- #189 Technologie
- #189beste Platzierung
- mehrmals pro WocheErscheinungsrhythmus
- 1 h 11typische Länge
- 20. Okt.letzte Folge
Two AI Researchers - Ravid Shwartz Ziv, and Allen Roush, discuss the latest trends, news, and research within Generative AI, LLMs, GPUs, and Cloud Systems.
Folgen
What’s Left for Mathematicians? | Daniel Litt
In this episode, we talked with Daniel Litt, a mathematician at the University of Toronto. In March 2025, Daniel bet that AI would not be able to autonomously produce an Annals of Mathematics-level paper by 2030. He has since conceded. He t
The Political Economy of AI: Governance, Bias, and the Future | Andy Hall (Stanford/Anthropic)
In this episode of the the information bottleneck podcast, we talked with Andy Hall, a Stanford professor and MTS at Anthropic. The conversation explores the political economy of AGI, analyzing how superintelligence could reshape job market
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 whi
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 DarkF
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 le
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
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 ep
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
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 par
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 G
World Models | John Langford (Microsoft AI Labs)
John Langford, one of the heads of Microsoft's AI Labs, the creator of Vowpal Wabbit, and a co-inventor of CAPTCHA, joins us to talk about world models. Transformers need orders of magnitude more data than humans to learn the same thing, an
Which Tabular Model Should You Actually Use? | David Holzmüller (INRIA)
Description Tabular data is still where most of machine learning actually happens in industry, and the field has changed a lot in the last few years. In this episode we talk with David Holzmüller, a researcher at INRIA and one of the people
Why You Can't Just Rent 1,000 GPUs | Charles Frye (Modal)
Charles Frye (Modal, ex-Weights & Biases, Berkeley PhD) joins Ravid and Allen to explain why modern AI research is bottlenecked by compute, and why simply buying more GPUs doesn't solve it. We cover the three problems every lab hits (underu
Stella Biderman (EleutherAI) - Open Source, AI Safety, and Who We Can Trust
Stella Biderman, Executive Director of EleutherAI, joins us the week an OpenAI model autonomously broke out of its sandbox and hacked Hugging Face. Stella calls it what she thinks it is, an offensive cyber operation, and argues it's part of
Why Deep Learning Finally Works on Tables | Frank Hutter (Prior Labs)
In this episode, Frank Hutter joins us to talk about TabPFN and why tabular data is suddenly the hottest problem in deep learning. Frank is a professor at the University of Freiburg and spent 15 years building the AutoML field before foundi
Surya Ganguli: The Physics of Intelligence
Surya Ganguli is a professor at Stanford and VP at General Catalyst, working at the intersection of physics, neuroscience, and AI. He started in string theory, moved to theoretical neuroscience, and now uses tools from statistical physics t
Text Diffusion Models with Brendan O'Donoghue (Google DeepMind)
Brendan O'Donoghue, research director at Google DeepMind, makes the case for text diffusion as a real alternative to autoregressive generation. He walks through how discrete diffusion works, why diffusion samples are far more diverse and wh
Nathan Lambert: Inside Post-Training and the Open Model Fight
Nathan Lambert spent three years as post-training lead at Ai2, where he built the OLMo models, and he writes Interconnects, one of the most-read technical newsletters in AI. He left Ai2 in June and is now working on a new project. He's also
Daphne Koller - The Future of AI in Biology and Drug Discovery
Daphne Koller wrote the book that many of us learned probabilistic graphical models from, founded Coursera, and now runs insitro, which is trying to make drug discovery a machine-learning problem. We start with the bitter lesson. She agrees
RL Was Broken at Every Level - With Joseph Suarez (PufferAI)
In this episode, Joseph Suarez from PufferAI explains why he thinks RL never had an algorithm problem, but it had a code problem. Every part of the standard RL stack was running about a thousand times slower than it should have been, and on
The Model Found a Way Out - with Florian Brand (Prime Intellect)
Florian Brand builds evals at Prime Intellect. The premise of the conversation is that writing a benchmark is the easy part now. Keeping the model from cheating it is the job, and it takes longer than the benchmark itself. We get into why h
Pierre-Carl Langlais on Building Models from Data You Can Account For
Most labs build language models by scraping the web and filtering afterward. Pierre-Carl Langlais runs it the other way around. At Pleias, the French-German lab he co-founded, the models are built from data he can actually account for, whic
Dhruv Batra: The Browser Is a Robotics Problem - From Embodied AI at Meta to Web Agents at Yutori
Dhruv Batra spent years leading Embodied AI at Meta, training virtual robots to navigate photorealistic 3D scans of real buildings with pure reinforcement learning. Then he left to co-found Yutori and build agents for a very different envir
How to Turn Research Into Billion-Dollar Companies, with Ion Stoica
Ion Stoica has done what almost no academic ever does — repeatedly turned university research into billion-dollar companies. He co-founded Databricks (now valued at over $100 billion), Anyscale, Arena AI and Conviva, while his Berkeley lab
Kaggle Grandmasters, Agent Skills, and Why Everyone Is Overfitting with Jean-Francois Puget (NVIDIA)
Jean-Francois Puget is a Director and Distinguished Engineer at NVIDIA, where he leads the Kaggle Grandmasters team, and he's ranked third on Kaggle's all-time list. We caught him on the day NVIDIA announced Nemotron Ultra and its new agent
AI Agents and The Golden Age of Asking Questions with Dimitris Papailiopoulos (MSR/UW-Madison)
In this episode, we talked with Dimitris Papailiopoulos, researcher at Microsoft Research's AI Frontiers lab and professor at the University of Wisconsin, about doing research in the age of agents. Dimitris told us about the Sunday morning
Why All Models Learn the Same Thing with Phillip Isola (MIT)
Phillip Isola, professor at MIT, joins us to talk about representation learning: what makes a representation good, why different models seem to converge on similar representations, and whether pre-training is really over. We discuss the pla
AI for Science with Qichao Hu (Molecular Universe / SES AI)
Most AI-for-science companies are selling shovels. Qichao Hu wants the gold. In this episode, we talk with Qichao, the founder and CEO of Molecular Universe, the AI-for-science platform that grew out of SES AI, a high-energy-density battery
Infrastructure for AI at Scale - With Benny Chen (Fireworks AI)
We talk a lot on this show about RL, agents, and the move between pre-training and post-training, but not enough about the layer everything actually runs on. Benny Chen, co-founder of Fireworks AI, one of the largest inference platforms aro
Broken Peer Review, AI, and Worms — with Oded Rechavi
Oded Rechavi is a biologist at Tel Aviv University and the co-founder of QED, a company building AI to review scientific work. He's also spent years studying worms. We start with what's wrong with peer review and grant funding: why it takes
