The Information Bottleneck

Ravid Shwartz-Ziv & Allen Roush
  • 5.0 (6)
  • Technology

Two AI Researchers - Ravid Shwartz Ziv, and Allen Roush, discuss the latest trends, news, and research within Generative AI, LLMs, GPUs, and Cloud Systems.

  1. 1d ago

    John Platt (Google): AI for Climate and Science

    John Platt is a Google Fellow and leads the Applied Science team at Google Research, the group working on climate and science. He's also the person behind SMO and Platt scaling. In this episode, we talk with him about how Google decides which scientific problems are worth its time, and what happens when AI meets real physical systems. A big part of the conversation is contrails. A small share of flights creates most of the warming from contrails, which together add up to roughly 1% of human-caused warming. John explains how his team detects contrails from weather satellites and predicts where they will form. In trials with American Airlines, rerouting flights cut contrail formation by 62% with almost no extra fuel. We also get into ERA, Google's empirical research assistant, and where AI agents actually help scientists. John sees them taking over the drudgery of building models and munging data, while humans keep the taste and the rigor. He shares why he's skeptical of gradient-free optimization, why the bitter lesson runs into energy and data center limits, and why physical AI that makes manufacturing cheaper could matter a lot for climate. We finish with FireSat, a satellite constellation (about 50 satellites at full scale) designed to catch wildfires while they're still small enough to put out. Timeline00:00 Intro 01:09 How Google picks science problems: comparative advantage 04:15 Projects that didn't work: RNA aptamers and tool/problem fit 05:46 DNA, RNA and XNA 07:41 Contrails and why they warm the planet 13:01 Three AI systems for avoiding contrails 15:23 Cloud seeding and why cloud physics is hard 18:17 Airline trials: 62% fewer contrails 19:46 Navier-Stokes, wind tunnels and where simulation still fails 25:07 Will agents replace scientists? ERA and scorable tasks 31:36 Overfitting, Kaggle and the half-pixel bug 33:41 Agents that exploit bugs: Lean and Goodhart's law 36:45 Gradient-free optimization 38:53 SMO, SVMs and hinge loss in neural networks 42:12 The bitter lesson and the limits of scaling 43:36 Where agents will help science in the next 12 months 45:13 Robotics and making climate tech cheaper to build 48:22 AI risk and misuse 50:51 Climate outlook: past 2°C 54:39 Why AI won't just solve climate change 57:04 Room-temperature superconductors, fusion and HVDC 1:02:25 FireSat: detecting wildfires from orbit 1:05:16 Why existing satellites aren't enough 1:07:53 The economics of 50 satellites 1:09:34 False positives 1:11:16 Closing TopicsComparative advantage as the test for which science problems Google takes onContrails: the physics, the AI pipeline, and the American Airlines trialsERA and AI agents for scientific softwareWhat scientists still do that agents can’t: taste, creativity, rigorOverfitting, Goodhart’s law, and agents exploiting bugsGradient-free methods, SMO, and hinge lossThe bitter lesson and the physical limits of scalingClimate outlook, fusion, and physical AIRoom-temperature superconductors and long-distance power transmissionFireSat and early wildfire detectionMusic - "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0

    John Platt (Google): AI for Climate and Science

  2. 6d ago

    Yuandong Tian on Recursive Self-Improvement

    Yuandong Tian spent many years at Meta FAIR and recently left to co-found Recursive Superintelligence, a company building AI that improves itself. In this episode, he tells us why. We start with his work on computer Go, where he built DarkForest before AlphaGo came out. A few years later came OpenGo, which played Korean professionals on a single GPU and didn't lose a game. He then tried to bring RL to real-world problems and found that the design of the action space mattered more than the algorithm. We also talk about Coconut, his paper on reasoning in latent space instead of tokens, and why frontier models still aren't trained that way. The second half is about recursive self-improvement. Yuandong wrote his last paper at Meta together with GPT-5 and says it made him 6 to 10 times faster. That convinced him his own job could be replaced within five years. We ask him where agents still fall short and whether a new architecture can really beat transformers at scale. He also gives his view on the calls to restrict self-improving AI and makes the case for open source. Recursive is hiring in San Francisco and London: talent@recursive.com Topics * Computer Go: DarkForest, AlphaGo and OpenGo * Gradient-free optimization * Action space design and neural architecture search * Coconut and reasoning in latent space * Understanding how neural networks learn representations * Grokking, and writing a paper with GPT-5 * Recursive self-improvement and coding agents * New architectures vs. transformers * The NanoGPT speedrun * Data efficiency and robotics * Restricting self-improving AI * Open source models Timeline 0:00 Intro 0:45 From CMU to deep learning, and the AlexNet debates 5:35 AlphaGo and beating Go pros on a single GPU 10:27 Gradient-free optimization 13:16 Diffusion models and diversity 16:23 RL on real problems: why the action space matters 20:34 AutoML and architecture search 22:05 Coconut and reasoning in latent space 26:07 Why latent reasoning hasn't caught on 30:36 Adapting research to the LLM era 34:04 Why he left Meta to work on recursive self-improvement 37:10 Do we still need humans? 39:37 Where agents fall short 43:08 Can new architectures beat transformers at scale? 47:29 The hardest part of research to automate 50:16 What comes next for AI 52:52 Should self-improving AI be restricted? 54:11 Open source models 56:43 Hiring at Recursive Music - "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0

