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The Information Bottleneck

Ravid Shwartz-Ziv & Allen Roush

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

Sito del podcast

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Episodi

79 episodi

  1. Physical AI for Science: Why It’s Harder Than Coding | Rishabh Agarwal (Periodic Labs) 1:01:24
  2. Dan Card: AI Hasn’t Changed Cybersecurity As Much As You Think 1:21:04
  3. Why Video Games Still Don’t Use Modern AI | Julian Togelius (NYU) 1:13:23
  4. What’s Left for Mathematicians? | Daniel Litt 1:18:18
  5. The Political Economy of AI: Governance, Bias, and the Future | Andy Hall (Stanford/Anthropic) 1:24:03
  6. John Platt (Google): AI for Climate and Science 1:12:16
  7. Yuandong Tian on Recursive Self-Improvement 58:23
  8. Chris Manning: Language Is the Real Unlock for Intelligence 55:51
  9. Why AI Still Can't See wtih Andrew Dai 58:17
  10. Can AI Replace Doctors? Zachary Lipton on the Future of Healthcare and AI 1:31:17
  11. Sara Hooker on the End of Static AI 1:35:51
  12. Tiny Recursive Models Beat the Giants - Alexia Jolicoeur-Martineau (Microsoft) 52:22
  13. Continual Learning Is the Next Bottleneck | Rohan Anil (Core Automation ) 1:11:09
  14. World Models | John Langford (Microsoft AI Labs) 1:05:39
  15. Which Tabular Model Should You Actually Use? | David Holzmüller (INRIA) 55:40
  16. Why You Can't Just Rent 1,000 GPUs | Charles Frye (Modal) 1:03:00
  17. Stella Biderman (EleutherAI) - Open Source, AI Safety, and Who We Can Trust 1:06:25
  18. Why Deep Learning Finally Works on Tables | Frank Hutter (Prior Labs) 1:18:06
  19. Surya Ganguli: The Physics of Intelligence 1:25:22
  20. Text Diffusion Models with Brendan O'Donoghue (Google DeepMind) 1:09:16
  21. Nathan Lambert: Inside Post-Training and the Open Model Fight 1:15:01
  22. Daphne Koller - The Future of AI in Biology and Drug Discovery 1:02:36
  23. RL Was Broken at Every Level - With Joseph Suarez (PufferAI) 1:03:28
  24. The Model Found a Way Out - with Florian Brand (Prime Intellect) 57:20
  25. Pierre-Carl Langlais on Building Models from Data You Can Account For 1:05:39
  26. Dhruv Batra: The Browser Is a Robotics Problem - From Embodied AI at Meta to Web Agents at Yutori 1:07:58
  27. How to Turn Research Into Billion-Dollar Companies, with Ion Stoica 49:27
  28. Kaggle Grandmasters, Agent Skills, and Why Everyone Is Overfitting with Jean-Francois Puget (NVIDIA) 59:08
  29. AI Agents and The Golden Age of Asking Questions with Dimitris Papailiopoulos (MSR/UW-Madison) 1:13:11
  30. Why All Models Learn the Same Thing with Phillip Isola (MIT) 1:11:28
  31. AI for Science with Qichao Hu (Molecular Universe / SES AI) 1:00:56
  32. Infrastructure for AI at Scale - With Benny Chen (Fireworks AI) 1:05:50
  33. Broken Peer Review, AI, and Worms — with Oded Rechavi 1:18:04
  34. Will AI Take Our Jobs? With Alex Imas (Google/University of Chicago) 1:29:01
  35. Why AI Benchmarks Are Lying to You - with Wenhu Chen (Meta/University of Waterloo) 1:19:03
  36. Jürgen Schmidhuber - Part 2: JEPA, the Road to AGI, and Who Really Invented Modern AI 1:29:29
  37. Jürgen Schmidhuber - World Models, RL, and the Year that changed AI (Part 1) 1:37:56
  38. AI for Science and the Thermodynamics of Generative AI - with Max Welling (UvA, CuspAI) 1:13:46
  39. After Math Falls, What's Next? with Julia Kempe (NYU/Meta) 1:14:43
  40. Intelligence in an Open World - with Mengye Ren (NYU) 59:16
  41. Language, Cognition, and the Limits of LLMs - with Tal Linzen (NYU/Google) 1:23:26
  42. The Principles of Diffusion Models - with Jesse Lai (Sony AI) 55:52
  43. Inside xAI, and the Bet on AI Math - with Christian Szegedy (Math Inc) 1:12:32
  44. Reasoning Models and Planning - with Rao Kambhampati (Arizona State) 1:11:53
  45. What Actually Matters in AI? - with Zhuang Liu (Princeton) 1:09:56
  46. The Future of Coding Agents with Sasha Rush (Cursor/Cornell) 1:24:52
  47. The Hidden Engine of Vision with Peyman Milanfar (Google) 1:24:25
  48. Reinventing AI From Scratch with Yaroslav Bulatov 57:46
  49. Why Healthcare Is AI's Hardest and Most Important Problem with Kyunghyun Cho (NYU) 1:18:18
  50. Diffusion LLM & Why the Future of AI Won't Be Autoregressive - Stefano Ermon (Stanford /Inception) 49:18