Uploaded February 2024 | Updated September 2026, 1 week ago
deeplearningtheory.com
(Daniel A. Roberts, Sho Yaida, Boris Hanin)
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Machine Learning Street Talk
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The Principles of Deep Learning Theory - Daniel A. Roberts Ph.D
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![The Universal Hierarchy of Life - Prof. Chris Kempes [SFI]](https://i.ytimg.com/vi/iwClZ-7OweY/mqdefault.jpg "The Universal Hierarchy of Life - Prof. Chris Kempes [SFI]
What is life? - asks Chris Kempes, a professor at the Santa Fe Institute.
Chris explains that scientists are moving beyond a purely Earth-based, biological view and are searching for a universal theory of life that could apply to anything, anywhere in the universe. He proposes that things we dont normally consider alive—like human culture, language, or even artificial intelligence; could be seen as life forms existing on different substrates.
To understand this, Chris presents a fascinating three-level framework:
- Materials: The physical stuff life is made of. He argues this could be incredibly diverse across the universe, and we shouldnt expect alien life to share our biochemistry.
- Constraints: The universal laws of physics (like gravity or diffusion) that all life must obey, regardless of what its made of. This is where different life forms start to look more similar.
- Principles: At the highest level are abstract principles like evolution and learning. Chris suggests these computational or optimization rules are what truly define a living system.
A key idea is convergence – using the example of the eye. Its such a complex organ that youd think it evolved only once. However, eyes evolved many separate times across different species. This is because the physics of light provides a clear target, and evolution found similar solutions to the problem of seeing, even with different starting materials.
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Prof. Chris Kempes:
https://www.santafe.edu/people/profile/chris-kempes
TRANSCRIPT:
https://app.rescript.info/public/share/Y2cI1i0nX iuZitvlguHvaVLQTwPX1Y_E1EHxV0i9I
TOC:
00:00:00 - Introduction to Chris Kempes and the Santa Fe Institute
00:02:28 - The Three Cultures of Science
00:05:08 - What Makes a Good Scientific Theory?
00:06:50 - The Universal Theory of Life
00:09:40 - The Role of Material in Life
00:12:50 - A Hierarchy for Understanding Life
00:13:55 - How Life Diversifies and Converges
00:17:53 - Adaptive Processes and Defining Life
00:19:28 - Functionalism, Memes, and Phylogenies
00:22:58 - Convergence at Multiple Levels
00:25:45 - The Possibility of Simulating Life
00:28:16 - Intelligence, Parasitism, and Spectrums of Life
00:32:39 - Phase Changes in Evolution
00:36:16 - The Separation of Matter and Logic
00:37:21 - Assembly Theory and Quantifying Complexity
REFS:
Developing a predictive science of the biosphere requires the integration of scientific cultures [Kempes et al]
https://www.pnas.org/doi/10.1073/pnas.2209196121
Seeing with an extra sense (“Dangerous prediction”) [Rob Phillips]
https://www.sciencedirect.com/science/article/pii/S0960982224009035
The Multiple Paths to Multiple Life [Christopher P. Kempes & David C. Krakauer]
https://link.springer.com/article/10.1007/s00239-021-10016-2
The Information Theory of Individuality [David Krakauer et al]
https://arxiv.org/abs/1412.2447
Minds, Brains and Programs [Searle]
https://home.csulb.edu/~cwallis/382/readings/482/searle.minds.brains.programs.bbs.1980.pdf
The error threshold
https://www.sciencedirect.com/science/article/abs/pii/S0168170204003843
Assembly theory and its relationship with computational complexity [Kempes et al]
https://arxiv.org/abs/2406.12176")
![Explosive AI Timeline Predictions [Gary Marcus, Daniel Kokotajlo, Dan Hendrycks]](https://i.ytimg.com/vi/j13ySJLvdOc/mqdefault.jpg "Explosive AI Timeline Predictions [Gary Marcus, Daniel Kokotajlo, Dan Hendrycks]
What if the most powerful technology in human history is being built by people who openly admit they dont trust each other? In this explosive 2-hour debate, three AI experts pull back the curtain on the shocking psychology driving the race to Artificial General Intelligence—and why the people building it might be the biggest threat of all. Kokotajlo predicts AGI by 2028 based on compute scaling trends. Marcus argues we havent solved basic cognitive problems from his 2001 research. The stakes? If Kokotajlo is right and Marcus is wrong about safety progress, humanity may have already lost control.