    Yuandong Tian on Recursive Self-Improvement

  3. Sep 27

    Chris Manning: Language Is the Real Unlock for Intelligence

    Chris Manning is a legend in NLP. He's a professor of linguistics and computer science at Stanford and ran the Stanford AI Lab. His course CS224N, Natural Language Processing with Deep Learning, is where a whole generation of researchers learned the field. If you've worked on anything in NLP over the last 25 years, you've probably used his work. We start with what linguistics gave machine learning that ML wouldn't have figured out on its own, and what's left for NLP researchers now that LLMs handle most of the classic tasks. Chris thinks the open problems have moved up the stack to pragmatics and dialogue. Models are good at using context but still sound confident when they shouldn't. Then we get into his recent work on how language models learn verb classes. Small GPT-2 models seem to form abstract categories right away instead of memorizing verbs one at a time, and Chris explains why distributed representations push them in that direction. The second half is about meaning and representation. Chris makes the case that Yann LeCun underrates the role of language in intelligence, and revisits his debate with Emily Bender over whether text alone can teach meaning. We finish with ReFT, which steers a frozen model by editing its hidden states, and whether concepts really live in linear subspaces. Timeline 00:00 Intro 01:05 What linguistics gave machine learning 03:27 Is NLP losing its focus on language? 06:29 What's left for NLP researchers in the LLM era 09:37 The next frontier: pragmatics and dialogue 13:01 Constructed languages and AI-to-AI communication 15:35 Do language models learn categories first? 23:43 Why transformers learn abstractions early 26:37 Does it hold at scale? 28:30 LeCun, JEPA and the role of language in intelligence 34:54 Can text alone teach meaning? The octopus debate 40:45 Diffusion language models vs transformers 43:39 ReFT: editing representations instead of weights 50:34 Weights or representations: where knowledge lives 52:37 Do concepts really live in linear subspaces? Topics NLP, computational linguistics, large language models, learning dynamics, meaning from form, world models, JEPA, diffusion LMs, ReFT, representation learning, interpretability Music "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0

    Chris Manning: Language Is the Real Unlock for Intelligence

  4. Sep 24

    Why AI Still Can't See wtih Andrew Dai

    Andrew Dai spent over a decade at Google Brain and DeepMind, where he co-wrote the 2015 paper that introduced language model pre-training followed by fine-tuning, and later co-led pre-training data for Gemini. He's now co-founder and CEO of Elorian AI, which is building models for visual reasoning. We talk about how his pre-training result started as a bug, why next-token prediction scales better than other objectives, and what went wrong for Google in the early LLM race. The second half is about vision: why today's frontier models still can't count objects in a photo, why he thinks reasoning is fundamentally visual, and how his view of world models differs from JEPA. Chapters 00:00 Intro 00:48 Andrew's background and the accidental discovery of pre-training 07:10 Why next-token prediction scales 10:09 How Google fell behind and the early days of Gemini 18:28 What makes training data good 28:16 Where visual understanding breaks down 46:01 World models, JEPA and robotics 55:27 Generation vs understanding, and what's next for Elorian Topics Pre-training and fine-tuningScaling and next-token predictionGemini and Google's AI historyData quality and synthetic dataVisual reasoning and countingWorld models and JEPAMusic "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0

