, Turing, and the Measure of Humanity.  Today, we go from Turing to Proust.

Fresh off raising a monster $15B, Marc Andreessen has lived through multiple computing platform shifts firsthand, from Mosaic and Netscape to cofounding A16z. In this episode, Marc joins swyx and Alessio in a16z's legendary Sand Hill Road office to argue that AI is not just another hype cycle, but the payoff of an “80-year overnight success”: from neural nets and expert systems to transformers, reasoning models, coding, agents, and recursive self-improvement. He lays out why he thinks this moment is different, why AI is finally escaping the old boom-bust pattern, and why the real bottleneck may be less about models than about the messy institutions, incentives, and social systems that struggle to absorb technological change.This episode was a dream come true for us, and many thanks to Erik Torenberg for the assist in setting this up. Full episode on YouTube!We discuss:* Marc's long view on AI: from the 1980s AI boom and expert systems to AlexNet, transformers, and why he sees today's moment as the culmination of decades of compounding technical progress* Why “this time is different”: the jump from LLMs to reasoning, coding, agents, and recursive self-improvement, and why Marc thinks these breakthroughs make AI real in a way prior cycles were not* AI winters vs. “80-year overnight success”: why the field repeatedly swings between utopianism and doom, and why Marc thinks the underlying researchers were mostly right even when the timelines were wrong* Scaling laws, Moore's Law, and what to build: why he believes AI scaling laws will continue, why the outside world is messier than lab purists assume, and how startups can still create durable value on top of rapidly improving models* The dot-com crash and AI infrastructure risk: Marc's comparison between today's AI capex boom and the fiber/data-center overbuild of 2000, plus why he thinks this cycle is different because the buyers are huge cash-rich incumbents and demand is already here* Why old NVIDIA chips may be getting more valuable: the pace of software progress, chronic capacity shortages, and the idea that even current models are “sandbagged” by supply constraints* Open source, edge inference, and the chip bottleneck: why Marc thinks local models, Apple Silicon, privacy, trust, and economics all point toward a major role for edge AI* American vs. Chinese open source AI: DeepSeek as a “gift to the world,” why open models matter not just because they're free but because they teach the world how things work, and how open source strategies may shift as the market consolidates* Why Pi and OpenClaw matter so much: Marc's claim that the combination of LLM + shell + filesystem + markdown + cron loop is one of the biggest software architecture breakthroughs in decades* Agents as the new “Unix”: how agent state living in files allows portability across models and runtimes, and why self-modifying agents that can extend themselves may redefine what software even is* The future of coding and programming languages: why Marc thinks software becomes abundant, why bots may translate freely across languages, and why “programming language” itself may stop being a salient concept* Browsers, protocols, and human readability: lessons from Mosaic and the web, why text protocols and “view source” mattered, and how similar principles may shape AI-native systems* Real-world OpenClaw use: health dashboards, sleep monitoring, smart homes, rewriting firmware on robot dogs, and why the most aggressive users are discovering both the power and danger of agents first* Proof of human vs. proof of bot: why Marc thinks the internet's bot problem is now unsolvable via detection alone, and why biometric + cryptographic proof of human becomes necessaryTimestamps* 00:00 Marc on AI's “80-Year Overnight Success”* 00:01 A Quick Message From swyx* 01:44 Inside a16z With Marc Andreessen* 02:13 The Truth About a16z's AI Pivot* 03:29 Why This AI Boom Is Not Like 2016* 06:33 Marc on AI Winters, Hype Cycles, and What's Different Now* 10:09 Reasoning, Coding, Agents, and the New AI Breakthroughs* 12:13 What Founders Should Build as Models Keep Improving* 16:33 AI Capex, GPU Shortages, and the Dot-Com Crash Analogy* 24:54 Open Source AI, Edge Inference, and Why It Matters* 33:03 Why OpenClaw and PI Could Change Software Forever* 41:37 Agents, the End of Interfaces, and Software for Bots* 46:47 Do Programming Languages Even Have a Future?* 54:19 AI Agents Need Money: Payments, Crypto, and Stablecoins* 56:59 Proof of Human, Internet Bots, and the Drone Problem* 01:06:12 AI, Management, and the Return of Founder-Led Companies* 01:12:23 Why the Real Economy May Resist AI Longer Than Expected* 01:15:53 Closing ThoughtsTranscriptMarc: Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. Having said that, I think what's actually happened is an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years where that was controversial. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right?Which is like, it's an overnight success ‘cause it's like bam, you know, chat GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.If I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough.swyx: Before we get into today's episode, I just have a small message for listeners. Thank you. We will not be able to bring you the ai, engineering, science, and entertainment contents that you so clearly want if you didn't choose to also click in and tune into our content.We've been approached by sponsors on an almost daily basis, but fortunately enough of you actually subscribed to us to keep all this sustainable without ads, and we wanna keep it that way. But I just have one favor to ask all of you. The single, most powerful, completely free thing you can do is to click that subscribe button.It's the only thing I'll ever ask of you, and it means absolutely everything to me and my team that works so hard to bring the in space to you each and every week. If you do it, I promise you will never stop working to make the show even better. Now, let's get into it.Alessio: Hey everyone, welcome to the Lidian Space Pockets. This is CIO, founder Kernel Labs, and I'm joined by s Swix, editor of Lidian Space.swyx: Hello. And we're in a 16 Z with a, uh, mark G and welcome.Marc: Yes, yes. A and what, half of 16? Something like that. A one. Exactly,swyx: exactly. Uh, apparently this is the, the final few days in your, your current office.You're moving across the road.Marc: Uh, we're, yeah. We have a, we have some, we have some projects underway, but yeah, this is actually, oh, this is the original. We're in actually the original office. We're in the, we're in the, we're, we're in the whole thing.swyx: It's beautiful. Yeah. Great.Marc: Thank you.swyx: So I have to come out, uh, this is a, you know, I wanted to pick a spicy start in October, 2022.I just made friends with Roone and, uh, I wanted to give him something to sort of be spicy about. And I said, uh. Uh, it'll never not be funny. The A 16 Z was constantly going. The future is where the smart people choose to spend their time and then going deep into crypto and not in ai. And that was in October 22nd, 2022.And Ruen says there was an internal meeting in a 16 Z to reorient around Gen ai. Obviously you have, but was there a meeting? What, what was that?Marc: I mean, I don't, look, I've been doing AI since the late eighties.swyx: Yeah.Marc: So I, I don't know, like all that, as far as I'm concerned, this stuff is all Johnny cum lately.Yeah. You, I mean, look, we've been doing ar entire existence. I mean, we've been doing AI machine learning deep, you know, deeply. We've been doing this stuff way from the beginning. Obviously a AI is just core to computer science. I, I, I actually view them as like quite, uh, quite continuous. Um, you know, Ben and I both have computer science degrees.Um, you know, we, we both, Ben, Ben and I actually both are world enough to remember the actual AI boom in the 1980s. Yeah. There was like a, there was a big AI boom at the time. Um, and there was a, was names like expert systems. Um, and they of like lisp and lisp machines. Uh, I, I coded in lisp. I was coding a lisp in 1989.When that was the, the language of the AI future. Um, yeah. So this is something that we're like completely, you completely comfortable with. I've been doing the whole time and are very enthusiastic aboutswyx: is there a strong, like this time is different because, uh, my closest analog was 20 16 17. It was an AI boom.Mm-hmm. And it petered out very, very quickly. Um, we, it just, it just in terms of investingMarc: sort of, sort of,swyx: yeah. Investment, investment excitement.Marc: Although that's really when the, the, the Nvidia phenomenon really, it was, I would say it was in that period when it was very clear that at, at the time it, the vocabulary was more machine learning, but it, it was very clear at that time that machine learning was hitting some sort of takeoff point.Alessio: Yeah.Marc: Well, and as you guys, you guys have talked about this at length on, on your thing, but, you know, if you really track what happened, I think the real story is, it was, it was the Alex net, uh, basically breakthrough in like 2013. That was the, that was the real knee in the curve. Um, and then it was obviously the transformer breakthrough in 17.Alessio: Yeah.Marc: Um, and then everything that followed. But, but, you know, look, machine learning, you know, there were, you know, look, uh, I mean look, I've been working, you know, I've been working with, uh, one of my, you know, kind of projects working with Facebook since 2004. Um, and on the board since 2007, and of course, you know, they, they started using machine learning very early, um, and, you know, have used it basically, you know, for like 20 years for, you know, content, you know, feed optimization and advertising optimization.And obviously many, you know, financial services. You know, many, many, many companies, many different sectors have been doing this. And so it's like one of these things, it's like, it's not a, it's not a single thing. Like it's, it's like, it's like layers, right? Yeah. Um, and, and the layers arrive at different paces and, but they kind of build up.swyx: Yeah.Marc: Uh, they kind of build up over time and then, and then, yeah. And then look, in retrospect, it was 2017 was kind of the, you know, the key, the key point with the trans transformer and then. And then as you guys know, there was this really weird like four year period where it's like the, the transformer existed and then it was just like,swyx: let's go.Yeah.Marc: Well, but, but it was just, but, but between 2020, but between 2017 and 2021, I mean, that was the era of which like companies like Google had internal chat Botts, but they weren't letting anybody use them.swyx: Yeah.Marc: Right. And then, you know, and then OpenAI developed Chat GT or GPT two, and then they told everybody, this is way too dangerous to deploy.Right. Yeah. You know, we can't possibly let normal people, normal people use this thing. And then you, you guys, I'm sure remember AI Dungeon, um mm-hmm. So the o for, there was like a year where like the only way for a normal person to use GP T three was in, in AI dungeon.Alessio: Yeah.Marc: And so you, you, we would do this, you'd go in there and you'd pretend to play Dungeons and Dragons.In reality, you're just trying to talk to talk to GPT. And so there was this, you know, there was this long, you know, and I, you know, the big, big companies, you know, big companies are cautious and, you know, the big companies were cautious. It, it, by the way, it took open ai. You know, they, they, they talk about this, it took open AI time to actually adjust, you know, kind of re redirect their researchswyx: path.I, I think, uh, let say Rosewood, right? Uh, the, the dinner that founded OpenAI was right there.Marc: Right, right. But that, that dinner would've taken place in 20swyx: 18Marc: 19. The formation of OpenAI Uhhuh as late as 2018.swyx: Uh, uh, sorry. Uh, no, I'm, I'm, I'm, I'm wrong. Probably It should be 20. Yeah. They just celebrated a 10 year anniversary, so it it is 2025.Yeah, so, so 2015?Marc: Yeah. 2015. Yeah. 2015. But then, uh, um, Alec Radford did G PT one in what, probablyswyx: mm-hmm. 17, 18,Marc: yeah. 17, 18. So it, yeah. For, and then, and then they didn't really, and then GPT three was what? 2020? 2020.swyx: 2020.Marc: Because that became copilot immediately. Even open ai, which has been, you know, the leader of, of this thing in the last decade, you know, e even they had to adapt and, and, and lean into the new thing.And so. Um, yeah, I, I think it's just this process of basically sort of wave after wave layer after layer, you know, building on itself. And then you kind of get these catalytic moments where, where the whole thing pops and, and obviously that's what's happening now.swyx: Is it useful to think about will there be any ai, winter?‘cause there's always these patterns. Like, is this, in the summer is something I constantly think about because do I get, do I just like. Just get endlessly hyped and just trust that I will only be early and never wrong or right. Well, are we, will there be a winter?Marc: So there's something about, say the following.There's something about AI that has led to this repeated pattern. Um, and, and, and you guys know this,swyx: it's summer, winter, summer,Marc: winter, summer, winter, summer, winter. And it goes back 80 years. Yeah. 80 years. Uh, so the original neural network paper was 1943. Right. Which is, which is amazing. Uh, that it was, it was far back that long.And then there was you, if you guys have ever talked about this on your show, but there was this, uh, there was a big, uh, there was an a GI conference at Dartmouth University in 1950. 55. 55, yeah. And they got a NSF grant to, uh, for the, all the AI experts at the time to spend the summer together. And they figured if they had 10 weeks together, they could get a GI, uh, at the other end.And they got their, by the way, they got the grant, they got the 10 weeks and then, you know, 1955, you know. No, no. A GI. And like I said, I, I lived through the eighties version of this where there was a big, a big boom and a crash. And so, so there is this thing, and there, there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.Um, and, and it's probably on both sides of like the, the, the boom bus cycle. You, you kind of see that play out. Having said that, I think what's actually happened is like just, and you know, and we now know in retrospect like an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years or that was controversial. And, and we now know that that's the case. And so we, we now, you know, everything we're building on today just sort of derives from the original idea in 1943. And so, so in retrospect, we, we now know that like, these, these guys are right.They, they, you know, they would get the timing wrong and they thought, you know, capabilities would arrive faster, or they were, it could be turned into businesses sooner or whatever, but like, they were fundamentally, the, the scientists who worked on this over the course of decades were fundamentally correct about what they were doing.And, and the, and the payoff from, from, from all their work is happening now. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right? Which is like, it's an overnight success.‘cause it's like bam, you know, chat, GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.Um, and thinking, and look, there were AI researchers who spent their entire lives. They got their PhD. They, they worked for, they've researched for 40 years. They retired in a lot of cases, they passed away and they never actually saw it work.swyx: Yeah. It's all sad.Marc: It is. It is sad. It's sad. Knewswyx: Jeff Hinton was like the last guy.Marc: Yeah. Yeah. Well, there were the guys, uh, was a guy, Alan Newell. I mean, there's tons of John McCarthy. You know, John McCarthy was like one of the inventors in the field. He's one of the guys who organized the Dartmouth Conference and you know, he taught at Stanford for 40 years. Wow. And passed, you know, passed away, I don't know, whatever, 10, 10 years ago or something.Never, never actually go. Got to see it happen. But like, it is amazing in retrospect, like, these guys were incredibly smart and they worked really hard and they were correct. So anyway, so then it's like, okay, you know, say history doesn't repeat, but it rhymes. It's like, okay, does that mean that there's gonna be another, like, you know, basically boom buzz cycle.And I, I will tell you, like, let, like in a sense, like yes, everything goes through cycles and, you know, people get overly enthusiastic and overly depressed and there's, there's a time, there's a timelessness to that. Having said that, there's just no question. Um, so the form, the foremost dangerous words in investing this time are, this time is different.Do you know the 12 most dangerous words investing? No. The four most d foremost dangerous words in investing are this time is different. Yeah. Um, the 12 most dangerous words. And so like, I'll tell you what's different. Like now it's working like, like there's just no, I mean, look, there's just no question.And by the way, I, I'll just give you guys my take. Like L LLMs, like from, from basically the Chad G PT moment through to spring of 25. I think you could still, I think well intention, well, and of. Form skeptics could still say, oh, this is just pattern completion. And oh, these things don't really understand what they're doing.And you know, the hall hallucination rates are way too high. And, you know, this is gonna be great for creative writing and creating, you know, Shakespeare and so sonnets and, you know, as, as rap lyrics or whatever, like, it's gonna be great and all that stuff, but we're not gonna be able to harness this to make this relevant in, you know, coding or in medicine or in law or in, you know, you know, kind of feels that, you know, kind of really, really matter.And I think basically it was the reasoning breakthrough. It, it was oh one and then R one that basically answered that question basically said, oh no, we're gonna be able to actually turn this into something that's gonna work in the real world. And, and then obviously the coding breakthrough over the, over basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.Mm-hmm. Where you're just like, alright, if, if, you know, if Linus Tova is saying that the AI coding is no better than he is like. Like, that's, that's never happened before. That's theswyx: benchmark.Marc: Yeah. That's never happened before. And so now we know that it's, it's gonna sweep through coding and, and then, and then we, we know, you know, we know that if it's gonna work in coding, it's gonna work in everything else.Right. It's just then, because that's, that's like, that's like, that's like the hardest in many ways. That's the hardest example. And how everything else is gonna be a, a derivative of that. And then on top of that, we just got the agent breakthrough, you know, with Open Claw, which is fantastic. Which is amazing and incredibly powerful.And then we just got the, the, um, the auto research, uh, you know, the, the self-improvement. You know, we're now into the self-improvement breakthrough. And so the, so the way I think about it is we've had four fundamental breakthroughs in functionality, l OMS reasoning, uh, agents, um, and then, uh, and, and then now RSI, um, and, and they're all actually working.Um, and so I'm, I'm just, as you like, you can tell I'm jumping outta my shoes. Like, like this is, like this is it like this, this is the culmination of 80 years worth of worth of work, and this is the time it's becoming real.Alessio: Yeah.Marc: I, I'm completely convinced.Alessio: I think the anxiety that people feel is like during the transistor era, yet Mors law, and it's like, all right, we understand why these things are getting better.We understand the physics of it. Yeah. With ai, it's. It's so jagged in like the jumps where like, like you said, it's like in three months you have like this huge jump like, and people are like, well this can keep happening. Right? But then it keeps happening,Marc: it'll keep happening.Alessio: And so like how do you think about also timelines of like what's we're building?I think we always have this question with guests, which is like, you know, should you spend time building harness for a model versus like the next model just gonna do it one shot in the lead space. Right. And how does that inform, like how you think about the shape of the technology? You know, you talk about how it's a new computing platform.If you have a computing platform, then like every six months it like drastically changes in what it looks like. It's hard to build companies on top of it.Marc: Yeah. So, so a couple things. So one is like, look, the, the Moore's law was what we now call a scaling law. Like Moore's Law was a scaling law and for your younger viewers, more Moore's Law was every chip chip chips either get twice as powerful or twice as cheap every, every 18 months.And that, and that and that, you know, that it's gotten more complicated in the last few years. But like that, that was like the 50 year trajectory of, of, of the computer industry. And then, and then by the way, and that's what took the mainframe computer from a $25 million current dollar thing into, you know, the phone in your pocket being, you know, a million times more powerful than that.Like that, you know, for, for 500 bucks. And so that, that was a scaling law. And then, and then, and then key to any scaling law, including Moore's Law and the AI scaling laws is, you know, they're not really laws, right? They're, they're, they're, they're predictions, but when they work, they become self-fulfilling predictions because they, they, they, they, they set a benchmark and, and then the entire industry, right?All the smart people in the industry kind of work to make sure that, that, that actually happens. And so they, they kind of motivate the breakthroughs that are required to, to keep that going. And, and in and in chips, that was a 50 year, that was a 50 year run. Right. And it, it was amazing. And it's still happening in, in some areas of, of chips.I think the same thing is happening with the, the core scaling laws. The core scaling laws. In, in, in ai, you know, they're, they're not really laws, but like they, they are basically. There are predictions and then they're motivating catalysts for the research work that is required to be. And, and, and, and by the way, also the investment, uh, dollars, um, uh, you know, required to basically keep, you know, keep the curves going and, and look, it, it is, it's gonna be complicated and it's gonna be variable and they're, you know, there're gonna be walls that are gonna look like they're fast approaching, and then they're gonna be, you know, engineers are gonna get to work and they're gonna figure out a way to punch through the walls.And obviously that's, you know, that's been happening a lot, you know, and then look, there's gonna be times when it looks like the walls have, you know, the, the, the laws have petered out and then they're gonna, they're gonna pick up again and surge and then, and then, and then it, it appears what's happening to the eyes is there's not multiple, you know, multiple scaling laws.Um, there's multiple areas of improvement. And, and I think, you know, I don't know how many more there are already yet to be discovered, but there are probably some more that we don't know about yet. You know, they, like, for example, there's probably some scaling law around, um, world models and robotics that we don't fully understand, you know, kind of acquisition of data at scale in the real world that we don't fully understand yet.So that, that, that one will probably kick in at some point here. There's a bunch of really smart people working on that. Um, and so, yeah, I, I think the expectation is that, that, you know, the, the scaling laws generally are gonna continue. Yeah. The, the pace of improvement will continue to move really fast.Um. To your question on like what to build. So, uh, I'm a complete believer the scaling laws are gonna continue. I'm a complete believer the capabilities are gonna keep getting amazing, um, you know, leaps and bounds. Uh, the part where I kind of part ways a little bit with how, what I would describe as the AI purists, um, you know, which is, which I would characterize as like the people who are.In many ways, the smartest people in the field, but also the people who spend their entire life, like at a lab, um, and have, have, I would say, have very little experience in the outside world. Um, the, the, the nuance I would offer is the outside world of 8 billion people and institutions and governments and companies and economic systems and social systems is really complicated.Um, and, um, and doesn't, you know, it it 8 billion people making collective decisions on planet Earth is not a simple process of like, just like you see this happening now. It's like a bunch of AI CEOs have this thing, which is just like, well, there's just this, they just all have this kind of thing when they talk in public where they're just like, well, there's these, these obvious set of things that so society to do.Alessio: Mm-hmm.Marc: And then they're like, society's not doing any of those things. Right. And it's like, how can society not, you know, what, whatever their theory is, how can society not see x, y, Z? Mm-hmm. And the answer is, well, society is number one. There's no single society, it's like 8 billion people. And they like all have a voice, and they all have a vote, like at the end of the day of how they, they react to change.And then, you know, it just like, it's just human reality is just really complicated and messy. Um, and, and, and so the specific answer to your question is like, as usual, it depends. Um, you know, it, it depends. Look, pe there's no question people are gonna, like, there's no question they're gonna be companies.It's already happening. There are companies that think that they're building value on top of the models and then they're just gonna get blissed by the, by the next model. There's no question that's happening. But I think there's no question also that just the process of adaptation of any technology into the real and into the real messy world of humanity is, is just going to be messy and complicated.It's, it's not going to be simple and straightforward. It's gonna be messy and complicated. And there are gonna be a lot of companies and a lot of products, um, uh, and in, in fact entire industries that are gonna get built to, to, to basically actually help all of this technology actually reach real people.Alessio: The amount of capital going into these companies, I mean, Dario talked about it on the Door Cash podcast and Door Cash was like, why don't you just buy 10 x more GPUs? And he is like, because I'm gonna go bankrupt if the model doesn't exactly hit the, the performance level. How do you think about that?Also as a risk on, you know, you guys are investors, open AI and thinking machines and world apps. It seems like we're leveraging the scaling loss at a pretty high rate, right? Like how comfortable, I guess, do you feel with the downside scenario, like, and say like things Peter out, you think you can kind of like restructure uh, these build outs and uh, you know, capital investments.Marc: Yeah. So should start by saying, so I live through the.com crash, um, and I can tell you stories for hours about the.com crash and it was horrible. No, it was awful. It was, it was, it was apocalyptic by the way. The, a lot of the.com crash was actually at the time, it was actually a telecom crash. It was a bandwidth crash.Like the, the thing that actually crashed, that wiped out all the money with the tele, the telecom companies.swyx: GlobalMarc: crossing. Global, global, yeah.swyx: I'm from Singapore and they, they laid so much cable o over over our oceans.Marc: Actually there was a scaling law in the.com. Era. And it was literally the, the US Commerce Department put out a report in 1996 and they said internet traffic was doubling every quarter.Um, and, and actually in 1995 and 1996, internet traffic actually did double every quarter. And so that became the scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber, anticipating that the demand for bandwidth is gonna keep doubling every quarter.Doubling every quarter though is like, you know, grains of chess and the chessboard, like at some point the numbers become extremely large. Right. And, and, and it really, and really what happened was the internet. The internet by the way, continuously kept growing basically since inception. And it's, you know, it's, it's continuously grown.It's never shrunk. And it's grown really fast compared to anything else. Mm-hmm. You know, in, in, in human history. But it wasn't doubling every quarter as of 19 98, 19 99. And so there was this gap in the expectation of what they thought was a scaling law versus reality. And that's actually what caused the.com crash, which was the, it they, they way over companies like global crossing way overbuilt fiber, which is sort of the, and by the way, fiber, telecom equipment, you know, so all the, all the networking gear, you know, and then, and then by the way, the actual physical data centers, like that was the beginning of the, of the, of the data center build and then, and the data center overbuild.And so you had that, but it was, it was literally, I think it was like $2 trillion got wiped out, right? It was like Jesus, it was like a big, it was. And by the way, the other, the other subtlety in it was the internet companies themselves never really had any debt. ‘cause tech, tech companies generally don't run on debt, but the telecom companies run on debt.Physical infrastructure companies run on debt. And so the companies like Global Crossing not just raise a lot of equity, they also raise a lot of debt. So they're highly levered. And so then you just do the thing. It's just like, okay, you have a highly levered thing where you're, you're just over, you're overbuilding capacity.Demand is growing, but not as fast as you hoped. And then boom, bankrupt. Right. And, and then it, and then it's like they say about the hotel industry, which is, it's always the third owner of a hotel that makes money. It has to go bankrupt twice, right? You have to wash out all of the over optimistic exuberance before it gets to actually a stable state.And then it makes money. So by the way, all of those data centers and all of those, all the fiber that they're in use, it's all in use today. Yeah. But 25 years later. But it, it, it took, and actually the elapsed time was, it took 15 years. It took 15 years from 2000 to 2015 to actually fill, fill up all that capacity.The cautionary warning is the, the overbuild can happen. Um, and, and, and, and, you know, you, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital, it's like, wow. It's just, I, I don't know how to invest in these crazy software things. For sure I can put build data centers and for sure I can buy GPUs that I can deploy, you know, compute grids and, and all these things.Um, and so, you know, if you're a pessimist, you could look at this and you could say, wow, this is like really set up to be able to basically replicate, you know, what we went through, what we went through in 2000. Obviously that would be bad. The counter argument, which is the one I I agree with, which is the counter on, on the other side is a couple things.One is the companies that are investing all the, the companies that are investing the money are like the bluest chip of companies. And so back, back, back in the, in the do, like Global Crossing was like a, it was like an entrepreneur. It was like a, a new venture, but like the money that's being deployed now at scale is Microsoft, and, you know, and Amazon and Google, Facebook and Facebook and Nvidia and, you know, these, these, these, and, and now you know, by the way, open ai philanthropic, which are now at like, you know, really serious size, um, you know, as companies with, you know, very serious revenue.These are very large scale companies with like, lots, lots of cash, lots of debt capacity that they've, they've never used. And so th this is institutional in a way that, that really wasn't at the time. And then the other is, at least for now, every dollar that's being put into anything that results in a running GPU is being turned into revenue right away.Like so, and you guys know this, like everybody's starved for capacity, everybody's starved for compute capacity and then, you know, all the associated things, memory and, and, and interconnected and everything else. Um, data center space. And so e every dollar right now that's being put into the ground is turning into revenue.And, and it, and in fact, I actually think there's an interesting thing happening, which is because everybody starve for capacity, the models that we actually have that we can use today are inferior versions of what we would have if not for the supply constraints. That's true. Um, if Right pose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful mm-hmm.The models would be much better. ‘cause you would just allocate a lot more money to training and you'd just build better models and they would be better. Um, and so we're, we're actually getting the sandbag version of the technology.swyx: Yeah. No. Everything we use is quantized because the, the labs have to keep the, the full versions,Marc: right?swyx: LikeMarc: we're not even getting the good stuff.swyx: Yeah.Marc: But, but getting the good stuff, it's, it's just, even if technical progress stops. Once there's like a much bigger build of like GPU manufacturing capacity and memory, you know, all, all the things that have to happen in the course of the next five or 10 years.Once it happens, even the current technology is gonna get, gonna get much better. And then as you know, like there's just like a million ways to use this stuff. Like there's just like a million use cases for this. Mm-hmm. Like, it, it, you know, this isn't just sending packets across a, a thing, whatever, and hoping that people find something to do with it.This is just like, oh, we apply intelligence into every domain of human activity. And then it works like incredibly well. Yeah. Um. Here's what I know, here's what I know. Um, in the next three or four year, it's like somewhere between three or four years out, basically everything is selling out. So like the, the entire supply chain is, is, is, is sold out or, or, or selling out.And so there, there's no, like, we're just gonna have like chronic supply shortage for, you know, for years to come. Um, there's going to be a response from the market that's gonna result in an enormous, you know, it's happening now. An enormous flood of investment in a new fab capacity and ev you know, every, everything else to be able to do that, at some point the supply chain constraints will unlock, you know, at least to some degree that will be another accelerant to industry growth when that happens.