Uploaded June 2025 | Updated September 2026, 1 week ago
Watch the explosive 2-hour debate here on MLST. Three AI experts pull back the curtain on the shocking psychology driving the race to Artificial General Intelligence.
Watch the explosive 2-hour debate here on MLST. Three AI experts pull back the curtain on the shocking psychology driving the race to Artificial General Intelligence.

![Type a Sentence, Get a Playable 3D World in 3 Seconds - Shlomi Fuchter & Jack Parker-Holder
This episode features Shlomi Fuchter and Jack Parker Holder from Google DeepMind, who are unveiling a new AI called Genie 3. The host, Tim Scarfe, describes it as the most mind-blowing technology he has ever seen. We were invited to their offices to conduct the interview (not sponsored).
Imagine you could create a video game world just by describing it. Thats what Genie 3 does. Its an AI world model that learns how the real world works by watching massive amounts of video. Unlike a normal video game engine (like Unreal or the one for Doom) that needs to be programmed manually, Genie generates a realistic, interactive, 3D world from a simple text prompt.
**SPONSOR MESSAGES***
Prolific: Quality data. From real people. For faster breakthroughs.
https://prolific.com/mlst?utm_campaign=98404559-MLST&utm_source=youtube&utm_medium=podcast&utm_content=script-gen
***
Here’s a breakdown of what makes it so revolutionary:
From Text to a Virtual World: You can type a drone flying by a beautiful lake or a ski slope, and Genie 3 creates that world for you in about three seconds. You can then navigate and interact with it in real-time.
Its Consistent: The worlds it creates have a reliable memory. If you look away from an object and then look back, it will still be there, just as it was. The guests explain that this consistency isnt explicitly programmed in; its a surprising, emergent capability of the powerful AI model.
A Huge Leap Forward: The previous version, Genie 2, was a major step, but it wasnt fast enough for real-time interaction and was much lower resolution.
Genie 3 is 720p, interactive, and photorealistic, running smoothly for several minutes at a time.
The Killer App - Training Robots: Beyond entertainment, the team sees Genie 3 as a game-changer for training AI. Instead of training a self-driving car or a robot in the real world (which is slow and dangerous), you can create infinite simulations. You can even prompt rare events to happen, like a deer running across the road, to teach an AI how to handle unexpected situations safely.
The Future of Entertainment: this could lead to a YouTube version 2 or a new form of VR, where users can create and explore endless, interconnected worlds together, like the experience machine from philosophy.
While the technology is still a research prototype and not yet available to the public, it represents a monumental step towards creating true artificial worlds from the ground up.
Jack Parker Holder [Research Scientist at Google DeepMind in the Open-Endedness Team]
https://jparkerholder.github.io/
Shlomi Fruchter [Research Director, Google DeepMind]
https://shlomifruchter.github.io/
TOC:
[00:00:00] - Introduction: The Most Mind-Blowing Technology Ive Ever Seen
[00:02:30] - The Evolution from Genie 1 to Genie 2
[00:04:30] - Enter Genie 3: Photorealistic, Interactive Worlds from Text
[00:07:00] - Promptable World Events & Training Self-Driving Cars
[00:14:21] - Guest Introductions: Shlomi Fuchter & Jack Parker Holder
[00:15:08] - Core Concepts: What is a World Model?
[00:19:30] - The Challenge of Consistency in a Generated World
[00:21:15] - Context: The Neural Network Doom Simulation
[00:25:25] - How Do You Measure the Quality of a World Model?
[00:28:09] - The Vision: Using Genie to Train Advanced Robots
[00:32:21] - Open-Endedness: Human Skill and Prompting Creativity
[00:38:15] - The Future: Is This the Next YouTube or VR?
