Machine Learning Street Talk (MLST)

This is a lively, no-holds-barred debate about whether AI can truly be intelligent, conscious, or understand anything at all — and what happens when (or if) machines become smarter than us.Dr. Mike Israetel is a sports scientist, entrepreneur, and co-founder of RP Strength (a fitness company). He describes himself as a "dilettante" in AI but brings a fascinating outsider's perspective.Jared Feather (IFBB Pro bodybuilder and exercise physiologist)The Big Questions:1. When is superintelligence coming?2. Does AI actually understand anything?3. The Simulation Debate (The Spiciest Part)4. Will AI kill us all? (The Doomer Debate)5. What happens to human jobs and purpose?6. Do we need suffering?Mikes channel: https://www.youtube.com/channel/UCfQgsKhHjSyRLOp9mnffqVgRESCRIPT INTERACTIVE PLAYER: https://app.rescript.info/public/share/GVMUXHCqctPkXH8WcYtufFG7FQcdJew_RL_MLgMKU1U---TIMESTAMPS:00:00:00 Introduction & Workout Demo00:04:15 ASI Timelines & Definitions00:10:24 The Embodiment Debate00:18:28 Neutrinos & Abstract Knowledge00:25:56 Can AI Learn From YouTube?00:31:25 Diversity of Intelligence00:36:00 AI Slop & Understanding00:45:18 The Simulation Argument: Fire & Water00:58:36 Consciousness & Zombies01:04:30 Do Reasoning Models Actually Reason?01:12:00 The Live Learning Problem01:19:15 Superintelligence & Benevolence01:28:59 What is True Agency?01:37:20 Game Theory & The "Kill All Humans" Fallacy01:48:05 Regulation & The China Factor01:55:52 Mind Uploading & The Future of Love02:04:41 Economics of ASI: Will We Be Useless?02:13:35 The Matrix & The Value of Suffering02:17:30 Transhumanism & Inequality02:21:28 Debrief: AI Medical Advice & Final Thoughts---REFERENCES:Paper:[00:10:45] Alchemy and Artificial Intelligence (Dreyfus)https://www.rand.org/content/dam/rand/pubs/papers/2006/P3244.pdf[00:10:55] The Chinese Room Argument (John Searle)https://home.csulb.edu/~cwallis/382/readings/482/searle.minds.brains.programs.bbs.1980.pdf[00:11:05] The Symbol Grounding Problem (Stephen Harnad)https://arxiv.org/html/cs/9906002[00:23:00] Attention Is All You Needhttps://arxiv.org/abs/1706.03762[00:45:00] GPT-4 Technical Reporthttps://arxiv.org/abs/2303.08774[01:45:00] Anthropic Agentic Misalignment Paperhttps://www.anthropic.com/research/agentic-misalignment[02:17:45] Retatrutidehttps://pubmed.ncbi.nlm.nih.gov/37366315/Organization:[00:15:50] CERNhttps://home.cern/[01:05:00] METR Long Horizon Evaluationshttps://evaluations.metr.org/MLST Episode:[00:23:10] MLST: Llion Jones - Inventors' Remorsehttps://www.youtube.com/watch?v=DtePicx_kFY[00:50:30] MLST: Blaise Agüera y Arcas Interviewhttps://www.youtube.com/watch?v=rMSEqJ_4EBk[01:10:00] MLST: David Krakauerhttps://www.youtube.com/watch?v=dY46YsGWMIcEvent:[00:23:40] ARC Prize/Challengehttps://arcprize.org/Book:[00:24:45] The Brain Abstractedhttps://www.amazon.com/Brain-Abstracted-Simplification-Philosophy-Neuroscience/dp/0262548046[00:47:55] Pamela McCorduckhttps://www.amazon.com/Machines-Who-Think-Artificial-Intelligence/dp/1568812051[01:23:15] The Singularity Is Nearer (Ray Kurzweil)https://www.amazon.com/Singularity-Nearer-Ray-Kurzweil-ebook/dp/B08Y6FYJVY[01:27:35] A Fire Upon The Deep (Vernor Vinge)https://www.amazon.com/Fire-Upon-Deep-S-F-MASTERWORKS-ebook/dp/B00AVUMIZE/[02:04:50] Deep Utopia (Nick Bostrom)https://www.amazon.com/Deep-Utopia-Meaning-Solved-World/dp/1646871642[02:05:00] Technofeudalism (Yanis Varoufakis)https://www.amazon.com/Technofeudalism-Killed-Capitalism-Yanis-Varoufakis/dp/1685891241Visual Context Needed:[00:29:40] AT-AT Walker (Star Wars)https://starwars.fandom.com/wiki/All_Terrain_Armored_TransportPerson:[00:33:15] Andrej Karpathyhttps://karpathy.ai/Video:[01:40:00] Mike Israetel vs Liron Shapira AI Doom Debatehttps://www.youtube.com/watch?v=RaDWSPMdM4oCompany:[02:26:30] Examine.comhttps://examine.com/