‘cause the products will get better and everything will get cheaper. Um, and so, so I know that's gonna happen. I know that, you know, the deployments, you know, the, the actual use cases are like really compelling. And then, like I said, you know, with reasoning and agents and so forth, like, I know they're just gonna get like much, much better from here.And so I, I, I know the capabilities are like really real and serious. I also know that the technical progress is not going to stop. It. It, it is excel. It is, is accelerating. Like the, the breakthroughs are are tremendous. I mean, even just month over month, the breakthroughs are really dramatic. And so, you know, I think if you were a cynic and there, there are cynics, you can look at 2000, you can find echoes.But I can't even imagine betting it that this is gonna like somehow disappoint and, you know, at least for years to come, I think it would be essentially suicidal to make that bet. Yeah. Um, it was that Michael Burry, uh, uh, that'sswyx: anMarc: interesting guy, huh? We'll pick on a guy. We'll pick, let's pick on one guy.We'll pick. Well ‘cause he did, he he came out with, it was, it was the, heswyx: doesn't mind.Marc: It was the Nvidia short. Right. He came with the Nvidia short. And then if you guys probably talked about this, which is the, the analysis now that like the current models are getting better faster at such a rate that if you are running an Nvidia, if you're running an Nvidia inference chip today, that's three years old, you're making more money on it today than you did three years ago because the pace of improvement of the software is, is faster than the, the, the depreciation cycle, the chip.And then my understanding is Google is running. I don't if they've, I don't know exactly what, uh, these are rumors that I've heard or maybe it's public, but, um, I think Google's running very old TPUs, very profitably. Ference. Yeah. And very profit and very profitably. Yeah. Um, and so, so it actually turns out, as far as I can tell, it's actually the opposite of the Beery thesis is actually.He was actually 180 degrees wrong. It's actually the, the, the, the old Nvidia chips are getting more valuable, which is something that's like literally never happened before. Like it's never been the case that you have an older model chip that becomes more valuable, not less valuable. And that, and again, that's an expression of the just ferocious pace of software progress.Ferocious pace of capability payoff. Yeah. Uh, that you're getting on the other side of this. And so I just, the idea of betting against that, like.swyx: Yeah. Yeah. Well, one ofMarc: my, it seems like an invitation to get your face ripped up.swyx: One of my early hits was like modeling the lifespan of the H 100 and h two hundreds and, and going like, you know, usually they advise like four to seven years and it was, you know, maybe you sort of realistically haircut cut it down to two to three.Yeah. But actually it's going up and not down. Yeah. And, and uh, that's, I mean that's, I think that's the dream. Uh, we are finding utilization and I think utilization solves all problems. Like, you can, you can find use, use cases for even like the poor, like even memory, we're having a shortage. Right. And, and even like the, the shittier versions of, of memory that we do have, we are finding use cases for it.So like That's great.Marc: Yeah.Alessio: How, how important is open source AI and kinda like edge inference in a world in which you have three years of supply crunch. Like, do you think in the, like, you know, if you fast forward like five years, like how do you think about inference, uh, in the data center versus at the edge?Marc: Well, so just to start, yeah. So I think, I think open source is very important for a bunch of reasons. I think edge, edge inference is very important for a bunch of reasons. I, I think just practically speaking, if we're just gonna have fundamental construc, supply crunches for the next, I mean, you, you guys know if you just project forward demand over the next three years, right?Yeah. Relative to supply, one of the, its main predictions you can do is what's gonna, what, what's gonna happen to the cost of, of inference in the core, uh, over the next three years? And like, it may rise dramatically, right? Like, so, so what is, and then is, is, you know, like the, the, the big model competition are subsidizing heavily right now.Right? Right. And so, so what's the, what will be the average person's, you know, per day, per month token cost, you know, three years from now to do all the things that they want to do. And I, I don't know, it's gonna. I mean, I have, you guys probably have friends, I have friends today who are paying a thousand dollars a day for open claw, for claw tokens to run open claw.Right? And so, okay. $30,000 a month. Right? And, and by the way, those, those friends have like a thousand more ideas of the things that they want their claw to do, right? Yeah. And so you, you could imagine there, there's like latent demand of up to, I don't know, five or $10,000 a day of, of, of tokens for a fully deployed, you know, per personal agent.Uh, and obviously consumers can't pay that, right? And so, so, but it gives you a sense of the fu of the fu of the future scope of demand, right? And so, so even, even if there's a 10 x improvement in price performance, that still, you know, goes to a hundred dollars a day, which is still way beyond what people can pay.Mm-hmm. So there's just gonna be like. Ferocious to me, by the way. The agent thing, the other interesting thing is I think the agent thing, so up until now, a lot of the constraints of GGPU constraints, I think the agent thing now also translates into CPU constraints. Mm-hmm. Right?swyx: CPU memory.Marc: Yes. CPU memory, right?And so, like the entire chip ecosystem is just gonna get wait,swyx: wait for network constraints, that that will be the killer.Marc: It's all bottleneck potentially for years. And so, so I, I think that Brad, and, and I think it's actually possible, I mean, generally inference costs are gonna keep coming down, but I think the, let's put it this way, the rate of decline, I think may level out here for a bit because of these supply constraints.And then at some point, maybe the lab stops subsidizing so much and that, that, that again, will be, be an issue. And so there's just gonna be so much more demand for inference than, than can be satisfied. Um, you know, kind of with the centralized model. And then, and then, you know, you guys know this, but like all the, just the dramatic, I mean just the dramatic innovations that have happened in the Apple silicon to be able to do, uh, inferences, it's quite amazing the level of effort being put.Like the open source guys are putting incredible effort into getting, you know, this recurring pattern where the big model will never run on a pc, and then six months later mm-hmm. Oh, it runs in a pc, right? It's like amazing. And there's very smart people working on that. So there's all that. And then look, there's also, you know.There's also like other, there's other motivators. There's other motivators which is just like, okay, how much trust are the big centralized model providers? You know, how much trust are they building in the market versus, you know, how much are, you know, at least for, in certain cases with some people, for certain use cases, people being like, well, I'm not willing to just like, turn everything over.So there, there, there's all the trust issues. Um, by the way, there's also just like straight up price optimization. There's many uses of AI where you don't need Einstein in the cloud. You just need like a, a a, a smart local model. There's also performance issues where you want, you know, you want, you know, you're gonna want your doorknob to have an AI model in it.Right. You know, to be able to, you know, do, um, you know, to be able to do access control. Um, obviously like everything with a chip is gonna have an AI model in it. Mm-hmm. And it, a lot of those are gonna be local. Um, and so, yeah. No, like I think, I think you're gonna have ti and then you're gonna, by the way, also wearable devices, you know, you don't wanna do a complete round trip.You want, you know, you, whatever your smart devices are, you want it to be like super low latency. Yeah.swyx: The question, do we care who makes it? Yeah. One of the biggest news this week was the collapse of AI two, the Allen Institute. Mm-hmm. One of the actual American open source model labs. Yeah. Um, and, uh, I'm not that optimistic on, on American open source.Yeah. Like you, you guys invested in MIS trial and MIS trial's doing extremely well outside of China. That's about it.Marc: Yeah. We'll see. We'll see. I look, I, number one, I do think we care. Uh, I do think we, I do think we care who makes it. Um, I would say this, the, the, the, the previous presidential administration wanted to kill it in the us Oh yeah.They wanted to drown in the bathtub. Um, and so they wanted to kill it. So at least we have a government now that actually like, actually wants it wants it to happen. And youswyx: earned to councilMarc: and Yeah. And the new and the P pcast. Yeah. So the, the, you know, this admin for whatever other political issues people have, which are many, you know, this administration has, I think a very enlightened view and in particular an enlightened view on AI and in particular on open source ai.Uh, and so they're very supportive. Um, my read is the Chi. The Chinese have a very, the various Chinese companies have a very specific reason to do open source, which is, they, they, they don't fundamentally, they don't think they can sell commercial, uh, AI outside of China right now. And or at least specifically not, not in the US for a combination of reasons.And so they, they kind of view, I think, open source AI as a bit of a loss leader against basically domestic, uh, you know, paid, paid services. And then kind of an, you know, kind of an ancillary products. You know, they're, they're very excited about it, by the way. I think it's great. I think it's great that they're doing it.Um, you know, I think Deeps seek was like a gift to the world. Um, I think. The great thing about open source, open source, the, the, the impact of open source is felt two ways. One is you, you get the software for free, but the other is you get to learn how it works, right? And so like the paper, the paper, the paper and, and the code, right?And the code. And so, like, for example, I thought this was amazing. So open comes out with L one and it's an amazing technical breakthrough, and it's just like, absolutely fantastic. But of course they don't explain how it works in detail. And then of course they hide the, they hide the reasoning traces, right?And, and then, and then, and then everybody's like, okay, this is great, but like, who's gonna be able to replicate this? Are other people gonna be able to do this? You know, is their secret sauce in there? And then our one comes out and it's just like, there's the code and there's the paper, and now the whole world knows how to do it.And then, you know, three months later, every other AI model is, is adding reasoning. And so, so you get this kind of double, like even if the Chinese models themselves are not the models that get used, the education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful.So that happens and then, I don't know. We'll, we'll see. You know, there are a bunch of American, you know, open source, you know, ai, uh, model companies. I mean, look, there's gonna be tremendous, you know, there already is. There's, you know, there's gonna be tre there's tremendous competition, uh, among the primary model companies.You know, there's, depending on how you count, there's like four or five, you know, big co model companies now that are, you know, kind of neck and neck, uh, in different ways. Um, uh, you know, and, and, and, um, you know, and then obviously Bo Bo both X and then MetAware involved are, you know, both have huge, you know, huge attempts to, you know, kind of, to kind of leapfrog underway.And then you've got, you know, a whole fleet of startups, new companies, including a whole bunch that we're backing, that are, you know, trying to come out with different approaches. And then you've got whatever it is. I don't know how, how many, how many, like main line foundation model companies are there in China at this point?It's probably six. It'sswyx: five Tigers is what they call it. Yeah. Uh, Quinn is in questionable because there's change in leadership,Marc: right?swyx: Yeah.Marc: But that, does that include, that includes like Moonshot,swyx: yes. Can deep seek, uh, uh, ZI, um, Quinn oh one is in there.Marc: Right. And then, um, and by dance and, and then you see,swyx: ance would be like the next tier ance.They weren't as prominent. They weren't, didn't haveMarc: a leading. Yeah. But they, you at least, you know, ance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming and, and so forth. And so, so, so like, look, here, here would be a thing you can anticipate, which is there are not these markets, there are not going to be between the US and China right now, there's like a dozen primary foundation model companies that are like at scale, at, at some level of a critical mass.It's not gonna be a dozen in three years, right? Like, it just because these industries don't bear a dozen, it's, it's gonna be three or you know, there's gonna be three or four big winners or maybe one or two big winners. And so there's gonna be like a whole bunch of those guys that are gonna have to figure out alternate strategies.Um, and I think like open source is one of those strategies. And so I, I think you could see like a whole, i, I, I think the questions like, who's gonna do open source? I think that could change really fast. I, I think that, that, that's a very dynamic thing. I think it's very hard to predict what happens. And, and I think it's very important.swyx: NVIDIA's doing a lot.Marc: Well, I was gonna say. Well, exactly. And then you're got Nvidia and then, and then, you know, just to, again, indu, there's an old thing in business strategy, which is called, uh, commoditize Compliments. Commoditize the compliment. That's right. And so if your Jensen is just kind of obvious, of course, you wanna commoditize the software.Yeah. And he's, and to his enormous credit, he's putting enormous resources behind that. And so maybe it, maybe it's literally Nvidia and I think that would be great.Alessio: Yeah. Uh, narrative violation to European projects, uh, in the, uh, damn.swyx: I'm hosting my, uh, Europe, uh, conference soon. And I got both of them.Alessio: They got us.They got us. MarkMarc: finished. They got us, us. Well, wait a minute. Where was Peter? So where was Steinberger when he did? In AustriaAlessio: was, yeah, yeah, yeah.Marc: He was in what? He was in Vienna. Oh, he was in Vienna. And then where is he now?swyx: Uh, he's moving to sf.Marc: Okay. Okay. Alright. Okay, there we go. And then, yeah, the PI guy, right?The PI guys are European.swyx: Yeah, they're also, they're buddies inAlessio: Australia. Mario's also there. Yeah.Marc: Right. And are they, yeah, they haven't announced yet. Any sort of change changed or have theyAlessio: No, they're, they have a company there.Marc: Okay. Got, okay. Good.Alessio: Good, good,good.Alessio: Um,Marc: yeah, good.swyx: Anyways, I think pie and open cloud very important software things and, and I just wanted you to just go off on what you think.Marc: Yeah. So I think in co the, the combination of the two of them I think is one of the 10 most important softwares. Openswyx: Claw got all the attention, but Right. Talk about pie,Marc: pi pie's, kind of the Yeah. PI's, PI's kind of the architectural breakthrough for those of us who are older. There was this whole thing that was very important in the world of software basically from like 1970 to, I don't know, it still is very important, but like 19, from 1973 to like basically the creation of Linux, which is basically this, this thing used to call like the Unix mindset.Like so, so, ‘cause there were all these different, you know, theories. There are all these different operating systems and mainframes and, and then you know, all these windows and Mac and all these things. And then there was this, but kind of behind it all was this idea of kind of the Unix mindset. And the Unix mindset was this thing where basically you don't have these, like, like in the old days, like, like the operating system that like made the computer industry really work, like in the 1960s mm-hmm.Was this thing called o os 360, which was this big operating system that IBM developed that was supposed to basically run everything. And it was this like giant monolithic architecture in the sky. It was like a, you know, it was like a giant castle. Um, of software. And, and by the way, it worked really well and they were very successful with it.But like, it was this huge castle in the sky, but it was this thing, it was almost unapproachable, which is like, you had to be kind of inside IBM or very close to IBM. And you had to really understand every aspect, how the system worked. And then the, the Unix sky is originally out of at and t and then out out of Berkeley, um, you know, came out and they said, no, let's have a completely different architecture.And the way architecture's gonna work is we're gonna have, we're gonna have a, a prompt and, and a, and a shell. And then, and then we're gonna, all, all the functionality is gonna be in the form of these discreet modules, and then you're gonna be able to chain the modules together. Mm-hmm. Yeah. And so like the, the, the op, it's almost like the operating, operating system itself is gonna be a programming language.Um, and then that led led to the, the, the sort of centrality of the shell. Um, and then that led to sort of, uh, you know, basically chaining together Unix tools. And then that led to the emergence of these, these scripting languages like Pearl, where you, you could basically kind of very easily do this, and then the shells got more sophisticated and then, and then, and then look like, you know, that, that, that number one, that worked and that, that was the world I grew up in.Like I was, I was a Unix guy. You know, sort of from, call it 1988 to, you know, kind of all, all the way through my work and it worked really well. It, it's in the background, um, you know, nor normal people don't need to, didn't need to necessarily know about it, but like, if you were doing like system architecture, application development, you, you, you knew all about it.Um, and then, you know, it's been in the background ever since. And, you know, look, your Mac still has a Unix shell, you know, kind of in there, and your iPhone still has a Unix shell kind of buried in there somewhere. So they're kind of in there. And then, you know, the Windows shell is kind of a, you know, sort of a weird derivative of that.But, um, you know, but look, the inter, the internet runs on Unix, um, and that smartphones, actually, both iOS and Android are Unix derivatives. And so, you know, kind of Unix did end up winning. But, but anyway, and then we just started taking that for granted. And then, and then so, so basically the, the way I think about what happened with Pie and then with Open Claw is basically what those guys figured out is, I always say the, the great breakthroughs are obvious in retrospect, right?Which is the best kind, the best kind. They weren't obvious at the time or somebody else would've done them already. Um, and so there is a, like a real conceptual leap, but then you look at it sort of the backwards looking and you're just like, oh, of course. Mm-hmm. Like the, the, to me those are always the best breakthroughs.Well, actually language models themselves are like that. It's just like, oh, next token completion. Oh, of course.swyx: Yeah. What other objective mattered?Marc: Yeah, exactly. But, but like it, right. But she's even saying it wasn't obvious until somebody actually did it. Right. And so the conceptual breakthrough is real and deep and powerful and, and very important.And so the way I think about pie and olaw is it's basically marrying the, the language model mindset to the un to the Unix, basically shell prompt mindset. And so it's, it's basically this idea that what, what, so what is an agent, right? And as, as, and as you know, like many smart people who have been trying to figure out what an agent is for, for, for decades, and they've had many architectures to build agents and the whole thing.And it turns out what is an agent. So it turns out what we now know is an agent is the following. It's, so it's a language model. And then above that, it's a ba, it's a bash shell. Um, so it's a, it's a Unix shell, and then it's, and then the agent has access, uh, has access to, to the shell. And, you know, hopeful, hopefully in a sandbox, maybe in, maybe in a sandbox.So it's, it's the model. Um, it's the shell. Um, and then it's a fi, it's a file system. Um, and then the state is stored in files. And then, you know, there's the markdown format for the, you know, for, for the files themselves. And then, and then there's basically what in Unix is called Aron job. There's a loop and then there's a heartbeat for the, there's heartbeat and, and the thing basically Wake Wakes up.Wakes up. So it's basically LLM plus shell, plus file system, plus markdown, plus kron. And it turns out that's an agent. And, and, and every part of that, other than the model is something that we already completely know and understand. And in fact, it turns out that like the latent power of the Unix shell is like extraordinary because basically like all, like, there's just like an, there's just enormous latent power in the shell.There's enormous numbers of Unix commands, there's enormous number of command line interfaces into all kinds of things already in the, you know, your entire, I mean your entire, just to start with, your computer runs on a shell. If you're running a Mac or a, or, or a phone, your computer, your computer's running on a shell, uh, already.And so like the full power of your computer is available at the command line level. Um, and then it turns out it's really easy to expose other functions as a command line interface. And so like this whole idea where we need like MCP and these like product mm-hmm. Fancy protocols, whatever, it's like, no, we don't, we just need like a command, command line thing.So that's the architecture. And then it turns out what is your agent? Your agent has a bunch of files starting a file system. And then there's the thing that just like completely blew my mind when I write my head around it as a result of this, which is like, okay. This means your agent is now actually independent of the model that it's running on.Because you can actually swap out a different LLM underneath your agent and your, your agent will change personality somewhat. ‘cause the model is different, but all of the state stored in the files will be retained.swyx: Yeah. Different instruction set, but you just compiledit.Marc: Right, exactly. And it's all right.It's like right. Swapping out a ship and recompiling, but it's, it's still, it's still your agent with all of its memories. Um, and with all of its capabilities. And then by the way, you can also swap out the shell, uh, so you can move it to a different execution environment that is also, is also a b shell, by the way, you can also switch out the file system, right.Uh, and you can, and you can, and you can swap out the, the, the heartbeat for the, the crown framework, the, the loop that the agent framework itself. And so your agent basically is ba basically at the end of the day, it's just. It's just, its files. Um, and then, and then there's of course it a openswyx: call.Marc: Yeah, it's, it's basically, it's, it's just the files.Um, and then by the way, as a consequence of that, the agent and then the agent itself, it turns out a couple important things. So one is it, it's, it, it can migrate itself, right? And so you're, you can instruct your agent, migrate yourself to a different, uh, runtime environment, migrate yourself to a different file system, migrate yourself to a different, you know, swap out the language model.Your agent will do all that stuff for you. And then there's the final thing, which is just amazing, which is the agent is the agent actually has full introspection. It actually, it actually knows about its own files and it could rewrite its own files. Right. Which by the way, is basically no widely deployed software system in history where the, the, the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself.Like that, that, I mean, there have been toy systems that have had that, but there, there's never been a widely deployed system that has that capability and then that leads you to the capability. That just like completely blew my mind when I wrap my head around it, which is you can tell the agent to add new functions and features to itself and it can do that.Extend yourself. Yeah. Right? Extend, extend yourself. Like extend yourself. Give yourself a new capability. Right? And so, and so literally it's just like you run into somebody at a party and they're like, oh, I have my open claw, do whatever, connect to my eat, sleep bed, and it gives me better advice and sleep.And you go home at night and you tell your claw, or if they're at the party, by the way, you tell your claw, oh, add this capability to yourself. And your claw will say, oh, okay, no problem. And it'll go out on the internet and it'll figure out whatever it needs and then it'll go out to claw code or whatever.It'll write whatever it needs. And then the next thing you know, it has this new capability. And so you don't even have to, like, you can have it upgrade itself without even having to, without having to do anything other than tell it that you want it to do that. And so anyway, so the, the combination of all this is just, I mean, this is just like a massive, incredible, I mean, it's just incredible.Like if I, if I were, if I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. Yeah. And again, pe people are gonna look at it and they already get this response. People are gonna look at it and they're gonna say, oh, well, where's the breakthrough?‘cause these, the, all of these components were already known before. Mm-hmm. But, but this is the key, the key to the breakthrough was by using all these components that were known before, you get all of the underlying capability of that's buried in there. And so all, and so for example, computer use all of a sudden just kind of falls, trivi, trivial.Of course it's gonna be able to use your computer. It has full access to the shell. Right. And then, and then you just, you, you give it access to a browser, and then you've got the computer and the browser and, and often away it goes. And, and then you've got all the abilities of the browser also. Um, yeah.And so, and so the capability unlock here is profound. My friends who are, you know, deepest into this, are having their claw do like a, like, literally like a thousand things in their lives. They have new ideas every day. They're just like constantly throwing new challenges at the thing. And by the way, it's early and, you know, these are, you know, these are prototypes and there are, you know, as you guys know, there's security issues.Yeah. And, and so, you know, there's a bunch of stuff to be ironed out, but the, the unlock of capability is just incredible.swyx: Yeah.Marc: And I, I have absolutely no doubt that everybody in the world is gonna, is gonna have at least, you know, an agent like this, if not an entire family of agents. And w

