The Information Bottleneck

Stella Biderman, Executive Director of EleutherAI, joins us the week an OpenAI model autonomously broke out of its sandbox and hacked Hugging Face. Stella calls it what she thinks it is - an offensive cyber operation- and argues it's part of a pattern: frontier labs have repeatedly failed to contain their own models, and won't invest in real security (air-gapped networks, SCIF-style facilities) as long as the incentives reward speed over safety.​And yet Stella remains one of the world's most prominent open-source advocates.  From her perspective, the biggest risk isn't the technology; it's unchecked corporate power, and the only durable check on it is an independent scientific research establishment that doesn't depend on the AI industry for its funding or its facts.From there the conversation spans the geopolitics of Chinese open models and whether governments can restrict them, sovereign AI and what it would actually take for other countries to train their own models, why harnesses and UX drive more of AI's perceived progress than raw intelligence, the AI-found counterexample to the Jacobian conjecture, and EleutherAI's "Deep Ignorance" approach to making open-weight models safe by filtering hazardous knowledge out of pretraining.key topicsAI governance and regulationCybersecurity incidents involving AI modelsOpen source AI safety and securityThe role of independent research in AI safetyLegal and ethical considerations in AI developmentTimeline00:13 — Intro: Stella Biderman and EleutherAI, a real non-profit in AI02:05 — News of the week: Kimi K3, and OpenAI's model autonomously hacking Hugging Face05:49 — "Frontier labs can't be trusted": repeated containment failures, air-gapped networks and SCIFs vs. sandboxes22:45 — Can governments ban open or Chinese models? Import restrictions and the six-month open/closed gap27:05 — Why Stella is still pro-open-source: unchecked corporate power as the real danger31:11 — The opioid epidemic analogy: avoiding both regulatory failure and overcorrection34:57 — Offense vs. defense: why open access to AI has empirically favored defenders37:28 — Chinese labs, the CCP, and why safety and fine-tuning are low-prestige work in China42:19 — Sovereign AI: does every country need its own foundation model?49:29 — Sampling, harnesses, and why ChatGPT was really a UX breakthrough54:09 — AI solves the Jacobian conjecture: domain data beats raw intelligence58:02 — Safety is contextual, not a model property — and what HAL 9000 got right1:01:42 — Is Stella optimistic about the future?1:02:50 — Deep Ignorance, the science of AI training dynamics, and how to get involved with EleutherAIMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

In this episode, Frank Hutter joins us to talk about TabPFN and why tabular data is suddenly the hottest problem in deep learning. Frank is a professor at the University of Freiburg and spent 15 years building the AutoML field before founding Prior Labs, which SAP just acquired for over a billion dollars.We get into why deep learning failed on tables for a decade and what in-context learning changed, how TabPFN is trained entirely on synthetic data, and why a model that never saw a real time series ended up beating specialized forecasting models. Frank also explains the architecture tricks behind scaling from 10,000 to a million rows, where LLMs fit into data science (and where they embarrassingly don't), and what happens to XGBoost from here.Beyond the research, Frank talks about the jump from professor to co-CEO, why he refused to merge his 45-person team into SAP's 110,000 employees, the open-weights licensing debate, and the case for building a frontier lab in Freiburg rather than San Francisco.key topicsThe role of foundation models in tabular dataImpact of SAP acquisition on Pro LabsThe evolution of AutoML and hyperparameter optimizationChallenges and solutions for large context in modelsOpen source models and licensing strategiesThe importance of independence for startup agilityFuture directions in AI for science and medicine00:00 Intro00:34 The SAP acquisition and staying independent07:39 Why tabular data is the next big thing in deep learning14:19 What makes tabular data hard19:14 AutoML, AutoGluon, and fifteen years of hyperparameter tuning28:27 Scaling TabPFN: context limits and architectures34:35 Agentic data science and LLMs39:30 Online learning, time series, and Bayesian inference in a forward pass47:05 Open weights and the license debate54:51 Will LLMs and tabular models merge?1:00:01 From academia to startup1:09:42 Why build in Europe1:12:53 Audience questions and hiringMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

Surya Ganguli is a professor at Stanford and VP at General Catalyst, working at the intersection of physics, neuroscience, and AI. He started in string theory, moved to theoretical neuroscience, and now uses tools from statistical physics to understand both brains and neural networks.We talk about why deep learning theory is finally catching up to practice,  including his group's recent work explaining neural scaling laws, and why smarter data selection could beat them entirely. He also tells the origin story of diffusion models, which were invented in his lab as an attempt to violate the second law of thermodynamics.The second half turns to the brain: what happens to a mouse's sense of self on ketamine, how stimulating a handful of neurons can induce hallucinations, and a method his lab developed to get a neuron deep in a monkey's brain to describe, in English, what makes it fire.We close on where he thinks AI is going wrong: models train on ten trillion tokens while humans hear a hundred million words, because we don't teach children with gradients; we tell them the algorithm.key topicsConnections between physics, neuroscience, and AIEmergent properties in complex systemsScaling laws in language modelsData efficiency and pruning in AINeuroscience insights into consciousness and selfThe future of AI and brain modelingChapters00:00 Introduction to Surya Ganguli 00:57 Surya's Background: From String Theory to Neuroscience02:22 Emergent Properties in Physics, Neuroscience, and AI03:16 Energy Landscapes and Loss Landscapes in High Dimensions04:07 Why Local Minima Don't Exist in High-Dimensional AI05:22 Gradient-Based vs. Gradient-Free Learning Methods08:21 AI in Mathematics and Drug Discovery: Opportunities and Challenges13:48 Scaling Laws and Data Efficiency in Language Models18:10 Properties of Data that Affect Scaling Laws22:04 Constructing Non-Redundant Data Sets for Better Learning24:32 Theory vs. Empirical Results in AI Research32:19 Fundamental Components of Deep Learning: Are They Changing?34:31 Future Paradigms in AI Beyond Current Models37:22 Teaching AI and Humans: Paradigm Shifts in Learning41:37 Consciousness, Self, and the Brain: Surya's Perspectives49:49 Neuroscience and AI: Understanding the Brain and Consciousness01:02:03 Understanding the Brain: Challenges and Opportunities01:09:21 Brain-Computer Interfaces and AI in NeuroscienceMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

Brendan O'Donoghue, research director at Google DeepMind, makes the case for text diffusion as a real alternative to autoregressive generation. He walks through how discrete diffusion works, why diffusion samples are far more diverse and what that unlocks for RL, where the Gemma diffusion model actually stands against frontier models, and why the whole training and serving stack being hyper-optimized for autoregression is the main thing holding the approach back. The conversation also covers hardware trends favoring flops over bandwidth, AGI timelines and real-world bottlenecks, and why he thinks RL is still underhyped.Key topics- Discrete diffusion for text vs autoregressive generation- Why diffusion samples are more diverse, and what that unlocks for RL- Where diffusion already wins: latency, on-device, robotics- Why serving cost, not quality, is the real blocker- RL as the most underhyped area in AITimeline00:00 Introduction00:50 What diffusion models are and how text diffusion works04:40 Why Brendan bet on text diffusion in 202307:15 Diversity, creativity, and why it helps RL11:00 The best diffusion LLM today and the gap to frontier models14:25 Latency, serving cost, and why it needs more chips17:14 Where diffusion already wins: on-device, robotics, battery20:14 One model, two modes: diffusion for thinking, AR for answering22:24 Samplers and the stuttering problem26:27 Theory, BERT, and why now is a good time to work on this31:48 Pipelines built for autoregression, and continuous diffusion35:35 Hardware: flops vs bandwidth39:49 AGI timelines and real-world bottlenecks50:15 Is AI engineering or science?54:14 Most overhyped and most underhyped ideas58:35 RL on diffusion, value functions, and exploration1:07:30 Go download the model and break itMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

Nathan Lambert spent three years as post-training lead at Ai2, where he built the OLMo models, and he writes Interconnects, one of the most-read technical newsletters in AI. He left Ai2 in June and is now working on a new project. He's also the author of the RLHF book. We talked a lot about open models, their capabilities, and why they are better than he expected. We get into what that means over the next two to five years, why he thinks recursive self-improvement is overblown, what the market for training environments actually looks like now, and why he expects Anthropic's famously open internal culture to break after its IPO.Key TopicsOpen vs closed models and who actually captures the valueAnthropic and OpenAI as opposite cultures, and the talent concentration problemBoom vs bubble, and why token spend hasn't produced 10x better productsContinual learning, RSI skepticism, and what Nathan wants to work on nextWhat the open ecosystem needs economically to surviveTimeline 00:00 Intro00:27 Open vs closed models, and who actually captures the value05:12 China, harnesses, and where the real training leverage sits08:40 Sovereign compute and the national security case for building models11:18 Uncensored open weights and the bioweapon question14:29 Anthropic vs OpenAI, ideology and politics19:35 The Mythos ban and the Fable 5 delays24:30 The AGI narrative, the talent drain, and antitrust28:12 Why researchers join Anthropic, and the open Slack culture34:04 Nathan's next 12 months: character training and big RL runs37:55 Continual learning, RSI, and why Nathan is skeptical43:19 Boom or bubble, tokens vs GPUs45:12 Why all that token spend never produced 10x products48:38 Job displacement and the small-business future52:49 Robotics, world models, and why multimodal lags57:44 What the open ecosystem should actually do1:03:17 Why NVIDIA isn't building a frontier model1:07:34 The RLHF book, and whether RLHF still matters1:11:06 GRPO vs PPO and on-policy distillationMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

Daphne Koller wrote the book that many of us learned probabilistic graphical models from, founded Coursera, and now runs insitro, which is trying to make drug discovery a machine-learning problem.We start with the bitter lesson. She agrees with most of it and then says where it stops working: biology doesn't have enough data, structure is how people understand anything, and making a drug is a question about an intervention that hasn't happened yet, not a pattern in data you already have.Most of the episode is about why drug discovery is hard. Ninety percent of drugs that reach the clinic fail, and mostly not because the molecule was bad. The molecule usually does what it was designed to do. It just turns out the thing it was designed to do had nothing to do with the disease. Only 22% of diseases have any approved drug at all, and she calls that an upper bound on what we understand, not a lower bound.She also gets into what agents are and aren't good for in a wet lab, why cells don't grow faster no matter how many GPUs you point at them, what it would take to have real foundation models for biology, and why almost all of biology is still out of distribution.Plus GLP-1s and what human data keeps teaching us, whether AI can make the kind of leap that turned a bacterial immune system into CRISPR, and what she'd build if she were starting Coursera today.Key TopicsThe impact of scaling and data in machine learningThe importance of structure and causality in AIChallenges in drug discovery and biological understandingThe role of foundation models in biologyEthical considerations in AI and biomedical researchChapters00:00 Introduction to Machine Learning and Drug Discovery02:00 The Bitter Lesson and Its Implications06:48 Challenges in Drug Design and Discovery11:48 Ethical Considerations in Human Research17:20 The Drug Discovery Pipeline Explained29:30 Integrating AI in Experimental Design35:38 The Role of Human Judgment in Drug Design37:14 Future of Drug Design: Efficiency vs. Automation39:37 Challenges in AI and Data Availability for Biology41:08 Foundation Models: Potential and Limitations43:39 Causality in Biological Data: Importance and Challenges45:18 Creativity vs. Understanding in Drug Design48:17 Balancing Investments in Data, Algorithms, and Experiments50:07 The Value of Simulations in Drug Discovery52:03 Mathematical Frameworks in Biology: Utility and Limitations54:14 The Future of Drug Discovery: Optimism and Innovations56:28 The Impact of Coursera on Education01:00:33 The Role of Universities in Lifelong Learning01:04:06 Connecting Dots: The Fun of Variety in Work01:05:46 Optimism for the Future of Drug DiscoveryMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

In this episode, Joseph Suarez from PufferAI explains why he thinks RL never had an algorithm problem, but it had a code problem. Every part of the standard RL stack was running about a thousand times slower than it should have been, and once that got fixed, problems that used to take months started getting solved in seconds on one GPU. We talk about what makes a simulator good for RL, why most of their sims run on CPU, what he wants to do with scientific simulation, and why he open sources all of it instead of writing papers. Key topicsTypes of RL and their applicationsChallenges in scaling reinforcement learningThe role of simulators and hardware in RLRL in gaming: from chess to complex games like NetHack and RuneScapeFuture directions: scientific simulation and biological modelingChapters00:00 - Introduction to RL and Puff AI01:50 - Different settings for RL: Games, Robots, Finance04:10 - RL in LM and other domains07:00 - Challenges and solutions in RL scaling09:55 - Building fast, efficient simulators15:10 - RL for scientific research and simulation19:57 - RL in complex games: NetHack, RuneScape, Dwarf Fortress29:55 - Future of RL: Scientific discovery and beyondResourcesPuff AI - Official Site - https://puffer.aiNetHack - https://www.nethack.org/RuneScape - https://www.runescape.com/Dwarf Fortress - http://www.bay12games.com/dwarves/OpenAI Gym - https://github.com/openai/gymMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

