The Information Bottleneck

We talked with Sasha Rush, researcher at Cursor and professor at Cornell, about what it actually feels like to we in the heart of the AI revolution and build coding agents right now. Sasha shared how these systems are changing day-to-day work and how it feels to develop these systems.
A big part of the conversation was about why coding has become such a powerful setting for these tools. We discussed what makes code different from other domains, why agents seem to work especially well there, and how much of today’s progress comes not just from better models, but from better ways of using them. Sasha also gave an inside look at how Cursor thinks about training coding models, long-running agents, context limits, bug finding, and the balance between autonomy and human oversight.
We also talked about the broader shift happening in software engineering. Are developers moving to a higher level of abstraction? Is this just a phase where we “babysit” models, or the beginning of a deeper change in how software gets built? Sasha had a very thoughtful perspective here, including what he’s seeing from students, researchers, and engineers who are growing up native to these tools.
More broadly, this episode is about what it means to do serious technical work in a moment when the tools are changing incredibly fast. Sasha brought both optimism and skepticism to the discussion, and that made this a really grounded conversation about where coding agents are today, what they are already surprisingly good at, and where all of this might be going next.

Timeline
00:00 Intro and Sasha joins us
01:11 What “coding agents” actually mean
02:34 Why coding became the breakout use case
08:56 Long-running agents and autonomous workflows
15:08 How these tools are changing the work of engineers
17:15 Are people just babysitting models right now?
22:11 How Cursor builds its coding models
26:29 Rewards, training, and what makes agents work
34:53 Memory, continual learning, and agent communication
38:00 How context compaction works in practice
41:29 Why coding agents recently got much better
50:31 Refactoring, maintenance, and self-improving codebases
52:16 Bug finding, oversight, and verification
54:43 Will this pace of progress continue?
56:42 Can this spread beyond coding?
58:27 The future of Cursor and coding agents
1:03:08 Model architectures beyond standard transformers
1:05:37 World models, diffusion, and what may come next
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
About: The 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.

How Denoising Secretly Powers Everything in AI

Peyman Milanfar is a Distinguished Scientist at Google, leading its Computational Imaging team. He's a member of the National Academy of Engineering, an IEEE Fellow, and one of the key people behind the Pixel camera pipeline. Before Google, he was a professor at UC Santa Cruz for 15 years and helped build the imaging pipeline for Google Glass at Google X. Over 35,000 citations.
Peyman makes a provocative case that denoising, long dismissed as a boring cleanup task, is actually one of the most fundamental operations in modern ML, on par with SGD and backprop. Knowing how to remove noise from a signal basically means you have a map of the manifold that signals live on, and that insight connects everything from classical inverse problems to diffusion models.
We go from early patch-based denoisers to his 2010 "Is Denoising Dead?" paper, and then to the question that redirected his research: if denoising is nearly solved, what else can denoisers do? That led to Regularization by Denoising (RED), which, if you unroll it, looks a lot like a diffusion process, years before diffusion models existed. We also cover how his team shipped a one-step diffusion model on the Pixel phone for 100x ProRes Zoom, the perception-distortion-authenticity tradeoff in generative imaging, and a new paper on why diffusion models don't actually need noise conditioning. The conversation wraps with a debate on why language has dominated the AI spotlight while vision lags, and Peyman's argument that visual intelligence, grounded in physics and robotics, is coming next.