La criptografía es el arte de la escritura secreta. ¿Cómo mandar un mensaje que solo pueda leer su destinatario? ¿Cuál fue la primera herramienta de codificación que se utilizó? ¿Cómo descifrar el código de un enemigo? Estas fascinantes preguntas son el origen de la criptografía, un campo que Alfre estudia con pasión. Su libro Criptorias se divide en dos partes: antes de Turing y después de Turing, considerado el padre de la criptografía moderna. La escritura oculta es tan antigua como la propia civilización.Me hace especial ilusión comercializar un producto que protegerá tus ahorros en estos tiempos inciertos. Pablo González Vidal, mi socio en El Proyecto K, ha configurado una magnífica cartera de inversión con una diversificación sectorial. La cartera, que invierte mediante ETFs de bajo coste, ofrece exposición de renta variable en 6 sectores: tecnología, salud, consumo, utilities, energía e inmobiliario. Todos ellos con un comportamiento distinto y con un peso previamente fijado, para así evitar una sobrerrepresentación. Se añade luego un porcentaje de renta fija y oro, en función de la respuestas en el perfil de riesgo, dándole la mayor robustez. Hemos decidido llamarla La Cartera K y funcionará como un roboadvisor que rebalanceará todas las posiciones automáticamente una vez al año. Puedes ya contratarla en inbestMe.La Cartera K. Invierte en lo que no cambia.La Cartera K es la evolución lógica de El Proyecto K. Abrimos el taller de inversión para que la gente aprendiera a construirse su propia estrategia diversificada. Ahora te damos la oportunidad de invertir directamente en una cartera que sigue los principios en los que creemos: indexación, activos descorrelacionados y bajos costes. Encontrarás todos los detalles aquí. Si quieres utilizar este nuevo vehículo de inversión para proteger tu capital, el proceso de alta no podría ser más simple: tienes que simplemente abrirte una cuenta en inbestMe y una vez dentro contratar tu propia Cartera K, ajustada a tu perfil de riesgo. Jordi Mercader es el CEO de inbestMe y quiero decir que no podríamos haber encontrado un socio mejor para lanzar este producto, en una plataforma de inversión que ofrece todas las garantías.Si tienes cualquier duda, escríbeme a joan@elproyectok.comÍndice:0:32 La vida antes de internet.10:42 Un software más libre.17:34 Request for Comments.27:43 ¿Qué es la criptografía?36:35 Los primeros mensajes secretos.45:26 Criptografía en la Edad Moderna.50:11 Romper el código para la guerra.58:37 La trágica vida de Alan Turing.1:09:46 Historia de una amistad.1:19:43 Todo son matemáticas.Apuntes:Criptoria; de Turing a Nakamoto. Alfre Mancera.La teoría de la información. Claude Shannon.La criptografía militar. Auguste Kerckhoffs.A declaration of the independence of cyberspace. John P. Barlow.The crypto anarchist manifesto. Timothy C. May.A cypherpunk's manifesto. Eric Hughes.Bitcoin: a peer-to-peer electronic cash system. Satoshi Nakamoto.

Jegyzetek FU: nem egészen úgy volt az AI és a rákos kutya gyógyulása sztori Hiánypótló tudományos eredmények rovat Mennyi ideig tart kiönteni egy üveg olívaolajat, részletesebb-pontosabb cikk itt Málnát és csipszet is ügyesen fog az ultraérzékeny robotkéz …amit a halak sugarasúszója (is) inspirált Ez meg itt a háztartási robot, akit ignorál a gazdája :( Dávid, mesélj nekünk már megint MI van rovat Az OpenAI bevezeti (?) a felnőtt módot Vagy mégsem Itt az új Turing-teszt, az emberiség utolsó védvonala “De jó, az AI-val majd kevesebbet kell dolgozni!” AI: “fogd meg a söröm” Szégyentelen önpromó alrovat: fontos, HIÁNYPÓTLÓ kutatás készült az AI-ról

Realities Remixed, formerly known as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.One of the most fundamental challenges in modern computing is the growing hardware–software mismatch. As Moore's Law slows and performance gains no longer come “for free,” software built on Turing‑era, sequential assumptions struggles to keep pace with today's highly parallel, heterogeneous hardware. That disconnect is now a central constraint on innovation.This week, Dave, Esmee, and Rob are joined by Peter Richards, advisor and AJ Guillon, founder of YetiWare, to explore why this mismatch persists, what it means for organizations today, and how emerging approaches may redefine the relationship between software and hardware in the years ahead. TLDR00:35 – Introduction01:04 – Hang out: How deep can we go, and what is the history of the compute era?07:00 – Dig in: The power demands of LLMs, data centers, scale, size and potato chips15:35 – Conversation with Peter Richards and AJ Guillon55:31 – Spring cleanup with a chainsaw and cycling GuestPeter Richards: https://www.linkedin.com/in/peter-richards-3b99688/AJ Guillon: https://www.linkedin.com/in/ajguillon/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

