The Information Bottleneck

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

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

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

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

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