[00:42:18] - The Next Step: Multi-Agent Simulations
[00:52:51] - Limitations: Thinking, Computation, and the Sim-to-Real Gap
[00:58:07] - Conclusion & The Future of Game Engines
REFS:
World Models [David Ha, Jürgen Schmidhuber]
https://arxiv.org/abs/1803.10122
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions [Rui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley]
https://arxiv.org/abs/1901.01753
Questioning Representational Optimism in Deep Learning [Akarsh Kumar, Jeff Clune, Joel Lehman, Kenneth O. Stanley]
The Fractured Entangled Representation Hypothesis
https://arxiv.org/pdf/2505.11581
TRANSCRIPT:
https://app.rescript.info/public/share/Zk5tZXk6mb06yYOFh6nSja7Lg6_qZkgkuXQ-kl5AJqM Type a Sentence, Get a Playable 3D World in 3 Seconds - Shlomi Fuchter & Jack Parker-Holder](https://i.ytimg.com/vi/ekgvWeHidJs/mqdefault.jpg)
![SCHMIDHUBER: HOW WE WILL LIVE WITH AIs
SPONSOR MESSAGES:
***
CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments.
https://centml.ai/pricing/
Juergen Schmidhuber — the man behind LSTMs, the 1991 linear transformer, the first generative adversarial networks, and artificial curiosity — sits down with Tim for a wide-ranging conversation about where AI came from, where it is going, and what it means for humanity.
This is the long-awaited second part of their interview, and Schmidhuber wastes no time. He opens with a provocation: the most influential invention of the twentieth century was not the transistor or the internet, but the Haber-Bosch process for synthesising fertiliser — the thing that made half of humanity possible. The twenty-first centurys equivalent, he argues, will be true artificial intelligence.
From there, the conversation traces the lineage of modern AI through Schmidhubers own work: the 1991 linear transformer (which scales linearly rather than quadratically), fast weight controllers, predictive coding, neural network distillation, and the GPU-powered deep learning revolution that his DanNet system helped ignite. He reflects on the hardware lottery, Nvidias rise, and why all of todays breakthroughs rest on algorithms invented in the previous millennium.
The middle section covers consciousness (modelled as a chunker-automatiser system), the path from AGI-as-tool to AGI-with-goals, the concept of Homo Ludens, and the geopolitics of the AI race between Europe, the US, and China. Schmidhuber then offers his characteristically optimistic take on existential risk: superintelligent AIs will be curious scientists fascinated by life, not terminators. He connects this to the Fermi paradox, speculating that Earth may be the first planet in our light cone to spawn an expanding AI bubble — a thought that carries, as he puts it, a lot of responsibility.
REFERENCES:
paper:
[00:05:45] The 1991 Linear Transformer (Unnormalized Fast Weight Controller)
https://people.idsia.ch/~juergen/FKI-147-91ocr.pdf
[00:11:00] Formal Theory of Creativity, Fun, and Intrinsic Motivation
https://arxiv.org/abs/0812.4360
[00:15:57] The Hardware Lottery
https://arxiv.org/abs/2009.06489
reference:
[00:25:00] NNAISENSE - AI for the Physical World
https://nnaisense.com/
book:
[00:35:00] Homo Ludens
https://en.wikipedia.org/wiki/Homo_Ludens
LINKS:
Full Transcript: https://app.rescript.info/share/ab733cfc4ca23500252b59b180c71f4a
Download PDF transcript: https://app.rescript.info/api/public/sessions/e20de2659d4ca617/pdf SCHMIDHUBER: HOW WE WILL LIVE WITH AIs](https://i.ytimg.com/vi/fZYUqICYCAk/mqdefault.jpg)
![An Astrophysicist Debunks the Singularity — Adam Becker
Astrophysicist Adam Becker, author of What Is Real?, joins Tim Scarfe to take apart the futures Silicon Valley keeps selling: the 2045 singularity, mind uploading, Mars colonies, and the AI apocalypse. His new book *More Everything Forever* argues these ideas are hugely influential, mostly evidence-free, and bankrolled by tech billionaires who need a story in which growth never ends.
Becker does the physics the boosters skip. Kurzweils law of accelerating returns rests on cherry-picked data, and every exponential ends. Grant Bezos his perpetual energy growth and humanity boils the oceans within a few centuries, then exhausts the observable universe in under 4,000 years. The stars are too far away, Mars dirt is poison, and the day the dinosaur-killing asteroid hit Earth was still nicer than any day on Mars. On AI, Becker calls LLMs pocket calculators for language: hallucination is the model doing exactly what it always does, and the intelligence explosion assumes intelligence is a single number you can buy with compute.