“Fiction has this unprecedented power in tech spaces. The more I started talking to engineers about their technical problems, the more I realized there’s so much more that humanities could offer.” –Nina Begus About Nina Begus Nina Begus is a researcher at the University of California, Berkeley, leading a research group on artificial humanities, and the founder of InterpretAI. She is author of Artificial Humanities: A Fictional Perspective on Language in AI, which received an Artificiality Institute Award, and First Encounters with AI. Webiste: ninabegus.com LinkedIn Profile: Nina Begus  Book: Artificial Humanities What you will learn How ancient myths and archetypes influence our understanding and design of AI Why the humanities—literature, philosophy, and the arts—are crucial for developing more thoughtful and innovative AI systems The dangers of limiting AI concepts to human-centered metaphors and the need for new, more expansive imaginaries How metaphors shape our interactions with AI products and the user experiences companies choose to enable The challenges and possibilities of imagining forms of machine intelligence and language beyond human templates Why collaboration between technical experts and humanists opens new frontiers for creativity and responsible technology What makes writing and artistic creation uniquely human, and how AI amplifies—not replaces—these impulses Practical ways artists, engineers, and thinkers can work together to explore new relationships and futures with AI Episode Resources Transcript Ross Dawson: Nina, it is wonderful to have you on the show. Nina Begus: Thank you for having me. Ross Dawson: You’ve written this very interesting book, Artificial Humanities, and I think there’s a lot to dig into. But what does that mean? What do you mean by artificial humanities? Nina Begus: Well, this was really a new framework that I’ve developed while I was working on the relationship between AI and fiction, and I started working on this about 15 years ago when I realized that fiction has this unprecedented power in tech spaces. So this is how it all started, but then the more I started talking to engineers about their technical problems, the more I realized there’s so much more that humanities could offer in this collaborative, generative approach that I’ve developed. I would say that now, as the field stands, it’s really a way to explore and demonstrate how humanities—as broad as science and technology studies, literary studies, film, philosophy, rhetoric, history of technology—how all of these fields can help us address the most pressing issues in AI development and use. And it’s been important to me that this approach uses traditional humanistic methods, theory, conceptual work, history, ethical approaches, but also that it’s collaborative and exploratory and experimental in this way that you can look back into the past and at the present to make a more informed choice about the future. You can speculate about different possibilities with it. Ross Dawson: Well, art is an expression of the human psyche, or even more, it is the fullest expression of humanity, and that’s what art tries to do. Also, I’m a deep believer in archetypes, human archetypes, and things which are intrinsic to who we are, and that’s something which you can only really uncover through the arts. Now we have arguably seen all these archetypes play out in real time, these modern myths being created right now in the stories being told of how AI is being created. So I think it’s extraordinarily relevant to look back at how we have depicted machines through our history and our relationship to them. Nina Begus: Yes, this is the reason why I started exploring this topic, actually, because there were so many ancient myths, these archetypal narratives that I’ve seen at the same time, both in technological products that were coming to the market and in the way technologists were thinking about it, and also in fictional products and films and novels in the way we imagined AI. I framed my book around the Pygmalion myth, but there are many, many other myths—Prometheus, Narcissus, the Big Brother narrative, and so on—that are very much doing work in the AI space. The reason why I chose the Pygmalion myth is because it’s so bizarre in many ways: you have this myth where a man creates an artificial woman, and then in the process of creation, falls in love with her. So there’s the creation of the human-like, and there’s also this relationality with the human-like. You would think this would not be a common myth, but quite the opposite—I found it everywhere I looked. It wasn’t called the Pygmalion myth, but the motif was there. I found it on the Silk Road, in ancient folk tales, in Native American folk tales, North Africa, and so on. So I think this kind of story is actually telling us a lot about how humans are not rational, how we have some very deeply embedded behaviors in us, and one of them is that we anthropomorphize everything, including machines.So I think this was a really important takeaway that we got already from the early days of AI with the first chatbot, Eliza. We’ve learned that that will be a feature of us relating to machines. Ross Dawson: So Joseph Campbell called the hero’s journey the monomyth, as in, there is a single myth. And I guess what you are doing here is—well, if you agree with that, which I’d be interested in—is that there are facets. The classic hero’s journey is quite simple, but there are facets of that monomyth, or something intrinsic to who we are, that is around this creation. And in this case, as you say, this relation we have with what we have created. Would you relate that at all to Joseph Campbell’s work? Nina Begus: I haven’t thought about it in this way, because I thought about myth and myths more and less of a storytelling issue, which here is definitely happening—the hero goes on a task, returns back changed, and maybe changes something in the community. The myths that I was looking into and the metaphors that I was exploring, primarily this huge metaphor of AI as a human mind, as an artificial reason—I think it works differently. It’s less of a narrative; it’s more of an imaginary of how or towards what we are building. I think this is a big problem, actually, because the imaginary around AI is very poor. What you get is mostly imagining machine intelligence on human terms, and a lot of people are bothered by that in the AI discourse—right, when you say the machine thinks, or the machine learns, or it has a mind, and some people go as far as to say it has consciousness. I think this kind of debate is actually not that productive. I think it’s more important to see how all these different AI products that we’ve created—and mostly when we talk about AI, people think of language models now—are very much designed as a sort of character, almost as an artificial human that, in literature, authors have been creating for a long time. So I think in that case, we can get back to a hero’s journey. But I think what I was looking at was actually more on the surface level of what kind of shortcuts we are using with these metaphors that we’re employing when building and using AI. I think the book makes a really good case showing that, yes, this is actually a very cultural technology. It’s very much informed by our imaginaries. One surprising part of it was really how hard it was to break out of this human mold. It was pretty much impossible to find examples of machines that are not exclusively human-like. I think Stanislaw Lem is one of the rare writers who can consistently deliver this kind of imaginary. Even looking at more recent works, like popular films such as Hollywood’s Ex Machina or Her, you can see how the technologists themselves would say, “Oh, we were influenced by this film,” in a way that it affirmed their product development trajectory. You can see it now, at this moment, with OpenAI launching companionship. So in many ways, not a lot has changed. Ross Dawson: Yeah, there’s a lot to dig into there. I just want to go back—in a sense, Pygmalion is a metaphor, but it’s also a myth. It is a story: creates a woman, and then falls in love with her, and then whatever happens from there. There is this, something happens, and then something else happens. That’s what a story is. I think that can impact the implicit metaphor, but coming back to the metaphor—so George Lakoff wrote the beautiful book Metaphors We Live By. I think the way the brain works is in metaphors and analogies to a very large degree. Some of those are enabling metaphors, and some of those are not very useful metaphors. I think part of your point is that some of the metaphors that we have for thinking about AI and machines are not useful. There may be, or we could create, some metaphors that are more useful. So, what are some of the most disabling metaphors, and what are some of the ones which could be more constructive? Nina Begus: Yes, So I think this main metaphor that I’ve mentioned—of AI as a human mind—is very limiting. I think it really limits the machinic potential to actually do something good with it. The fact that we’re still using the criteria that were made for humans, like different criteria developed on human language—the Turing test was one of them, right, a while ago. Now we have stricter ones. I think this tells you a lot about how we actually evaluate AI and how even these benchmarks that are supposed to be quantitative are actually often qualitative, often stories, like mini-narratives. But yeah, when we look at different metaphors in this space, there are other ones that also emerge from fiction. I mentioned the Big Brother, the AI as an Oracle, and we need to be aware that these ideas inform the very interaction we have with AI. If we think of it as a mirror, we’re going to use it differently—it’s almost as a bouncing board. If we think of it as a teacher, or as a coach, or as an assistant, it would again create a different use. So I think there are a lot of these metaphors that the companies themselves are trying to decide which one they will go with, because it completely changes the user and the interaction. I think they’re also very cultural, even though you might say, “Oh, it’s a categorical mistake to treat a machine as a human.” I think you can see this kind of treatment across, at least in part, and it doesn’t mean that we consider it human. It just means that we’re engaging with it on our own terms, as if it was human. Now, what could be productive? I do think metaphors, even if they’re not accurate, can be productive. My goal, really, with the book was to break out of this projection of what the machine could be, to find in this exploratory way other directions, other landscapes where we couldn’t go because we’re being limited by our imaginary, by our ideas. So in this way, I think humanistic approaches can be very helpful to designers, to technology builders, to artists, to explore the novelty that so many of these sectors are after. Ross Dawson: Yeah, and I guess people latch on to what they know. I think that’s part of the thing where with AI, “Oh, it’s like a human. Let’s treat it like a human, and let’s make it like a human.” It is, amongst other things, a lack of imagination. That’s where the humanities, the arts, can offer us—those who have the imagination to be able to envisage different possibilities or relationships. But I guess part of it is also that humans relate, and so we have learned to relate to other humans and also to other animals and hopefully to nature as well. But these are all established patterns of relating. So do we need to discover in ourselves new ways of relating to new categories—things which are not humans, not animals, and not nature? Nina Begus: Exactly, this is the exact problem we’re dealing with, and because we’re dealing with a yet unexplored, yet undefined relation, and we’re using old, outdated terms for that relation. This is why we don’t really have a good way of describing it and establishing it. It will take a while for this to develop, which is fine, but we need to realize that there are some concepts that we’re using that we better leave behind and go ahead by building new ones. This is why I think it’s really important to work in a more interdisciplinary collaboration, so that you can see what you can actually build from the technical perspective, so that you can see what these machines are actually capable of. Because you usually don’t know when you create them right?Machine learning is sort of exploratory by design. Ross Dawson: So, just to call it out more explicitly, what are the metaphors you think are the most destructive or most inappropriate, and what are some of the ones which you think are the most promising? Nina Begus: Well, I’m just writing on the Midas myth, which is sort of the opposite of the Pygmalion myth. With Pygmalion, you lean into that human imitation, but with Midas, you lean into the liminality that Midas presents as this sort of hybrid creature. I think leaning into the boundaries that we draw for ourselves—and now AI is not cooperating with them—this is where the productive part will be in actually creating something that has philosophical dignity, but also a kind of productive trajectory for the machines to go. I feel like we’re still in this first phase of developing AI, because when you look at it historically, we haven’t really moved from the conceptual and philosophical premises that were established in the 1940s, 50s, and 60s for this technology. We have now gotten the technology that caught up to the ideas from the 60s, but we’re still stuck in the same conceptual space. Ross Dawson: Yeah, very much so. And, you know, of course, what is AGI, which everyone talks about, is basically—the only way in which people seem to be able to frame it is as relative to humans, which is the only reference point we have. I mean, there’s, of course, animal intelligence, but that’s because of that. It is, again, that lack of imagination—saying, “Well, intelligence, oh, intelligence is what humans do, so let’s do something which is the same as that,” whereas there’s so much white space in what intelligence could be. I think this almost comes back to definition. When people say intelligence, the word, when they use the word intelligence, they are referring to what humans do. It’s not a general term, and so it all becomes a language problem as well, because we are so rooted to relating our language to human capabilities, as opposed to a more general potential. Nina Begus: Yes, I think you’re really on to something here, because I can see it also—because I work with animal communication researchers, and we’re finding things there that we didn’t find because we limited ourselves to thinking language is just a human production, that it needs a human subject. Now, as soon as we got rid of this presumption, we’re finding new things, things that are basically parallel to what we do in our language. So language is in a space of tension because it’s being attacked both from the animal side and from the machinic side, which is why I really focused on language in this book. It’s not a coincidence that we centered artificial intelligence in language as the interface, because this is how we relate to the world—this is our interface to talk to each other, to understand each other. I think the fact that language is coming under such pressure as an interface brings with it a lot of other concepts that are being challenged. Are only humans creative? Is there a natural creativity, machinic creativity? Is there a different kind of intelligence that’s maybe solely biological, embodied? How do we think about cognition? How do we think about culture? In AI and in the natural world, there’s so much that comes with it: agency, autonomy, freedom, community, which I think we will be grappling with for the next few decades, at least. Ross Dawson: I think you alluded before to the potential for AI to have its own languages.  Nina Begus: I’ts happening already. The reason why I like Stanislaw Lem so much is because he can actually think about a machine—back in the 1970s, he’s doing that—about a machine that’s not human-like, that’s not limited to human language. It is trained on human language, but then it goes its own way, where the human linguistic ceiling just cannot go anymore. We’re already seeing that in the models, in Berkeley’s Biological Artificial Intelligence Lab, in the models that are not large language models, but generative adversarial networks that are based on speech. We see that as they are learning the words, they are encoding some information into silences that we don’t know what it is. I think what’s really exciting to me are two things about language in machines. The first one is, what is this non-human production of language? We did not think that non-humans can produce language, even though we had parrots who had to crawl their way to us to speak in “humanese,” to show that they have some kind of intelligence—even if it’s just parroting, even if it’s just what we call imitation, which some people consider not to be intelligence. We’ve had these examples before, but now it’s gotten nuclear—on this scale that LLMs are performing, it’s really challenged a lot of our solely human attributes: creativity, storytelling. A lot of journalists come to me because there’s this existential fear of machines taking over their work and so on. So we’ve been thinking about those things, and now it’s actually happening. Ross Dawson: One of the other key points here, I think, is that humanity is—the arts—there’s so much, as you mentioned, in terms of fiction, in terms of films, in terms of visual arts, and many other artistic domains. We have reference points that we use, and the amount which people refer to the movie Her in the last years is pretty extraordinary, partly because it’s obviously coming very much true. I think the Ex Machina story is very interesting as well, as are many others in the past. But there is also this act of imagination. There are people who have written these books, who have crafted these films, who have created these things, and they are the ones who have been not just manifesting our human psyche, but also pushing that out and coming up with ideas which others haven’t had, to give us something. So one thing we can certainly do is mine and dig into what has been created. But is there a way to interface through this to this act of imagining, which can give us new artifacts and ways of thinking and ways of relating? Nina Begus: Yes, I think imagination and humanities in general are going to become more and more important, because AI will do a lot of technical work, but imaginaries—this is what we really excel at. It’s actually interesting to see how you think fiction is this unbounded landscape where you can imagine anything, and yet it’s really hard to find examples of machines that are beyond the human. Even these writers, like the screenwriters for Her and Ex Machina, create these completely Pygmalion-esque films, where you have an artificial woman leading a relationship with a human man, and so on. For the whole film, you have her act as a human-like entity. But then at the end of each of those films—well, particularly in Her—Spike Jonze really tried to break out of this and show her AI side. Basically, there was no language to describe it, so he resorted to a metaphor—the metaphor of a book, where Samantha, the operations assistant, explains that her world is falling apart, like the way words are floating further and further apart in a book. That’s how she’s able to describe it; that’s the closest she gets. And then in Ex Machina, Alex Garland really wanted to portray the world from the social robot Ava’s perspective in a visual way. He wrote down a scene, but he said, “I failed to execute it visually. I just couldn’t do it well.” So instead, he gave us a different scene that’s shot from afar, where Ava embarks onto a helicopter and she has to undergo her Turing test—the helicopter pilot cannot recognize her as a robot; he needs to think she’s a human woman. There have been attempts, I think even in Garland’s next film Annihilation, they’re trying to set the grounds for something that’s entirely new and hard to imagine. I think a big takeaway for us is this is very hard to do. Ross Dawson: Yes, well, given that context, I do want to—as in the human plus AI framing—given all of this, what is it that we can do or should be doing in order to amplify our humanity, our capabilities, the positive aspects of what it is to be human? How can we relate to or use AI in order to amplify the best of us? Nina Begus: Yeah, I actually had, while I was writing the book Artificial Humanities, this other dream project to work with writers—professional writers, creatives, people who live in a world of words—to see what they make of AI. I waited a little bit for the public’s polarized reactions to calm down a bit and gathered 16 writers, some of whom already made a space for themselves in the field, like Sheila Heti and Ken Liu and Ted Chiang, and then some of the more junior writers who I knew were thinking about that—a Netflix screenwriter, and so on. I gathered them to see—I think the creative people are really the answer here—I gathered them to see how they approach this very human part of the new human and AI collaboration zone. What was common across a lot of essays that are coming out in October under the title “First Encounters with AI” is this argument that, well, AI doesn’t have subjectivity, it doesn’t have emotions, it doesn’t have a body, it doesn’t have experience, it doesn’t have meaning—all of these things that really make us human, all of these parts that actually make art compelling and literature compelling. So Ken Liu’s argument, for example, was, let’s leave machines what they’re good at—they’re good at imitating and copying—and we’re good at interpreting, we’re good at creating and imagining. I think this is really a way to go with this. This catastrophizing that’s very present in the public discourse, I think, is a bit misleading. I wish we had a more nuanced approach to what’s actually happening, particularly in the space of writing. Obviously, AI is a groundbreaking technology that affects pretty much every one of us and all the sectors, but when it comes to writing, we just don’t think it’s killable. We think that there’s this perennial impulse that humans have to play with language, and that is not going to go away with AI. We’re just going to amplify it through AI, through this new possibility that has now opened in many ways. I like to think about AI as—you know, we’ve figured out how to fly. As soon as we figured out the physics of flight, we had planes and helicopters and drones and kites, and these are the new possibilities for human activities. In the same way, we figured out the machine learning principles, and now we have large language models and diffusion models, and we have GANs and so on, and there will be more. These are the new spaces of possibility that have opened for our activities, for our spirit to work on, but they do not replace the human in a meaningful way. It’s more about extension than it is about automation. Ross Dawson: Yeah, that’s a wonderful way of framing it. So where can people go to find out more about your work? Nina Begus: I have a pretty populated website with my name, ninabegus.com, where I write about my books, I write about my public work. I have videos on there, podcasts, links, and so on. I also have a pretty lively lab with a lot of collaborators and students, where a lot of what I imagined when writing Artificial Humanities—where a lot of collaborative projects happen. We have artists, we have engineers, we have philosophers that work on the same question, but come at it from very different backgrounds and with very different skills. I think this is becoming more and more important in the world of AI. Ross Dawson: Yes, yes, bringing all of those disciplines and frames and thinking together. That’s wonderful. I love what you’re doing—very important. I hope the messages ripple through, and obviously wonderful to be able to share this with the Humans Plus AI audience. Thank you so much. Nina Begus: Thank you, Ross, and thank you all for listening. The post Nina Begus on artificial humanities, AI archetypes, limiting and productive metaphors, and human extension (AC Ep38) appeared first on Humans + AI.

Dr Jakub Strychowski, wieloletni CTO z doktoratem z AI, opowiada o tym, jak sztuczna inteligencja realnie zmienia rynek IT. [more] Rozmawiamy m.in. o tym, czy juniorzy faktycznie muszą bać się o pracę, kiedy klienci zaczną sami 'promptować' działające aplikacje i dlaczego masowo generowany kod może stać się naszym największym koszmarem. Pełen opis odcinka, polecane materiały i linki oraz transkrypcję znajdziesz na: https://devmentor.pl/?p=16518 || devmentor.pl/rozmowa ⬅ Chcesz przebranżowić się do IT i poznać rozwiązania, które innym pozwoliły skutecznie znaleźć pracę? Jestem doświadczonym developerem oraz mentorem programowania – chętnie odpowiem na Twoje pytania o naukę programowania oraz świat IT. Umów się na bezpłatną, niezobowiązującą rozmowę! ~ Mateusz Bogolubow, twórca podcastu Pierwsze kroki w IT || devmentor.pl/podcast ⬅ Oficjalna strona podcastu

Neste episódio comentamos sobre as principais atualizações e desafios no mercado de tecnologia, trazendo uma análise objetiva sobre cibersegurança e proteção de dados. Ao longo da reprodução, você irá descobrir os recentes desdobramentos éticos do uso de inteligência artificial em contextos militares, envolvendo a recusa da Anthropic em aderir aos termos do Departamento de Defesa norte-americano e os impactos disso para a privacidade global. Você também irá aprender sobre o novo marco regulatório do Conselho Federal de Medicina para ferramentas automatizadas na área da saúde, compreendendo como as exigências da LGPD se aplicam à segurança da informação na proteção de dados médicos sensíveis. Além disso, você entenderá os detalhes do recente ataque hacker que causou graves incidentes de segurança no setor financeiro, e saberá identificar as vulnerabilidades críticas na integração de modelos de linguagem via protocolo MCP, como a perigosa injeção de prompts em servidores expostos. O host Guilherme Goulart compartilha ainda sua vivência no evento SecOps Summit, refletindo sobre a importância dos profissionais de segurança na governança corporativa. Por fim, você poderá avaliar como o uso excessivo do ChatGPT pode afetar a criatividade e gerar a homogeneização do pensamento. Para continuar acompanhando nossas discussões, não se esqueça de assinar o podcast na sua plataforma preferida, seguir nossos perfis nas redes sociais e avaliar o programa para apoiar o nosso trabalho. Esta descrição foi realizada a partir do áudio do podcast com o uso de IA, com revisão humana.     Visite nossa campanha de financiamento coletivo e nos apoie!  Conheça o Blog da BrownPipe Consultoria e se inscreva no nosso mailing Acesse WhisperSafe – Transcreva áudio e grave reuniões direto no seu computador, mesmo offline. Rápido, leve e pronto para usar com qualquer IA. Use o cupom SEGLEG50 para 50% de desconto na sua assinatura. ShowNotes Episódio citado – 2013-06-18 – Episódio #28 – PRISM – Privacidade X Segurança The Pentagon formally labels Anthropic a supply-chain risk Anthropic's Claude is suddenly the most popular iPhone app following Pentagon feud Anthropic vs. U.S. Department of War The Pentagon Can't Afford This A.I. Fight Statement from Dario Amodei on our discussions with the Department of War Employees across OpenAI and Google support Anthropic's lawsuit against the Pentagon AI safety leader says ‘world is in peril’ and quits to study poetry Microsoft & Anthropic MCP Servers at Risk of RCE, Cloud Takeovers AI Conundrum: Why MCP Security Can’t Be Patched Away MCP is the backdoor your zero-trust architecture forgot to close Ministério da Educação – REFERENCIAL PARA DESENVOLVIMENTO E USO RESPONSÁVEIS DE INTELIGÊNCIA ARTIFICIAL NA EDUCAÇÃO Nova resolução de uso de IA na CFM Artigo “When ChatGPT is Gone: Creativity Reverts and Homogeneity Persists“ BTG Pactual restabelece operações via Pix após ser alvo de ataque hacker BTG Pactual sofre ataque hacker e suspende operações via Pix PF investiga participação de funcionários no ataque hacker de R$ 100 milhões ao BTG Pactual Imagem do Episódio: A Torre de Babel — Pieter Bruegel