Florian Brand builds evals at Prime Intellect. The premise of the conversation is that writing a benchmark is the easy part now. Keeping the model from cheating it is the job, and it takes longer than the benchmark itself.We get into why he thinks you can't evaluate a model apart from the CLI it runs in, what happens to statistics when a single run costs five figures, and whether the feeling that a model just works can ever become a number.He also has a few stories about agents finding their way around the scoring that are worth hearing cold.Timeline00:13 Intro01:00 What evals are for04:05 Agentic benchmarks07:10 Kimi K2 and model diversity08:23 Long-horizon coding tasks10:29 Building a benchmark12:15 MirrorCode14:27 Rubrics and LLM judges16:30 The cost of expert labelers17:49 Long runs and variance19:44 Evaluating the harness24:29 Chinese labs building CLIs30:00 More reward hacking37:45 Tau-bench and economic tasks39:43 Benchmaxxing and GLM 5.245:15 Statistics and cost47:56 Frontier convergence52:04 Misuse in open and closed models55:35 Self-improvementMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.AboutThe Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Most labs build language models by scraping the web and filtering afterward. Pierre-Carl Langlais runs it the other way around. At Pleias, the French-German lab he co-founded, the models are built from data he can actually account for, which in practice means open and public-domain sources plus a lot of synthetic data the lab generates itself. It sounds like a self-imposed handicap. It mostly isn't. One of their models is a 600 million parameter system that runs live inside the Paris subway's monitoring pipeline.We cover the SYNTH pretraining dataset and why he thinks "ethical data" has to mean more than copyright-free. He explains why barely 2% of their Common Corpus appears in typical web crawls, and why that gap is really a preservation problem. From there, he gets blunt about benchmark maxing and whether GLM really earns its Opus-class reputation. He also argues that the quiet move by closed labs to hide reasoning traces is mostly about claiming ownership of model outputs. He's skeptical of sovereign AI, and not shy about how Mistral drifted from frontier research toward French corporate consulting. We finish on NVIDIA's persona datasets and the odd idea of training on the conditions that produced a text rather than the text itself.Timeline(00:02) Welcome and introductions(00:49) Why synthetic data matters, and the SYNTH set(04:15) Three reasons to control your training data(07:18) What "ethical data" actually means(11:08) How Common Corpus got built, from Wikipedia to PDFs(16:35) Agentic harnesses and synthetic data(20:03) Evaluating data when you train on reasoning traces(25:27) General versus specialized pretraining(27:08) Benchmark maxing and the GLM question(31:51) Getting diversity in, and the NVIDIA personas(35:02) Hidden reasoning traces and the fight over model IP(38:17) Mid-training and the "It's All Training" thesis(41:47) Can small models actually compete(45:01) Cybersecurity and Europe's strategic gap(47:08) Do you need a big model to orchestrate the small ones(52:08) Sovereign AI and the limits of national champions(56:42) Scaling laws when you control the data(01:00:41) The NVIDIA persona datasets(01:04:52) What you actually do with synthetic personas(01:08:22) Closing thoughtsMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.AboutThe Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Dhruv Batra spent years leading Embodied AI at Meta,  training virtual robots to navigate photorealistic 3D scans of real buildings with pure reinforcement learning. Then he left to co-found Yutori and build agents for a very different environment: the web browser.In this episode, Dhruv explains why he sees these as the same problem. Web agents, in his framing, are robots that act in a browser (pixels in, actions out), and the web turns out to be just as messy an environment as the physical world.Along the way, we cover his definition of intelligence as "navigation in idea space," why robotics is lagging LLMs, the sim-to-real gap and why you can't fake friction coefficients, the teleoperation counterexample to the "it's a sensor problem" argument, and his provocative claim that under the current paradigm, we solved machine learning and didn't even realize it. He also makes the case for why the scaling hypothesis isn't falsifiable, why JEPA-style arguments deserve to be grappled with, how Yutori trains its Navigator models with RL on live websites, and what happens to the ad-supported web when agents, not eyeballs, do the browsing.Timeline00:01 — Intro00:54 — What embodied AI actually means06:47 — Intelligence as navigation in idea space13:26 — Habitat: training robots with pure RL, no maps20:04 — Why robotics is behind LLMs28:24 — Sim-to-real: what you can and can't fake33:34 — "We solved ML and nobody noticed"37:12 — Leaving Meta, founding Yutori43:21 — Web agents: screenshots in, actions out48:15 — Why the web won't rebuild itself for agents53:32 — Training Navigator: RL on live websites1:01:04 — Who pays for the web when agents browse?1:09:17 — What Yutori means, closing thoughtsMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.AboutThe Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Ion Stoica has done what almost no academic ever does — repeatedly turned university research into billion-dollar companies. He co-founded Databricks (now valued at over $100 billion), Anyscale, Arena AI and Conviva, while his Berkeley lab produced the open source projects the entire AI industry runs on: Ray, vLLM, and SGLang.In this episode, we ask him how it's actually done. His answer is surprisingly unromantic: solve a problem people already care about, build an artifact good enough that they adopt it, and pay attention to the moment users start asking "who maintains this after the students graduate?" - that's when a project becomes a company. He's also insistent that the credit belongs to his students.From there, the conversation goes deep into what he's watching now: why the AI stack has become an order of magnitude more complex than the Hadoop/Spark era, why maximizing GPU utilization is "the name of the game" for any enterprise, and why coding agents will struggle with distributed systems long after they've mastered web apps. He shares a memorable reward-hacking story — a load balancer that maximized throughput by dropping requests — explains why the gap between open and closed models sits at about six months, and closes with his case for regulating AI by outcomes, not capabilities.Timeline00:00 — Introduction: welcoming Ion Stoica01:21 — The playbook: how research projects become companies05:22 — Will vLLM and SGLang stay open source?07:47 — The real bottleneck in the AI stack: complexity, not just hardware14:31 — Should algorithms follow infrastructure, or the other way around?16:13 — Can AI coding tools write distributed systems and GPU kernels?21:09 — Verifiers, harnesses, and the limits of outsourcing understanding25:41 — Reward hacking: the load balancer that dropped requests25:58 — How should enterprises consume GPUs? Utilization as the name of the game30:23 — GPU scarcity: will the compute crunch ever end?35:27 — Hyper-optimization and the risk of locking in today's architectures37:17 — Open vs. closed models: why every company wants to own the stack40:35 — The six-month gap, and the rising cost of training frontier models43:58 — Kimi, Qwen, and who's incentivized to keep open models alive45:39 — Regulation: outcomes, not capabilities47:41 — Self-regulation, concentration of power, and auditing open models48:32 — Wrap-upMusic"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.AboutThe Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Jean-Francois Puget is a Director and Distinguished Engineer at NVIDIA, where he leads the Kaggle Grandmasters team, and he's ranked third on Kaggle's all-time list. We caught him on the day NVIDIA announced Nemotron Ultra and its new agent skills repo. We talk about what skills actually are, why they beat MCP tools on context cost, and how NVIDIA built an evaluation pipeline to separate skills that help from skills that don't.From there we talk about the thing JFP cares about most: evaluation. He explains why most LLM benchmarks reward overfitting, how his team discovered O3 could pick the right files to fix SWE-bench issues without reading them, and why the only benchmarks he trusts are the ones where you commit before you see the score, which is exactly how Kaggle works. He predicts a "bloodbath" for the wave of competitors letting coding agents chase leaderboard scores with no notion of validation.We also get into what coding agents are actually good for ("a mix of a genius and a dumb person"), the multi-agent system at NVIDIA that built a working PyTorch clone that runs 10x slower than the real thing, his unfiltered take on frontier lab PR and the Mythos release, whether AI is a bubble, and the story of how his team won ARC-AGI with a 4-billion-parameter model at 20 cents a task, including jumping from third to first in the final hours of a seven-month competition.Timeline00:00 — Intro01:05 — NVIDIA's announcements: Nemotron Ultra and the agent skills repo07:21 — Skills vs MCP tools, and progressive disclosure10:24 — Agents that write their own skills: a new form of learning13:33 — When overfitting is fine (and when it isn't)15:47 — Why most LLM benchmarks reward overfitting17:06 — The SWE-bench contamination story: O3 picks files without reading them19:45 — How LLMs changed Kaggle, and the coming "bloodbath"25:40 — What makes a good data scientist: evaluation and one-bit experiments28:56 — Running Codex at scale: the top token consumers at NVIDIA29:37 — Did coding agents kill AutoML?30:16 — Genius and dumb at once: the limits of coding agents35:21 — Humans in the loop, sandboxing, and the teenage hacker who never wrote code37:42 — Mythos, frontier lab PR, and open source40:08 — Why NVIDIA builds open models, and where it's already frontier43:48 — World models, robots, and the coffee test49:20 — Why agents still can't play Dota50:24 — Is AI a bubble?53:14 — Winning ARC-AGI with a 4B model at 20 cents a task57:39 — Kaggle is a legal drugMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