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Guest Powerhouse
Gary Marcus - Cognitive scientist, author of Taming Silicon Valley, and AIs most prominent skeptic whos been warning about the same fundamental problems for 25 years (https://garymarcus.substack.com/)
Daniel Kokotajlo - Former OpenAI insider turned whistleblower who reveals the disturbing rationalizations of AI lab leaders in his viral AI 2027 scenario (https://ai-2027.com/)
Dan Hendrycks - Director of the Center for AI Safety who created the benchmarks used to measure AI progress and argues we have only years, not decades, to prevent catastrophe (https://danhendrycks.com/)
Transcript: http://app.rescript.info/public/share/tEcx4UkToi-2jwS1cN51CW70A4Eh6QulBRxDILoXOno
TOC:
Introduction: The AI Arms Race
00:00:04 - The Danger of Automated AI R&D
00:00:43 - The Rationalization: If we dont, someone else will
00:01:56 - Sponsor Reads (Tufa AI Labs & Google Gemini)
00:02:55 - Guest Introductions
The Philosophical Stakes
00:04:13 - What is the Positive Vision for AGI?
00:07:00 - The Abundance Scenario: Superintelligent Economy
00:09:06 - Differentiating AGI and Superintelligence (ASI)
00:11:41 - Sam Altman: A Decade in a Month
00:14:47 - Economic Inequality & The UBI Problem
Policy and Red Lines
00:17:13 - The Pause Letter: Stopping vs. Delaying AI
00:20:03 - Defining Three Concrete Red Lines for AI Development
00:25:24 - Racing Towards Red Lines & The Myth of Durable Advantage
00:31:15 - Transparency and Public Perception
00:35:16 - The Rationalization Cascade: Why AI Labs Race to Win
Forecasting AGI: Timelines and Methodologies
00:42:29 - The Case for Short Timelines (Median 2028)
00:47:00 - Scaling Limits: Compute, Data, and Money
00:49:36 - Forecasting Models: Bio-Anchors and Agentic Coding
00:53:15 - The 10^45 FLOP Thought Experiment
The Great Debate: Cognitive Gaps vs. Scaling
00:58:41 - Gary Marcuss Counterpoint: The Unsolved Problems of Cognition
01:00:46 - Current AI Cant Play Chess Reliably
01:08:23 - Can Tools and Neurosymbolic AI Fill the Gaps?
01:16:13 - The Multi-Dimensional Nature of Intelligence
01:24:26 - The Benchmark Debate: Data Contamination and Reliability
01:31:15 - The Superhuman Coder Milestone Debate
01:37:45 - The Driverless Car Analogy
The Alignment Problem
01:39:45 - Has Any Progress Been Made on Alignment?
01:42:43 - Fairly Reasonably Scares the Sh*t Out of Me
01:46:30 - Distinguishing Model vs. Process Alignment
Scenarios and Conclusions
01:49:26 - Garys Alternative Scenario: The Neurosymbolic Shift
01:53:35 - Will AI Become Jeff Dean?
