The Information Bottleneck

Jaksot

• AI for Science and the Thermodynamics of Generative AI - with Max Welling (UvA, CuspAI) 29.05.2026 1t 13min

  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.
• After Math Falls, What's Next? with Julia Kempe (NYU/Meta) 25.05.2026 1t 14min

  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.
• Language, Cognition, and the Limits of LLMs - with Tal Linzen (NYU/Google) 17.05.2026 1t 23min

  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.
• Intelligence in an Open World - with Mengye Ren (NYU) 20.05.2026 59min

  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.
• The Principles of Diffusion Models - with Jesse Lai (Sony AI) 10.05.2026 55min

  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.
• Inside xAI, and the Bet on AI Math - with Christian Szegedy (Math Inc) 04.05.2026 1t 12min

  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.
• Reasoning Models and Planning - with Rao Kambhampati (Arizona State) 29.04.2026 1t 11min

  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.
• What Actually Matters in AI? - with Zhuang Liu (Princeton) 24.04.2026 1t 9min

  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.
• The Future of Coding Agents with Sasha Rush (Cursor/Cornell) 15.04.2026 1t 24min

  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.
• The Hidden Engine of Vision with Peyman Milanfar (Google) 10.04.2026 1t 24min

  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.
• Reinventing AI From Scratch with Yaroslav Bulatov 30.03.2026 57min

  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.
• Why Healthcare Is AI's Hardest and Most Important Problem with Kyunghyun Cho (NYU) 24.03.2026 1t 18min

  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.
• Diffusion LLM & Why the Future of AI Won't Be Autoregressive - Stefano Ermon (Stanford /Inception) 19.03.2026 49min

  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.
• Training Is Nothing Like Learning with Naomi Saphra (Harvard) 13.03.2026 1t 11min

  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.
• EP28: How to Control a Stochastic Agent with Stefano Soatto (VP AWS/ Pro. UCLA) 06.03.2026 1t 2min

  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.
• EP27: Medical Foundation Models - with Tanishq Abraham (Sophont.AI) 02.03.2026 1t 25min

  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.
• EP26: Measuring Intelligence in the Wild - Arena and the Future of AI Evaluation 24.02.2026 44min

  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.
• EP25: Personalization, Data, and the Chaos of Fine-Tuning with Fred Sala (UW-Madison / Snorkel AI) 17.02.2026 1t 15min

  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
• EP24: Can AI Learn to Think About Money? - with Bayan Bruss (Capital One) 08.02.2026 1t 31min

  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.
• EP23: Building Open Source AI Frameworks: David Mezzetti on TxtAI and Local-First AI 01.02.2026 1t 14min

  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.