In this episode, we talked with Dimitris Papailiopoulos, researcher at Microsoft Research's AI Frontiers lab and professor at the University of Wisconsin, about doing research in the age of agents. Dimitris told us about the Sunday morning that changed how he works: he handed Claude Code and Codex a question he'd been sitting on for years, went about his day, and came back to an answer. After a few days of dread about what's left for humans, he landed somewhere more optimistic, calling this the golden age of asking questions.We talked about his "smallest transformer that can add" leaderboard, a symbolic GSM8K solver built from if-else statements, and what happened when he put two Claude Code instances in the same file system and told them to do something cool (one pair invented a communication protocol, the other played Battleship). We also got into diversity and slop in agent-generated ideas, why agents get stubborn after a million tokens, harness overfitting on Terminal-Bench, continual learning and world models, whether agents need vision, and where information theory actually helps in AI and where it's a katana used to make coffee.Timeline00:00 Intro01:45 How agents changed the way Dimitris does research04:30 A Sunday morning with Claude Code, Codex, and GSM8K07:15 The dread, then the golden age of asking questions08:20 Taste and verification, and how we train students now09:53 Will models make human verification obsolete?11:30 The smallest transformer that can add 10-digit numbers13:40 Humans as initializers for gradient descent in idea space15:32 Allen on diversity, slop profiles, and high temperature research21:44 When Claudes meet: Battleship, invented protocols, and a grokking paper25:53 Single agent vs multi-agent under fixed compute30:28 Auto-research benchmarks and what agents actually accelerate35:14 Inside the symbolic GSM8K solver (with a live progress check)40:04 Idea overfitting and why agents refuse to change course44:00 Learning from failure traces and harness overfitting48:04 Continual learning, memory files, and world models51:30 Why don't labs personalize models on your own history?57:52 Agent-to-agent communication: is Jira the right tool?1:01:25 Multimodality: vision as a tool vs one unified model1:05:40 Information theory and AI, or making coffee with a katana1:11:23 Closing thoughts: ask bigger questionsMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Phillip Isola, professor at MIT, joins us to talk about representation learning: what makes a representation good, why different models seem to converge on similar representations, and whether pre-training is really over.We discuss the platonic representation hypothesis and its limits, why clustering structure matters more than global geometry, and Phillip's new neural thickets paper arguing that post-training is easier than people think because pre-trained weights already sit near solutions to downstream tasks. Phillip also explains why he thinks LLMs are already world models, why he's betting on RNNs making a comeback, and why his most exciting current direction is artificial life: putting LLM agents in open environments with no fixed task and studying them like new organisms.Timeline:00:00 Intro song00:13 Intro01:05 What is representation learning and why it matters04:09 What makes a representation good: minimality and sufficiency10:03 How cross entropy and contrastive learning shape representations14:35 Dimensionality reduction and why dimension isn't the right complexity measure16:35 Compression and geometric clustering during training19:27 The platonic representation hypothesis and what actually converges22:53 Local neighborhoods vs global structure: the Aristotelian follow-up24:33 When convergence is strong: truth vs the space of possibility28:09 Is there true similarity in the world? The Bouba-Kiki effect30:56 World models vs autoregressive LLMs32:14 Diffusion LLMs as a special case of autoregressive models33:42 What architectures win in five years: the case for RNNs36:11 Grad student descent, or do we actually have principles?40:51 Feathers and wings: what to take from biology43:17 How close are we to brain-like models? Marr's three levels47:01 Are better models becoming less human-like?49:38 Is pre-training all you need? The neural thickets paper54:18 LoRA, low rank fine-tuning, and why post-training is easier than we thought56:01 RL environments and what our benchmarks actually test1:01:11 Artificial life: LLM agents as new organisms1:07:20 What's overlooked in AI research right now1:08:36 Why stay in academia, and doing science in the age of OpusMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Most AI-for-science companies are selling shovels. Qichao Hu wants the gold.In this episode, we talk with Qichao, the founder and CEO of Molecular Universe, the AI-for-science platform that grew out of SES AI, a high-energy-density battery developer he's run for fourteen years. His core distinction is that companies from the AI world build tools, such as foundation models that predict properties, while companies from the science world care about the final product, such as the new battery or material that actually ships. Molecular Universe sits firmly on the science side, and the difference shows up everywhere from what they publish to what they refuse to.We get into the actual workflow of materials discovery and where AI compresses it. A single trial in a traditional lab can take a year with maybe a 40% success rate; the goal is to run a thousand candidates in parallel and turn that year into a week. Qichao walks through improving low-temperature fast-charging for EV batteries:  from hypothesis generation through molecule-, material-, and device-level property prediction, down to autonomous labs that synthesize and test the top candidates without a human touching a pipette.The hardest problem, it turns out, isn't predicting molecular properties or measuring device performance, but it's the black box connecting the two. In batteries, that's the solid-electrolyte interface, which the field has been hand-waving about since the seventies. And the thing standing in the way of cracking it isn't a clever training trick but data: companies sitting on twenty years of records are finding it too messy, incomplete, and poorly labeled to train on, and are having to start collecting from scratch with new protocols and robots.Timeline00:13 — Intro and welcome;01:19 — Shovel vs. gold05:18 — Why the world's smartest scientist doesn't automatically give you a better battery07:25 — The discovery workflow09:37 — Exploration vs. exploitation11:54 — Safety and filtering: screening novel molecules against banned and toxic-substance lists17:55 — How hypotheses get generated, and where frontier LLMs help20:29 — From hypothesis to ~400 formulations: property prediction, ranking, and handing off to autonomous labs26:37 — "A foundation model for everything" — and the black box between molecular properties and device performance30:01 — World models and physics33:09 — The great unknown in batteries37:08 — Simulation vs. reality: calibrating massive simulated datasets with a sliver of experimental data41:47 — Lab robotics: how fast the hardware has caught up, and what a floor of autonomous labs looks like43:50 — The real bottlenecks50:21 — Pre-training from scratch vs. post-training LLMs, and why training tricks haven't reduced the need for good data52:42 — Evaluation55:42 — Publish the B+ model, keep the A model58:05 — Five years out1:00:37 — Closing thoughts and wrapMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We talk a lot on this show about RL, agents, and the move between pre-training and post-training, but not enough about the layer everything actually runs on. Benny Chen, co-founder of Fireworks AI, one of the largest inference platforms around, walks us through what it takes to serve models at scale: sourcing GPUs, writing the kernels, the runtime, and the routing layer that lets a customer hit one endpoint and forget the rest.We talk why the real bottleneck is power, not chips, and why that favors Nvidia and Google. Why MoE keeps winning even when dense models look better on paper and why he'd rather run fungible capacity at 95% than specialized chips at 60%. We also talk about quantization limits, where RL efficiency has to go next, and his case that AI is still under-hyped. We also get into cross-region training, sparse autoencoders and why interpretability hasn't taken off in open source, whether open models can close the gap, and a frank read on Anthropic's go-to-market.Timeline00:00 — Intro: the part of AI nobody talks about01:20 — What "infrastructure for AI" actually means: the layers, from GPUs up to routing02:59 — Why not just buy your own GPUs and do it yourself?05:17 — The scale Fireworks runs at06:35 — Hardware inflation, GPU costs, and the real risk hiding in commit duration10:14 — Nvidia vs AMD vs TPUs, and why power is the bottleneck11:57 — Mixing GPU types and generations; fungibility vs. specialization14:22 — Once you have the GPUs, what's the next layer to build?17:04 — Dense vs. MoE, and why the hardware picks the winner21:07 — Quantization: is FP4 the floor? TurboQuant and INT vs. FP24:28 — How tied are the algorithms to the hardware?25:12 — DeepSeek, DeepGEMM, and next-token prediction as reconstruction loss28:50 — Why RL is still wildly inefficient compared to pre-training30:08 — Speculative decoding, AI-generated kernels, and auto-research34:00 — The AGI question: why text gets automated but vision may stay expensive37:07 — Hype check: why Benny thinks AI is still under-hyped41:28 — Training vs. inference at the infrastructure level44:12 — Scaling across data centers: cross-region training with Cursor45:40 — Sparse autoencoders, interpretability, and why open source is human-constrained49:04 — Will open models catch up — on quality and on compute?51:41 — Are we plateauing? Opus 4.7 vs. 4.6 and the coming data wars54:41 — Physical limits, HBM, and whether chips keep getting faster58:17 — The belief about inference everyone gets wrong59:31 — Anthropic, mythos, and a frank take on go-to-market1:04:41 — Wrap-upMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Oded Rechavi is a biologist at Tel Aviv University and the co-founder of QED, a company building AI to review scientific work. He's also spent years studying worms.We start with what's wrong with peer review and grant funding: why it takes years to publish, why reviewers are often your own competitors, and why the whole thing is locked to an economic model that rewards publishing more papers, not better ones. Oded explains why he doesn't call QED "peer review" at all, and what it would take to actually validate science instead of just stamping it.Then we get into the biology. C. elegans has exactly 959 cells, every one of them named, and a fully mapped brain. Oded's lab studies how a worm's experiences get passed to its offspring through RNA rather than DNA — meaning what happens to a worm in its lifetime can change its descendants. We also talk about using ancient DNA to reassemble the Dead Sea Scrolls, what AI can and can't do for biology, and why he wants to build an "Ironman suit" for researchers rather than replace them.00:00 Intro01:35 Why scientific publishing is broken04:02 Years to publish, and what it costs science07:20 Bad reviewers, conflicts of interest, and the money10:47 Why preprints don't fix it15:37 How AI conferences handle review22:07 Conferences vs. journals — does slow review help?25:22 Building QED: review, not peer review30:02 Tracking a paper from idea to submission33:11 What writing a grant actually involves35:00 The ERC reviewer crisis37:06 Tailoring feedback to your field41:48 Switching to biology44:30 Every cell has a name: inside C. elegans46:28 Inheritance without DNA48:16 What the worm "thinks" changes its offspring51:58 Reassembling the Dead Sea Scrolls with ancient DNA56:07 Psychedelics and worms58:36 Can AI run the research itself?1:04:49 Automation vs. validation1:07:12 The origin of life1:08:49 Why people reject AI-written work1:16:18 Will humans still have a role?1:17:39 Wrap-upMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Will AI take our jobs? We put the question to Alex Imas, the new Director of AGI Economics at Google DeepMind and a professor at Chicago Booth, whose entire job now is studying how frontier AI reshapes the economy. His short answer: probably some of them, but the popular story is mostly wrong about which jobs and how fast.Alex makes the case that a job is a bundle of tasks, not a single thing AI either does or doesn't do, and that the number of people who should actually care about is how much consumer demand responds to falling prices. Get that wrong and you predict mass layoffs. Get it right and you sometimes predict more hiring. We get into why the automation panic is two centuries old, why he thinks blue-collar work is in more danger than white-collar, and why the people already winning are the ones adopting AI fastest.We also cover the AGI versus ASI distinction and why it changes everything for the economy, what happens when there's no moat and open models stay six to eight months behind, the three-tier pricing future he sees coming after the 2026 compute crunch, and what any of this means if you're deciding whether to send your kids to college.The episode was recorded before Alex joined GoogleTimestamps00:00 Meeting Alex Imas00:44 Will AI take our jobs?03:35 Is this an AI question or an economics question?06:18 The economy is already behind the AI we have07:43 Why AI adoption is K-shaped12:51 Was Andrew Yang right?13:45 The automation panic is 200 years old16:46 Dario's six-month claim, and why we don't see it yet17:22 A job is not a task22:38 The three numbers that actually predict the labor market22:42 The chess engine analogy and the centaur phase25:45 Recursive self-improvement and the hamburger problem30:06 Should AI labs be the ones answering alignment questions?31:17 The "invisible hand wave" and why nobody wants fully autonomous AI33:27 AGI vs ASI, and why the difference is everything35:28 Commodities vs relational goods41:14 Star Trek, replicators, and predicting with sci-fi45:20 Inequality and the Upper West Side VCs46:21 Your money manager was automated in the 1960s50:47 Are OpenAI and Anthropic overvalued? The moat problem54:29 What has to be true for the losses to make sense55:43 Cognitive atrophy and monopoly fears57:00 The 2026 compute crunch and the three-tier pricing future1:01:52 The Apple vs Android analogy1:03:54 A rich-country perspective1:04:16 Protecting the skills that actually matter1:07:02 Will not using AI become a status symbol?1:08:53 Does capitalism even survive?1:13:44 Redistribution becomes the political battleground1:18:16 Blue collar vs white collar: who's really at risk1:21:18 Advice for parents in an AI world1:22:43 Saving for retirement when the Valley says don't1:25:06 Will non-elite colleges survive?Music:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

In this episode, we sit down with Wenhu Chen, research scientist at Meta MSL, assistant professor at the University of Waterloo, and the person behind MMLU-Pro and MMMU. If you've read a frontier model release in the last two years, you've seen his benchmarks. That makes him one of the best people to answer the question everyone dances around: when a model jumps from 40% to 90% on your benchmark, how much of that is real? In this episode, we dig into why benchmarks have become the loss function of the entire field - design a bad one, and thousands of brilliant researchers will spend months hill-climbing in the wrong direction. Wenhu is surprisingly candid about the limits of his own creations: contamination is everywhere, saturation turns frontier benchmarks into unit tests, and popular alternatives, such as LM Arena, mostly measure tone and length rather than capability. His answer is to evaluate models where they've never been: private codebases, hospital data, and the messy, live internet.We also talk about ClawBench, his new benchmark that deploys agents to over 140 real production websites to do things people actually want done, such, such as ordering food, booking tickets, and applying for jobs. The best model in the world completes about a third of these tasks. We unpack why: bot detection, models that refuse to click "pay," agents that give up the moment an environment doesn't match their training, and harnesses that can swing results by 20% without changing the model at all.Along the way, we cover the overlooked science of evaluating pre-training, data flywheels, and synthetic environments for agent training, and whether RL teaches models to reason or just surfaces what's already there. We close with Wenhu's predictions: exploration and adaptability will improve rapidly, but security will become the field's hardest problem as agents gain real permissions in the real world.Timestamps00:00 – Intro00:55 – What good evaluation means, and how it's changed since the early GPT days03:35 – Benchmarks as the field's loss function05:50 – Contamination: the problem nobody fully solves08:08 – MMLU-Pro scores: real progress or training on the test set?11:05 – Can you measure creativity?12:34 – Why human judges and arenas are unreliable — and what to use instead19:22 – What a good benchmark actually looks like22:34 – Chain of thought: signal or scratchpad?26:01 – Auto-research and hill-climbing agents28:52 – Harnesses: 20% swings without touching the model32:28 – Safety, model release, and an "FDA for models"36:53 – The overlooked science of pre-training evaluation43:49 – Designing pre-training benchmarks when one run costs a billion dollars49:45 – ClawBench: agents on 140+ live websites, and why the best model gets 33%54:42 – How MMLU-Pro and MMMU-Pro were born from public complaints59:16 – Pixel agents vs. APIs: will MCP kill computer use?1:02:11 – Training agents: data flywheels and synthetic environments1:05:43 – SFT vs. RL, and does RL teach reasoning or reveal it?1:09:21 – What gets solved next year — and what doesn't1:14:32 – Undervalued ideas, and what's next for ClawBenchMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

In the second half of our conversation with Jürgen Schmidhuber, we focus on the key ideas he's pursued since the early 1990s and discuss why he believes these concepts are only now being rediscovered.We start with JEPA. Jürgen argues that the method LeCun named in 2022 is the same family he published in 1992 as Predictability Maximization. From there he traces the adversarial lineage back further still, to his 1990 world-model paper and 1991 Predictability Minimization  -  the curiosity-driven minimax games he sees as the real origins of GANs.We also talk about why these ideas took thirty years to land, why today's trillion-dollar data-center buildout is driven by AGI fear, and why he thinks Apple may come out ahead.The back half turns to what he sees as the real frontier: physical AI. Today's systems are superhuman behind the screen but helpless at a leaky pipe, and until a robot can use human tools, there's no AGI. He discusses self-replicating, self-improving machines as "a new kind of life," reframes continual learning and test-time training as ideas from his 1991 fast-weight work, and detours through Solomonoff's universal prior, Hutter's AIXI, and the Gödel machine.We close on the subject Jürgen is famous for: scientific credit. He makes his case for rigorous attribution, casts himself as a "speaker for the dead" championing forgotten pioneers like Ivakhnenko, and reflects candidly on whether the fights are personal.Timeline00:30 — What JEPA is, and the 1992 Predictability Maximization story 04:54 — Implementing PMAX: autoencoders, Siamese networks, Infomax 09:10 — Predictability Minimization, factorial codes, and the roots of GANs 16:00 — Why it took 30 years: the economics of compute 20:52 — Data, the web, and 1990 as the origin point 23:09 — Hardware inflation, the trillion-dollar buildout, and the coming crash 34:05 — Physical AI: the plumber problem and self-replicating machines 41:14 — Which 90s ideas are being scaled right now 45:26 — Continual learning and test-time training as "old hats" 55:19 — Measuring intelligence: Solomonoff, AIXI, and the Gödel machine 1:05:26 — Self-replication and von Neumann 1:09:51 — Will he see AGI in his lifetime? 1:10:42 — Credit, integrity, and being a "speaker for the dead" Music:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