We often think of Large Language Models (LLMs) as all-knowing, but as the team reveals, they still struggle with the logic of a second-grader. Why can’t ChatGPT reliably add large numbers? Why does it "hallucinate" the laws of physics? The answer lies in the architecture. This episode explores how *Category Theory* —an ultra-abstract branch of mathematics—could provide the "Periodic Table" for neural networks, turning the "alchemy" of modern AI into a rigorous science.In this deep-dive exploration, *Andrew Dudzik*, *Petar Velichkovich*, *Taco Cohen*, *Bruno Gavranović*, and *Paul Lessard* join host *Tim Scarfe* to discuss the fundamental limitations of today’s AI and the radical mathematical framework that might fix them.TRANSCRIPT:https://app.rescript.info/public/share/LMreunA-BUpgP-2AkuEvxA7BAFuA-VJNAp2Ut4MkMWk---Key Insights in This Episode:* *The "Addition" Problem:* *Andrew Dudzik* explains why LLMs don't actually "know" math—they just recognize patterns. When you change a single digit in a long string of numbers, the pattern breaks because the model lacks the internal "machinery" to perform a simple carry operation.* *Beyond Alchemy:* deep learning is currently in its "alchemy" phase—we have powerful results, but we lack a unifying theory. Category Theory is proposed as the framework to move AI from trial-and-error to principled engineering. [00:13:49]* *Algebra with Colors:* To make Category Theory accessible, the guests use brilliant analogies—like thinking of matrices as *magnets with colors* that only snap together when the types match. This "partial compositionality" is the secret to building more complex internal reasoning. [00:09:17]* *Synthetic vs. Analytic Math:* *Paul Lessard* breaks down the philosophical shift needed in AI research: moving from "Analytic" math (what things are made of) to "Synthetic" math [00:23:41]---Why This Matters for AGIIf we want AI to solve the world's hardest scientific problems, it can't just be a "stochastic parrot." It needs to internalize the rules of logic and computation. By imbuing neural networks with categorical priors, researchers are attempting to build a future where AI doesn't just predict the next word—it understands the underlying structure of the universe.---TIMESTAMPS:00:00:00 The Failure of LLM Addition & Physics00:01:26 Tool Use vs Intrinsic Model Quality00:03:07 Efficiency Gains via Internalization00:04:28 Geometric Deep Learning & Equivariance00:07:05 Limitations of Group Theory00:09:17 Category Theory: Algebra with Colors00:11:25 The Systematic Guide of Lego-like Math00:13:49 The Alchemy Analogy & Unifying Theory00:15:33 Information Destruction & Reasoning00:18:00 Pathfinding & Monoids in Computation00:20:15 System 2 Reasoning & Error Awareness00:23:31 Analytic vs Synthetic Mathematics00:25:52 Morphisms & Weight Tying Basics00:26:48 2-Categories & Weight Sharing Theory00:28:55 Higher Categories & Emergence00:31:41 Compositionality & Recursive Folds00:34:05 Syntax vs Semantics in Network Design00:36:14 Homomorphisms & Multi-Sorted Syntax00:39:30 The Carrying Problem & Hopf FibrationsPetar Veličković (GDM)https://petar-v.com/Paul Lessardhttps://www.linkedin.com/in/paul-roy-lessard/Bruno Gavranovićhttps://www.brunogavranovic.com/Andrew Dudzik (GDM)https://www.linkedin.com/in/andrew-dudzik-222789142/---REFERENCES:Model:[00:01:05] Veohttps://deepmind.google/models/veo/[00:01:10] Geniehttps://deepmind.google/blog/genie-3-a-new-frontier-for-world-models/Paper:[00:04:30] Geometric Deep Learning Blueprinthttps://arxiv.org/abs/2104.13478https://www.youtube.com/watch?v=bIZB1hIJ4u8[00:16:45] AlphaGeometryhttps://arxiv.org/abs/2401.08312[00:16:55] AlphaCodehttps://arxiv.org/abs/2203.07814[00:17:05] FunSearchhttps://www.nature.com/articles/s41586-023-06924-6[00:37:00] Attention Is All You Needhttps://arxiv.org/abs/1706.03762[00:43:00] Categorical Deep Learninghttps://arxiv.org/abs/2402.15332