The sting is that Becker thinks the doomers are sincere. Yudkowsky, Bostrom and the effective altruists are not grifters, he says, just wrong, and their warnings that AI could end the world feed the same growth story the money depends on. He closes with his own prescription: take social problems seriously, regulate the whole tech industry, and tax billionaires out of existence.
TIMESTAMPS:
00:00:00 Cold open and the thesis of More Everything Forever
00:04:24 Kurzweils singularity and the physical limits of exponential growth
00:14:02 High agency and the fantasy of imprinting humanity on the cosmos
00:16:55 Mind uploading, functionalism, and embodied cognition
00:24:24 AI psychosis and anthropomorphizing LLMs
00:26:24 Calculators, hallucination, and the limits of scale
00:32:20 Yudkowsky and the intelligence-explosion argument
00:40:37 True believers, venture capital, and the sci-fi growth narrative
00:47:21 From Extropians to EA: utilitarianism and longtermism
00:53:50 Brain worms and Beckers prescription: take social science seriously
00:56:49 Why the AI-ethics discourse is broken
01:01:42 The eugenics and IQ argument against intelligence
01:06:07 Why space settlement fails: Mars, the moon, and orbital data centers
01:10:42 Billionaire myths and the search for purpose
01:13:38 Tax billionaires, regulate tech: closing prescriptions
REFERENCES:
book:
[00:00:07] More Everything Forever (Adam Becker, 2025)
https://www.hachettebookgroup.com/titles/adam-becker/more-everything-forever/9781541619593/
[00:00:15] What Is Real? (Adam Becker, 2018)
https://en.wikipedia.org/wiki/What_Is_Real%3F
[00:15:46] What We Owe the Future (Will MacAskill, 2022)
https://www.hachettebookgroup.com/titles/william-macaskill/what-we-owe-the-future/9781541618626/
other:
[00:00:27] Dreaming Against the Machine (podcast)
https://www.dreamingagainstthemachine.com
[00:01:04] The Useful Idiots of AI Doomsaying (Adam Becker, The Atlantic, 2025)
https://www.theatlantic.com/books/archive/2025/09/what-ais-doomers-and-utopians-have-in-common/684270/
concept:
[00:14:03] Agency (philosophy)
https://en.wikipedia.org/wiki/Agency_(philosophy)
[00:18:08] Embodied cognition
https://plato.stanford.edu/entries/embodied-cognition/
[00:19:06] Functionalism
https://plato.stanford.edu/entries/functionalism/
[00:20:46] Good regulator theorem
https://en.wikipedia.org/wiki/Good_regulator
[00:33:35] Intelligence explosion
https://en.wikipedia.org/wiki/Intelligence_explosion
[00:34:14] Instrumental convergence
https://en.wikipedia.org/wiki/Instrumental_convergence
[00:36:26] Orthogonality thesis
https://en.wikipedia.org/wiki/Orthogonality_thesis
[00:38:06] Intentional stance
https://en.wikipedia.org/wiki/Intentional_stance
[00:44:23] Effective altruism
https://en.wikipedia.org/wiki/Effective_altruism
[00:47:32] Extropianism
https://en.wikipedia.org/wiki/Extropianism
[00:49:50] Utilitarianism
https://en.wikipedia.org/wiki/Utilitarianism
[00:52:15] Longtermism
https://en.wikipedia.org/wiki/Longtermism
[00:56:02] Human biodiversity (HBD)
https://en.wikipedia.org/wiki/Human_biodiversity
[01:05:00] Recursive self-improvement
https://en.wikipedia.org/wiki/Recursive_self-improvement
[01:09:25] Speed of light
https://en.wikipedia.org/wiki/Speed_of_light
person:
[00:04:49] Ray Kurzweil
https://en.wikipedia.org/wiki/Ray_Kurzweil
[00:15:42] Will MacAskill
https://en.wikipedia.org/wiki/William_MacAskill
[00:17:37] Adrian Daub
https://en.wikipedia.org/wiki/Adrian_Daub
[00:26:05] Shannon Vallor — The AI Mirror
https://en.wikipedia.org/wiki/Shannon_Vallor
[00:33:05] Eliezer Yudkowsky
https://en.wikipedia.org/wiki/Eliezer_Yudkowsky
[00:35:21] Nick Bostrom
https://en.wikipedia.org/wiki/Nick_Bostrom
[00:50:22] Peter Singer
https://en.wikipedia.org/wiki/Peter_Singer
[00:57:45] Timnit Gebru
https://en.wikipedia.org/wiki/Timnit_Gebru
[01:04:53] I. J. Good
https://en.wikipedia.org/wiki/I._J._Good An Astrophysicist Debunks the Singularity — Adam Becker](https://i.ytimg.com/vi/fgsmq8f3sWQ/mqdefault.jpg)


![Why Frontier AI Labs Fight to Hide Chain of Thought — Ilia Shumailov & Alexander Panfilov
Tim Scarfe speaks with Ilia Shumailov and Alexander Panfilov about their paper, Stealing Reasoning Traces from Proprietary LLM APIs.