When we say 'the cloud' what we mean is 'the data centre'. Globally, data centres are projected to consume over 1000 terawatt hours in 2026. What does that mean for energy production, distribution, and consumption? Guest Phil Harris, Cerio President and CEO, joins thinkenergy to shed light on something we all rely on but may not fully understand. From efficiency to sustainability, environmental concerns to Cerio's role improving how data centres manage energy. Listen in for the future of cloud computing.  - Related links  ●       Cerio: https://www.cerio.ai/ ●       Phil Harris on LinkedIn: https://www.linkedin.com/in/paharris/  ●       Trevor Freeman on LinkedIn: https://www.linkedin.com/in/trevor-freeman-p-eng-8b612114  ●       Hydro Ottawa: https://hydroottawa.com/en     To subscribe using Apple Podcasts: https://podcasts.apple.com/us/podcast/thinkenergy/id1465129405 To subscribe using Spotify: https://open.spotify.com/show/7wFz7rdR8Gq3f2WOafjxpl To subscribe on Libsyn: http://thinkenergy.libsyn.com/ --- Subscribe so you don't miss a video: https://www.youtube.com/user/hydroottawalimited Follow along on Instagram: https://www.instagram.com/hydroottawa Stay in the know on Facebook: https://www.facebook.com/HydroOttawa Keep up with the posts on X: https://twitter.com/thinkenergypod --- Transcript: Trevor Freeman  00:07 Welcome to think energy, a podcast that dives into the fast, changing world of energy through conversations with industry leaders, innovators and people on the front lines of the energy transition. Join me, Trevor Freeman, as I explore the traditional, unconventional and up and coming facets of the energy industry. If you have any thoughts, feedback or ideas for topics we should cover, please reach out to us at thinkenergy@hydroottawa.com. Hi everyone, and welcome back. Data centres have come up a number of times on this show, and for very good reason, they have become a key underpinning technology for so much of our lives, every time we pull out that phone from our pockets to pull up directions or buy something online or doom, scroll on your social media or new site of choice, every time you use your phone stream a movie, leverage an AI model, whatever you end up using it for, it's funny as I read this list, I'm sure there's like some university student out there who's thinking, man, what is this old man talking about? We don't use our phones for that, whatever the kids are doing these days, whatever we're doing these days with our phones, with our computers, our tablets, et cetera. All of that leverages infrastructure that most of us have never seen and, quite frankly, probably don't really understand we talk about the cloud like it's this amorphous, nebulous thing, but in reality, we're talking about real hardware in a real building that uses real energy, mainly electricity, a lot of water. And this isn't really new, like we've been leveraging centralized data centres for many years now, but what is changing is the scale of the data centres that we're seeing now, and the pace of growth in computing power that we need to do, the things that we want to do, and that our data centres are able to deliver. So just to throw a few numbers at it, the traditional data centre servers that maybe power the early days of on demand online streaming services, for example, they used anywhere from five to 15 kilowatts per rack. But modern server racks that are used to power AI searches, for example, can hit anywhere from 60 to 100 kilowatts per rack. This is great from a power output per rack perspective, but it means massive energy needs, and that is showing up in the size of load requests that we're seeing from new data centres. New data centres today are asking for service connections that are orders of magnitude higher than those built even just five years ago, globally, data centres are projected to consume over 1000 terawatts in 2026 or terawatt hours, sorry, in 2026 and just a quick kind of refresher from high school or wherever you would have learned this, a terawatt is 1000 gigawatts, which is 1000 megawatts. So 1000 terawatt hours, which is roughly equivalent to the annual electricity demand from the country of Japan, an entire country. So given all of this, there are a lot of incentives to find ways to maximize efficiency and reduce some of that energy demand, and that's where my next guest, Phil Harris and his company Cerio come into play. I'll let Phil get into the details of exactly what Cerio does, but essentially, their goal is to reimagine the data centre to maximize sustainability and reduce energy needs. Phil is Cerio's President and CEO, and has been in the networking and data centre industry for over 35 years, including at well known companies like Intel and Cisco. And I'm really excited about this conversation. One to understand, how do we make data centres a little bit more efficient, or maybe a lot more efficient, but also just to really understand, like, what are we talking about when we talk about a data centre? What is actually happening, what is physically inside these buildings, and we'll get into a little bit of that in our conversation. So Phil, welcome to the show.   Phil Harris  04:13 Well, thanks, Trevor. I appreciate it.   Trevor Freeman  04:13 So Phil, obviously we're here today to talk about your work building sustainable data centres, or trying to make data centres a little bit more sustainable. But before we get into that. You know, you've spent your career, you know, decades of your career at different tech giants. Let's call them in telecisco to to mention, you've seen quite a bit of change. No doubt, over your time, has that changed, like, does this industry change linearly? Does it grow fairly steady, or is it kind of big jumps? And are we on the cusp of any major shifts? What can you kind of tell us about the future of this, this sector, data, tech, etc?   Phil Harris  04:48 It's interesting, I think, as companies start, and I was at companies like Cisco, for example, when it was a very small company to when it was very large company. And this should be no surprise for anybody, the bigger the company gets, the harder. It is to change, and they really find that the only way they change is when they absolutely have to, not because they want to, and that's a combination of just inertia and shareholders expectations and a whole bunch of things. So I would say that the bigger the company is, the harder is them, for them to react. And so I think small, nimble companies tend to do much better when there's a lot of transformational technology and development and changes in the overall ecosystem we live in. I think just the second part of your question, you know, I look at the current situation as a point in time where a lot of companies will have to make some significant changes, simply because we're hitting too many walls, technological walls, commercial walls, geopolitical walls, that are really sort of confining what people can do. So I think what's going to about to happen is we're about to see a significant change, and this is not atypical in the industry. If we think about back into the into the start of what we would think of today as computer science around mainframes that were happening in the 60s. You know, for about a decade and a half, two decades, there was a lot of dominance around a particular way of doing things. And then some new innovational technology came along that rapidly changed, that scaled out, and it went from a very dominant set of players to a much larger number of smaller players who could then provide more innovation and more scale and more choice. And I think we're about to see that transition occurring as well.   Trevor Freeman  06:25 So is this, is there sort of like an analogous time, 10 years ago, 20 years ago? Are we on the cusp of, like, the big, the big change that we've seen before? Like, what would you compare this to? You know, in the last 2030, years?   Phil Harris  06:40 Yeah. I mean, I think there's been eras of compute. And if we say, I mean, we can find analogies outside of the compute world, but let's just stay in the compute, computing science world. I gave the mainframe example as one, and then we went to what we call client server, which scaled out rapidly. Telephony. We went from large, big telephone exchanges that started in in the government space, went to very large organizations. Now, basically we've completely scaled out how we make phone calls to use that now 20th century as a terminology. Nobody really makes telephone calls anymore. And we went through this with cloud computing and the Internet, where there was a change in the approach to the way we did things that suddenly gave us a scale out mentality, rather than a scale up mentality. And I think that's what we have to key in on here. Is it that we can take some of you? I was on a panel yesterday where we were talking about scale, and I say, well, to scale or not to scale? That is not the question. It's how do we scale? Do we continue to scale up, which is the current model, or do we start to think about scaling out, which is a more distributed model? So we go from a small number of big things to a large number of smaller things. And typically in computer science, whatever you want to start, storage, compute, memory, telephony, everything we've ever done goes through this arc.   Trevor Freeman  07:59 Yeah, it's it's interesting, and it's, there's obviously my brain's gonna immediately try and find those, those similarities between my world that I live in on the energy side of things. And it's the same question, like, there, there's, there is no path where we're not expanding the amount of energy we need. We're not going to be using more energy. But there are different ways to do that, and there are different paths we can take the business as usual that just grow, grow, grow, decentralized energy production and large scale transmission. Or there's a combination of like, grow those things, but also find alternative methods. More ders more sort of like close to consumer energy sources and storage, et cetera, et cetera. And people that listen to this podcast know I kind of go on ad nauseam about this. So lots of similarities. There another kind of framing or foundational thing that I want to talk through before we really get into the meat of our conversation is helping ground both myself and our listeners, and what exactly we're talking about here. So we, we all use, whether we know it or not, we use, you know, like cloud computing constantly, whether it's in our calls, how we're using the internet, using AI, more, more frequently. Now, what is the physical reality behind that? What's actually happening? What is the term data centre? What is a data centre for our listeners here? What does that look like?   Phil Harris  09:26 Yeah, let's start there. That's a great question. We started recognizing that the amount of power and space required for computers in companies and government in all sorts of different applications was getting larger than we could put in a room, in a closet near maybe where people were using it. We had to sort of create dedicated space, because the power requirements, the cooling requirements, just the noise. You can't hear this, but just in my basement, I have a few different compute systems that my wife continues to tell me is keeping my neighborhood awake. The reality is the environmentals of these things became very difficult. So we created these purpose built locations that had then different requirements in terms of access and facilities and power and cooling and staffing. And so they became a new way of thinking about building compute infrastructure at a building level, not just at the individual computers themselves. So a data is usually a very large room or building, I should say that houses large amounts of compute and storage and other networking equipment. There's a whole range of different technologies that go into a data centre that allows us to process information. That's what a data centre is. To give you some analogies in the US, there's about nearly 6000 data centres, depending on how you measure a data centre. In Canada, we have about 400 in Europe, there's about 750 that we can identify as standalone data centres. You can probably find more places where computers are outside of people's homes, but that's about the ratio we're looking at.   Trevor Freeman  10:59 And we're seeing, I think, and tell me if I'm wrong here, like, all this talk about the AI proliferation, data centre proliferation, we're seeing an expansion of these. Is that we're seeing the size of these data centres expand, or we're seeing just more of them popping up. Like, what does it mean when we say we're seeing, like, data centre growth because of AI, what does that mean?   Phil Harris  11:24 Well, it's fascinating, because now our worlds collide, because the way we now think about how to describe a data centre isn't in the square footage or the number of computers, it's in how much power it consumes, and we now measure it in megawatts, and it starts in 10 megawatts, or single digit megawatts, very small data centres, into average size data centres in the 10s of megawatts, up to now the hundreds and the gigawatts of consumption that you look at these hyperscalers. But I think we have to put this into a sort of a human scale. It helps us to put this in human scale. If I were to go back to ChatGPT actually about now, 15 months ago. ChatGPT-4. If you were to put that data centre footprint into the province of Ontario, for example, where you and I both are right now, it would be the equivalent of a million internal combustion engine cars driving 30 kilometers a day, if you ever drive up the 401 you probably don't want to see another million cars on the 401 Yeah, but that's the amount of energy that we can think of in terms of a data centre of that scale.   Trevor Freeman  12:33 Yeah, and again, kind of putting it in the electrical industry's terms, what we consider as a large load so we have a specific designation of a large load request that is anything five megawatts and higher. And like, up until recently, we would get one or two of those every once in a while, like, it's pretty rare to get a large load request. We are seeing large load requests coming in at a near constant pace now, like the number of large load requests we're getting, and a lot of it is because of this, not all because of data centres or anything like that, but a lot of them are certainly driven by that need for more more computing power, more facilities that support that.   Phil Harris  13:18 That's right. And at the same time, we're seeing a demand on on energy around now home, EV charging, and other aspects of the general distribution of the power, everything's taking a step function. But if I could just say one thing to your point about before I was seven megawatts, was a high load, then we may need to change that scale. It's almost inefficient to build a data centre unless you're somewhere above the 10 megawatt range, because at that point, get somebody else to do it for you.   Trevor Freeman  13:42 Interesting, yeah, and that's where it's sort of like, almost like, renting space in a data centre for a request of that size. Interesting, something that you know, I've seen kind of in your in your writing, on your on your blogs, is the idea that traditional data centres are really built for peak capacity, which absolutely mirrors the power industry. We build our electrical grids for peak capacity, and obviously that leads to a fair amount of inefficiencies. So if you're building just a peak capacity, if you're not at peak capacity, there is an inefficiency happening. There something that you identified. It's a stat from your research talks about graphics processing unit usage rates as low as 20 or 25% so I'm assuming that means kind of like three quarters of that hardware is sitting idle or not being used valuably. Tell us a little bit about what, what Cerio what you're doing, what your composable architecture specifically is doing to reclaim that wasted power and cooling capacity,   Phil Harris  14:44 Yeah, and so it starts off with your the premise you correctly raised is that, if we think about the the equipment, the physical equipment, and how we put these devices and these components together in a data centre, the same model we've been using today is, is about 3035, Years old in terms of individual compute systems, where we run applications, software that has memory and central processing units, those typical things you have in a laptop, or you have every computer. But then we put these accelerators, these GPUs, companies like Nvidia now are the one most valuable companies on the planet, if not the most valuable planet company on the planet, because that's the technology they develop. But we're trying to put these new class of accelerators into an existing compute model which wasn't designed for this. So then itself now starts to fragment the ability to leverage those resources in a data centre. And as you accurately said, it's interesting. If I could geek out on this a little bit for the energy consumer in the room, please. Do we think? We think about the notion not only the megawatts of power going into the data but we we think about what we call power usage efficiency. And that basically says, whatever the power delivered to a data centre, how much of that is applicable to the IT systems in that data centre, a good, well run, efficient data centre is about 1.2 that means about 1.2 times the amount of power that's used is delivered. Your home, for example, is about 30 times the amount of power we use is what's delivered. We are very inefficient from our home use, by the way. But that's another problem to solve in another podcast, but in this case, that's all true until we then ask the question, but what's actually being used at that equipment? And that's now in that 25 to 30% range at any point in time, and we refer to that as stranded and idle assets that, for whatever reason, aren't where the application is or aren't applicable to be used for the application that moment because they're in some other box, or it's a time of day when people use equipment. And by the way, equipment like that isn't being used 24 by seven, but it's drawing power 24 by seven, right? So there's lots of inherent inefficiencies in that model. So what we do is we provide the ability to dynamically have pools of resources where we can dynamically attach resources to a compute system as required, at the scale you're required, and allowing you to be much more efficient in the timing of that and the amount of equipment required to meet your end solution. And by doing that, we can increase the number of accelerators that you apply to a compute system, which inherently means you are much more efficient in those compute systems, because it's not just the computers. As I said before, there's storage, there's firewalls, there's load balances, there's networking equipment, all of that can now be much more efficiently used. All of that is drawing power.   Trevor Freeman  17:35 So is the idea, then, that the equipment not being used, or when you're at a lower demand time in terms of computing power, you've got physical equipment idling, sort of in more idle mode, drawing less resources that you can then ramp up so the peak amount of equipment still there. You're just being more efficient with it when it's not being used. And you've developed a way to sort of dynamically pull that in. Is that what I'm hearing.   Phil Harris  18:00 Exactly, I'll give you an example. A data centre here in Toronto wanted to have a block of 128 GPUs. They could have, they could they could service their customers with, with the current systems they were using previously to deploying our infrastructure, they had to require deploy, actually, 200 GPUs and a very large number of servers in the to house those GPUs. By deploying this area technology, they brought that down to 136 actual GPUs, and they reduced the number of compute platforms by a factor of four. So they reduced it by 75%.   Trevor Freeman  18:35 Yeah, that's fantastic,   Phil Harris  18:36 With exactly the same outcomes to their customers. With no no contention for resources, no oversubscription of resources, just more efficient use of those resources.   Trevor Freeman  18:46 Gotcha. So still able to meet that peak demand, but not sort of firing up that equipment when it's not needed.   Phil Harris  18:53 Well, not just not firing it, not having to have as much stranded equipment, because we can use all the equipment all the time.   Trevor Freeman  19:01 Gotcha. Okay, so in when I was kind of setting up that last question, I used the term composable architecture, and I'll admit that I pulled that from your material. Help me understand what that means. So you know that I've also seen you use composable infrastructure sounds a bit abstract, like, what? What are we talking about here? What does that actually look like?   Phil Harris  19:20 When a consumer, or someone who's building a data centre buys their computer equipment, they usually will actually buy the computers, the GPUs, the storage and other things at the same time, and they will get delivered together, and that box now becomes a unit of compute capacity. But the thing about that is whether you're able to use that entire capacity, the length in which that's a useful there's a lot of innovation churn right now as new things are coming through very quickly. But that box is now solid. You know, it's statically built for the rest of its life. Pretty much, it's very expensive. IBM did a study to take a server out of a rack, these big, six foot racks or bigger, where. These servers are housed with lots of wires going into them, power and data and all sorts of things. It's about $1,000 a minute to take one of those servers out of the rack and either change something that's broken, update something so they just don't get taken out of the rack. Because the average time to take a server out of the rack is about an hour. The math on that's pretty simple. So if I'm spending $60,000 to upgrade a 20,030 $1,000 server, I'm just gonna leave it there and buy another one. So that creates more of these stranded assets. So composability says, Let's separate these things into, as I said, pools of resources, compute accelerators and other devices, and have a fabric between them that allows us to, in real time, assemble a compute system that I need. That's the composing part as I need it, because I can now take the resources anywhere in my data centre, if you've got the right fabric, which we've built that allows you then to real time build that compute system with exactly the same capabilities, exactly the same performance, and without having to change any of your software or the way the service work. Everything has to be off the shelf to make this work, and that's what we've built.   Trevor Freeman  21:05 Got you. So, two of the terms, and you'll forgive me, this is sort of a new sector for me. Two of the terms that are used as metrics to determine performance are power usage, effectiveness, and you've kind of talked about, you know, GPU usage. Is the industry moving more towards that GPU usage metric? Is that just something that you guys are kind of leading the curve on? Or where are we at on that?   Phil Harris  21:34 Oh no, this is very much the industry way of describing not just efficiency, but requirements. And we use very weird terms for this. Every industry has their weird term. Weird terminology, and we're now moving to the for example, in AI, the number of tokens per second when you and I put a request or a question into ChatGPT or CoPilot or chord, whatever we use, those words get translated into tokens, actually numbers. Every compute system is just a big calculator. At the end of the day, we do, we do massive processing on numbers. How many of those tokens can I put into the system? How long does it take to process those tokens and give me a response? And the tokens per second, per watt is now what we're asking. So how many tokens a second, and what power per token is it costing me to process information? And that's the interesting way of thinking about how AI, for example, and that's value started this conversation will be measured is the most amount of tokens per second, per watt. Now, right now, we're focusing on tokens per second. We're not looking at the last denominator, which is watts. So that's why these data centres are getting so ridiculous. Ridiculously large. And you know, we even heard it in the in the State of the Union address in the United States earlier in the week, where, you know, there's now the administration pushing cloud vendors and AI vendors to say, Hey, pretty soon you're gonna be on your own about delivering power. Because, quite frankly, the way you're going. It's going to become untenable to think about that from a national grid perspective. Now, I think that may be a little bit into the future, but I don't think it's a completely unreasonable sentiment at this point.   Trevor Freeman  23:12 Yeah, and I mean, you're talking about, and we talked earlier about the just the scale of energy usage here is reaching a new height, a new level. And if we break it down to the individual racks, you know, these racks of servers or processors that you've got in your data centre, we're now talking about anywhere from 50 kilowatts to 100 kilowatts of cooling need. And that's the big driver of energy usage, I think, is correct here is the cooling need per rack multiplied by, of course, big numbers to get those, you know, 5-10-20-30, megawatt data cetnre we're talking about when we talk about cooling and we talk about, you know, hot spots within a data centre, how does your approach differ from kind of the standard way of doing it.   Phil Harris  24:02 So that's a great question, and I think we should explain why the cooling part, it's a bit like buying really good, expensive wagyu steak every day and then having to spend a lot of money on a gym membership to then go and burn off those calories. So we put all this power into power these compute systems, but then we have to keep them cool, and the harder they that, the faster they run, the more powerful they run, the hotter they get. But we need to cool them. So there's this relationship between the more power we draw, the more cooling we need, and cooling is becoming, as I said, that sort of trade off for performance. Now there's lots of exotic ways of cooling computer systems. We can just blow air across them. We can have a liquid like the radiator in your car, or we can literally drop these compute systems into bars of solvents. Ferdinand Porsche, I like to use of other industry analogies. Ferdinand Porsche, the guy who obviously designed the first Porsches and the VW Beetle, realized if I could distribute the heat of the engine block with a horizontal block, I could blow air across it. It was much more efficient than trying to put a radiator to actually cool down the engine block the way that other cars who have the engine in the front, and it's because of surface area. Now, if I've got to put all my GPUs and CPUs and memory close together, either in the same box or the same rack, that concentration of heat needs to be addressed with cooling. One of the ways we can address this is not only to be very selected when I compose the GPU, it's the only time it's drawing power, but also I can spread them out through my data centre by having a fabric that allows me to connect them to the compute systems with the same performance, but now I can distribute my heat generation. That means I can cool more efficiently, just like that Fernand Porsche analogy of the of the Porsche 911 because now heat over over, spread of distance and surface area is a more efficient way, which means it won't mean that we won't ever get to liquid cooling. I don't think immersion cooling is a good idea for lots of other reasons. It's a necessity, more than an optimization, but we can defer the complexity, the cost of those exotic cooling systems if we're more efficient in a way we use and design our data centres.   Trevor Freeman  26:18 And I guess there's a similar description there of, if you're concentrating all that heat in a specific, you know, physical area within a bigger building room, whatever you want to call it, that that cooling system is having to work to that peak cooling need, so to that hot spot effectively. But it's not working just on that spot. It's working across the whole physical area. If you're spreading that cooling need out across the whole room, one the peak is a little bit lower, and you're just more effectively using your whole cooling system. Is that fair to say?   Phil Harris  26:52 And that's exactly the right way of looking at this. And think about it from this perspective as well. The reason we have to cool is because if we don't call sufficiently, those devices become very unreliable and reduce a useful lifespan without going into who, because they keep this information confidential. But one large cloud provider in the US, for example, a GPU that normally has a lifespan of at least three years, is going down to about nine months right now. And the reason for that reduction the lifespan of the use of that GPU, is because of the heating characteristics within these boxes that are getting even with all these cooling mechanisms are becoming now a reduction in the lifespan. So that means we have to create even, remember, I said what it costs to take a system out of a rack. That means we don't have to apply an efficient and effective cooling strategy, our power strategy and cooling trategy, then we start hitting problems very quickly.   Trevor Freeman  27:50 Got you okay. Okay, so there's a mantra that I admit I hadn't seen before until kind of reading some of your material. It's, it's friends. Don't let friends build data centres. And I think it's referring to, you know, this, this move. And there's so many industries that kind of do this cycle of centralization to decentralization, and the sort of data movement went towards that centralization, and you saw these big, massive data centres. But there's, there's kind of a move now back to, let's call it decentralization or repatriation of data. And so for various geopolitical reasons, organizations, companies, governments, are wanting to pull their data back home and have it kind of be more in their control, living in their own servers. So how are you or how is Cerio helping companies kind of get back into the data centre business or repatriate their data without, kind of, you know, getting into the troubles that led for to that centralization in the first place?   Phil Harris  28:55 Yeah, and by the way, I can't take real credit for that quote. Cole Crawford, who was one of the early guys at Facebook before it became META, and was one of the leading voices in the Open Compute platform movement, which is try and standardize how we do these things. Cole is now the CEO of a company called Vapor IO, and what he was really saying is, it's so complicated and difficult to run data centres, let alone building the capital expense. AI isn't just one thing. There's lots of stages in the workflow of AI. We train these big models. You have heard of large language models like ChatGPT or copilot, but what we use them for the results of those trained models is what we call inference. Now you'll now hear about agentic AI, where we turn those results into actions. Okay, that's the agency part of agentic. Well, the use of AI in the corporate world is now becoming, as you said, both regulated, but from an intellectual property perspective, it's about how I control my data and my information. Because if I put that all into somebody else's large language model, I basically put. Populated somebody else's large language model with what might be my proprietary information or information that's very sensitive, and it's one of the reasons why you'll hear in the press about anthropic for example, trying to put guardrails around the use of their AI, because they're very sensitive to this. Most enterprises, governments of all sorts, have realized, though, they need to have run this in their own data centres, because they need to have control over this in control over this information and the use of this information, that's the repatriation you're talking about, moving these workloads now into the organization that previously said, Hey, cloud computing can take this problem. We're going to now figure out how enterprises, which are far many more of them in far more diverse locations, can now build their own data centres and get the right power, the right efficiency, the right capabilities at the right cost.   Trevor Freeman  30:47 Does that open the door? I mean, earlier, you talked about, you know, if we're talking about a five megawatt data centre, it's almost not worth it. You know, that's just sort of renting space in someone else's. How does that track with an organization that won't have enough data or enough computing power, whatever the metric is to warrant a 30 megawatt data centre for their own data, but wants to get that that control, wants to bring it more in house, is our is your technology helping those smaller data centres exist? Is that the correlation there?   Phil Harris  31:18 We can now move it into one of the things that we another couple of terms that may be an maybe not your your listeners may not be familiar with in the compute world or the data centre world, we talk of brownfield and Greenfield. Brownfield is that which is already there. Greenfield is something I have to build new. A lot of the Brownfield world is what is the predominant sort of quantity of compute power on the planet is primarily brownfield The question is, can I take that existing infrastructure and put the capabilities we've been describing in this discussion into those brownfields? So I can reduce the cost of the expansion of that because I can reuse the compute equipments there, I can now add just the discrete GPU technology, for example, into an existing data centre that doesn't therefore blow the power budget or the cooling envelope within that environment, but I can still now start taking advantage as I figure out what my larger plans are, and at the same time, how do we have a tier of providers? I'll give you an example. There's a company in, again, in Canada, think on who are building a data centre in in Ottawa, it's going to have its own liquid natural LNG as its source of power for its own power requirements. Why? Because they can have the power they need as they need it in that location, and they can provide that secure infrastructure for both government and private enterprises, and think on is certainly in Canada, one of those companies that's really seen to be a trusted partner in this. So it will be a bit of what can I do myself? How do I have a trusted partner? We think of sovereign AI a lot. That means trust more than anything, and that's becoming the new mechanism of thinking about this.   Trevor Freeman  33:04 Thinking about the environmental impact of tech and of data. We've talked about the energy usage here, but there's also the physical aspect to it. Of the pace of improvement in technology means we see obsolescence, or we see kind of technology being outdated fairly quickly. We all, like on the personal level. We all see this with our cell phones, our smartphones, our whatever tech we have at home that seems to be out of date fairly soon. I think that the stat, or that the saying that's out there is, you know, tech is kind of obsolete or becomes trash within three years. Obviously, this is not sustainable. Is this part of the drive of what you're doing? Is it? Are you looking to sort of extend the life of the physical equipment you've touched on this a little bit, but maybe expand a little bit on that?   Phil Harris  33:52 Yeah, this goes a little bit back to that Brownfield-Greenfield discussion. But one way of looking at I guess, is when I put all of these components into what the classic model, the current model, I put my central processing unit, my memory, my storage, my GPUs, all in the same box. What is the thing in that box that I want to take advantage of as new innovation happens, versus that which is happening over a slower evolutionary cycle? Well, right now, if I put everything in the same compute unit. Go back to my cost of taking that box out of the rack. I'm pretty much limited by the slowest innovation curve within that platform. Now as what I can take advantage over time. Interestingly, GPUs are innovating currently at a clip of about once a year. Nvidia comes out the new generation of GPUs once a year, but now we're getting more GPUs into the market. We're getting much more diversity, and that diversity means I'll have more options more often. But if my compute system itself is only innovating once every three years to your point, then if I don't decouple these things, if I don't have the ability to separate these innovations. Curves. I'm always stuck with the slowest innovation curve. One of the things we've done at serial with the fabric we've built and the platform we've built is to allow you now to, if you like, dislocate those innovation curves and those options, so as new technology comes along, I can apply it to the things that are innovating slower and still get the outcomes I'm looking for. And that will significantly increase the existing lifespan of equipment that's in people's data centre.   Trevor Freeman  35:26 So, looking at a data centre of the future, and not, you know, not far into the future, let's say 5-10, years from now, are we seeing some of the same technology still exist within that data centre, or is it, you know, everything gets cycled out within like, what's the generation of a data centre, for example? Like, how often, or how soon will we see it all cycle out?   Phil Harris  35:48 I think you there's a there's a technical answer to that, and the financial answer to that. The depreciation model, so that the capital infrastructure can be written off people's books over a three or five year window is very typical. So we see that there's just a financial inhibition to changing more or faster than that three to five year window. The technical churn, as I said, is happening much more rapidly in the technologies that are drawing most power but providing most capability. So one of the things that we're looking at is how companies now start leasing infrastructure, because if they lease the infrastructure, they can now recycle that and bring new technology in faster into their organizations. But to do that, you've got to have the ability to bring new technology in and not be stuck with these static systems that we have today. So there's a set of financial instruments, and now with work that Cerio is doing, technical capabilities that allow customers to really continue to innovate. So there's no real, hey, it's going to be all churned out in three years. I'll continue to innovate over those three years, reciting the technology that can stay where it is and bringing new technologies as it becomes available at the right financial model.   Trevor Freeman  36:56 I'm curious about what that innovation is. So you talked about Nvidia, kind of essentially a new GPU every year. There's a new version every year. What is the innovation? Are they just is it getting faster and more compute power, and therefore it's pulling more energy? And is that just like a perpetual increase, or is it kind of same compute power, less energy, like, do we ever see, I guess what I'm what I'm getting at with this little bit of a ramble here is, do we ever see that that rate of change in energy usage start to flatten out and come down while we still can grow our computing power? Or does energy usage just continue to grow? Like, are we on a bit of a path with no end right now,   Phil Harris  37:44 History taught us a little bit about this. Gordon Moore, who was one of the founders of Intel actually, we had this term called Moore's Law, and Moore's Law was basically this idea that every 18 months we'll double the number of transistors on a piece of silicon. Now, for those in the computer science world, we understand what that means. For the rest of the world, the Trans World. The transistor is the smallest unit of technology within the computer. It's the basic building block of how we build computers. The central processing is all the GPUs. They all come down to taking literally silicon and in a foundry, we call them, figuring out how to make as many transistors interconnect with each other in a in a smaller area as possible, or the most amount of transistors we can. So a bit of a geeky answer to your question. But the way that we look at how each innovation improves is, are we increasing the number of transistors, which means we can do more math? Remember, all we're doing is processing numbers.   Trevor Freeman  38:41 Per unit, per physical unit, right?   Phil Harris  38:43 Per physical unit.   Trevor Freeman  38:44 Okay.   Phil Harris  38:45 And the way we do that is in these big foundries that process all this silicon into these components. They have, what are called process nodes and the and literally how we etch a transistor, it's called lithography onto a piece of silicon. Tells us the power of that piece of silicon and the more I can etch. So we get into what we call the nanometer scale, or what we call a process node. So every time, if you really look into the spec sheets of Nvidia, every generation, they'll talk about how many nanometers their silicon process is based on. Because the smaller I can get that number, the more transistors I can have on the same amount of silicon, the more processing I have, but every transistor takes power. So with more transistors, I require more power, even though in the same physical space, it looks like the same amount of silicon. Therefore, your question was a great one. Do we ever get to zero nanometers? Well, no, we're going to hit a wall here eventually. So then the question is, that's the scale up model. Try and make one thing as big as possible. How about if we make lots of things powerful, but we have more of them in China, the last year, we heard of deep seek. Deep seek was a Chinese government sponsored effort to try and come up with a. Much more cost effective way of doing the equivalent to ChatGPT. They didn't do that with bigger GPUs. They did it with much smaller GPUs, but many more of them. And that comes back to how efficient I am in deploying lots of things together. And that goes back to my earlier point about we start with scale up. Inevitably, in the industry, we go to scale out.   Trevor Freeman  40:22 And is it fair to say that the power usage per transistor, is that fairly static? Like, is there efficiencies to gain there? Or your GPU is going to use more power because you're packing more transistors into it, and once you hit that wall, that's going to be the power consumption level, is that, right?   Phil Harris  40:43 Well, this is the games that these silicon manufacturers, like Intel, AMD, Nvidia, they're all trying to figure out how to sort of figure out new and interesting ways of packaging all of the silicon in these processing units. And we've got a whole industry and science around the packaging mechanism to make those tiles, and that we now think of them as little tiles of processing power, and some that will be doing very specific jobs. Some will be doing very general jobs. It's now getting to the point where the science around the packaging of these dyes or these tiles is as much as the of the of the innovation, as the actual tiles and the processing on them. So it's an extremely complex technical problem, and we are hitting some walls here, which is why I go back to my earlier point. We're now reaching a point where is it just a technical problem we're solving, or a technical, operational and commercial problem we have to think about? And this is that wall that wall that you asked me about right at the beginning of this conversation. Are we about to hit a wall? And the answer is, yes.   Trevor Freeman  41:46 Interesting. I mean, I'm always fascinated by like, what are the what are the really smart people in the industry focusing their time on? And it's so that's why we're talking to you. Of you know, you're looking at, how do we operationalize this. How do we get the most efficient combination and structure of what we're doing here? There's folks that are looking at, how do we pack the most computing power efficiency into these specific units? I guess there's an aspect of, how do we cool this in the in the most effective way, like, what's, how do we, you know, drive down the cooling power needed? What else is out there, in terms of, like, we have smart people focused on this efficiency. What's the thing that's missing from that, that sort of list?   Phil Harris  42:36 Well, I think maybe what's going on right now. And if I could just add a, unfortunately, just one more layer of complexity.  Remember said we were processing silicon? Well, the Earth's got lots of silicon, but we don't have lots of places to process that silicon. The companies that are formed to process silicon into these processing units, we call them foundries. The world's largest is TSMC, based in Taiwan. And then we have Intel, we have Samsung, we have a few others around the world. Global Foundry is another one. There is a limit, physical limit, because these foundries are huge and they take decades of development and optimization. So if we start breaking ground on a new foundry tomorrow, we'll see output in about five years. So we have a constrained supply. So if I'm if I'm Jensen at Nvidia or any of the big silicon manufacturers, I'm going to optimize that relatively constrained supply to where I'm going to get the best return on my investment. And that's why this scale up model is happening. So given that we know that we won't have any more foundry capacity of scale for another couple of years, at least, then the reality is we've got to think differently about how we're thinking about the processing of that silicon. Do I want just ever bigger processes that become more expensive, more limited in where I can deploy them. And quite frankly, the top 15 consumers in the world of silicon consume about 80% of that silicon, if not more. How do I democratize that? Again, it goes from scale up to a scale out model, where I can use that same processing capacity to produce more silicon.   Trevor Freeman  44:20 Fascinating. Yeah, I just, I took us down a little bit of a nerd out path. You had me really interested in that. Okay, so last question here, we hear this term for a bunch of different reasons. Around the world right now we're hearing this term democratizing, happening a lot, and I know you've talked about democratizing, AI, what does that mean? What does that mean to you, or describe that for us?   Phil Harris  44:48 Yeah, I think it really means. Going back to my last point about if 15 big consumers of silicon are going to consume the vast majority of verbal supply chain, that makes the. At a losing proposition for the rest of the organizations and the rest of the governments and the rest of the individuals on the planet. So how do we make sure that AI can be built both responsibly from a sustainability perspective, right? And I don't mean just the ecological side, but that's important here too, but also from the ability to I was on a panel yesterday between the UK Government and the Canadian government, where we're looking at how do countries around the world have the ability to control their own destiny? And there's this whole notion of sovereignty and AI sovereignty right now that isn't because people want to have closed walls around them, that you want to have choice. They don't want to be dictated to by very dominant players where they, quite frankly, don't have the buying power to compete. You know that the amount of capital going into some of the AI companies, we saw $30 billion going into anthropic last week. That's actually a small increase in their capitalization relative to the other big AI players on the planet. That's $30 billion so we've got to think to ourselves, is that a sustainable model commercially? And the answer is no. So we've got to have technology. We've got to have the right ability to deliver power. We've got to have the right designs of data centres that can keep them cooled in an effective and efficient and responsible way. And we've got to be able to give them enough power to make them viable, to make them useful. That's the democratization we all have to be focused on.   Trevor Freeman  46:25 And we need every, I guess, to sort of round of the point is we need everybody to be able, everybody being, you know, whatever, major industry, countries, whoever, to be able to access that equally, so that we don't have to rely on the major players out there in order to do those things you just said, gotcha.   Phil Harris  46:41 That's exactly right. And look, there'll always be a pyramid here. There always has been a technology. There's always still the big players, right? But the question is, have the big players the stifled out the ability for smaller players to come up, innovate, provide choice, provide alternative ways of looking at things, and that's what got to make sure that we keep the and this always relies on some new technology coming along that enables that. Sarah believes that we've created that next layer in the stack, if you like, of technologies that gives us that opportunity to rethink the innovation curve going forward.   Trevor Freeman  47:14 Very fascinating. Phil, thanks for your time. I really appreciate it. This has been super interesting. It's not an area that I often get to spend my time thinking about so is great to chat today. As as you know, we always kind of round out our interviews with the same series of questions to our guests. So what's a book that you've read that you think everybody should read?   Phil Harris  47:34 Well, I'm not sure I can recommend this for everybody. One of the people who basically, along the lines of some of the things I've been talking about today, who revolutionized the computer world was a gentleman by the name of Linus Torvald in Helsinki in Finland. At the time, he's now based in the States, he realized that there was a dominance around how the operating systems on computers, the things that run the software, was limiting, basically, innovation choice and forcing us down a very closed path. So he wrote something called Linux, which was a new operating system. So be on your phone, your TV, your microwave that's running Linux today. Interesting because there wasn't an operating system that we could then generally deploy. That meant there was more developers had the ability to write applications, more hardware vendors could now have software they could run on their on their platforms. He gave the world a new innovation curve. And every time this happens to my last point, good things happen. Very good things happen for the world, for every individual on the planet. And Linus was one of those individuals who saw that need. And so his book, just for fun, and he's a very quirky guy, as you can probably imagine, is a great book about his philosophical approach to what it takes to change really big problems. And I would encourage all of you just to even just read the first few chapters. It's a fascinating view of how an incredibly smart man, smart individual took on probably one of the biggest problems we had in the 20th and 21st Century of computing, and solved it by recognizing you take a different path.   Trevor Freeman  49:11 Yeah, very cool.   Phil Harris  49:12 As far as shows, um, I don't know. I'm one of these guys. I've got two 13 year old daughters. So my wife and I get to watch TV for a very limited amount of time where we can watch it, about the things we want to watch, so we tend to sort of cram things in. But I'm a huge Aaron Sorkin fan, so if I ever need something on a rainy day to go back just to think about how the world could be, I watch the West Wing. It's a show that's imaginary. It's got incredible script writing, it's got incredible character development, but it really talks about how to think about doing the right thing as well. Now, whether you agree with the politics or not, that's a different question, but just the thought that smart thinking solves big problems, again, sort of It's a bit like the Linus Torvald book. It just speaks to me about sometimes we can solve big problems. With individuals or people who just had the right way of thinking about things.   Trevor Freeman  50:00 Yeah, I think that's the kind of, you know, call it entertainment, because it is entertainment, but it's the entertainment that sticks with you, and that we go back to time and again, is the ones that we can also, like, see the the underlying philosophy, or, you know, theory of change that goes into that entertainment. And it's, it's fun to watch. It's, you know, either humorous or dramatic or whatever, but there's still that underlying message. And I think, yeah, West Wing is a great example of of that. There's a handful of those other sort of classic shows that are in that line too. A free round trip flight anywhere in the world. Where would you go?   Phil Harris  50:40 This is hard. My wife and I were talking about this the other day, and I've had the luxury of traveling just about everywhere. I think there's 15 countries on the planet I haven't been to, but if I ever want to go to one place is Bali. And there's two reasons. One, my wife and I went there for a honeymoon, and it was the beginning of the most important chapter of my life by far. And secondly, it's because it has that balance of everything. It's I love to scuba dive. I love the rainforests, the jungle, the architecture, the people, the food. It just brings everything into one package for me. And so it just again. It's those things that sort of speak to you emotionally and also intellectually. It's one of those things that I could always go back too.   Trevor Freeman  51:26 Fantastic. Who is someone that you admire?   Phil Harris  51:29 In history or today?   Trevor Freeman  51:32 You pick, anything.   Phil Harris  51:33 that's fascinating. I think historically it's under Brit it's hard not to go back to some of my forebears, or my country's forebears, Alan Turing, who, against all adversity, social, political, technical, came up with an inspirational way of thinking about solving what are deemed to be unsolvable. And again, it's a tragic story. I think we've all, if you see the movie that was made about his life, it's a very tragic story, but it's an inspirational story about how, again, if you just take a different approach to solving what seems to be an unsolvable problem, you can you get smart people together. Doesn't have to be a big army of people. I think so. Turing is one of those people that always comes back for me t think, wow, if I could have just some of his courage and some of his imagination and some of his intellect, I'd be a very happy person.   Trevor Freeman  52:29 Yeah, and it's almost, I mean, obviously, a brilliant man, but it's the willing to think in a different way, or willing to approach a problem in a different way that I mean, there's a long list in history of major turning points that are as a result of someone thinking in a different way or doing something in a different way. And I think that's a great example of it.   Phil Harris  52:49 Just about the entire course of human life are in the midpoint of the 20th century, change on that, that man's inspiration, that man's imagination.   Trevor Freeman  52:57 Yeah, and that's, that's not an understatement. That's fantastic. Okay, last question, what's something about, kind of the energy sector, or, you know, your sector that that you're really excited about, or something that you see in the future that you're really excited about?   Phil Harris  53:09 Actually, I see it now, to be honest, there are things in the future. Hey, I have two 13 year old kids. I want to have a sustainable ecology and world environment for them to live in and bring their own families up in. And I think about how we can use power more efficiently, but how we can make it look sustainability is important. I want to see renewable, sustainable energy for the general world as a thesis right now. It's how we can be much more efficient in the use of power and the right power delivery. And I think, as I said, I gave the think on example, that's incredibly exciting, because now, if we can do that at scale, that's an opportunity to do that democratization that I spoke about. So when I think about the things that really excited me about the data centre world, the world I live in, actually that power generation and power availability in a clean, effective, well managed fashion is exactly what we need right now, while the rest of us are solving these transistor problems.   Trevor Freeman  54:04 Yeah, it's, I mean, our listeners are probably going to roll their eyes, because I say this all the time, but one of the things that excites me the most is seeing like we're in a period of change, and that's a really exciting time to be working in this and I kind of hear that from you in your sector as well, and I see it in mine, in the energy sector of we're actually getting to see some of this innovation, some of these like leaps and bounds forward. That's not to say there aren't still problems. It's not to say there aren't steps backwards as well. But it's very cool to be working on this in a time when we're seeing that change, and that's kind of what I'm hearing from you as well. Indeed. Awesome. Phil, thanks so much for your time. I really appreciate it. This has been great. Chatting with you.   Phil Harris  54:42 Trevor, the pleasure is all mine. Thank you.   Trevor Freeman  54:44 Fantastic. Take care.   Phil Harris  54:46 Take care.   Trevor Freeman  54:47 Thanks for tuning in to another episode of the thinkenergy podcast. Don't forget to subscribe wherever you listen to podcasts, and it would be great if you could leave us a review. It really helps to spread the word. As always, we would love to hear from you whether. Feedback, comments or an idea for a show or a guest, you can always reach us at thinkenerg@hydroottawa.com.

