The Information Bottleneck

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.

Timeline
00:00  Intro
00:55  1991 in Munich, and why that lab mattered
02:38  "I'm not very smart"  and why compute getting 10× cheaper every 5 years changed everything
04:25  Chess as an AI proxy
08:27  Artificial curiosity in the 90s vs. today's RL exploration
09:10  Why RL is harder than supervised learning
20:48  Coding agents vs. robots, and how a baby learns its own hands
26:20  Compression as understanding
33:40  What's actually missing on the road to AGI
37:30  Why millisecond-by-millisecond planning is stupid
47:44  Convergence to LLMs, GPUs, and how far we still are from the Bremermann limit
51:49  Unsupervised learning, factorial codes, and predictability minimization
58:12  Credit assignment: the fights with LeCun and the Nobel critique
1:02:13  On his last name becoming a verb
1:05:17  The award system's missing peer review
1:07:03  Closed labs and the decline of open research
1:13:23  Audience questions
1:34:02  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: trimmed

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 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.

Timeline
00:00 — Intro
00:47 — Are we happy with the LLM era?
03:14 — Embodiment and physical AI
08:05 — Does "AGI" even matter as a term?
11:34 — Verifiers, RL, and why math/coding are tractable
13:17 — What actually shifted to make materials discovery work
14:42 — From molecules to biology and wet labs
16:26 — Working with real labs: timescales, friction, and the "Mira" agent
20:29 — Balancing simulators vs. experiments: the exploration–exploitation trade-off
23:44 — Active learning for experimental design
24:23 — Why active learning hasn't been central to LLMs
25:24 — A general loop for ML-for-science across domains
27:10 — Foundation models for chemistry: a "mother ship" plus a zoo of fine-tuned models
30:04 — Quantum mechanics, interpretation, and AI as a creative theorist
31:54 — World models and Yann LeCun's view; priors vs. learning
34:57 — Should world knowledge be explicit? (responding to Stefano Ermon)
36:41 — Vision: equivariance vs. transformers, and the role of optimization
40:32 — Best model for molecular properties in 10 years? Will transformers survive?
43:16 — CuspAI's climate focus and what motivated it
47:10 — One platform for every material class — what transfers and what doesn't
48:42 — Where does the risk of human extinction really come from?
51:06 — The "pause AI" debate and the arms-race reality
52:40 — Regulating powerful models: government vs. self-regulation
55:16 — Who should design AI regulation?
56:29 — The new book
1:00:31 — Compression, the information bottleneck, and renormalization
1:03:30 — The role of foundational principles in modern AI
1:04:06 — Waves in computing, the brain, and the next wave of innovation
1:07:11 — 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: trimmed
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.

Julia Kempe on Why Math Will Fall Next, Superhuman Provers, and the Return of the Renaissance Researcher

In 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.
Timeline
00:00 — Introductions
01:00 — Defining reasoning and verifiable domains
04:00 — Lean, Mathlib, and the formalization of mathematics
10:00 — 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 work
18:00 — Why physics is harder than math
22:00 — Moravec's paradox, evolution, and why robotics lags behind language
27:00 — The Renaissance is back: generalist researchers in the age of AI
29:00 — Advising students: math, programming, and what core education still matters
32:00 — Teaching and assessment when GPT can do the homework
35:00 — Anti-AI backlash, energy costs, and the security threat
40:00 — Scaling vs. efficiency
42:00 — Model collapse, synthetic data, and what's left to squeeze from the internet
44:00 — What's exciting next: AI for science, safety, robotics, memory, and planning
47:00 — Annotation costs as a proxy
50:00 — Superhuman models and what security even means against them
52:00 — AlphaGo as precedent for verifiable superhuman performance
54:00 — Hallucination, the Mirage paper, and whether these are solvable problems
56:00 — Why coding isn't fully solved yet
58:00 — Agent security, prompt injection, and the Wild West of deployed agents
1:01:00 — Regulation: what's needed and what's possible
1:04:00 — Advice for PhD students and what research academia should pursue
1:09:00 — Startup opportunities: robotics, security, and AI for finance
1:12:00 — Closing thoughts: use the tools, and build grassroots AI for good
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
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 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.
Timeline
00:00 — Intro and welcome
01:24 — What is intelligence? Defining it relative to objectives and open environments
04:19 — Is intelligence really the path to human flourishing, or is it productivity?
04:57 — Safety, scalable oversight, and whether stronger models help or hurt
06:09 — What does "alignment" actually mean?
07:18 — Centralized vs. decentralized models: objectivity vs. personal meaning
08:50 — Hinton vs. LeCun: where Mengye stands on AI risk
10:29 — Bottom-up vs. top-down architectures and feedback loops
21:28 — Biology and AI: inspiration, not blueprint
24:14 — Biological plausibility, spiking nets, and where the analogy breaks
25:39 — JEPA, Mamba, and architectures beyond the transformer
27:31 — Language as a special modality: abstraction built for communication
29:04 — Are we too locked into the current paradigm? Risk of creativity collapse
30:09 — Synthetic data, simulation, and the brain's own generative models
31:43 — World models and physical AI: how babies actually learn 33:03 — The case for smaller, continually learning models
37:02 — The role of academic research in a frontier-lab world
39:47 — Why LLMs aren't funny: the creativity gap
40:35 — What research areas matter most: embodiment, continual learning, creativity
42:05 — Creativity is bounded by experience — and why bottom-up sampling isn't enough
45:35 — Agent-to-agent communication and the limits of sub-agents
46:39 — Model confidence, epistemic uncertainty, and forecasting
49:44 — Tokenization, static vs. dynamic worlds, and always-learning systems
52:20 — Latent variables, JEPA, and why frontier models skip them
53:40 — The future of ML education when AI writes the experiments
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
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 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.

Timeline
00:13 — Intro and what cognitive science means
02:16 — Using computational simulations to understand how humans learn language
05:26 — How children learn language vs. how LLMs are pre-trained
07:53 — Why mainstream LLMs are not good models of humans
10:07 — Comparing humans and models with eye-tracking and reading behavior
13:52 — Sensory modalities, smell, and how much you can learn from language alone
16:03 — Animal cognition and decoding animal communication
17:00 — Nature vs. nurture, inductive biases, and what transformers can and can't learn
21:21 — Instilling inductive biases through synthetic languages
27:34 — The bouba/kiki effect and cross-linguistic sound symbolism
28:33 — Latent causal structure in language and whether models discover it
31:13 — Does knowing linguistics help build better models?
35:07 — World models: what they mean, and why transformers encode geometry but don't use it
39:13 — Tokenization, and why Tal doesn't like it
41:35 — Scaling laws and the inverse-U curve of model quality vs. human fit
44:34 — Where the human–model mismatch comes from: architecture, memory, and data
47:08 — Diffusion language models and sentence planning
48:21 — Data quality, synthetic data, and curriculum effects
50:54 — Comparing models at different training stages to human development; BabyLM
54:40 — What level of the model should we actually probe? Representations vs. behavior
1:01:04 — Mechanistic interpretability, Deep Dream, and human dreaming
1:02:11 — Cognitive neuroscience, intracranial recordings, and working memory
1:10:31 — 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 classroom
1:20:54 — 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: trimmed
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.