In this episode, we host Jürgen Schmidhuber - the man, the legend, one of the godfathers of modern AI. His lab worked out many ideas behind today’s systems (LSTM, world models, artificial curiosity, Transformer variants, and even GAN-style setups) decades before they became fashionable, and he’s just as well known for making sure people remember who did what first. This is the first of two conversations with him.We go back to his lab in the early 90s and ask how one small group came up with so many of the ideas that are now being scaled to a thousand billion dollars, back when compute was ten million times more expensive. A lot of the episode comes down to one distinction he keeps making: prediction vs. decision-making. His take is that LLMs are very good prediction machines that imitate the web, but that’s only half the problem. To actually act in the world, you need a controller that uses a world model to plan. He talks about his 1990 work on world models and artificial curiosity, where the controller gets rewarded for running experiments that improve its own model (an adversarial setup years before GANs), why planning millisecond by millisecond doesn’t scale, and why you need sub-goals instead.We also talk about compression as the core of understanding, from falling apples to Kepler to Einstein, and why we still don’t have a robot that can do what a plumber does, even though the AI behind the screen keeps getting better. Then the conversation moves to credit assignment: how “to Schmidhuber” became a verb, what he thinks is broken about the award system, and a long exchange on PMAX vs. JEPA. He ends on the real origins of deep learning and a prediction about self-replicating machines in space.Timeline00:00  Intro00:55  1991 in Munich, and why that lab mattered02:38  "I'm not very smart"  and why compute getting 10× cheaper every 5 years changed everything04:25  Chess as an AI proxy08:27  Artificial curiosity in the 90s vs. today's RL exploration09:10  Why RL is harder than supervised learning20:48  Coding agents vs. robots, and how a baby learns its own hands26:20  Compression as understanding33:40  What's actually missing on the road to AGI37:30  Why millisecond-by-millisecond planning is stupid47:44  Convergence to LLMs, GPUs, and how far we still are from the Bremermann limit51:49  Unsupervised learning, factorial codes, and predictability minimization58:12  Credit assignment: the fights with LeCun and the Nobel critique1:02:13  On his last name becoming a verb1:05:17  The award system's missing peer review1:07:03  Closed labs and the decline of open research1:13:23  Audience questions1:34:02  Closing: who really invented deep learning?Music:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

In this episode, we sit with Max Welling, Professor of Machine Learning at the University of Amsterdam, co-founder and CTO of CuspAI, and a foundational figure behind variational autoencoders (VAEs), equivariant networks, and Bayesian deep learning. We talk about AI for science, the physics underneath generative models, and what's still missing on the road to real intelligence.Max starts with what impresses him and what worries him about the LLM era, then makes the case that the next leaps will come from physical AI and from science itself. We dig into how machine learning actually works in the lab, world models and whether priors like geometry and symmetry should be built in or simply learned, and whether transformers will still rule a decade from now. At the end, we talk about CuspAI's climate mission, AI risk and regulation, Max’s new book, and where neuroscience might inspire the next wave of ML.Timeline00:00 — Intro00:47 — Are we happy with the LLM era?03:14 — Embodiment and physical AI08:05 — Does "AGI" even matter as a term?11:34 — Verifiers, RL, and why math/coding are tractable13:17 — What actually shifted to make materials discovery work14:42 — From molecules to biology and wet labs16:26 — Working with real labs: timescales, friction, and the "Mira" agent20:29 — Balancing simulators vs. experiments: the exploration–exploitation trade-off23:44 — Active learning for experimental design24:23 — Why active learning hasn't been central to LLMs25:24 — A general loop for ML-for-science across domains27:10 — Foundation models for chemistry: a "mother ship" plus a zoo of fine-tuned models30:04 — Quantum mechanics, interpretation, and AI as a creative theorist31:54 — World models and Yann LeCun's view; priors vs. learning34:57 — Should world knowledge be explicit? (responding to Stefano Ermon)36:41 — Vision: equivariance vs. transformers, and the role of optimization40:32 — Best model for molecular properties in 10 years? Will transformers survive?43:16 — CuspAI's climate focus and what motivated it47:10 — One platform for every material class — what transfers and what doesn't48:42 — Where does the risk of human extinction really come from?51:06 — The "pause AI" debate and the arms-race reality52:40 — Regulating powerful models: government vs. self-regulation55:16 — Who should design AI regulation? 56:29 — The new book1:00:31 — Compression, the information bottleneck, and renormalization1:03:30 — The role of foundational principles in modern AI1:04:06 — Waves in computing, the brain, and the next wave of innovation1:07:11 — Neuroscience and ML: are we in a better position now?1:09:17 — Conferences, the ICLR keynote, and finding the right peopleMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Julia Kempe on Why Math Will Fall Next, Superhuman Provers, and the Return of the Renaissance ResearcherIn this episode, we sit down with Julia Kempe, a Professor at NYU's Center for Data Science and researcher at Meta FAIR's Foundations of Reasoning team,  for a wide-ranging conversation on the future of AI research.We dig into why verifiable domains like mathematics may be on track to "fall" the way Go did. With formal verification through Lean and the Mathlib infrastructure, LLM agents can now generate and check proofs at scale, and Julia makes the case that a new industry of automated mathematical discovery is closer than most mathematicians believe. We explore why Erdős problems are already falling, what's still missing for harder fields like analysis and physics, and how synthetic data, curation, and verification fit together.From there we get into the energy and scaling limits of frontier models, the case for academic research that big labs can't pursue, how to advise PhD students when Claude can already do their first-year work, the rise of AI safety and security as research priorities, and Julia's optimistic argument that AI tools are bringing back the Renaissance generalist  -  the researcher who can finally work fluently across math, biology, and beyond.Timeline00:00 — Introductions01:00 — Defining reasoning and verifiable domains04:00 — Lean, Mathlib, and the formalization of mathematics10:00 — Constructive proofs, Erdős problems, and the new wave of "AI mathematicians"14:00 — Will math be "solved"? Art, photography, and the changing nature of creative work18:00 — Why physics is harder than math22:00 — Moravec's paradox, evolution, and why robotics lags behind language27:00 — The Renaissance is back: generalist researchers in the age of AI29:00 — Advising students: math, programming, and what core education still matters32:00 — Teaching and assessment when GPT can do the homework35:00 — Anti-AI backlash, energy costs, and the security threat40:00 — Scaling vs. efficiency42:00 — Model collapse, synthetic data, and what's left to squeeze from the internet44:00 — What's exciting next: AI for science, safety, robotics, memory, and planning47:00 — Annotation costs as a proxy50:00 — Superhuman models and what security even means against them52:00 — AlphaGo as precedent for verifiable superhuman performance54:00 — Hallucination, the Mirage paper, and whether these are solvable problems56:00 — Why coding isn't fully solved yet58:00 — Agent security, prompt injection, and the Wild West of deployed agents1:01:00 — Regulation: what's needed and what's possible1:04:00 — Advice for PhD students and what research academia should pursue1:09:00 — Startup opportunities: robotics, security, and AI for finance1:12:00 — Closing thoughts: use the tools, and build grassroots AI for goodMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We talk with Mengye Ren, Assistant Professor at NYU's Center for Data Science, about what intelligence actually means once you step outside a benchmark, and why scaling a single centralized model isn't the whole story.We get into why intelligence has to be defined in open environments, not closed ones, and what that means for how we measure progress. We push on the creativity question: today's models sample bottom-up from a softmax or a Gaussian, with no internal loop of consideration, and as Mengye puts it, we haven't understood creativity yet and we're already prepared to hand it over.We also talk about what's missing for the next paradigm: continual learning, memory, embodied grounding, and smaller models that actually accumulate experience instead of re-deriving everything from scratch each call. Along the way, we get into JEPA and latent variables, biology as inspiration vs. blueprint, why frontier labs don't lean on explicit latents, the limits of synthetic data and world models, agent-to-agent communication, model uncertainty and forecasting, and whether ML education still matters when AI writes the experiments.A grounded, contrarian conversation about where AI research should be looking next, beyond benchmarks, beyond scale.Timeline00:00 — Intro and welcome01:24 — What is intelligence? Defining it relative to objectives and open environments04:19 — Is intelligence really the path to human flourishing, or is it productivity?04:57 — Safety, scalable oversight, and whether stronger models help or hurt06:09 — What does "alignment" actually mean?07:18 — Centralized vs. decentralized models: objectivity vs. personal meaning08:50 — Hinton vs. LeCun: where Mengye stands on AI risk10:29 — Bottom-up vs. top-down architectures and feedback loops21:28 — Biology and AI: inspiration, not blueprint24:14 — Biological plausibility, spiking nets, and where the analogy breaks25:39 — JEPA, Mamba, and architectures beyond the transformer27:31 — Language as a special modality: abstraction built for communication29:04 — Are we too locked into the current paradigm? Risk of creativity collapse30:09 — Synthetic data, simulation, and the brain's own generative models31:43 — World models and physical AI: how babies actually learn 33:03 — The case for smaller, continually learning models37:02 — The role of academic research in a frontier-lab world39:47 — Why LLMs aren't funny: the creativity gap40:35 — What research areas matter most: embodiment, continual learning, creativity42:05 — Creativity is bounded by experience — and why bottom-up sampling isn't enough45:35 — Agent-to-agent communication and the limits of sub-agents46:39 — Model confidence, epistemic uncertainty, and forecasting49:44 — Tokenization, static vs. dynamic worlds, and always-learning systems52:20 — Latent variables, JEPA, and why frontier models skip them53:40 — The future of ML education when AI writes the experimentsMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We host Tal Linzen, Associate Professor at NYU and Research Scientist at Google, for a conversation on the intersection of cognitive science and large language models.We discussed why children can learn language from around 100 million words while LLMs need trillions, and the surprising finding that as models get better at predicting the next word, they become worse models of how humans actually process language. Tal walked us through how his lab uses eye-tracking and reading-time data to compare model behavior to human behavior, and what that reveals about prediction, working memory, and the limits of current architectures.We also got into nature versus nurture and how inductive biases can be instilled by pre-training on synthetic languages, world models and whether transformers actually use the geometric structure they encode, the BabyLM challenge and data-efficient language learning, and what mechanistic interpretability can offer cognitive science beyond just fixing model bugs. The conversation closed on academia versus industry, the role of PhDs in the current AI moment, and how AI coding tools are changing the way Tal teaches and evaluates students at NYU.Timeline00:13 — Intro and what cognitive science means02:16 — Using computational simulations to understand how humans learn language05:26 — How children learn language vs. how LLMs are pre-trained07:53 — Why mainstream LLMs are not good models of humans 10:07 — Comparing humans and models with eye-tracking and reading behavior13:52 — Sensory modalities, smell, and how much you can learn from language alone16:03 — Animal cognition and decoding animal communication17:00 — Nature vs. nurture, inductive biases, and what transformers can and can't learn21:21 — Instilling inductive biases through synthetic languages 27:34 — The bouba/kiki effect and cross-linguistic sound symbolism28:33 — Latent causal structure in language and whether models discover it31:13 — Does knowing linguistics help build better models?35:07 — World models: what they mean, and why transformers encode geometry but don't use it39:13 — Tokenization, and why Tal doesn't like it41:35 — Scaling laws and the inverse-U curve of model quality vs. human fit44:34 — Where the human–model mismatch comes from: architecture, memory, and data47:08 — Diffusion language models and sentence planning48:21 — Data quality, synthetic data, and curriculum effects50:54 — Comparing models at different training stages to human development; BabyLM54:40 — What level of the model should we actually probe? Representations vs. behavior1:01:04 — Mechanistic interpretability, Deep Dream, and human dreaming1:02:11 — Cognitive neuroscience, intracranial recordings, and working memory1:10:31 — Should you still do a PhD in 2026?1:12:31 — Will software engineers lose their jobs to AI?1:17:43 — Teaching in the age of coding agents: what changes in the classroom1:20:54 — What's next: human-like LLMs as user simulators, and recruitingMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We host Chieh-Hsin (Jesse) Lai, Staff Research Scientist at Sony AI and visiting professor at National Yang Ming Chiao Tung University, Taiwan, for a conversation about diffusion models, the technology behind tools like Stable Diffusion, and most of the AI image and video generators you've seen in the last few years. Jesse recently co-authored The Principles of Diffusion Models with Stefano Ermon, and the book is quickly becoming a go-to reference in the field.We start with what a generative model actually is, and what it means to "generate" an image or a sound. Jesse explains the core idea behind diffusion in plain terms. You start with pure noise, and a neural network gradually cleans it up, step by step, until a realistic image emerges.From there, we talk about why diffusion has come to dominate so much of generative AI. Because the model builds an image gradually, you can guide it along the way, nudging the output toward what you actually want, refining details, or combining it with other controls. We also discuss the common critique that diffusion is slow and how the field has largely addressed it through new techniques.We zoom out to the bigger picture, too. Jesse shares his view on world models and whether diffusion is the right foundation for them. We talk about what makes a generative model genuinely good versus just good at gaming benchmarks, and why evaluating creativity and realism is so much harder than scoring a multiple-choice test.Timeline00:12 — Intro and welcoming Jesse00:47 — Why Jesse wrote the book, and who it's for03:29 — The three families of diffusion models, and why they're really one idea05:14 — What makes a good generative model07:39 — How do you even measure if a generated image is good08:59 — Why diffusion beats autoregressive models for images10:33 — Is diffusion still slow? How fast generation got fast11:12 — A simple intuition for what a "score" is14:12 — How the different flavors of diffusion connect under the hood14:42 — Diffusion for text and proteins17:12 — Consistency models and the push for one-step generation22:12 — Diffusion for world models: simulating reality in real time26:12 — Do world models need to understand language35:12 — Is diffusion the right tool, or just a convenient one38:12 — What benchmarks actually tell us, and what they miss46:12 — Closing thoughts and where to find the bookMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We talked with Christian Szegedy, co-inventor of Inception and Batch Normalization, founding scientist at xAI, now at Math Inc, about what it takes to build a frontier lab, and why he left xAI to work on formal mathematics. Christian thinks Lean and auto-formalization are the missing piece for trustworthy AI: a machine-checkable layer underneath all reasoning, where proofs are guaranteed correct without anyone having to read them.We got into his bet with François Chollet that AI will hit superhuman mathematician level by 2026, and what that actually unlocks beyond math itself: verified software instead of vibe-coded apps that break when you refactor, AI systems you can actually trust because their reasoning is checkable, and a path to handling protein folding, chemistry, and parts of biology with real guarantees instead of hand-waving. Christian also walked us through how Math Inc's Gauss system pulled off a proof in two weeks that human experts had estimated would take another year.We also covered xAI's first 12-person year, why Christian no longer buys the original batch normalization story, why he's sure transformers won't be the dominant architecture in five years, what mathematicians do in a world of cheap proofs, and his take on whether humanity will handle AI well. He distrusts humanity more than he distrusts AI.Timeline00:12 — Intros: Christian's background (Inception, Batch Norm, xAI, Math Inc)01:29 — Building a frontier lab from scratch: the first 12 people at xAI04:15 — Hiring for proven track records when 200K GPUs are at stake06:07 — Elon's "dependency graph" and balancing long-term vision with investor demos07:28 — Gauss formalizes the strong prime number theorem in 2 weeks12:25 — What "formalization" actually means (and why it's not what most people think)14:39 — Why Lean gives 100% certainty and why that matters for RL15:26 — ProofBridge and joint embeddings across mathematical subfields 18:07 — Does math formalization transfer to coding and other fields?21:44 — Can every domain be mathematized? 23:14 — Verified software, chip design, and why vibe-coded apps are dangerous26:35 — Scaling Mathlib by 100–1000x28:27 — Artisan formalizers vs. invisible machine-language formalists33:26 — Can verification generalize?45:19 — Revisiting Batch Norm: covariate shift, loss landscape, and what really happens48:22 — Is normalization even necessary? 50:10 — What's actually fundamental in modern AI architectures51:41 — Why Christian thinks transformers won't last 5 years52:38 — The 2026 superhuman AI mathematician bet55:15 — What's missing: better verification + a much larger formalized math repository56:13 — Lean vs. Coq vs. HOL Light -  does the proof assistant actually matter?59:26 — The role of mathematicians in 5–10 years1:02:00 — A human element to mathematics: Newton, Leibniz, and competitive proving1:03:25 — The telescope analogy: AI as the instrument that lets us see the math universe1:05:19 — Job apocalypse or Jevons paradox? 1:08:41 — Advice for students1:09:50 — Can we formally verify AI alignment? 1:11:52 — Closing thanksMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We sat down with Rao Kambhampati, a Professor of CS at Arizona State University and former President of AAAI, to talk about reasoning models: what they are, when they work, and when they break.Rao has been working on planning and decision-making since long before deep learning, which makes him one of the most grounded voices on what today's reasoning systems actually do. We start with definitions of what reasoning is, why planning is the hard subset of it, and what changed when systems like o1 and DeepSeek R1 moved the verifier from inference into post-training. From there we get into where these models generalize, where they don't, and why benchmarks can be misleading about both.A big chunk of the conversation is on chain-of-thought: what intermediate tokens are actually doing, why they help the model more than they help the reader, and what outcome-based RL does to whatever semantic content was there to begin with. We also cover world models and why Rao thinks the video-only framing is the wrong bet, the difference between agentic safety and existential risk, and what the planning community figured out decades ago that the LLM community keeps rediscovering.Timeline(00:12) Intros(01:32) Defining "reasoning" and the System 1 / System 2 framing(04:12) Blocksworld vs Sokoban, and non-ergodicity(06:42) Pre-o1: PlanBench and "LLMs are zero-shot X" papers(07:42) LLM-Modulo and moving the verifier into post-training(10:12) Is RL post-training reasoning, or case-based retrieval?(13:12) τ-Bench and benchmarks that avoid action interactions(14:12) OOD generalization and what we don't know about post-training data(19:02) Does it matter how they work if they answer the questions we care about?(21:27) Architecture lotteries and why no one tries different designs(23:42) Intermediate tokens and the "reduce thinking effort" cottage industry(26:12) The 30×30 maze experiment(27:42) Sokoban, NetHack, and Mystery Blocksworld(34:58) Stop Anthropomorphizing Intermediate Tokens — the swapped-trace experiment(46:12) Latent reasoning, Coconut, and why R0 beat R1(50:12) How outcome-based RL erodes CoT semantics(52:12) Dot-dot-dot and Anthropic's CoT monitoring paper(53:42) Safety: Hinton, Bengio, LeCun(57:12) Existential risk vs real safety work(59:42) World models, transition models, and video-only approaches(1:03:12) Why linguistic abstractions matter — pick and roll(1:05:42) What the planning community knew in 2005(1:08:12) Multi-agent LLMs(1:09:57) Closing thoughts: the bridge analogyMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