Le monde de l'intelligence artificielle vient de connaître un séisme financier et technologique. Yann LeCun, l'un des « parrains » français du deep learning et lauréat du prix Turing, a officialisé le lancement de sa start-up, Advanced Machine Intelligence (AMI Labs), avec une levée de fonds record de 1,03 milliard de dollars. Ce tour de table, l'un des plus importants jamais réalisés en phase d'amorçage en Europe, propulse immédiatement la jeune pousse parisienne au rang de licorne.Rupture avec les modèles de langage (LLM)Cette annonce marque un tournant philosophique majeur. Jusqu'à présent, le secteur était dominé par les grands modèles de langage (LLM) comme ChatGPT. Cependant, Yann LeCun ne cache plus ses divergences avec l'approche actuelle, qu'il juge limitée. Selon lui, les LLM ne font que prédire le mot suivant sans véritable compréhension du réel. Ils sont incapables de raisonner, de planifier ou d'appréhender les lois physiques élémentaires.Pour dépasser ces limites, AMI Labs mise sur les « World Models » (modèles de monde). L'idée est de créer une IA capable d'apprendre de manière autonome en observant le monde, à l'instar d'un enfant qui comprend la gravité en voyant un objet tomber. Ces systèmes s'appuient sur l'architecture JEPA (Joint Embedding Predictive Architecture) pour modéliser les interactions physiques et logiques, permettant ainsi à l'IA d'anticiper les conséquences d'une action dans un environnement complexe et multidimensionnel.Un soutien massif et stratégiqueLe prestige du fondateur a attiré un casting d'investisseurs exceptionnel. Le tour de table a été co-dirigé par des fonds comme Bezos Expeditions (Jeff Bezos) et Cathay Innovation, avec la participation de géants industriels tels que Nvidia, Samsung et Toyota. La France est également en première ligne avec le soutien de Bpifrance et de grandes fortunes comme Xavier Niel (Iliad), la famille Mulliez ou le groupe Dassault.L'avenir de l'IA ancré dans le réelL'objectif à court terme n'est pas de sortir un produit de consommation immédiat, mais de bâtir une infrastructure scientifique solide. Les fonds serviront principalement à acquérir une puissance de calcul colossale (GPU) et à recruter les meilleurs chercheurs mondiaux à Paris, New York et Montréal.À terme, ces « World Models » pourraient révolutionner la robotique domestique, l'industrie automobile et l'automatisation complexe. En apprenant à comprendre le monde physique plutôt que de simplement manipuler le langage, AMI Labs ambitionne de donner naissance à une intelligence artificielle véritablement autonome et dotée de « bon sens ». Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