    Why AI Still Can't See wtih Andrew Dai

  5. Sep 21

    Can AI Replace Doctors? Zachary Lipton on the Future of Healthcare and AI

    Zachary Lipton is an associate professor at Carnegie Mellon University and a co-founder of Abridge, a healthcare AI company (and a jazz saxophonist!). His research spans machine learning, healthcare, and the broader impact of AI. In this episode, we discuss what AI can (and cannot yet) do in healthcare. We talk about why medicine is harder to automate than coding, AI scribes and clinical decision support, drug discovery, whether AI could eventually replace doctors, and the role of open models and intelligent routing. In the second half, we turn to the future of AI research: whether academia can still compete, what AI PhD students should work on, and how Zach thinks about automation and the future of research. Timeline00:00 — Introduction 00:28 — Why healthcare is hard for AI 05:00 — Zach’s path into healthcare AI 22:13 — Where AI can have the biggest impact 32:01 — Can AI replace doctors? 41:41 — Open models and AI infrastructure 56:20 — The future of AI research 1:03:03 — What should AI PhD students work on? 1:27:00 — Advice for researchers and founders TopicsAI in healthcare • AI doctors • drug discovery • clinical decision support • open-source AI • AI research • academia vs. industry • future of work Music "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0

    Can AI Replace Doctors? Zachary Lipton on the Future of Healthcare and AI

  6. Sep 16

    Sara Hooker on the End of Static AI

    What comes after scaling? We talk with Sara Hooker, co-founder and CEO of Adaptation Lab, about why the next generation of AI may look very different from today's static models. Sara argues that models should continuously adapt to new tasks, data, users, and environments—and that doing this efficiently will require rethinking much more than fine-tuning. We discuss continual learning, AutoScientist and automated research, why non-verifiable tasks may become the next major bottleneck, and why interfaces could be as important as the models themselves. We also get into open vs. closed models, distillation and Chinese AI labs, AI regulation and safety, cybersecurity and biorisk, AI companionship, and what may eventually come after Transformers and tokenization. TopicsContinuous learning and adaptive AIFine-tuning, memory, and AutoScientistAI agents and automated researchNon-verifiable tasks and human feedbackAdaptive interfacesOpen vs. closed models and distillationAI safety, regulation, cyber risk, and bioriskAI companionship and persuasionThe limits of TransformersMultilingual models and tokenizationChapters00:00 — Introduction 02:15 — Why start another AI lab? The return of research 05:46 — What continuous learning actually means 12:04 — Should every company have its own adapting model? 13:59 — Fine-tuning and platforms like Tinker 18:04 — AutoScientist and automated optimization 22:52 — Can AI really improve its own research? 28:38 — The problem of non-verifiable tasks 31:30 — Human feedback and the limits of exponential progress 34:43 — Why the AI interface matters 40:36 — Distillation, China, and open models 49:05 — Open-model licensing 52:19 — Will open models catch closed models? 58:43 — AI regulation and compute thresholds 1:03:07 — AI safety and agent failures 1:10:19 — Biorisk vs. cybersecurity 1:14:03 — Persuasion, AI companionship, and overlooked risks 1:20:41 — Where will AI have the biggest real-world impact? 1:25:41 — What is missing from current AI architectures? 1:29:03 — Neurosymbolic AI 1:31:30 — Multilingual models and tokenization 1:34:02 — Byte-level models and alternatives to tokenization 1:35:03 — Closing Music "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0

    Sara Hooker on the End of Static AI

  7. Sep 15

    Tiny Recursive Models Beat the Giants - Alexia Jolicoeur-Martineau (Microsoft)

    Alexia Jolicoeur-Martineau is a Principal Researcher at Microsoft and the author of "Less is More: Recursive Reasoning with Tiny Networks," the paper behind the Tiny Recursive Model that hit about 45% on ARC-AGI-1 with a fraction of the parameters of frontier systems. It won the 2025 ARC Prize paper award. She read the hierarchical reasoning paper, thought the potential was real and the explanation was not, and rebuilt it without the mouse brains: a small network that carries a hidden state and a current answer, thinks for a few steps, updates, and repeats, with the gradient truncated at each loop. We get into why puzzles suit this and autoregression doesn't, why she thinks LLMs are bad at molecules and more data won't fix it, and what she'd do with a trillion dollars. Timeline 00:01 Intro01:06 Leaving biostatistics, and why the field stagnated06:47 GANs, diffusion, and research on four GPUs12:58 What was wrong with the hierarchical reasoning paper16:31 Tiny recursive models explained without the biology22:35 Why puzzles favor recursion over left to right generation24:15 Is the bitter lesson really bitter?27:28 With infinite compute, would you still want small models?32:00 Self improvement, memory, and a trillion dollars37:01 Test time compute beyond chain of thought40:41 Why chain of thought fails on molecules45:17 Is there a universal representation?48:06 What people are already building with TRM55:22 Fixed point models and DEQMusic "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0Topics Tiny Recursive Models and the ARC-AGI resultsWhat the hierarchical reasoning model was really doingDeep supervision and truncated backpropLooping transformers and parameter efficiencyWhy puzzles favor whole-context iteration over left to right generationTest time compute beyond chain of thoughtLatent reasoning and the Coconut line of workWhy LLMs fail on chemistry and physicsRepresentation learning and whether a universal representation exists