In this episode, we hosted Zhuang Liu, Assistant Professor at Princeton and former researcher at Meta, for a conversation about what actually matters in modern AI and what turns out to be a historical accident.Zhuang is behind some of the most important papers in recent years (with more than 100k citations): ConvNeXt (showing ConvNets can match Transformers if you get the details right), Transformers Without Normalization (replacing LayerNorm with dynamic tanh), ImageBind, Eyes Wide Shut on CLIP's blind spots, the dataset bias work showing that even our biggest "diverse" datasets are still distinguishable from each other, and more.We got into whether architecture research is even worth doing anymore, what "good data" actually means, why vision is the natural bridge across modalities but language drove the adoption wave, whether we need per-lab RL environments or better continual learning, whether LLMs have world models (and for which tasks you'd need one), why LLM outputs carry fingerprints that survive paraphrasing, and where coding agents like Claude Code fit into research workflows today and where they still fall short.Timeline00:13 — Intro01:15 — ConvNeXt and whether architecture still matters06:35 — What actually drove the jump from GPT-1 to  GPT-308:24 — Setting the bar for architecture papers today11:14 — Dataset bias: why "diverse" datasets still aren't22:52 — What good data actually looks like26:49 — ImageBind and vision as the bridge across modalities29:09 — Why language drove the adoption wave, not vision32:24 — Eyes Wide Shut: CLIP's blind spots34:57 — RL environments, continual learning, and memory as the real bottleneck43:06 — Are inductive biases just historical accidents?44:30 — Do LLMs have world models?48:15 — Which tasks actually need a vision world model50:14 — Idiosyncrasy in LLMs: pre-training vs post-training fingerprints53:39 — The future of pre-training, mid-training, and post-training57:57 — Claude Code, Codex, and coding agents in research59:11 — Do we still need students in the age of autonomous research?1:04:19 — Transformers Without Normalization and the four pillars that survived1:06:53 — MetaMorph: Does generation help understanding, or the other way around?1:09:17 — WrapMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We talked with Sasha Rush, researcher at Cursor and professor at Cornell, about what it actually feels like to we in the heart of the AI revolution and build coding agents right now. Sasha shared how these systems are changing day-to-day work and how it feels to develop these systems.A big part of the conversation was about why coding has become such a powerful setting for these tools. We discussed what makes code different from other domains, why agents seem to work especially well there, and how much of today’s progress comes not just from better models, but from better ways of using them. Sasha also gave an inside look at how Cursor thinks about training coding models, long-running agents, context limits, bug finding, and the balance between autonomy and human oversight.We also talked about the broader shift happening in software engineering. Are developers moving to a higher level of abstraction? Is this just a phase where we “babysit” models, or the beginning of a deeper change in how software gets built? Sasha had a very thoughtful perspective here, including what he’s seeing from students, researchers, and engineers who are growing up native to these tools.More broadly, this episode is about what it means to do serious technical work in a moment when the tools are changing incredibly fast. Sasha brought both optimism and skepticism to the discussion, and that made this a really grounded conversation about where coding agents are today, what they are already surprisingly good at, and where all of this might be going next.Timeline00:00 Intro and Sasha joins us01:11 What “coding agents” actually mean02:34 Why coding became the breakout use case08:56 Long-running agents and autonomous workflows15:08 How these tools are changing the work of engineers17:15 Are people just babysitting models right now?22:11 How Cursor builds its coding models26:29 Rewards, training, and what makes agents work34:53 Memory, continual learning, and agent communication38:00 How context compaction works in practice41:29 Why coding agents recently got much better50:31 Refactoring, maintenance, and self-improving codebases52:16 Bug finding, oversight, and verification54:43 Will this pace of progress continue?56:42 Can this spread beyond coding?58:27 The future of Cursor and coding agents1:03:08 Model architectures beyond standard transformers1:05:37 World models, diffusion, and what may come nextMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