The core bug sounds deceptively simple: providers return encrypted reasoning state so conversations can be resumed or forked. But those blobs can be replayed across users and sibling models. A smaller model can ask the provider to decrypt the trace, then repeat the hidden reasoning in plain text. The discussion covers leaked private data, a broadly reusable jailbreak, poisoned agent traces, chain-of-thought monitoring, responsible disclosure, and possible defenses.
Ilia Shumailov is an AI and security researcher, formerly at Google DeepMind, who completed his Cambridge PhD under Ross Anderson. Alexander Panfilov is a PhD researcher at the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems, working on AI safety, adversarial machine learning, and LLM red-teaming. They close by separating the demonstrated jailbreaking threat from ordinary benign distillation, and by arguing for controlled experiments over sweeping claims.
TIMESTAMPS:
00:00:00 Intro montage
00:01:33 Portable encrypted thought and decoded reasoning
00:24:55 How the attack works and what it means
00:39:04 Doom, defense, and scientific restraint
REFERENCES:
paper:
[00:00:00] Stealing Reasoning Traces from Proprietary LLM APIs
https://arxiv.org/abs/2608.09867
[00:09:22] Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
https://arxiv.org/abs/2507.11473
[00:11:30] Reasoning Models Don’t Always Say What They Think
https://www.anthropic.com/research/reasoning-models-dont-say-think
[00:37:22] PostTrainBench: Can LLM Agents Automate LLM Post-Training?
https://arxiv.org/abs/2603.08640
[00:41:02] Large-scale online deanonymization with LLMs
https://arxiv.org/abs/2602.16800
other:
[00:09:28] OpenAI and Hugging Face partner to address security incident during model evaluation
https://openai.com/index/hugging-face-model-evaluation-security-incident/
[00:10:22] Claude, GPT, and Gemini All Struggle to Evade Monitors
https://metr.org/notes/2025-08-22-claude-gpt-gemini-struggle-evade-monitors/
tool:
[00:42:08] Isabelle proof assistant
https://isabelle.in.tum.de/
RESCRIPT:
https://app.rescript.info/share/07fc38276e0823dc9b8986c32e202c7f Why Frontier AI Labs Fight to Hide Chain of Thought — Ilia Shumailov & Alexander Panfilov](https://i.ytimg.com/vi/gasgivVCl2U/mqdefault.jpg)


![The Hidden Math Behind All Living Systems
Dr. Sanjeev Namjoshi, a machine learning engineer who recently submitted a book on Active Inference to MIT Press, discusses the theoretical foundations and practical applications of Active Inference, the Free Energy Principle (FEP), and Bayesian mechanics. He explains how these frameworks describe how biological and artificial systems maintain stability by minimizing uncertainty about their environment.
Namjoshi traces the evolution of these fields from early 2000s neuroscience research to current developments, highlighting how Active Inference provides a unified framework for perception and action through variational free energy minimization. He contrasts this with traditional machine learning approaches, emphasizing Active Inferences natural capacity for exploration and curiosity through epistemic value.
The discussion covers key technical concepts like Markov blankets,
generative models, and the distinction between continuous and discrete implementations. Namjoshi explains how Active Inference moved from continuous state-space models (2003-2013) to discrete formulations (2015-present) to better handle planning problems.