Is a car that wins a Formula 1 race the best choice for your morning commute? Probably not. In this sponsored deep dive with Prolific, we explore why the same logic applies to Artificial Intelligence. While models are currently shattering records on technical exams, they often fail the most important test of all: **the human experience.**Why High Benchmark Scores Don’t Mean Better AIJoining us are **Andrew Gordon** (Staff Researcher in Behavioral Science) and **Nora Petrova** (AI Researcher) from **Prolific**. They reveal the hidden flaws in how we currently rank AI and introduce a more rigorous, "humane" way to measure whether these models are actually helpful, safe, and relatable for real people.---Key Insights in This Episode:* *The F1 Car Analogy:* Andrew explains why a model that excels at the "Humanities Last Exam" might be a nightmare for daily use. Technical benchmarks often ignore the nuances of human communication and adaptability.* *The "Wild West" of AI Safety:* As users turn to AI for sensitive topics like mental health, Nora highlights the alarming lack of oversight and the "thin veneer" of safety training—citing recent controversial incidents like Grok-3’s "Mecha Hitler."* *Fixing the "Leaderboard Illusion":* The team critiques current popular rankings like Chatbot Arena, discussing how anonymous, unstratified voting can lead to biased results and how companies can "game" the system.* *The Xbox Secret to AI Ranking:* Discover how Prolific uses *TrueSkill*—the same algorithm Microsoft developed for Xbox Live matchmaking—to create a fairer, more statistically sound leaderboard for LLMs.* *The Personality Gap:* Early data from the **Humane Leaderboard** suggests that while AI is getting smarter, it is actually performing *worse* on metrics like personality, culture, and "sycophancy" (the tendency for models to become annoying "people-pleasers").---About the HUMAINE LeaderboardMoving beyond simple "A vs. B" testing, the researchers discuss their new framework that samples participants based on *census data* (Age, Ethnicity, Political Alignment). By using a representative sample of the general public rather than just tech enthusiasts, they are building a standard that reflects the values of the real world.*Are we building models for benchmarks, or are we building them for humans? It’s time to change the scoreboard.*Rescript link:https://app.rescript.info/public/share/IDqwjY9Q43S22qSgL5EkWGFymJwZ3SVxvrfpgHZLXQc---TIMESTAMPS:00:00:00 Introduction & The Benchmarking Problem00:01:58 The Fractured State of AI Evaluation00:03:54 AI Safety & Interpretability00:05:45 Bias in Chatbot Arena00:06:45 Prolific's Three Pillars Approach00:09:01 TrueSkill Ranking & Efficient Sampling00:12:04 Census-Based Representative Sampling00:13:00 Key Findings: Culture, Personality & Sycophancy---REFERENCES:Paper:[00:00:15] MMLUhttps://arxiv.org/abs/2009.03300[00:05:10] Constitutional AIhttps://arxiv.org/abs/2212.08073[00:06:45] The Leaderboard Illusionhttps://arxiv.org/abs/2504.20879[00:09:41] HUMAINE Framework Paperhttps://huggingface.co/blog/ProlificAI/humaine-frameworkCompany:[00:00:30] Prolifichttps://www.prolific.com[00:01:45] Chatbot Arenahttps://lmarena.ai/Person:[00:00:35] Andrew Gordonhttps://www.linkedin.com/in/andrew-gordon-03879919a/[00:00:45] Nora Petrovahttps://www.linkedin.com/in/nora-petrova/Event:Algorithm:[00:09:01] Microsoft TrueSkillhttps://www.microsoft.com/en-us/research/project/trueskill-ranking-system/Leaderboard:[00:09:21] Prolific HUMAINE Leaderboardhttps://www.prolific.com/humaine[00:09:31] HUMAINE HuggingFace Spacehttps://huggingface.co/spaces/ProlificAI/humaine-leaderboard[00:10:21] Prolific AI Leaderboard Portalhttps://www.prolific.com/leaderboardDataset:[00:09:51] Prolific Social Reasoning RLHF Datasethttps://huggingface.co/datasets/ProlificAI/social-reasoning-rlhfOrganization:[00:10:31] MLCommonshttps://mlcommons.org/