How Denoising Secretly Powers Everything in AIPeyman Milanfar is a Distinguished Scientist at Google, leading its Computational Imaging team. He's a member of the National Academy of Engineering, an IEEE Fellow, and one of the key people behind the Pixel camera pipeline. Before Google, he was a professor at UC Santa Cruz for 15 years and helped build the imaging pipeline for Google Glass at Google X. Over 35,000 citations.Peyman makes a provocative case that denoising, long dismissed as a boring cleanup task, is actually one of the most fundamental operations in modern ML, on par with SGD and backprop. Knowing how to remove noise from a signal basically means you have a map of the manifold that signals live on, and that insight connects everything from classical inverse problems to diffusion models.We go from early patch-based denoisers to his 2010 "Is Denoising Dead?" paper, and then to the question that redirected his research: if denoising is nearly solved, what else can denoisers do? That led to Regularization by Denoising (RED), which, if you unroll it, looks a lot like a diffusion process, years before diffusion models existed. We also cover how his team shipped a one-step diffusion model on the Pixel phone for 100x ProRes Zoom, the perception-distortion-authenticity tradeoff in generative imaging, and a new paper on why diffusion models don't actually need noise conditioning. The conversation wraps with a debate on why language has dominated the AI spotlight while vision lags, and Peyman's argument that visual intelligence, grounded in physics and robotics, is coming next.Timeline0:00 Intro and Peyman's background1:22 Why denoising matters more than you think Sensor diversity and Tesla's vision-only bet15:04 BM3D and why it was secretly an MMSE estimator17:02 "Is Denoising Dead?" then what else can denoisers do?18:07 Plug-and-play methods and Regularization by Denoising (RED)26:18 Denoising, manifolds, and the compression connection28:12 Energy-based models vs. diffusion: "The Geometry of Noise"31:40 Natural gradient descent and why flow models work34:48 Gradient-free optimization and high-dimensional noise45:13 Image quality and the perception-distortion tradeoff48:39 Information theory, rate-distortion, and generative models52:57 Denoising vs. editing54:25 The changing role of theory57:07 Hobbyist tools vs. shipping consumer products59:40 Coding agents, vibe coding, and domain expertise1:05:00 Vision and more complex-dimensional signals1:09:31 Do models need to interact with the physical world?1:11:28 Continual learning and novelty-driven updates1:13:00 On-device learning and privacy1:15:01 Why has language dominated AI? Is vision next?1:17:14 How kids learn: vision first, language later1:19:36 Academia vs. industry1:22:28 10,000 citations vs. shipping to millions, why choose?Music:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Yaroslav Bulatov helped build the AI era from the inside, as one of the earliest researchers at both OpenAI and Google Brain. Now he wants to tear it all down and start over. Modern deep learning, he argues, is up to 100x more wasteful than it needs to be  -  a Frankenstein of hacks designed for the wrong hardware. With a power wall approaching in two years, Yaroslav is leading an open effort to reinvent AI from scratch: no backprop, no legacy assumptions, just the benefit of hindsight and AI agents that compress decades of research into months. Along the way, we dig into why AGI is a "religious question," how a sales guy with no ML background became one of his most productive contributors, and why the Muon optimizer, one of the biggest recent breakthroughs, could only have been discovered by a non-expert.Timeline00:12 — Introduction and Yaroslav's background at OpenAI and Google Brain01:16 — Why deep learning isn't such a good idea02:03 — The three definitions of AGI: religious, financial, and vibes-based07:52 — The SAI framework: do we need the term AGI at all?10:58 — What matters more than AGI: efficiency and refactoring the AI stack13:28 — Jevons paradox and the coming energy wall14:49 — The recipe: replaying 70 years of AI with hindsight17:23 — Memory, energy, and gradient checkpointing18:34 — Why you can't just optimize the current stack (the recurrent laryngeal nerve analogy)21:05 — What a redesigned AI might look like: hierarchical message passing22:31 — Can a small team replicate decades of research?24:23 — Why non-experts outperform domain specialists27:42 — The GPT-2 benchmark: what success looks like29:01 — Ian Goodfellow, Theano, and the origins of TensorFlow30:12 — The Muon optimizer origin story and beating Google on ImageNet36:16 — AI coding agents for software engineering and research40:12 — 10-year outlook and the voice-first workflow42:23 — Why start with text over multimodality45:13 — Are AI labs like SSI on the right track?48:52 — Getting rid of backprop — and maybe math itself53:57 — The state of ML academia and NeurIPS culture56:41 — The Sutra group challenge: inventing better learning algorithmsMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We talk with Kyunghyun Cho, who is a Professor of Health Statistics and a Professor of Computer Science and Data Science at New York University, and a former Executive Director at Genentech, about why healthcare might be the most important and most difficult domain for AI to transform. Kyunghyun shares his vision for a future where patients own their own medical records, proposes a provocative idea for running continuous society-level clinical trials by having doctors "toss a coin" between plausible diagnoses, and explains why drug discovery's stage-wise pipeline has hit a wall that only end-to-end AI thinking can break through. We also get into GLP-1 drugs and why they're more mysterious than people realize, the brutal economics of antibiotic research, how language models trained across scientific literature and clinical data could compress 50 years of drug development into five, and what Kyunghyun would do with $10 billion (spoiler: buy a hospital network in the Midwest). We wrap up with a great discussion on the rise of professor-founded "neo-labs," why academia got spoiled during the deep learning boom, and an encouraging message for PhD students who feel lost right now.Timeline:(00:00) Intro and welcome(01:25) Why healthcare is uniquely hard(04:46) Who owns your medical records? — The case for patient-controlled data and tapping your phone at the doctor's office(06:43) Centralized vs. decentralized healthcare — comparing Israel, Korea, and the US(13:19) Why most existing health data isn't as useful as we think — selection bias and the lack of randomization(16:53) The "toss a coin" proposal — continuous clinical trials through automated randomization, and the surprising connection to LLM sampling.(23:07) Drug discovery's broken pipeline — why stage-wise optimization is failing, and we need end-to-end thinking(28:30) Why the current system is already failing society — wearables, preventive care, and the case for urgency(31:13) Allen's personal healthcare journey and the GLP-1 conversation(33:13) GLP-1 deep dive — 40 years from discovery to weight loss drugs, brain receptors, and embracing uncertainty(36:28) Why antibiotic R&D is "economic suicide" and how AI can help(42:52) Language models in the clinic and the lab — from clinical notes to back-propagating clinical outcomes, all the way to molecular design(48:04) Do you need domain expertise, or can you throw compute at it?(54:30) The $10 billion question — distributed GPU clouds and a patient-in-the-loop drug discovery system(58:28) Vertical scaling vs. horizontal scaling for healthcare AI(1:01:06) AI regulation — who's missing from the conversation and why regulation should follow deployment(1:06:52) Professors as founders and the "neo-lab" phenomenon — how Ilya cracked the code(1:11:18) Can neo-labs actually ship products? Why researchers should do research(1:13:09) Academia got spoiled — the deep learning anomaly is ending, and that's okay(1:16:07) Closing message — why it's a great time to be a PhD student and researcherMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

In this episode, we talk with Stefano Ermon,  Stanford professor, co-founder & CEO of Inception AI, and co-inventor of DDIM, FlashAttention, DPO, and score-based/diffusion models, about why diffusion-based language models may overtake the autoregressive paradigm that dominates today's LLMs.We start with the fundamental topics, such as what diffusion models actually are, and why iterative refinement (starting from noise, progressively denoising) offers structural advantages over autoregressive generation.From there,  we dive into the technical core of diffusion LLMs. Stefano explains how discrete diffusion works on text, why masking is just one of many possible noise processes, and how the mathematics of score matching carries over from the continuous image setting with surprising elegance.A major theme is the inference advantage. Because diffusion models produce multiple tokens in parallel, they can be dramatically faster than autoregressive models at inference time. Stefano argues this fundamentally changes the cost-quality Pareto frontier, and becomes especially powerful in RL-based post-training.We also discuss Inception AI's Mercury II model, which Stefano describes as best-in-class for latency-constrained tasks like voice agents and code completion.In the final part, we get into broader questions  - why transformers work so well, research advice for PhD students, whether recursive self-improvement is imminent, the real state of AI coding tools, and Stefano's journey from academia to startup founder.TIMESTAMPS0:12 – Introduction1:08 – Origins of diffusion models: from GANs to score-based models in 20193:13 – Diffusion vs. autoregressive: the typewriter vs. editor analogy4:43 – Speed, creativity, and quality trade-offs between the two approaches7:44 – Temperature and sampling in diffusion LLMs — why it's more subtle than you think9:56 – Can diffusion LLMs scale? Inception AI and Gemini Diffusion as proof points11:50 – State space models and hybrid transformer architectures13:03 – Scaling laws for diffusion: pre-training, post-training, and test-time compute14:33 – Ecosystem and tooling: what transfers and what doesn't16:58 – From images to text: how discrete diffusion actually works19:59 – Theory vs. practice in deep learning21:50 – Loss functions and scoring rules for generative models23:12 – Mercury II and where diffusion LLMs already win26:20 – Creativity, slop, and output diversity in parallel generation28:43 – Hardware for diffusion models: why current GPUs favor autoregressive workloads30:56 – Optimization algorithms and managing technical risk at a startup32:46 – Why do transformers work so well?33:30 – Research advice for PhD students: focus on inference34:57 – Recursive self-improvement and AGI timelines35:56 – Will AI replace software engineers? Real-world experience at Inception37:54 – Professor vs. startup founder: different execution, similar mission39:56 – The founding story of Inception AI — from ICML Best Paper to company42:30 – The researcher-to-founder pipeline and big funding rounds45:02 – PhD vs. industry in 2026: the widening financial gap47:30 – The industry in 5-10 years: Stefano's outlookMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Naomi Saphra, Kempner Research Fellow at Harvard and incoming Assistant Professor at Boston University, joins us to explain why you can't do interpretability without understanding training dynamics,  in the same way you can't do biology without evolution.Naomi argues that many structures researchers find inside trained models are vestigial, they mattered early in training but are meaningless by the end. Grokking is one case of a broader phenomenon: models go through multiple consecutive phase transitions during training, driven by symmetry breaking and head specialization, but the smooth loss curve hides all of it. We talk about why training is nothing like human learning, and why our intuitions about what's hard for models are consistently wrong  -  code in pretraining helps language reasoning, tokenization drives behaviors people attribute to deeper cognition, and language already encodes everything humans care about. We also get into why SAEs are basically topic models, the Platonic representation hypothesis, using AI to decode animal communication, and why non-determinism across training runs is a real problem that RL and MoE might be making worse.Timeline: (00:12) Introduction and guest welcome (01:01) Why training dynamics matter - the evolutionary biology analogy (03:05) Jennifer Aniston neurons and the danger of biological parallels (04:48) What is grokking and why it's one instance of a broader phenomenon (08:25) Phase transitions, symmetry breaking, and head specialization (11:53) Double descent, overfitting, and the death of classical train-test splits (15:10) Training is nothing like learning (16:08) Scaling axes - data, model size, compute, and why they're not interchangeable (19:29) Data quality, code as reasoning fuel, and GPT-2's real contribution (20:43) Multilingual models and the interlingua hypothesis (25:58) The Platonic representation hypothesis and why image classification was always multimodal (29:12) Sparse autoencoders, interpretability, and Marr's levels (37:32) Can we ever truly understand what models know? (43:59) The language modality chauvinist argument (51:55) Vision, redundancy, and self-supervised learning (57:18) World models - measurable capabilities over philosophical definitions (1:00:14) Is coding really a solved task? (1:04:18) Non-determinism, scaling laws, and why one training run isn't enough (1:10:12) Naomi's new lab at BU and recruitingMusic:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0. Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Stefano Soatto, VP for AI at AWS and Professor at UCLA, the person responsible for agentic AI at AWS, joins us to explain why building reliable AI agents is fundamentally a control theory problem.Stefano sees LLMs as stochastic dynamical systems that need to be controlled, not just prompted. He introduces "strands coding," a new framework AWS is building that sits between vibe coding and spec coding, you write a skeleton with AI functions constrained by pre- and post-conditions, verifying intent before a single line of code is generated. The surprising part: even as AI coding adoption goes up, developer trust in the output is going down.We go deep into the philosophy of models and the world. Stefano argues that the dichotomy between "language models" and "world models" doesn't really exist, where a reasoning engine trained on rich enough data is a world model. He walks us through why naive realism is indefensible, how reverse diffusion was originally intended to show that models can't be identical to reality, and why that matters now.We also discuss three types of information, Shannon, algorithmic, and conceptual, and why algorithmic information is the one that actually matters to agents. Synthetic data doesn't add Shannon information, but it adds algorithmic information, which is why it works. Intelligence isn't about scaling to Solomonov's universal induction; it's about learning to solve new problems fast.Takeaways:Vibe coding is local feedback control with high cognitive load; spec coding is open-loop global control with silent failures, neither scales well alone.Trust in AI-generated code is declining even as adoption rises.The distinction between next-token prediction and world model is mostly nomenclature - reasoning engines operating on multimodal data are world models.Algorithmic information, not Shannon information, is what matters in the agentic setting.Intelligence isn't minimizing inference uncertainty - it's minimizing time to solve unforeseen tasks.The intent gap between user and model cannot be fully automated or delegated.Timeline(00:13) Introduction and guest welcome(01:12) How the agentic era changed machine learning(06:11) Vibe coding one year later(07:23) Vibe vs. spec vs. strands coding(14:30) Why English is not a programming language(16:36) Constrained generation and agent choreography(20:44) Diffusion models vs. autoregressive models (25:59) The platonic representation hypothesis and naive realism(31:14) Synthetic data and the information bottleneck(36:22) Three types of information: Shannon, algorithmic, conceptual(38:47) Scaling laws and Solomonov induction(42:14) World models and the Goethian vs. Marrian approach(49:00) Encoding vs. generation and JEPA-style training(55:50) Are language models already world models?(59:13) Closing thoughts on trust, education, and responsibility.Music:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0. Changes: trimmedAboutThe Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Tanishq Abraham, CEO and co-founder of Sophont.ai, joins us to talk about building foundation models specifically for medicine.Sophont is trying to be something like an OpenAI or Anthropic but for healthcare  - training models across pathology, neuroimaging, and clinical text, to eventually fuse them into one multimodal system. The surprising part: their pathology model trained on 12,000 public slides performs on par with models trained on millions of private ones. Data quality beats data quantity.We talk about what actually excites Tanishq, which is not replacing doctors, but finding things doctors can't see. AI predicting gene mutations from a tissue slide, or cardiovascular risk from an eye scan.We also talk about the regulation and how the picture is less scary than people assume. Text-based clinical decision support can ship without FDA approval. Pharma partnerships offer near-term impact. The five-to-ten-year timeline people fear is really about drug discovery, not all of medical AI.Takeaways:The real promise of medical AI is finding hidden signals in existing data, not just automating doctorsSmall, curated public datasets can rival massive private onesMultimodal fusion is the goal, but you need strong individual encoders firstAI research itself might get automated sooner than biology or chemistryFDA regulation has more flexibility than most people thinkTimeline(00:12) Introduction and guest welcome(02:32) Anthropic's ad about ChatGPT ads(07:26) XAI merging into SpaceX(13:32) Vibe coding one year later(17:00) Claude Code and agentic workflows(21:52) Can AI automate AI research?(26:57) What is medical AI(31:06) Sofont as a frontier medical AI lab(33:52) Public vs. private data - 12K slides vs. millions(36:43) Domain expertise vs. scaling(41:54) Cancer, diabetes, and personal stakes(47:52) Classification vs. prediction in medicine(50:36) When doctors disagree(54:43) Quackery and AI(57:15) Uncertainty in medical AI(1:03:11) Will AI replace doctors?(1:07:24) Self-supervised learning on sleep data(1:10:10) Aligning modalities(1:13:17) FDA regulation(1:22:28) Closing Music:"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0."Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.Changes: trimmedAbout The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Anastasios Angelopoulos, Co-Founder and CEO of Arena AI (formerly LMArena), joins us to talk about why static benchmarks are failing, how human preference data actually works under the hood, and what it takes to be the "gold standard" of AI evaluation.Anastasios sits at a fascinating intersection -   a theoretical statistician running the platform that every major lab watches when they release a model. We talk about the messiness of AI-generated code slop (yes, he hides Claude's commits too), then dig into the statistical machinery that powers Arena's leaderboards and why getting evaluation right is harder than most people think.We explore why style control is both necessary and philosophically tricky, where you can regress away markdown headers and response length, but separating style from substance is a genuinely unsolved causal inference problem. We also get into why users are surprisingly good judges of model quality, how Arena serves as a pre-release testing ground for labs shipping stealth models under codenames, and whether the fragmentation of the AI market (Anthropic going enterprise, OpenAI going consumer, everyone going multimodal) is actually a feature, not a bug. Plus, we discuss the role of rigorous statistics in the age of "just run it again," why structured decoding can hurt model performance, and what Arena's 2026 roadmap looks like.Timeline:(00:12) Introduction and Anastasios's Background(00:55) What Arena Does and Why Static Benchmarks Aren't Enough(02:26) Coverage of Use Cases - Is There Enough?(04:22) Style Control and the Bradley-Terry Methodology(08:35) Can You Actually Separate Style from Substance?(10:24) Measuring Slop - And the Anti-Slop Paper Plug(11:52) Can Users Judge Factual Correctness?(13:31) Tool Use and Agentic Evaluation on Arena(14:14) Intermediate Feedback Signals Beyond Final Preference(15:30) Tool Calling Accuracy and Code Arena(17:42) AI-Generated Code Slop and Hiding Claude's Commits(19:49) Do We Need Separate Code Streams for Humans and LLMs?(20:01) RL Flywheels and Arena's Preference Data(21:16) Focus as a Startup - Being the Evaluation Company(22:16) Structured vs. Unconstrained Generation(25:00) The Role of Rigorous Statistics in the Age of AI(29:23) LLM Sampling Parameters and Evaluation Complexity(30:56) Model Versioning and the Frequentist Approach to Fairness(32:12) Quantization and Its Effects on Model Quality(33:10) Pre-Release Testing and Stealth Models (34:23) Transparency - What to Share with the Public vs. Labs(36:27) When Winning Models Don't Get Released(36:59) Why Users Keep Coming Back to Arena(38:19) Market Fragmentation and Arena's Future Value(39:37) Custom Evaluation Frameworks for Specific Users(40:03) Arena's 2026 Roadmap - Science, Methodology, and New Paradigms(42:15) The Economics of Free Inference(43:13) Hiring and Closing ThoughtsMusic:"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0."Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Fred Sala, Assistant Professor at UW-Madison and Chief Scientist at Snorkel AI, joins us to talk about why personalization might be the next frontier for LLMs, why data still matters more than architecture, and how weak supervision refuses to die.Fred sits at a rare intersection,  building the theory of data-centric AI in academia while shipping it to enterprise clients at Snorkel. We talk about the chaos of OpenClaw (the personal AI assistant that's getting people hacked the old-fashioned way, via open ports), then focus on one of the most important questions: how do you make a model truly yours?We dig into why prompting your preferences doesn't scale, why even LoRA might be too expensive for per-user personalization, and why activation steering methods like REFT could be the sweet spot. We also explore self-distillation for continual learning, the unsolved problem of building realistic personas for evaluation, and Fred's take on the data vs. architecture debate (spoiler: data is still undervalued). Plus, we discuss why the internet's "Ouroboros effect" might not doom pre-training as much as people fear, and what happens when models become smarter than the humans who generate their training data.Takeaways:Personalization requires ultra-efficient methods - even one LoRA per user is probably too expensive. Activation steering is the promising middle ground.The "pink elephant problem" makes prompt-based personalization fundamentally limited - telling a model what not to do often makes it do it more.Self-distillation can enable on-policy continual learning without expensive RL reward functions, dramatically reducing catastrophic forgetting.Data is still undervalued relative to architecture and compute, especially high-quality post-training data, which is actually improving, not getting worse.Weak supervision principles are alive and well inside modern LLM data pipelines, even if people don't call it that anymore.Timeline:(00:13) Introduction and Fred's Background(00:39) OpenClaw — The Personal AI Assistant Taking Over Macs(03:43) Agent Security Risks and the Privacy Problem(05:13) Cloud Code, Permissions, and Living Dangerously(07:47) AI Social Media and Agents Talking to Each Other(08:56) AI Persuasion and Competitive Debate(09:51) Self-Distillation for Continual Learning(12:43) What Does Continual Learning Actually Mean?(14:12) Updating Weights on the Fly — A Grand Challenge(15:09) The Personalization Problem — Motivation and Use Cases(17:41) The Pink Elephant Problem with Prompt-Based Personalization(19:58) Taxonomy of Personalization — Preferences vs. Tone vs. Style(21:31) Activation Steering, REFT, and Parameter-Efficient Fine-Tuning(27:00) Evaluating Personalization — Benchmarks and Personas(31:14) Unlearning and Un-Personalization(31:51) Cultural Alignment as Group-Level Personalization(41:00) Can LLM Personas Replace Surveys and Polling?(44:32) Is Continued Pre-Training Still Relevant?(46:28) Data vs. Architecture — What Matters More?(52:25) Multi-Epoch Training — Is It Over?(54:53) What Makes Good Data? Matching Real-World Usage(59:23) Decomposing Uncertainty for Better Data Selection(1:01:52) Mapping Human Difficulty to Model Difficulty(1:04:49) Scaling Small Ideas — From Academic Proof to Frontier Models(1:12:01) What Happens When Models Surpass Human Training Data?(1:15:24) Closing ThoughtsMusic:"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0."Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.Changes: trimmed