It's episode 228 and time for us to talk about Computers and Computer Science books! We discuss technology, digital humanities, coding, and more! You can download the podcast directly, find it on Libsyn, or get it through Apple Podcasts or your favourite podcast delivery system. In this episode Anna Ferri | Meghan Whyte | Matthew Murray

In this episode, Stewart Alsop III sits down with Tom Faye — experimenter, author of The 90 Day Client Acquisition Code, and founder of Carbon Credits Marketplace — to talk about solar energy, off-grid living, and the solarpunk vision of a technology-powered utopia. They cover everything from perovskite solar cells and portable container-based solar systems, to carbon credits, ESG investing, and blockchain verification of clean energy output. The conversation also winds through AI training data, business automation, and the data labeling industry before circling back to some bigger questions about human nature, geopolitics, and what genuine self-reliance looks like in 2025. You can find Tom and his work at Carbon Credits Marketplace on LinkedIn and his energy consumption data visualization is also shared there. His book The 90 Day Client Acquisition Code is available for those looking to explore business automation further.Timestamps00:00 Introduction to Tom Fay and his work01:03 Understanding Solar Punk: Utopian Tech and Culture02:15 Current State of Solar Technology and Storage03:45 Living Off-Grid: Solar, Batteries, and Remote Work06:11 Solar Energy in Africa: Challenges and Opportunities12:21 Powering Communities with Mobile Solar Solutions16:50 The Vision of Solar Punk: Self-Sufficient Communities22:54 Existing Examples: Great Barrier Island and Others26:06 Overfishing, Environmental Challenges, and Technological Solutions28:34 Using Technology to Address Second-Order Environmental Problems36:35 Data, AI, and the Future of Energy Management43:13 Carbon Credits, Blockchain, and ESG Reporting45:27 The Geopolitics of Green Energy and Resource Control46:53 How to Connect with Tom Fay and Future ProjectsKey InsightsSolarpunk represents a genuine near-future possibility, not just an aesthetic. As solar panels and lithium batteries become cheaper and more efficient, the vision of abundant, decentralized clean energy is becoming a practical reality rather than a utopian fantasy.Perovskite solar cells are pushing efficiency roughly 22% beyond conventional panels, and the bigger revolution happening right now is on the storage side — cheaper, higher-capacity batteries are what will truly unlock solar's potential at scale.Africa may leapfrog the West on solar adoption, just as it leapfrogged landlines with mobile phones. People in energy-scarce countries viscerally understand the value of clean power in a way that people in the West, accustomed to reliable grids, simply don't.Portable solar container units — self-contained, deployable systems — already exist and are making off-grid energy viable for farms, mines, remote lodges, and even data centers, with a roughly five-to-one solar-to-load footprint required.Carbon credits generated from verified solar output, tracked via IoT smart meters and stamped on blockchain, represent a long-term business opportunity that survives political shifts because institutional investors and banks operate on independent ESG mandates.AI training data is a present and real economic opportunity, but a shrinking one. The window for humans — especially lawyers, scientists, and specialists — to get paid for their expertise is closing fast as labs pivot toward synthetic data generation.True self-reliance comes down to four things: food, water, power, and transportation. With solar and Starlink, the gap between remote wilderness and connected civilization has essentially collapsed — something unimaginable even a generation ago.

This episode of the Anglotopia Podcast delves into the hidden history of Bletchley Park, exploring its origins, growth, and the significant role it played during World War II. Dr. Chris Smith joins us to discuss the secrecy surrounding the operations, the organizational structure, and the cryptanalysis processes that led to the breaking of the Enigma code. The discussion also highlights the impact of Bletchley Park's intelligence on military operations, the social dynamics and gender roles within the workforce, and the legacy of this crucial establishment in British history. We also unpack some of the myths around Bletchley Park, Alan Turing, and the development of the first computers. Links Chris Smith at Coventry University “The Last Cambridge Spy: John Cairncross, Bletchley Codebreaker and Soviet Double Agent” by Chris Smith Friends of Anglotopia Club "The Hidden History of Bletchley Park" (Palgrave Macmillan, 2015) Episode 78: "Did Churchill Know? Unraveling the Myths of the Coventry Blitz" Bletchley Park Museum Takeaways Bletchley Park grew rapidly due to the demands of war. The workforce at Bletchley Park was predominantly women. Secrecy was maintained through strict measures and the Official Secrets Act. Bletchley Park's structure was organized into specialized huts for efficiency. The Enigma machine was a complex cipher system with vulnerabilities. Intelligence from Bletchley Park significantly influenced military strategies. Alan Turing's contributions were pivotal but not the sole focus of Bletchley Park's success. The legacy of Bletchley Park continues to be relevant in discussions of intelligence and secrecy. Social dynamics at Bletchley Park reflected broader class and gender issues in British society. Bletchley Park is now a museum, preserving its history and contributions. Sound Bites "Churchill says that Bletchley is his goose which lays these golden eggs and never cackles. Well, actually some of them did cackle, but on the whole it's a remarkable feat that they kept it as secret as they did." — Chris on the limits of wartime secrecy. "If you wanted to produce an accurate movie about Bletchley Park, it would probably be a woman working on a typewriter for ten hours a day. That doesn't produce a very interesting narrative for the audience." — Chris on Hollywood vs. reality. "159 quintillion possible settings. If you tried to brute force this one letter at a time, that period of time is longer in seconds than the universe has existed." — Chris on the power of Enigma. "Enigma can never encipher a letter into itself. You can press A 26 times and you'll never get A again. That's an inherent weakness." — Chris on how the unbreakable code was broken. "The person who sort of handed them the Official Secrets Act had a gun on the table. The implication was that if you break this secret, this could carry the death penalty." — Chris on how secrecy was enforced. "The British basically sell Enigma machines to other countries after the war but just don't tell them that they can break them. That's a big reason it stayed secret." — Chris on why the secret lasted until 1974. "There's this idea that Bletchley is a meritocracy, but it isn't. This is a very middle class institution." — Chris on class at Bletchley Park. "Alan Turing would chain his mug to his radiator. He'd ride his bicycle wearing a government-issued gas mask because of his hay fever. He's quite an odd guy, but obviously very brilliant." — Chris on the real Turing. "They actually invent a fictional spy called Boniface working in the German high command. Every time they talked about ultra intelligence, they attributed it to Boniface." — Chris on how they disguised their source. "The Bletchley Park Trust rescued this place from the jaws of destruction at the hands of property developers. They were going to knock it all down and make way for housing." — Chris on how close we came to losing Bletchley Park. Chapters 00:00 Introduction to Bletchley Park 02:02 The Role of Bletchley Park in WWII 03:20 Choosing Bletchley Park: Strategic Decisions 06:13 The Growth of Bletchley Park 11:08 Maintaining Secrecy at Bletchley Park 15:58 The Structure and Organization of Bletchley Park 20:35 Understanding Codes: Enigma and Beyond 25:35 Utilizing Intelligence: The Process at Bletchley Park 34:01 The Legacy of Bletchley Park's Secrets 01:00:36 anglotopia-podcast-outro.mp4 Video Version

Ne Nedenmiş'in bu bölümünde Alan Turing'in hayatından kısa bir kesit paylaşırken günümüz yapay zekalarının da ilhamı olan Turing testinin

Every time you type a message, unlock your phone, or trust a computer to make a decision, you're relying on the ideas of someone you may never have heard of and probably never learned about at school. That person is Alan Turing. He was a British mathematician, logician, and wartime codebreaker, and one of the most important figures in modern science and technology. Turing helped lay the foundations of modern computing. He played a crucial role in breaking Nazi codes during the Second World War. And he asked questions about machines that still shape how we think about artificial intelligence today. His influence is everywhere, from the security that protects your data to the algorithms behind AI. In this episode, I want to explore Alan Turing's life, his scientific achievements, and the legacy he left behind, before connecting his story to my Greatest Scientist of All Time series. Conversation Club - https://thinkinginenglish.blog/2026/02/23/376-who-was-alan-turing-english-vocabulary-lesson/ TRANSCRIPT - ⁠https://thinkinginenglish.blog/2026/02/16/375-do-we-live-in-a-surveillance-society-prepositions-of-place-english-grammar-lesson/⁠ AD Free Episode - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/thinkinginenglish⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Thinking in English Bonus Podcast -⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/collection/869866⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ YouTube Channel -⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@thinkinginenglishpodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠INSTAGRAM - thinkinginenglishpodcast (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/thinkinginenglishpodcast/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠) ⁠⁠⁠⁠⁠⁠⁠ My Editing Software (Affiliate Link) - ⁠⁠⁠⁠⁠https://descript.cello.so/BgOK9XOfQdD⁠⁠⁠⁠⁠ Borough by Blue Dot Sessions Contact ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠advertising@airwavemedia.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to advertise on Thinking in English. Thinking in English is part of the ⁠⁠⁠⁠⁠Airwave Media podcast network.⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Olive Song from MiniMax shares how her team trains the M series frontier open-weight models using reinforcement learning, tight product feedback loops, and systematic environment perturbations. This crossover episode weaves together her AI Engineer Conference talk and an in-depth interview from the Inference podcast. Listeners will learn about interleaved thinking for long-horizon agentic tasks, fighting reward hacking, and why they moved RL training to FP32 precision. Olive also offers a candid look at debugging real-world LLM failures and how MiniMax uses AI agents to track the fast-moving AI landscape. Use the Granola Recipe Nathan relies on to identify blind spots across conversations, AI research, and decisions: https://bit.ly/granolablindspot LINKS: Conference Talk (AI Engineer, Dec 2025) – https://www.youtube.com/watch?v=lY1iFbDPRlwInterview (Turing Post, Jan 2026) – https://www.youtube.com/watch?v=GkUMqWeHn40 Sponsors: Claude: Claude is the AI collaborator that understands your entire workflow, from drafting and research to coding and complex problem-solving. Start tackling bigger problems with Claude and unlock Claude Pro's full capabilities at https://claude.ai/tcr Tasklet: Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai CHAPTERS: (00:00) About the Episode (04:15) Minimax M2 presentation (Part 1) (17:59) Sponsors: Claude | Tasklet (21:22) Minimax M2 presentation (Part 2) (21:26) Research life and culture (26:27) Alignment, safety and feedback (32:01) Long-horizon coding agents (35:57) Open models and evaluation (43:29) M2.2 and researcher goals (48:16) Continual learning and AGI (52:58) Closing musical summary (55:49) Outro PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

Alexander Embiricos is the Head of Codex at OpenAI, leading the development of the company's flagship AI coding systems that power automated software generation, debugging and developer workflows. Under his leadership, Codex has become one of the most widely adopted AI developer platforms.  AGENDA: 05:13 Will Coding Be Automated? Why AI Could Create More Engineers, Not Fewer 07:17 Do We Need PMs? The "Undefined" Product Role and When It Matters 08:06 The Real AGI Bottleneck: Human Prompting, Validation, and "Too Much Effort" 13:04 Three Phases of Agents: Coding → Computer Use → Productized Workflows 13:52 Enterprise Reality Check: Security, Permissions, and Safe Agentic Browsing 17:57 Is Inference the New Sales and Marketing?  18:49 What % of Codex Was Written by AI? 21:33 Do OpenAI Use AI for Code Review? 23:31 Is there any stickiness to AI coding tools? 28:22 What Does "Winning" Mean at OpenAI? Mission, Competition, and Moats 32:04 The Future UI: Chat or Voice 34:10 Agent-to-Agent Workflows: Designing for Approvals, Compliance, and Automation 35:39 Do Coding Models Have a Data Moat? 36:50 How does Codex View Data: Will They Build Their Own Mercor and Turing? 37:27 How Does Codex View Consumer: Will They Compete with Lovable? 41:56 Benchmarks vs "Vibes": How People Actually Judge Models 42:43 Cursor's Edge and the Case for Building Your Own Models 47:37 Is SaaS Dead? What Still Defends Value (Humans + Systems of Record) 51:28 Talent Wars and Career Advice for New Engineers in the AI Era 01:01:03 Guardrails, the Fully AI-Managed Stack, and a 10-Year Vision for Everyone      

