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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
Reinforcement Learning
Textai
Gpt-5
AI Coding
Joint Embedding Predictive Architectures
Natural Language Processing
Diffusion Models
Reasoning Models
World Models
NVIDIA
Local-First AI
Semantic Search

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

PublishesWeeklyEpisodes43Founded10 months ago
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ScienceTechnology

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Artwork for The Information Bottleneck

Latest Episodes

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) de... more

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. ... more

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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 Reasoni... more

YouTube

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 h... more

YouTube

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Recent Guests

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)
Mengye Ren
Assistant Professor at NYU Center for Data Science
New York University
Episode: Intelligence in an Open World - with Mengye Ren (NYU)
Tal Linzen
Professor at NYU; Research scientist at Google
New York University; Google
Episode: Language, Cognition, and the Limits of LLMs - with Tal Linzen (NYU/Google)
Christian Szegedy
AI researcher, co-inventor of Inception and batch normalization; co-founder of XAI and Math Inc
Math Inc; XAI
Episode: Inside xAI, and the Bet on AI Math - with Christian Szegedy (Math Inc)
Subbarau Kamphampati
Professor at Arizona State University
Arizona State University
Episode: Reasoning Models and Planning - with Rao Kambhampati (Arizona State)
Zhuang Liu
Assistant Professor at Princeton
Princeton University
Episode: What Actually Matters in AI? - with Zhuang Liu (Princeton)
Sasha Rush
Researcher at Cursor and professor at Cornell
Cursor; Cornell University
Episode: The Future of Coding Agents with Sasha Rush (Cursor/Cornell)
Peyman Milanfar
Distinguished scientist at Google since 2012, leading the imaging team
Google
Episode: The Hidden Engine of Vision with Peyman Milanfar (Google)
Yaroslav Bulatov
AI researcher, early researcher at OpenAI and Google Brain
OpenAI, Google Brain
Episode: Reinventing AI From Scratch with Yaroslav Bulatov

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
#147
Canada/Technology
Apple Podcasts
#24
Israel/Technology
Apple Podcasts
#40
Finland/Technology
Apple Podcasts
#164
Netherlands/Technology
Apple Podcasts
#178
Switzerland/Technology
Apple Podcasts
#242
South Africa/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. The MAD Podcast with Matt Turck
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The Information Bottleneck launched 10 months ago and published 43 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. Julia Kempe
2. Mengye Ren
3. Tal Linzen
4. Christian Szegedy
5. Subbarau Kamphampati
6. Zhuang Liu
7. Sasha Rush
8. Peyman Milanfar

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