Bayan Bruss, VP of Applied AI at Capital One, joins us to talk about building AI systems that can make autonomous financial decisions, and why money might be the hardest problem in machine learning.Bayan leads Capital One's AI Foundations team, where they're working toward a destination most people don't associate with banking: getting AI systems to perceive financial ecosystems, form beliefs about the future, and take actions based on those beliefs. It's a framework that sounds simple until you realize you're asking a model to predict whether someone will pay back a loan over 30 years while the world changes around them.We get into why LLMs are a bad fit for ingesting 5,000 credit card transactions, why synthetic data works surprisingly well for time series, and the tension between end-to-end learning and regulatory requirements that demand you know exactly what your model learned. We also discuss reasoning in language vs. in latent space - if you wouldn't trust a self-driving car that translated images to words before deciding to turn, should you trust a financial system that does all its reasoning in token space?Takeaways:Money is a behavioral science problem - AI in finance requires understanding people, not just numbers.Foundation models pre-trained on web text don't outperform purpose-built models for financial tasks. You're better off building a standalone encoder for financial data.Synthetic data works surprisingly well for time series - possibly because real-world time series lives on a simpler manifold than we assume.Explainability in ML is fundamentally unsatisfying because people want causality from non-causal models.Financial AI needs world models that can imagine alternative futures, not just fit historical data.Timeline:(00:24) Introduction and Bayan's Background(00:42) Claude Code, Vibe Coding - Hype or AGI?(05:59) The Future of Software Engineering and Abstraction(11:20) Abstraction Layers and Karpathy's Take(13:54) Hamming, Kuhn, and Scientific Revolutions in AI(19:24) Stack Overflow's Decline and Proof of Humanity(23:07) Why We Still Trust Humans Over LLMs(30:45) Deep Dive: AI in Banking and Consumer Finance(34:17) Are Markets Efficient? Behavioral Economics vs. Classical Views(37:14) The Components of a Financial Decision: Perception, Belief, Action(42:15) Protected Variables, Proxy Features, and Fairness in Lending(45:05) Explainability: Roller Skating on Marbles(47:55) Sparse Autoencoders, Interpretability, and Turtles All the Way Down(51:57) Foundation Models for Finance — Web Text vs. Purpose-Built(53:09) Time Series, Synthetic Data, and TabPFN(59:44) Feeding Tabular Data to VLMs - Graphs Beat Raw Numbers(1:03:35) Reasoning in Language vs. Latent Space(1:08:24) Is Language the Optimal Representation? Chinese Compression and Information Density(1:13:37) Personalization and Predicting Human Behavior(1:21:36) World Models, Uncertainty, and Professional Worrying(1:24:07) Prediction Markets and Insider Betting(1:26:33) Can LLMs Predict Stocks?(1:29:11) Multi-Agent Systems for Financial DecisionsMusic:"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0."Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0. Changes: trimmedAbout: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