01:58:41 - Takeoff Speeds and Exceeding Human Intelligence
02:03:19 - Final Disagreements and Closing Remarks
REFS:
Gary Marcus (2001) - The Algebraic Mind
https://mitpress.mit.edu/9780262632683/the-algebraic-mind/
00:59:00
Gary Marcus & Ernest Davis (2019) - Rebooting AI
https://www.amazon.co.uk/Rebooting-AI-Building-Artificial-Intelligence-ebook/dp/B07MYLGQLB
01:31:59
Gary Marcus (2024) - Taming Silicon Valley
https://www.amazon.co.uk/Taming-Silicon-Valley-Ensure-Works-ebook/dp/B0CQWWM94N
00:03:01
Ajeya Cotra (2020) - Forecasting TAI with Biological Anchors
https://www.alignmentforum.org/posts/KrJfoZzpSDpnrv9va/draft-report-on-ai-timelines
00:53:15
Daniel Kokotajlo (2021) - What 2026 looks like
https://www.lesswrong.com/posts/6Xgy6CAf2jqHhynHL/what-2026-looks-like
00:55:00
Dan Hendrycks et al. (2021) - Measuring Massive Multitask Language Understanding (MMLU)
https://arxiv.org/abs/2009.03300
00:03:48
Dan Hendrycks et al. (2021) - Measuring Mathematical Problem Solving With the MATH Dataset
https://arxiv.org/abs/2103.03874
00:03:48
Aitor Lewkowycz, Anders Andreassen et al. (2022) - Solving Quantitative Reasoning Problems with Language Models (Minerva)
https://arxiv.org/abs/2206.14858
01:21:45
Apple Research (2024) - GSM-Symbolic/The Illusion of Thinking
https://arxiv.org/abs/2410.05229
https://machinelearning.apple.com/research/illusion-of-thinking
00:59:15")
![Can Outsourcing Thinking Make Us Dumber? [Prof. David Krakauer]](https://i.ytimg.com/vi/jXa8dHzgV8U/mqdefault.jpg "Can Outsourcing Thinking Make Us Dumber? [Prof. David Krakauer]
Prof. David Krakauer, President of the Santa Fe Institute argues that we are fundamentally confusing knowledge with intelligence, especially when it comes to AI.
He defines true intelligence as the ability to do more with less—to solve novel problems with limited information. This is contrasted with current AI models, which he describes as doing less with more; they require astounding amounts of data to perform tasks that dont necessarily demonstrate true understanding or adaptation. He humorously calls this really shit programming.
David challenges the popular notion of emergence in Large Language Models (LLMs). He explains that the tech communitys definition—seeing a sudden jump in a models ability to perform a task like three-digit math—is superficial. True emergence, from a complex systems perspective, involves a fundamental change in the systems internal organization, allowing for a new, simpler, and more powerful level of description. He gives the example of moving from tracking individual water molecules to using the elegant laws of fluid dynamics. For LLMs to be truly emergent, wed need to see them develop new, efficient internal representations, not just get better at memorizing patterns as they scale.
Drawing on his background in evolutionary theory, David explains that systems like brains, and later, culture, evolved to process information that changes too quickly for genetic evolution to keep up. He calls culture evolution at light speed because it allows us to store our accumulated knowledge externally (in books, tools, etc.) and build upon it without corrupting the original.
This leads to his concept of exbodiment, where we outsource our cognitive load to the world through things like maps, abacuses, or even language itself.
We create these external tools, internalize the skills they teach us, improve them, and create a feedback loop that enhances our collective intelligence.
However, he ends with a warning. While technology has historically complemented our deficient abilities, modern AI presents a new danger. Because we have an evolutionary drive to conserve energy, we will inevitably outsource our thinking to AI if we can. He fears this is already leading to a diminution and dilution of human thought and creativity. Just as our muscles atrophy without use, he argues our brains will too, and we risk becoming mentally dependent on these systems.