What if everything we think we know about AI understanding is wrong? Is compression the key to intelligence? Or is there something more—a leap from memorization to true abstraction? In this fascinating conversation, we sit down with **Professor Yi Ma**—world-renowned expert in deep learning, IEEE/ACM Fellow, and author of the groundbreaking new book *Learning Deep Representations of Data Distributions*. Professor Ma challenges our assumptions about what large language models actually do, reveals why 3D reconstruction isn't the same as understanding, and presents a unified mathematical theory of intelligence built on just two principles: **parsimony** and **self-consistency**.**SPONSOR MESSAGES START**—Prolific - Quality data. From real people. For faster breakthroughs.https://www.prolific.com/?utm_source=mlst—cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economyHiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst—**END**Key Insights:**LLMs Don't Understand—They Memorize**Language models process text (*already* compressed human knowledge) using the same mechanism we use to learn from raw data. **The Illusion of 3D Vision**Sora and NeRFs etc that can reconstruct 3D scenes still fail miserably at basic spatial reasoning**"All Roads Lead to Rome"**Why adding noise is *necessary* for discovering structure.**Why Gradient Descent Actually Works**Natural optimization landscapes are surprisingly smooth—a "blessing of dimensionality" **Transformers from First Principles**Transformer architectures can be mathematically derived from compression principles—INTERACTIVE AI TRANSCRIPT PLAYER w/REFS (ReScript):https://app.rescript.info/public/share/Z-dMPiUhXaeMEcdeU6Bz84GOVsvdcfxU_8Ptu6CTKMQAbout Professor Yi MaYi Ma is the inaugural director of the School of Computing and Data Science at Hong Kong University and a visiting professor at UC Berkeley. https://people.eecs.berkeley.edu/~yima/https://scholar.google.com/citations?user=XqLiBQMAAAAJ&hl=en https://x.com/YiMaTweets **Slides from this conversation:**https://www.dropbox.com/scl/fi/sbhbyievw7idup8j06mlr/slides.pdf?rlkey=7ptovemezo8bj8tkhfi393fh9&dl=0**Related Talks by Professor Ma:**- Pursuing the Nature of Intelligence (ICLR): https://www.youtube.com/watch?v=LT-F0xSNSjo- Earlier talk at Berkeley: https://www.youtube.com/watch?v=TihaCUjyRLMTIMESTAMPS:00:00:00 Introduction00:02:08 The First Principles Book & Research Vision00:05:21 Two Pillars: Parsimony & Consistency00:09:50 Evolution vs. Learning: The Compression Mechanism00:14:36 LLMs: Memorization Masquerading as Understanding00:19:55 The Leap to Abstraction: Empirical vs. Scientific00:27:30 Platonism, Deduction & The ARC Challenge00:35:57 Specialization & The Cybernetic Legacy00:41:23 Deriving Maximum Rate Reduction00:48:21 The Illusion of 3D Understanding: Sora & NeRF00:54:26 All Roads Lead to Rome: The Role of Noise00:59:56 All Roads Lead to Rome: The Role of Noise01:00:14 Benign Non-Convexity: Why Optimization Works01:06:35 Double Descent & The Myth of Overfitting01:14:26 Self-Consistency: Closed-Loop Learning01:21:03 Deriving Transformers from First Principles01:30:11 Verification & The Kevin Murphy Question01:34:11 CRATE vs. ViT: White-Box AI & ConclusionREFERENCES:Book:[00:03:04] Learning Deep Representations of Data Distributionshttps://ma-lab-berkeley.github.io/deep-representation-learning-book/[00:18:38] A Brief History of Intelligencehttps://www.amazon.co.uk/BRIEF-HISTORY-INTELLIGEN-HB-Evolution/dp/0008560099[00:38:14] Cyberneticshttps://mitpress.mit.edu/9780262730099/cybernetics/Book (Yi Ma):[00:03:14] 3-D Vision bookhttps://link.springer.com/book/10.1007/978-0-387-21779-6<TRUNC> refs on ReScript link/YT