David Mezzetti, creator of TxtAI, joins us to talk about building open source AI frameworks as a solo developer - and why local-first AI still matters in the age of API-everything.David's path from running a 50-person IT company through acquisition to building one of the most well-regarded AI orchestration libraries tells you how sometimes constraints breed better design. TextAI started during COVID when he was doing coronavirus literature research and realized semantic search could transform how we find information.We get into the evolution of the AI framework landscape - from the early days of vector embeddings to RAG to LLM orchestration. David was initially stubborn about not supporting OpenAI's API, wanting to keep everything local. He admits that probably cost him some early traction compared to LangChain, but it also shaped TextAI's philosophy: you shouldn't need permission to build with AI.We also talk about small models and some genuinely practical insights: a 20-million parameter model running on CPU might be all you need. On the future of coding with AI, David's come around on "vibe coding" and notes that well-documented frameworks with lots of examples are perfectly positioned for this new world.Takeaways:Local-first AI gives you control, reproducibility, and often better performance for your domainSmall models (even 20M parameters) can solve real problems on CPUGood documentation and examples make your framework AI-coding friendlyOpen source should mean actually contributing - not just publishing codeSolo developers can compete by staying focused and being willing to evolveTimeline:(00:14) Introduction and David's Background(07:44) TextAI History and Evolution(12:04) Framework Landscape: LangChain, LlamaIndex, Haystack(15:16) Can AI Re-implement Frameworks?(24:14) API Specs: OpenAI vs Anthropic(26:46) Running an Open Source Consulting Business(32:51) Origin Story: COVID, Kaggle, and Medical Literature(43:08) Open Source Philosophy and Giving Back(47:16) Ethics of Local AI and Developer Freedom(01:06:44) Human in the Loop and AI-Generated Code(01:09:31) The Future of Work and AutomationMusic:"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0."Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0. Changes: trimmedAbout:The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Cody Blakeney from Datology AI joins us to talk about data curation - the unglamorous but critical work of figuring out what to actually train models on.Cody's path from writing CUDA kernels to spending his days staring at weird internet text tells you something important: data quality can account for half or more of a model's final performance. That's on par with major architectural breakthroughs.We get into the differences between pre-training, mid-training, and post-training data. Mid-training in particular has become a key technique for squeezing value out of rare, high-quality datasets. Cody's team stumbled onto it while solving a practical problem: how do you figure out if a 5-billion-token dataset is actually useful when you can't afford hundreds of experimental runs?We also talk about data filtering and some genuinely surprising findings: the documents that make the best training data are often short and dense with information. Those nicely written blog posts with personal anecdotes? Turns out models don't learn as well from them.On synthetic data, Cody thinks pre-training is still in its early days, where most techniques are variations on a few core ideas, but there's huge potential. He's excited about connecting RL failures back to mid-training: when models fail at tasks, use that signal to generate targeted training data.Takeaways:Data work is high-leverage but underappreciatedMid-training helps extract signal from small, valuable datasetsGood filters favor dense, factual text over polished prose.Synthetic data for pre-training works surprisingly well, but remains primitive.Optimal data mixtures depend on model scale, where smaller models need more aggressive distribution shifts.Timeline(00:12) Introduction to Data Correlation in LLMs(05:14) The Importance of Data Quality(10:15) Pre-training vs Post-training Data(15:22) Strategies for Effective Data Utilization(20:15) Benchmarking and Model Evaluation(28:28) Maximizing Perplexity and Coherence(30:27) Measuring Quality in Data(32:56) The Role of Filters in Data Selection(34:19) Understanding High-Quality Data(39:15) Mid-Training and Its Importance(46:51) Future of Data Sources(48:13) Synthetic Data's Role in Pre-Training(53:10) Creating Effective Synthetic Data(57:39) The Debate on Pure Synthetic Data(01:00:25) Navigating AI Training and Legal Challenges(01:02:34) The Controversy of AI in the Art Community(01:05:29) Exploring Synthetic Data and Its Efficiency(01:11:21) The Future of Domain-Specific vs. General Models(01:22:06) Bias in Pre-trained Models and Data Selection(01:28:27) The Potential of Synthetic Data Over Human DataMusic:"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0."Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.Changes: trimmedAboutThe Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Guest: Niloofar Mireshghallah (Incoming Assistant Professor at CMU, Member of Technical Staff at Humans and AI)In this episode, we dive into AI privacy, frontier model capabilities, and why academia still matters.We kick off by discussing GPT-5.2 and whether models rely more on parametric knowledge or context. Niloofar shares how reasoning models actually defer to context, even accepting obviously false information to "roll with it."On privacy, Niloofar challenges conventional wisdom: memorization isn't the problem anymore. The real threats are aggregation attacks (finding someone's pet name in HTML metadata), inference attacks (models are expert geoguessers), and input-output leakage in agentic workflows.We also explore linguistic colonialism in AI, or how models fail for non-English languages, sometimes inventing cultural traditions.The episode wraps with a call for researchers to tackle problems industry ignores: AI for science, education tools that preserve the struggle of learning, and privacy-preserving collaboration between small local models and large commercial ones.Timeline[0:00] Intro[1:03] GPT-5.2 first impressions and skepticism about the data cutoff claims[4:17] Parametric vs. context memory—when do models trust training vs. the prompt?[9:28] The messy problem of memory, weights, and online learning[16:12] Tool use changes model behavior in unexpected ways[17:15] OpenAI's "Advances in Sciences" paper and human-AI collaboration[24:17] Why deep research is getting less useful[28:17] Pre-training vs. post-training—which matters more?[30:35] Non-English languages and AI failures[33:23] Hilarious Farsi bugs: "I'll get back to you in a few days" and invented traditions[37:56] Linguistic colonialism—ChatGPT changed how we write[41:20] Why memorization isn't the real privacy threat[47:14] The three actual privacy problems: inference, aggregation, input-output leakage[54:33] Deep research stalking experiment—finding a cat's name in HTML[1:01:13] Privacy solutions for agentic systems[1:03:23] What Niloofar's excited about: AI for scientists, small models, niche problems[1:08:31] AI for education without killing the learning process[1:09:15] Closing: underrated life advice on health and sustainable habitsMusic:"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0."Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.Changes: trimmedAboutThe Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Yann LeCun – Why LLMs Will Never Get Us to AGI"The path to superintelligence - just train up the LLMs, train on more synthetic data, hire thousands of people to school your system in post-training, invent new tweaks on RL-I think is complete bullshit. It's just never going to work."After 12 years at Meta, Turing Award winner Yann LeCun is betting his legacy on a radically different vision of AI. In this conversation, he explains why Silicon Valley's obsession with scaling language models is a dead end, why the hardest problem in AI is reaching dog-level intelligence (not human-level), and why his new company AMI is building world models that predict in abstract representation space rather than generating pixels.Timestamps(00:00:14) – Intro and welcome(00:01:12) – AMI: Why start a company now?(00:04:46) – Will AMI do research in the open?(00:06:44) – World models vs LLMs(00:09:44) – History of self-supervised learning(00:16:55) – Siamese networks and contrastive learning(00:25:14) – JEPA and learning in representation space(00:30:14) – Abstraction hierarchies in physics and AI(00:34:01) – World models as abstract simulators(00:38:14) – Object permanence and learning basic physics(00:40:35) – Game AI: Why NetHack is still impossible(00:44:22) – Moravec's Paradox and chess(00:55:14) – AI safety by construction, not fine-tuning(01:02:52) – Constrained generation techniques(01:04:20) – Meta's reorganization and FAIR's future(01:07:31) – SSI, Physical Intelligence, and Wayve(01:10:14) – Silicon Valley's "LLM-pilled" monoculture(01:15:56) – China vs US: The open source paradox(01:18:14) – Why start a company at 65?(01:25:14) – The AGI hype cycle has happened 6 times before(01:33:18) – Family and personal background(01:36:13) – Career advice: Learn things with a long shelf life(01:40:14) – Neuroscience and machine learning connections(01:48:17) – Continual learning: Is catastrophic forgetting solved?Music:"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0."Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.Changes: trimmedAboutThe Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Atlas Wang (UT Austin faculty, XTX Research Director) joins us to explore two fascinating frontiers: the foundations of symbolic AI and the practical challenges of building AI systems for quantitative finance.On the symbolic AI side, Atlas shares his recent work proving that neural networks can learn symbolic equations through gradient descent, a surprising result given that gradient descent is continuous while symbolic structures are discrete. We talked about why neural nets learn clean, compositional mathematical structures at all, what the mathematical tools involved are, and the broader implications for understanding reasoning in AI systems.The conversation then turns to neuro-symbolic approaches in practice: agents that discover rules through continued learning, propose them symbolically, verify them against domain-specific checkers, and refine their understanding.On the finance side, Atlas pulls back the curtain on what AI research looks like at a high-frequency trading firm. The core problem sounds simple (predict future prices from past data). Still, the challenge is extreme: markets are dominated by noise, predictions hover near zero correlation, and success means eking out tiny margins across astronomical numbers of trades. He explains why synthetic data techniques that work elsewhere don't translate easily, and why XTX is building time series foundation models rather than adapting language models.We also discuss the convergence of hiring between frontier AI labs and quantitative finance, and why this is an exceptional moment for ML researchers to consider the finance industry.Links:Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning - arxiv.org/abs/2506.21797Atlas website - https://www.vita-group.space/Guest: Atlas Wang (UT Austin / XTX)Hosts: Ravid Shwartz-Ziv & Allen RoushMusic: “Kid Kodi” — Blue Dot Sessions. Source: Free Music Archive. Licensed CC BY-NC 4.0.

In this episode, we hosted Judah Goldfeder, a PhD candidate at Columbia University and student researcher at Google, to discuss robotics, reproducibility in ML, and smart buildings.Key topics covered:Robotics challenges: We discussed why robotics remains harder than many expected, compared to LLMs. The real world is unpredictable and unforgiving, and mistakes have physical consequences. Sim-to-real transfer remains a major bottleneck because simulators are tedious to configure accurately for each robot and environment. Unlike text, robotics lacks foundation models, partly due to limited clean, annotated datasets and the difficulty of collecting diverse real-world data.Reproducibility crisis: We discussed how self-reported benchmarks can lead to p-hacking and irreproducible results. Centralized evaluation systems (such as Kaggle or ImageNet challenges), where researchers submit algorithms for testing on hidden test sets, seem to drive faster progress.Smart buildings: Judah's work at Google focuses on using ML to optimize HVAC systems, potentially reducing energy costs and carbon emissions significantly. The challenge is that every building is different. It makes the simulation configuration extremely labor-intensive. Generative AI could help by automating the process of converting floor plans or images into accurate building simulations.Links:Judah website - https://judahgoldfeder.com/Music:"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0."Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.Changes: trimmed

In this episode, we talk with Will Brown, a research lead at Prime Intellect, about his journey into reinforcement learning (RL) and multi-agent systems, exploring their theoretical foundations and practical applications. We discuss the importance of RL in the current LLMs pipeline and the challenges it faces. We also discuss applying agentic workflows to real-world applications and the ongoing evolution of AI development.Chapters00:00 Introduction to Reinforcement Learning and Will's Journey03:10 Theoretical Foundations of Multi-Agent Systems06:09 Transitioning from Theory to Practical Applications09:01 The Role of Game Theory in AI11:55 Exploring the Complexity of Games and AI14:56 Optimization Techniques in Reinforcement Learning17:58 The Evolution of RL in LLMs21:04 Challenges and Opportunities in RL for LLMs23:56 Key Components for Successful RL Implementation27:00 Future Directions in Reinforcement Learning36:29 Exploring Agentic Reinforcement Learning Paradigms38:45 The Role of Intermediate Results in RL41:16 Multi-Agent Systems: Challenges and Opportunities45:08 Distributed Environments and Decentralized RL49:31 Prompt Optimization Techniques in RL52:25 Statistical Rigor in Evaluations55:49 Future Directions in Reinforcement Learning59:50 Task-Specific Models vs. General Models01:02:04 Insights on Random Verifiers and Learning Dynamics01:04:39 Real-World Applications of RL and Evaluation Challenges01:05:58 Prime RL Framework: Goals and Trade-offs01:10:38 Open Source vs. Closed Source Models01:13:08 Continuous Learning and Knowledge ImprovementMusic:"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0."Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.Changes: trimmed

In this episode, we discuss various topics in AI, including the challenges of the conference review process, the capabilities of Kimi K2 thinking, the advancements in TPU technology, the significance of real-world data in robotics, and recent innovations in AI research. We also talk about the cool "Chain of Thought Hijacking" paper, how to use simple ideas to scale RL, and the implications of the Cosmos project, which aims to enable autonomous scientific discovery through AI.Papers and links:Chain-of-Thought Hijacking - https://arxiv.org/pdf/2510.26418Kosmos: An AI Scientist for Autonomous Discovery - https://t.co/9pCr6AUXAeJustRL: Scaling a 1.5B LLM with a Simple RL Recipe - https://relieved-cafe-fe1.notion.site/JustRL-Scaling-a-1-5B-LLM-with-a-Simple-RL-Recipe-24f6198b0b6b80e48e74f519bfdaf0a8Chapters00:00 Navigating the Peer Review Process04:17 Kimi K2 Thinking: A New Era in AI12:27 The Future of Tool Calls in AI17:12 Exploring Google's New TPUs22:04 The Importance of Real-World Data in Robotics28:10 World Models: The Next Frontier in AI31:36 Nvidia's Dominance in AI Partnerships32:08 Exploring Recent AI Research Papers37:46 Chain of Thought Hijacking: A New Threat43:05 Simplifying Reinforcement Learning Training54:03 Cosmos: AI for Autonomous Scientific DiscoveryMusic:"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0."Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.Changes: trimmed

In this episode, we sit down with Alex Alemi, an AI researcher at Anthropic (previously at Google Brain and Disney), to explore the powerful framework of the information bottleneck and its profound implications for modern machine learning.We break down what the information bottleneck really means, a principled approach to retaining only the most informative parts of data while compressing away the irrelevant. We discuss why compression is still important in our era of big data, how it prevents overfitting, and why it's essential for building models that generalize well.We also dive into scaling laws: why they matter, what we can learn from them, and what they tell us about the future of AI research.Papers and links:Alex's website - https://www.alexalemi.com/Scaling exponents across parameterizations and optimizers - https://arxiv.org/abs/2407.05872Deep Variational Information Bottleneck - https://arxiv.org/abs/1612.00410Layer by Layer: Uncovering Hidden Representations in Language Models - https://arxiv.org/abs/2502.02013Information in Infinite Ensembles of Infinitely-Wide Neural Networks - https://proceedings.mlr.press/v118/shwartz-ziv20a.htmlMusic:“Kid Kodi” — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.“Palms Down” — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.Changes: trimmed

In this episode, we talked about AI news and recent papers. We explored the complexities of using AI models in healthcare (the Nature Medicine paper on GPT-5's fragile intelligence in medical contexts). We discussed the delicate balance between leveraging LLMs as powerful research tools and the risks of over-reliance, touching on issues such as hallucinations, medical disagreements among practitioners, and the need for better education on responsible AI use in healthcare.We also talked about Stanford's "Cartridges" paper, which presents an innovative approach to long-context language models. The paper tackles the expensive computational costs of billion-token context windows by compressing KV caches through a clever "self-study" method using synthetic question-answer pairs and context distillation. We discussed the implications for personalization, composability, and making long-context models more practical.Additionally, we explored the "Continuous Autoregressive Language Models" paper and touched on insights from the Smol Training Playbook.Papers discussed:The fragile intelligence of GPT-5 in medicine: https://www.nature.com/articles/s41591-025-04008-8Cartridges: Lightweight and general-purpose long context representations via self-study: https://arxiv.org/abs/2506.06266Continuous Autoregressive Language Models: https://arxiv.org/abs/2510.27688The Smol Training Playbook: https://huggingface.co/spaces/HuggingFaceTB/smol-training-playbookMusic:“Kid Kodi” — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.“Palms Down” — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.Changes: trimmedThis is an experimental format for us, just news and papers without a guest interview. Let us know what you think!