RESCRIPT LINK (interactive transcript):
https://app.rescript.info/public/share/nCL8fdE_m3J6fA3SHlreCADpkWpbXaEF1JQ14z6N7Y8
TOC:
[00:00:00] Intelligence: Doing more with less
[00:02:10] Why brains evolved: The limits of evolution
[00:05:18] Culture as evolution at light speed
[00:08:11] True meaning of emergence: More is Different
[00:10:41] Why LLM capabilities are not true emergence
[00:15:10] What real emergence would look like in AI
[00:19:24] Symmetry breaking: Physics vs. Life
[00:23:30] Two types of emergence: Knowledge In vs. Out
[00:26:46] Causality, agency, and coarse-graining
[00:32:24] Exbodiment: Outsourcing thought to objects
[00:35:05] Collective intelligence & the boundary of the mind
[00:39:45] Mortal vs. Immortal forms of computation
[00:42:13] The risk of AI: Atrophy of human thought
David Krakauer
President and William H. Miller Professor of Complex Systems
https://www.santafe.edu/people/profile/david-krakauer
REFS:
Large Language Models and Emergence: A Complex Systems Perspective
David C. Krakauer, John W. Krakauer, Melanie Mitchell
https://arxiv.org/abs/2506.11135
Filmed at the Diverse Intelligences Summer Institute:
https://disi.org/")
![AI HAS A BODY PROBLEM... [Dr. Maxwell Ramstead]](https://i.ytimg.com/vi/jsIt2sTB_vo/mqdefault.jpg "AI HAS A BODY PROBLEM... [Dr. Maxwell Ramstead]
This episode features Dr. Maxwell Ramstead and Jason Fox both from Noumenal discussing why current AI approaches fall short for real-world applications and whats needed for true physical AI.
The guests argue that todays AI systems, including large language models, are fundamentally stuck in data space - they only process patterns in data rather than understanding the physical world that generates that data.
Maxwell uses Platos Cave as a powerful metaphor: like prisoners seeing only shadows on a wall, LLMs interact with representations of reality (text, images) rather than reality itself.
Rather than building monolithic models, Noumenal is creating a compositional system - essentially a marketplace of models where specialized AI components can be dynamically combined and deployed to robots.
Sponsor messages:
Tufa AI Labs are hiring for ML Engineers and a Chief Scientist in Zurich/SF. They are top of the ARCv2 leaderboard!
https://tufalabs.ai/
TRANSCRIPT:
http://app.rescript.info/public/share/DVaCBhYF3Y-1Q2kA2N5-YkV4kOGBNZc5KLrDwntLznU
https://www.noumenal.ai/
https://x.com/mjdramstead
https://scholar.google.ca/citations?user=ILpGOMkAAAAJ&hl=fr
https://x.com/jasongfox?lang=en-GB
TOC
Opening & Context
00:00:00 - Opening Hook: Why Create a Physical AI Company?
00:01:59 - Sponsor: Tufa AI Labs
00:02:30 - Guest Introductions: Maxwell Ramstead & Jason Fox
00:05:18 - Noumenal Background
Core Problems with Current AI
00:09:30 - The Embodiment Problem: Why Bodies Matter
00:10:15 - LLMs Lack Physical Grounding
00:12:00 - AI Stuck in Platos Cave
00:16:15 - Language as Wrong Compression for Physics
00:17:22 - The Exhaustion of Static Datasets
00:19:54 - Humans as the Grounding for LLMs
Philosophical Foundations
00:28:00 - Fractured vs. Deep Understanding
00:32:15 - Defining Real: When You Bump Into Things
00:37:00 - Emergence: Weak vs. Strong Causal Power
00:41:45 - The Free Energy Principle Explained
00:44:15 - Constraints: How the Universe Builds Things
Objects, Intelligence & Grounding
00:46:15 - What Is an Object? From Data to Physics
00:51:00 - Learning Primitives & Predictive Grip
00:55:58 - There Is No General Intelligence
01:00:15 - The Human-AI Feedback Loop
01:03:08 - The Irony of LLM Specialization
01:06:05 - LLMs as Tools vs. Autonomous Agents
01:08:45 - Hallucinating Capabilities: The Third Leg Problem
The Noumenal Solution
01:09:00 - A Marketplace of Specialized Models
01:13:45 - Dynamic Skill Loading: Phone a Friend
01:16:15 - Learning from Brain Evolution
01:18:00 - Business Model Critique: Why OpenAI Wont Work
01:22:30 - The Physical Dataset Problem
Implementation & Future
01:22:30 - Community-Driven Data Collection
01:24:45 - Jim Fans Physical Turing Test
01:26:30 - Enterprise vs. Consumer Models
01:27:22 - Docker for Robotics: The Technical Architecture
01:30:12 - Reproducibility in Learning Systems
01:32:00 - Closing Thoughts")