He sees Active Inference as being at a similar stage to deep learning in the early 2000s - poised for significant breakthroughs but requiring better tools and wider adoption. While acknowledging current computational challenges, he emphasizes Active Inferences potential advantages over reinforcement learning, particularly its principled approach to exploration and planning.
Namjoshi advocates for balanced oversight that enables innovation while maintaining appropriate safeguards. He expresses particular concern about the rapid pace of AI development potentially outpacing our understanding of risks and regulatory frameworks.
Dr. Sanjeev Namjoshi
https://snamjoshi.github.io/
TOC:
1. Theoretical Foundations: AI Agency and Sentience
[00:00:00] 1.1 Intro
[00:04:30] 1.2 Free Energy Principle and Active Inference Theory
[00:11:16] 1.3 Emergence and Self-Organization in Complex Systems
[00:19:11] 1.4 Agency and Representation in AI Systems
[00:29:59] 1.5 Bayesian Mechanics and Systems Modeling
2. Technical Framework: Active Inference and Free Energy
[00:38:37] 2.1 Generative Processes and Agent-Environment Modeling
[00:42:27] 2.2 Markov Blankets and System Boundaries
[00:44:30] 2.3 Bayesian Inference and Prior Distributions
[00:52:41] 2.4 Variational Free Energy Minimization Framework
[00:55:07] 2.5 VFE Optimization Techniques: Generalized Filtering vs DEM
3. Implementation and Optimization Methods
[00:58:25] 3.1 Information Theory and Free Energy Concepts
[01:05:25] 3.2 Surprise Minimization and Action in Active Inference
[01:15:58] 3.3 Evolution of Active Inference Models: Continuous to Discrete Approaches
[01:26:00] 3.4 Uncertainty Reduction and Control Systems in Active Inference
4. Safety and Regulatory Frameworks
[01:32:40] 4.1 Historical Evolution of Risk Management and Predictive Systems
[01:36:12] 4.2 Agency and Reality: Philosophical Perspectives on Models
[01:39:20] 4.3 Limitations of Symbolic AI and Current System Design
[01:46:40] 4.4 AI Safety Regulation and Corporate Governance
5. Socioeconomic Integration and Modeling
[01:52:55] 5.1 Economic Policy and Public Sentiment Modeling
[01:55:21] 5.2 Free Energy Principle: Libertarian vs Collectivist Perspectives
[01:58:53] 5.3 Regulation of Complex Socio-Technical Systems
[02:03:04] 5.4 Evolution and Current State of Active Inference Research
6. Future Directions and Applications
[02:14:26] 6.1 Active Inference Applications and Future Development
[02:22:58] 6.2 Cultural Learning and Active Inference
[02:29:19] 6.3 Hierarchical Relationship Between FEP, Active Inference, and Bayesian Mechanics
[02:33:22] 6.4 Historical Evolution of Free Energy Principle
[02:38:52] 6.5 Active Inference vs Traditional Machine Learning Approaches
Transcript and shownotes with refs and URLs:
https://www.dropbox.com/scl/fi/qj22a660cob1795ej0gbw/SanjeevShow.pdf?rlkey=w323r3e8zfsnve22caayzb17k&st=el1fdgfr&dl=0
SELECTED REFS:
[0:02:45] Fristons original Free Energy Principle paper (Nature Reviews Neuroscience, 2010) - foundational text establishing FEP
[0:35:55] Schrödingers What is Life? (1944) - pioneering work connecting physics and biology
[0:44:30] Bayes Theorem - fundamental mathematical framework underlying probabilistic inference
[0:58:25] Shannons Mathematical Theory of Communication (1948) - established information theory
[1:18:20] Parr, Pezzulo & Fristons Active Inference (MIT Press) - comprehensive synthesis of the field
[1:24:05] Kahnemans Thinking, Fast and Slow - seminal work on dual-process theory of cognition
[1:25:55] Simons concept of satisficing - fundamental contribution to bounded rationality theory
[2:23:05] Dawkins The Selfish Gene (1976) - influential evolutionary theory perspective
[2:34:15] MacKays work on information theory and machine learning - bridged information theory and modern ML The Hidden Math Behind All Living Systems](https://i.ytimg.com/vi/hf18w6CuY8o/mqdefault.jpg)