“How do you tend to respond when you do not know?” We had this question in our Journal Circle a couple of weeks ago. It’s at the heart of many issues in our world right now. How do we hold it?When do we conceal it?Where do we turn for knowledge?And what do we do with it when we acquire it? That’s what we explore in this episode of The Gentle Rebel Podcast. https://youtu.be/QRAS1dib_GM Our Relationship With Not Knowing I find this advert baffling. A couple are wandering around the Leeum Museum in South Korea. They didn't know it was big; they only gave themselves an hour. He thinks a roof tile is a book. Even when his phone corrects him, they skip off giggling without listening to the information. It reminds me of a billboard from the AI company Turing that says the quiet part out loud: “We teach AGI to think, reason, and code—so you don’t have to.” Are we being encouraged to outsource our thinking and reasoning, not to support and deepen our cognitive abilities, but to replace them? Are they saying we don’t have to think or reason anymore? Even if that’s not the intention, it’s certainly the outcome of using many tools like this. There seems to be a disregard for the sacred delight of human consciousness, thought processes, and creativity. And a subtle quest to eliminate mystery, curiosity, and the learning that comes from not knowing. Yet not knowing has always been central to human potential. It is the driving force of creativity, innovation, and deeper connection to the worlds within, around, and between us. Open and Closed Stances As people reflected in our Journal Circle, a thread emerged: openness vs closedness. Closed not-knowing: defensive, protective, secretive. Open not-knowing: curious, relational, exploratory. Closedness can feel tight. Clenched. Like rushing to paint over the threat of embarrassment or being found out. Openness can feel spacious. Physically expansive, deeper, and less pressured. Where the uncertainty is met with an invitation into possibility and curiosity rather than grasping, clinging, and defensiveness. We explore several ways this plays out in everyday life. Pretending To Know One response to not knowing is pretending to know. We’ve probably all done it. Nodding along when everyone else seems to understand. Staying quiet because asking a question feels risky. Research in 2007 found that children aged 14 months to five years ask an average of 107 questions per hour. By the time they reach late primary school, many stop asking questions altogether. In the episode, I share an anecdote from research led by Susan Engel, where a ninth grader is stopped mid-question with the instruction: “No questions now, please; it's time for learning.” Within institutional settings, our natural curiosity and creativity can be left behind, and if questions are deemed disruptive or inappropriate, we may simply pretend to know and struggle quietly. This is especially true for many more introverted and sensitive people, who are already generally disposed to slot in around others without drawing much attention to themselves. Child-like Curiosity A child doesn’t see their lack of knowledge as a reason to be ashamed. It’s underpinned by the electric buzz of connection. Everything is new, mysterious, and waiting to be explored. For an adult moving through and out of a rigid system, not knowing can feel like an exposing story in which their worth as a human is assessed. Pretending to know can become an adaptive strategy. A way to keep the peace. A way to belong. There's also the technological version, prominent in many AI tools people rely on for accurate information. These systems are designed to always produce an answer, even when they are wrong. This reflects the kind of closed pretending that aims to foster a perception of expertise, so those listening believe that the source’s confidence equates to competence. But pretending doesn't only come from intentional deception. It can stem from stories we absorb, linking knowledge with worth: “I must know in order to be useful.”“I must be useful in order to be accepted.” Letting go of that story can be liberating. Saying “I Don't Know” “I don't know” is an option. A surprisingly radical one. When it is open, it creates space to explore our unknowing. An open “don’t know” admits not knowing with hands turned towards learning and discovery. It might come with an inner spark and the freedom from performance. A closed “I don't know” shuts things down. It can signal indifference or defensiveness. Sometimes that boundary is healthy. Sometimes it is armour. Being “In The Know” There is also the social currency of being “in the know.” Trends. News. Other people's business. Ignorance can feel like bliss. It can also feel like exclusion. From a closed place, being in the know becomes about control. From an open place, it can become a source of connection. The ability to link ideas, introduce people, and catalyse collaboration. Knowing What's Best Another response to uncertainty is doubling down on certainty. We are pattern-seeking creatures. We build cognitive maps to navigate a complex world. But when ambiguity feels overwhelming, certainty can feel like solid ground, even if it's forged, manufactured, and brittle. Closedness says “this is how it is”, refuses nuance, and punishes curiosity and accountability as disrespect, insolence, and rudeness. Open wisdom looks different. It sits shoulder to shoulder, acknowledges nuance, and is willing to say, “I don't know the best thing to do here.” Admitting one does not know can be a radical act in cultures that equate doubt with weakness and desperately seek a way to explain and understand everything, even without empirical evidence. Knowing That We Don't Know In a 1933 essay lamenting the rise of the Nazi movement in Germany, Bertrand Russell wrote, “The fundamental cause of the trouble is that in the modern world the stupid are cocksure, while the intelligent are full of doubt.” Charles Bukowski said something similar when giving advice to budding writers: “But the problem is that bad writers tend to have the self-confidence, while the good ones tend to have self-doubt.“ These quotes highlight the importance of knowing what we do not know — and recognising the limits of our own perspective. This took us to a detour into the Dunning–Kruger effect, which is the idea that we can speak confidently about subjects precisely because we don't yet know what we don't know. Reading Maps and Navigating Life “I don't know, but I am aware of where to look to figure it out.” In The Return To Serenity Island course, we map elements of life, seeing it as a treasure laden island. Not knowing is a door to connection, curiosity, creativity, and exploration. But it can also feel disorienting, confusing, and alienating at times. Maps help disorientation become orientation-in-progress without strict instructions or someone else’s path to follow. They can bring us home to ourselves.

Thousands are dead, Iran’s economy is collapsing, and the nation is on the brink of unrest, yet Dr. Hormoz Shariat says God is moving powerfully behind the scenes. In this urgent conversation, Sean sits down again with the “Billy Graham of Iran” to hear how many Iranians are rejecting Islam, turning to Christ, and how the underground church is growing amid persecution and chaos. This is a rare glimpse into both the darkness and the spiritual awakening unfolding in Iran right now. WATCH: Why Iranian Muslims are Turing to Jesus (https://youtu.be/_tdPBR7i7rw) *Get a MASTERS IN APOLOGETICS or SCIENCE AND RELIGION at BIOLA (https://bit.ly/3LdNqKf) *USE Discount Code [smdcertdisc] for 25% off the BIOLA APOLOGETICS CERTIFICATE program (https://bit.ly/3AzfPFM) *See our fully online UNDERGRAD DEGREE in Bible, Theology, and Apologetics: (https://bit.ly/448STKK) FOLLOW ME ON SOCIAL MEDIA: Twitter: https://x.com/Sean_McDowell TikTok: https://www.tiktok.com/@sean_mcdowell?lang=en Instagram: https://www.instagram.com/seanmcdowell/ Website: https://seanmcdowell.org Discover more Christian podcasts at lifeaudio.com and inquire about advertising opportunities at lifeaudio.com/contact-us.

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

Jonathan Siddharth is the founder and CEO of Turing, a $2.2 billion AI company that provides coding and reasoning data to train frontier models for OpenAI, Google, Meta, Anthropic and more. Turing's mission is to accelerate superintelligence to drive economic growth. In this episode of World of DaaS, Jonathan and Auren discuss:How Turing creates expert data for frontier modelsWhy SaaS is dying in the age of AI agentsDisrupting the $30 trillion market for digital knowledge workBuilding a stage five company cultureYou can find Auren Hoffman on X at @auren and Jonathan Siddharth on X at @jonsidd.Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com)

...we Tossed our way into oblivion with the exciting new show Pluribus in this one.Hello audient!The new show Pluribus comes at us with all the niceties, trappings and plot pitfalls  to be found in our rot-attacked brains. While the first Season leaves many more questions than answers, leaving large holes in the logic and the story, it makes for a perfect Tossers episode; a real conceptual skeet-shooting playground.Pluribus deals with an alien(?) hive mind that has taken over every person on Earth except for very few who had an "incompatible" genetic material. The fact that this hive speaks and behaves in an almost one-to-one parody on the "personality" of ChatGPT in our increasingly compartmentalized realities and interactions with one-another (like in Social Media) makes the connection to AI-related issues almost inescapable.We'll mention here just one particularly interesting toss we cam across, concerning the 'body' (the 'animal' part of Aristotle's famous definition of the "Human" as 'a talking animal'). It juts out of the narrative like a sore thumb, like an unmourned loss: what does it mean for the main protagonist to "fall in love" with a body from the hive mind? How does a hive mind approach real issues of attachment, like pain and discord, as they arise through the "affair" with the protagonist? What is the role - within our attachments - of the body's memories, its unique history, when it is "pluribussed" like that?This also connected to a recurring theme in the Tossers' arsenal, the ethical imperative we inherited from our Derrida(ddy), the one we express as "Never let the dead bury the dead." As the bodies of the entire world's population become an indistinct mass grave, a Frankenstein-monster-cum-Turing-machine, it becomes both overly relevant and no longer relevant: when the dead are recycled into food for the "hive-people" and yet you can fall in love with a person whose body is completely controlled by the "everyone" of the hive, including going and sleeping with another "survivor" (of the assimilation) while expressing love to our protagonist.Confused? We hope so. But it is a rather productive confusion, we believe, in our day and age. Rather appropriately, all Stars made an appearance here in one way or another.The rest will be told to our one, loyal (and virtual!) audient, and her flaming lips...

Scott and Wes sit down with Dimitri Metropolis to explore the wild edges of TypeScript—from running Doom in the type system to building tools like Typeslayer. They dig into Turing-complete types, performance limits, and what the future might hold for TypeScript and programming languages as a whole. Show Notes 00:00 Welcome to Syntax! 00:27 Dimitri Metropolis Introduction 01:29 What is Doom in TypeScript? 03:10 TypeScript Types and Turing Completeness 04:06 Project Overview and Challenges 04:57 ASCII Art and Visual Representation 06:50 Performance Issues with TypeScript 09:27 Brought to you by Sentry.io 09:51 Typeslayer Tool Introduction 16:19 Building in Tauri 20:54 Challenges around packaging 24:03 Future of TypeScript and AI 27:40 Is the Go-based compiler significantly faster? TSperf 30:23 Should there be something to follow Typescript? 36:27 Staying up to date with WASM. 37:08 SquiggleConf Overview 38:26 Hosting a conference 40:45 What are your thoughts on Zig? 45:07 Vibe coding as an end goal 50:01 Sick Picks & Shameless Plugs Sick Picks Dimitri: pullfrog Shameless Plugs Dimitri: Michigan TypeScript on YouTube Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Lords: * Erica * Micah * https://www.reddit.com/r/micahwrites/ Topics: * Puerto Rico branded holiday jams * Chive drama on Reddit * https://www.reddit.com/r/KitchenConfidential/comments/1o0j6hq/cuttingacupofchiveseverydayuntilthereddit/ * How to cure tinnitus (maybe) * The Ballad of Blasphemous Bill * https://www.poetryfoundation.org/poems/46647/the-ballad-of-blasphemous-bill * In defense of making movies sequels until they're good again Microtopics: * Being finished with horrible shit. * Being in the middle of things forever. * The Minutes of the Intermittent Meetings of the Society of Apocryphal Gentlefolk, by Dark Art * Coming down from the high of PiCoSteveMo. * Explaining PiCoSteveMo to someone like it's their first time at Rocky Horror Picture Show. * Two things I'm willing to sacrifice to play PiCoSteveMo games. * Putting your PICO-8 game in a CRT filter. * Lawnmower Man, based on the title by Stephen King. * Developing a field system in Puerto Rico. * Winston's face appearing to the extent that Zoom thinks it's part of my chest. * A deafening hospital siren playing while you're trying to have a good time at the beach. * Pirate-themed massage. * Stealing the windsurfing gear and going for a ride. * Walking past the site of a pirate massage and fatal accident holding a solo cup. * Getting pushed off the road by seven full-sized Coca Cola trucks led by a Santa Sleigh and followed by a party truck with a giant octagonal speaker spreading holiday cheer. * Charging more for a well-traveled Coca Cola. * Holiday-Branded Traffic Jams. * Shipping your worst wine to India and it turns out that the sea voyage turns it into your best wine. * Spanish Milk. * Visiting Puerto Rico during linear time. * The Puerto Rican version of Sleep No More in which Bad Bunny might pull you into a dark corner for a one-on-one and it's not clear whether he works for the event or if he's just another attendee. * Day 57 of chopping chives on Reddit. * Drawing airplanes crashing into the chives that are too long. * Working with (and living with) the Chive Lord. * Comparing Day 1 chives with day 55 chives. * Finding Yoshi in a pile of chopped chives. * A job that exists. (But not one you get paid for.) * Asking the robot to add heart shapes to your food processor chives. * These are the Days of our Chives. * Each Sale I Drink a Glass of Water. * Self-hosting memes and Turing-complete memes. * Phase canceling your tinnitus. * Not wanting to look it up because then you'd know. * Curing tinnitus with extremely specific grenades. * A party where everyone is constantly singing their personal tinnitus tones. * Why don't we get bass tinnitus? * Can you cure bass tinnitus with snail caviar? * The native word for white people who are doing poorly in Alaska. * The ice worms wriggling their purple heads through the crust of the pale blue snow. * Pine trees cracking like little guns in the silence of the wood. * Prankster Bill dying with his arms and legs outstretched so that he won't fit in his coffin. * Poems that demand to be performed with a banjo. * Whether they have banjos in Alaska. * Having fun with the way words sound. * I'm not gonna make it – but I can be an X shape. * The Cremation of Sam McGee. * Burning your house down to get the insurance money to buy a telescope. * Making bad art until it becomes good. * Tremors 3: Back to Perfection. * A giant worm monster drilling up out of the ground in order to slice chives. * Really dwelling on how much you don't have in common with other people. * Six topics (and Shrieker Island) * A dollmaker on the run after making farcical plays about Hitler. * The Saved by the Bell themed music video featuring the same actors as the Final Destination movie it's promoting. * Would you take 90 minutes off of your life to have not seen Final Destination 4? * The replacement for the 1 to 10 pain scale where you decide which Final Destination movie you'd be willing to watch to take the pain away. * Low pain awareness. * Chess boxing win/loss ratios.

In this episode of the Crazy Wisdom podcast, host Stewart Alsop sits down with Josh Halliday, who works on training super intelligence with frontier data at Turing. The conversation explores the fascinating world of reinforcement learning (RL) environments, synthetic data generation, and the crucial role of high-quality human expertise in AI training. Josh shares insights from his years working at Unity Technologies building simulated environments for everything from oil and gas safety scenarios to space debris detection, and discusses how the field has evolved from quantity-focused data collection to specialized, expert-verified training data that's becoming the key bottleneck in AI development. They also touch on the philosophical implications of our increasing dependence on AI technology and the emerging job market around AI training and data acquisition.Timestamps00:00 Introduction to AI and Reinforcement Learning03:12 The Evolution of AI Training Data05:59 Gaming Engines and AI Development08:51 Virtual Reality and Robotics Training11:52 The Future of Robotics and AI Collaboration14:55 Building Applications with AI Tools17:57 The Philosophical Implications of AI20:49 Real-World Workflows and RL Environments26:35 The Impact of Technology on Human Cognition28:36 Cultural Resistance to AI and Data Collection31:12 The Bottleneck of High-Quality Data in AI32:57 Philosophical Perspectives on Data35:43 The Future of AI Training and Human Collaboration39:09 The Role of Subject Matter Experts in Data Quality43:20 The Evolution of Work in the Age of AI46:48 Convergence of AI and Human ExperienceKey Insights1. Reinforcement Learning environments are sophisticated simulations that replicate real-world enterprise workflows and applications. These environments serve as training grounds for AI agents by creating detailed replicas of tools like Salesforce, complete with specific tasks and verification systems. The agent attempts tasks, receives feedback on failures, and iterates until achieving consistent success rates, effectively learning through trial and error in a controlled digital environment.2. Gaming engines like Unity have evolved into powerful platforms for generating synthetic training data across diverse industries. From oil and gas companies needing hazardous scenario data to space intelligence firms tracking orbital debris, these real-time 3D engines with advanced physics can create high-fidelity simulations that capture edge cases too dangerous or expensive to collect in reality, bridging the gap where real-world data falls short.3. The bottleneck in AI development has fundamentally shifted from data quantity to data quality. The industry has completely reversed course from the previous "scale at all costs" approach to focusing intensively on smaller, higher-quality datasets curated by subject matter experts. This represents a philosophical pivot toward precision over volume in training next-generation AI systems.4. Remote teleoperation through VR is creating a new global workforce for robotics training. Workers wearing VR headsets can remotely control humanoid robots across the globe, teaching them tasks through direct demonstration. This creates opportunities for distributed talent while generating the nuanced human behavioral data needed to train autonomous systems.5. Human expertise remains irreplaceable in the AI training pipeline despite advancing automation. Subject matter experts provide crucial qualitative insights that go beyond binary evaluations, offering the contextual "why" and "how" that transforms raw data into meaningful training material. The challenge lies in identifying, retaining, and properly incentivizing these specialists as demand intensifies.6. First-person perspective data collection represents the frontier of human-like AI training. Companies are now paying people to life-log their daily experiences, capturing petabytes of egocentric data to train models more similarly to how human children learn through constant environmental observation, rather than traditional batch-processing approaches.7. The convergence of simulation, robotics, and AI is creating unprecedented philosophical and practical challenges. As synthetic worlds become indistinguishable from reality and AI agents gain autonomy, we're entering a phase where the boundaries between digital and physical, human and artificial intelligence, become increasingly blurred, requiring careful consideration of dependency, agency, and the preservation of human capabilities.

Backtracking on your standard of living. What America 250 could have looked like with Dome at the helm. What happened with Dan leaving the FBI? Turing around a criminal organization. Communism is the religion of the malcontent wherever you go. Follow The Jesse Kelly Show on YouTube: https://www.youtube.com/@TheJesseKellyShowSee omnystudio.com/listener for privacy information.

✨《我想聽你說 2 Popcorn Talks 2》歡樂對話卡牌組全新推出

✨《我想聽你說 2 Popcorn Talks 2》歡樂對話卡牌組全新推出

Jonathan Siddharth is Founder and CEO of Turing, one of the fastest-growing AI companies advancing frontier models. Jonathan has led the company to an astonishing $350M ARR with just $225M raised and a profitable company. A Stanford-trained AI scientist, Jonathan previously helped pioneer natural language search at Powerset, which was acquired by Microsoft. AGENDA: 03:35 Data, Compute, Algorithms: What is Most Abundant? What is Lacking Most? 09:18 What Does No One Know About AI's Data Requirements That Everyone Should? 17:05 The Biggest Challenges Enterprises Have with AI Adoption 20:38 Why Will 99% of Knowledge Work Will be Gone in 10 Years 27:12 How Will Data-Driven Feedback Loops Replace Technology as the Moat 36:08 Who Wins the Data Labelling Market? Who Loses? 38:23 Is Revenue BS in Data Labelling? Are Players Calling GMV Revenue?  45:20 Why is SaaS Dead in a World of AI? 51:23 Will the Phone be the Primary User Interface to an AI World? 57:07 Quickfire Round    

Neo The Home Robot: The future is here! Or is it? A new AI robot dubbed NEO has been announced but is it just an Indian guy using VR to control him. Zoom Court Fails: A cop shows up to Zoom court with no pants. Are we going to just let this happen!? Andy Richter and Adrien Skye: Andy Richter survives another night during the Halloween episode! How far can the Fandy's go!? We also get an alert that the Adrien Skye listening party is going on! THE BEAR!, FUCK YOU, WATCH THIS!, MICHAEL JACKSON!, THRILLER!, HALLOWEEN!, THIRSTY THURSDAY!, THRILLER DANCE!, ZOMBIE MJ!, MEMORIES!, FAVORITE PART!, MAKING OF VHS!, RICK BAKER!, BEHIND THE SCENES!, COREYWEEN HANG!, HOME ROBOT!, TECHNOLOGY!, NEO!, PUPPET!, REMOTELY RUN!, INDIA!, MAID!, VR HEADSET!, GOGGLES!, SCARY ACCENT!, NORWEGIAN!, ROBOT ATTACKED!, EGG CHARGER!, GAY AS HELL!, GLEB!, FIRE A GUN!, VIOLENCE!, POSSESSOR!, ADS!, REDWOOD AI!, DR ANDY SKILONAKIS!, TURING!, ZOOM COURT FAILS!, DRAG RACING!, DISORDERLY!, BLUNT!, DANCING WITH THE STARS!, ANDY!, HALLOWEEN NIGHT!, STILL ALIVE!, FOG!, DANCING!, HIDE!, FANDY!, GO HOME!, MORMON WIVES!, BABY!, DANCE!, RSV!, HOSPITAL!, HALLOWEEN!, GOTHSPEL!, CIRCUS QUEEN!, PLASTIC STANDARDS!, VAMPIRES BALLAD!, I'M DOWN!, PITTS OF HELL!, BETTER NOW!, LIVE!, CLUB!, BAR!  You can find the videos from this episode at our Discord RIGHT HERE!