    Tiny Recursive Models Beat the Giants - Alexia Jolicoeur-Martineau (Microsoft)

  8. Sep 10

    Continual Learning Is the Next Bottleneck | Rohan Anil (Core Automation )

    Rohan Anil spent eleven and a half years at Google, where he went from writing memory allocators to large-scale linear solvers, then optimization at Google Brain, where he co-developed distributed Shampoo and led optimization for PaLM and Gemini pre-training, including the work that produced Gemini Flash. He then joined Anthropic's pre-training team, and left before the IPO to co-found Core Automation with Jerry Tworek (ex-VP of Research at OpenAI). We talk with him about how Brain worked at its peak, why he left two of the world's best labs, and what he thinks is missing from today's models. Rohan's view is that pre-training and RL were split by organizational convenience rather than by science. Pre-training builds a prior, and RL sharpens it to the tasks we care about, and neither gives a model a way to absorb new data or learn from its own experience once it is deployed. Post-training more every day plateaus, on-policy distillation plateaus, and in-context learning only goes as far as the context does. He argues the next architecture needs better ways to fold in new knowledge at inference time, and that this is a fundamental optimization question rather than a harness-engineering one. We also get into why coding agents still fail on low-level systems work, his take on Muon, why second-order methods matter once you leave the noise-dominated regime, and why nobody can yet use a few million GPUs for a single training run. Timeline 00:00 Intro 01:09 From computer vision to Google systems engineering 02:37 Large-scale linear solvers and sparse features 06:01 Getting into optimization: SDCA and Yonghui Wu's team 07:29 Joining the Shampoo crew 09:35 The Google Brain ethos, and why 2017 to 2019 was special 14:23 Is open research going to keep winning? 15:53 Frontier models are only as good as the prior you give them 17:30 Missing the language model wave, then Common Crawl and online distillation 18:31 Paternity leave, DALL-E Mini, and the 14 days that became two years 20:32 PaLM, Gemini pre-training, and Gemini Flash 23:59 The Shampoo origin story: Tomer Koren's two-week proof 26:54 Why leave Google for Anthropic 30:20 Why leave Anthropic for a startup 31:33 Meeting Jerry Tworek at Dolores Park 34:00 What Core Automation is building 36:17 Continual learning and the pre-training vs RL split 39:11 Why coding agents fail at kernels and low-level pipelines 42:00 The QR factorization kernel competition and reward hacking 44:57 Numerics, verification, and hardware that keeps changing 46:07 Are LLMs creative, or just good at search? 49:50 Getting models to extrapolate instead of interpolate 52:03 Why did we ever call it pre-training? 55:02 What RL is really learning 56:27 Competing with the big labs with fewer people 58:31 Will kernel generation keep old GPUs alive? Amdahl's law 1:01:58 Open source plans 1:02:55 Audience question: agentic optimizers 1:04:53 Audience question: Muon, Shampoo, and the future of second-order methods 1:08:58 Hiring at Core Automation key topics Journey from Google Brain to startupEvolution of AI research and optimizationPre-training and reinforcement learningKernel optimization and system efficiencyOpen source AI and collaborative researchChallenges in AI creativity and explorationFuture directions in continual learning and model scaling Music"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0

    Continual Learning Is the Next Bottleneck | Rohan Anil (Core Automation )
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Ratings & Reviews

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About

Two AI Researchers - Ravid Shwartz Ziv, and Allen Roush, discuss the latest trends, news, and research within Generative AI, LLMs, GPUs, and Cloud Systems.

Information

  • Creator
    Ravid Shwartz-Ziv & Allen Roush
  • Years Active
    2025 - 2026
  • Episodes
    74
  • Rating
    Explicit
  • Copyright
    © 2026 Ravid Shwartz-Ziv & Allen Roush
  • Show Website
    The Information Bottleneck

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