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Artwork for The Information Bottleneck

The Information Bottleneck

Ravid Shwartz-Ziv & Allen Roush
Artificial Intelligence
Machine Learning
Openai
Large Language Models
AI Research
Generative AI
Language Models
Data Curation
World Models
Reinforcement Learning
Textai
Representation Learning
Gpt-5
AI Coding
Joint Embedding Predictive Architectures
Natural Language Processing
Diffusion Models
Reasoning Models
Artificial General Intelligence
NVIDIA

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

PublishesTwice weeklyEpisodes51Foundeda year ago
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ScienceTechnology

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Artwork for The Information Bottleneck

Latest Episodes

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 that chang... more

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 platonic rep... more

YouTube

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 develope... more

YouTube

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 around, walks... more

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Recent Guests

Phillip Isola
Professor at MIT, expert in representation learning
Massachusetts Institute of Technology
Episode: Why All Models Learn the Same Thing with Phillip Isola (MIT)
Qichao Hu
Founder and CEO of Molecular Universe, part of Ses Ai
Molecular Universe / Ses Ai
Episode: AI for Science with Qichao Hu (Molecular Universe / SES AI)
Benny Chen
Co-founder of Fireworks AI
Fireworks AI
Episode: Infrastructure for AI at Scale - With Benny Chen (Fireworks AI)
Oded Rechavi
Professor at Tel Aviv University, biologist, and co-founder of QED Science
Tel Aviv University / QED Science
Episode: Broken Peer Review, AI, and Worms — with Oded Rechavi
Alex Imas
Professor at University of Chicago, expert on AI and labor markets
University of Chicago
Episode: Will AI Take Our Jobs? With Alex Imas (Google/University of Chicago)
Wenhu Chen
Researcher at Meta AI/MSL and Assistant Professor at the University of Waterloo
Meta AI/MSL; University of Waterloo
Episode: Why AI Benchmarks Are Lying to You - with Wenhu Chen (Meta/University of Waterloo)
Jürgen Schmidhuber
Renowned AI researcher and AI foundation thinker
IDSIA / University of Lugano (context from dialogue)
Episode: Jürgen Schmidhuber - Part 2: JEPA, the Road to AGI, and Who Really Invented Modern AI
Max Welling
Professor of machine learning, CTO and co-founder of CuspAI
University of Amsterdam / CuspAI
Episode: AI for Science and the Thermodynamics of Generative AI - with Max Welling (UvA, CuspAI)
Julia Kempe
Professor at NYU Center for Data Science and Meta researcher
New York University; Meta
Episode: After Math Falls, What's Next? with Julia Kempe (NYU/Meta)

Hosts

Ravid Shwartz Ziv
Host of The Information Bottleneck
Allen Roush
Co-host of the show

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#48
Israel/Technology
Apple Podcasts
#115
Japan/Technology
Apple Podcasts
#162
Sweden/Technology
Apple Podcasts
#210
South Africa/Technology
Apple Podcasts
#218
India/Technology
Apple Podcasts
#235
South Korea/Technology

Talking Points

Recent interactions between the hosts and their guests.

Intelligence in an Open World - with Mengye Ren (NYU)
Q: What is the value of continual learning versus offline training, and can models learn in a truly embodied way in real-world environments?
He argues continual learning is essential for embodied agents operating in the real world due to data scarcity, non-stationarity, and the need for continual adaptation, though offline pre-training will likely remain part of the pipeline.
Intelligence in an Open World - with Mengye Ren (NYU)
Q: Do you think we will see self-improvement in the near future in AI, where models improve themselves with minimal human intervention?
Mengye explains there is no consensus yet and discusses potential fundamental limits, but notes ongoing exploration of architectures and learning strategies that could enable continual improvement without relying solely on traditional backpropagation.
What Actually Matters in AI? - with Zhuang Liu (Princeton)
Q: Do you think architecture is the primary driver of AI progress, or do data and training regimes matter more?
The guest argues that while architecture matters, the data, scale, and how we train (the interface and objectives) largely determine performance; you can achieve frontier-level results with different architectures if you get the training data and process right, and you often see diminishing returns chasing purely architectural changes without broader data and training considerations.
Reinventing AI From Scratch with Yaroslav Bulatov
Q: What is your approach to building better models while addressing energy efficiency and memory constraints?
Start from energy-aware toy tasks, design with a clear energy budget in mind, and progressively scale while measuring energy per unit of useful capability. Consider architecture shifts (like local hierarchical message passing) and embrace open, iterative experimentation rather than chasing a single benchmark.
Reinventing AI From Scratch with Yaroslav Bulatov
Q: Do you think AI coding agents will replace software engineers or researchers, or will they augment human labor?
They will augment, increasing what engineers and researchers can accomplish, but not replace them entirely. The trend is toward integrating agents into workflows to amplify productivity, with humans providing direction, evaluation, and creative problem-solving.

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Frequently Asked Questions About The Information Bottleneck

What is The Information Bottleneck about and what kind of topics does it cover?

A tech-forward discussion series that centers on the latest research trends, practical engineering challenges, and strategic implications of generative AI, LLMs, and related infrastructures. Episodes frequently explore topics like diffusion models, world models, reinforcement of safety and alignment, and the evolving roles of AI in mathematics, healthcare, coding, and education. Guests tend to be leading researchers or industry practitioners from universities and big tech who unpack complex concepts (from formalization and verification to agentic AI and open-world learning) with hands-on examples, debates about data quality versus architecture, and considerations for production deployment. Expect rigorous technical talk blended with pragmat... more

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1. Machine Learning Street Talk (MLST)
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The Information Bottleneck launched a year ago and published 51 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on The Information Bottleneck?

Recent guests on The Information Bottleneck include:

1. Phillip Isola
2. Qichao Hu
3. Benny Chen
4. Oded Rechavi
5. Alex Imas
6. Wenhu Chen
7. Jürgen Schmidhuber
8. Max Welling

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