Timeline
0:00 Intro and Peyman's background
1:22 Why denoising matters more than you think Sensor diversity and Tesla's vision-only bet
15:04 BM3D and why it was secretly an MMSE estimator
17:02 "Is Denoising Dead?" then what else can denoisers do?
18:07 Plug-and-play methods and Regularization by Denoising (RED)
26:18 Denoising, manifolds, and the compression connection
28:12 Energy-based models vs. diffusion: "The Geometry of Noise"
31:40 Natural gradient descent and why flow models work
34:48 Gradient-free optimization and high-dimensional noise
45:13 Image quality and the perception-distortion tradeoff
48:39 Information theory, rate-distortion, and generative models
52:57 Denoising vs. editing
54:25 The changing role of theory
57:07 Hobbyist tools vs. shipping consumer products
59:40 Coding agents, vibe coding, and domain expertise
1:05:00 Vision and more complex-dimensional signals
1:09:31 Do models need to interact with the physical world?
1:11:28 Continual learning and novelty-driven updates
1:13:00 On-device learning and privacy
1:15:01 Why has language dominated AI? Is vision next?
1:17:14 How kids learn: vision first, language later
1:19:36 Academia vs. industry
1:22:28 10,000 citations vs. shipping to millions, why choose?
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
About: The 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.

In this episode, we sit down with Peyman Milanfar, Distinguished Scientist at Google, where he leads the Computational Imaging team. Peyman is a member of the National Academy of Engineering, an IEEE Fellow, and one of the key minds behind the imaging pipeline in Google Pixel phones. Before joining Google, he was a professor of Electrical Engineering at UC Santa Cruz for 15 years, and he helped develop the imaging pipeline for Google Glass during his time at Google X. With over 35,000 citations and decades of work at the intersection of image processing and AI, Peyman makes a compelling case that denoising, long dismissed as a "digital janitor" task, is actually one of the most fundamental operations in modern machine learning, on par with SGD and backpropagation.
We trace the full arc from classical denoising algorithms to modern diffusion models. Peyman explains how early denoisers implicitly learned from image patches, how the "Is Denoising Dead?" paper in 2010 led him to ask what else denoisers could do beyond cleaning up noise, and how that question opened the door to regularization by denoising and, eventually, to the diffusion models powering image generation today.
We also dig into the practical side, including how Peyman's team shipped a one-step diffusion model on the Pixel phone for 100x ProRes Zoom, the challenges of controlling hallucinations in generative models for consumer products, and why understanding physics and the image formation process still matters in the age of large models.
The conversation wraps with a big-picture debate: why has language dominated the AI spotlight while vision lags behind? Peyman argues that visual intelligence is coming next, and that, unlike language, vision requires grounding in the physical world through robotics, world models, and continuous learning. He also reflects on his journey from professor to industry researcher and why he wouldn't trade the ability to take ideas from theory to millions of users.
Timeline
0:13 Intro
1:42 Why denoising matters
3:20 History of denoising
5:57 How denoisers work
9:39 Why phones need denoising
12:54 Tesla's vision-only bet
14:14 BM3D's dominance
16:58 "Is Denoising Dead?"
18:21 Regularization by Denoising (RED)
24:26 RED looks like diffusion
26:19 Denoising & manifolds
28:42 Energy-based vs. diffusion models
33:46 Blind denoisers
40:30 Diffusion for text
45:44 Perception-distortion tradeoff
53:05 Denoising vs. editing
57:01 ComfyUI & democratization
58:51 One-step diffusion on Pixel
59:51 Coding agents & domain expertise
1:02:45 Diffusion for music
1:06:53 World models & continuous learning
1:15:01 Why vision will overtake language
1:21:12 Professor vs. Google
1:25:08 Wrap-up
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
About: The 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.