Pedro Domingos, author of the bestselling book "The Master Algorithm," introduces his latest work: Tensor Logic - a new programming language he believes could become the fundamental language for artificial intelligence.Think of it like this: Physics found its language in calculus. Circuit design found its language in Boolean logic. Pedro argues that AI has been missing its language - until now.**SPONSOR MESSAGES START**—Build your ideas with AI Studio from Google - http://ai.studio/build—Prolific - Quality data. From real people. For faster breakthroughs.https://www.prolific.com/?utm_source=mlst—cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economyHiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst—**END**Current AI is split between two worlds that don't play well together:Deep Learning (neural networks, transformers, ChatGPT) - great at learning from data, terrible at logical reasoningSymbolic AI (logic programming, expert systems) - great at logical reasoning, terrible at learning from messy real-world dataTensor Logic unifies both. It's a single language where you can:Write logical rules that the system can actually learn and modifyDo transparent, verifiable reasoning (no hallucinations)Mix "fuzzy" analogical thinking with rock-solid deductionINTERACTIVE TRANSCRIPT:https://app.rescript.info/public/share/NP4vZQ-GTETeN_roB2vg64vbEcN7isjJtz4C86WSOhw TOC:00:00:00 - Introduction00:04:41 - What is Tensor Logic?00:09:59 - Tensor Logic vs PyTorch & Einsum00:17:50 - The Master Algorithm Connection00:20:41 - Predicate Invention & Learning New Concepts00:31:22 - Symmetries in AI & Physics00:35:30 - Computational Reducibility & The Universe00:43:34 - Technical Details: RNN Implementation00:45:35 - Turing Completeness Debate00:56:45 - Transformers vs Turing Machines01:02:32 - Reasoning in Embedding Space01:11:46 - Solving Hallucination with Deductive Modes01:16:17 - Adoption Strategy & Migration Path01:21:50 - AI Education & Abstraction01:24:50 - The Trillion-Dollar WasteREFSTensor Logic: The Language of AI [Pedro Domingos]https://arxiv.org/abs/2510.12269The Master Algorithm [Pedro Domingos]https://www.amazon.co.uk/Master-Algorithm-Ultimate-Learning-Machine/dp/0241004543 Einsum is All you Need (TIM ROCKTÄSCHEL)https://rockt.ai/2018/04/30/einsum https://www.youtube.com/watch?v=6DrCq8Ry2cw Autoregressive Large Language Models are Computationally Universal (Dale Schuurmans et al - GDM)https://arxiv.org/abs/2410.03170 Memory Augmented Large Language Models are Computationally Universal [Dale Schuurmans]https://arxiv.org/pdf/2301.04589 On the computational power of NNs [95/Siegelmann]https://binds.cs.umass.edu/papers/1995_Siegelmann_JComSysSci.pdf Sebastian Bubeckhttps://www.reddit.com/r/OpenAI/comments/1oacp38/openai_researcher_sebastian_bubeck_falsely_claims/ I am a strange loop - Hofstadterhttps://www.amazon.co.uk/Am-Strange-Loop-Douglas-Hofstadter/dp/0465030793 Stephen Wolframhttps://www.youtube.com/watch?v=dkpDjd2nHgo The Complex World: An Introduction to the Foundations of Complexity Science [David C. Krakauer]https://www.amazon.co.uk/Complex-World-Introduction-Foundations-Complexity/dp/1947864629 Geometric Deep Learninghttps://www.youtube.com/watch?v=bIZB1hIJ4u8Andrew Wilson (NYU)https://www.youtube.com/watch?v=M-jTeBCEGHcYi Mahttps://www.patreon.com/posts/yi-ma-scientific-141953348 Roger Penrose - road to realityhttps://www.amazon.co.uk/Road-Reality-Complete-Guide-Universe/dp/0099440687 Artificial Intelligence: A Modern Approach [Russel and Norvig]https://www.amazon.co.uk/Artificial-Intelligence-Modern-Approach-Global/dp/1292153962