Yaroslav Bulatov helped build the AI era from the inside, as one of the earliest researchers at both OpenAI and Google Brain. Now he wants to tear it all down and start over. Modern deep learning, he argues, is up to 100x more wasteful than it needs to be  -  a Frankenstein of hacks designed for the wrong hardware. With a power wall approaching in two years, Yaroslav is leading an open effort to reinvent AI from scratch: no backprop, no legacy assumptions, just the benefit of hindsight and AI agents that compress decades of research into months. Along the way, we dig into why AGI is a "religious question," how a sales guy with no ML background became one of his most productive contributors, and why the Muon optimizer, one of the biggest recent breakthroughs, could only have been discovered by a non-expert.
Timeline
00:12 — Introduction and Yaroslav's background at OpenAI and Google Brain
01:16 — Why deep learning isn't such a good idea
02:03 — The three definitions of AGI: religious, financial, and vibes-based
07:52 — The SAI framework: do we need the term AGI at all?
10:58 — What matters more than AGI: efficiency and refactoring the AI stack
13:28 — Jevons paradox and the coming energy wall
14:49 — The recipe: replaying 70 years of AI with hindsight
17:23 — Memory, energy, and gradient checkpointing
18:34 — Why you can't just optimize the current stack (the recurrent laryngeal nerve analogy)
21:05 — What a redesigned AI might look like: hierarchical message passing
22:31 — Can a small team replicate decades of research?
24:23 — Why non-experts outperform domain specialists
27:42 — The GPT-2 benchmark: what success looks like
29:01 — Ian Goodfellow, Theano, and the origins of TensorFlow
30:12 — The Muon optimizer origin story and beating Google on ImageNet
36:16 — AI coding agents for software engineering and research
40:12 — 10-year outlook and the voice-first workflow
42:23 — Why start with text over multimodality
45:13 — Are AI labs like SSI on the right track?
48:52 — Getting rid of backprop — and maybe math itself
53:57 — The state of ML academia and NeurIPS culture
56:41 — The Sutra group challenge: inventing better learning algorithms
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
About: The 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.

We talk with Kyunghyun Cho, who is a Professor of Health Statistics and a Professor of Computer Science and Data Science at New York University, and a former Executive Director at Genentech, about why healthcare might be the most important and most difficult domain for AI to transform. Kyunghyun shares his vision for a future where patients own their own medical records, proposes a provocative idea for running continuous society-level clinical trials by having doctors "toss a coin" between plausible diagnoses, and explains why drug discovery's stage-wise pipeline has hit a wall that only end-to-end AI thinking can break through. We also get into GLP-1 drugs and why they're more mysterious than people realize, the brutal economics of antibiotic research, how language models trained across scientific literature and clinical data could compress 50 years of drug development into five, and what Kyunghyun would do with $10 billion (spoiler: buy a hospital network in the Midwest). We wrap up with a great discussion on the rise of professor-founded "neo-labs," why academia got spoiled during the deep learning boom, and an encouraging message for PhD students who feel lost right now.

Timeline:
(00:00) Intro and welcome
(01:25) Why healthcare is uniquely hard
(04:46) Who owns your medical records? — The case for patient-controlled data and tapping your phone at the doctor's office
(06:43) Centralized vs. decentralized healthcare — comparing Israel, Korea, and the US
(13:19) Why most existing health data isn't as useful as we think — selection bias and the lack of randomization
(16:53) The "toss a coin" proposal — continuous clinical trials through automated randomization, and the surprising connection to LLM sampling.
(23:07) Drug discovery's broken pipeline — why stage-wise optimization is failing, and we need end-to-end thinking
(28:30) Why the current system is already failing society — wearables, preventive care, and the case for urgency
(31:13) Allen's personal healthcare journey and the GLP-1 conversation
(33:13) GLP-1 deep dive — 40 years from discovery to weight loss drugs, brain receptors, and embracing uncertainty
(36:28) Why antibiotic R&D is "economic suicide" and how AI can help
(42:52) Language models in the clinic and the lab — from clinical notes to back-propagating clinical outcomes, all the way to molecular design
(48:04) Do you need domain expertise, or can you throw compute at it?
(54:30) The $10 billion question — distributed GPU clouds and a patient-in-the-loop drug discovery system
(58:28) Vertical scaling vs. horizontal scaling for healthcare AI
(1:01:06) AI regulation — who's missing from the conversation and why regulation should follow deployment

(1:06:52) Professors as founders and the "neo-lab" phenomenon — how Ilya cracked the code
(1:11:18) Can neo-labs actually ship products? Why researchers should do research
(1:13:09) Academia got spoiled — the deep learning anomaly is ending, and that's okay
(1:16:07) Closing message — why it's a great time to be a PhD student and researcher

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

About: The 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.