A nice list. But, it doesn't actually go much beyond electronics. In terms of quality of life, I think some of these "conveniences" are a downgrade in practice. I miss blockbuster. I miss watching my favourite shows when they aired on a TV schedule. I miss 90s gaming. I miss being able to unplug.

My full interview with MLStreetTalk has just been posted. I really enjoyed this conversation! We talk about the bitter lesson, scientific discovery, Bayesian inference, mysterious phenomena, and key principles for building intelligent systems. www.youtube.com/watch?v=M-jT...

I'm excited to be giving a keynote talk at the AutoML conference 9-10 am at Cornell Tech tomorrow! I'm presenting "Prescriptions for Universal Learning". I'll talk about how we can enable automation, which I'll argue is the defining feature of ML. 2025.automl.cc/program/

Research doesn't go in circles, but in spirals. We return to the same ideas, but in a different and augmented form.

CDS/Courant Professor Andrew Gordon Wilson (@andrewgwils.bsky.social) argues mysterious behavior in deep learning can be explained by decades-old theory, not new paradigms: PAC-Bayes bounds, soft biases, and large models with a soft simplicity bias. nyudatascience.medium.com/deep-learnin...

Regardless of whether you plan to use them in applications, everyone should learn about Gaussian processes, and Bayesian methods. They provide a foundation for reasoning about model construction and all sorts of deep learning behaviour that would otherwise appear mysterious.

A common takeaway from "the bitter lesson" is we don't need to put effort into encoding inductive biases, we just need compute. Nothing could be further from the truth! Better inductive biases mean better scaling exponents, which means exponential improvements with computation.

Gould mostly recorded baroque and early classical. He only recorded a single Chopin piece, as a one-off broadcast. But like many of his efforts, it's profoundly thought provoking, the end product as much Gould as it is Chopin. I love the last mvt (20:55+). www.youtube.com/watch?v=NAHE...

Whatever you do, just don't be boring.

I had a great time presenting "It's Time to Say Goodbye to Hard Constraints" at the Flatiron Institute. In this talk, I describe a philosophy for model construction in machine learning. Video now online! www.youtube.com/watch?v=LxuN...

Excited to be presenting my paper "Deep Learning is Not So Mysterious or Different" tomorrow at ICML, 11 am - 1:30 pm, East Exhibition Hall A-B, E-500. I made a little video overview as part of the ICML process (viewable from Chrome): recorder-v3.slideslive.com#/share?share...

Our new ICML paper discovers scaling collapse: through a simple affine transformation, whole training loss curves across model sizes with optimally scaled hypers collapse to a single universal curve! We explain the collapse, providing a diagnostic for model scaling. arxiv.org/abs/2507.02119 1/3

Excited about our new ICML paper, showing how algebraic structure can be exploited for massive computational gains in population genetics.

Machine learning is perhaps the only discipline that has become less mature over time. A reverse metamorphosis, from butterfly to caterpillar.

AI this, AI that, the implications of AI for X... can we just never talk about AI again?

Really excited about our new paper, "Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion". We explain the mysterious success of masking diffusion to propose new diffusion models that work well in a variety settings, including proteins, images, and text!

A really outstanding interview of Terence Tao, providing an introduction to many topics, including the math of general relativity (youtube.com/watch?v=HUkB...). I love relativity, and in a recent(ish) paper we also consider the wave maps equation (section 5, arxiv.org/abs/2304.14994).

AI benchmarking culture is completely out of control. Tables with dozens of methods, datasets, and bold numbers, trying to answer a question that perhaps no one should be asking anymore.

We have a strong bias to overestimate the speed of technological innovation and impact. See past claims about autonomous driving, AI curing diseases... or the timeline in every sci-fi book ever written. Where is my flying car?

My new paper "Deep Learning is Not So Mysterious or Different": arxiv.org/abs/2503.02113. Generalization behaviours in deep learning can be intuitively understood through a notion of soft inductive biases, and formally characterized with countable hypothesis bounds! 1/12

I had a great time talking with @anilananth.bsky.social as part of the Simons Institute Polylogues. We cover universal learning, generalization phenomena, how transformers are both surprisingly general but also limited, and the difference between statistics and ML! www.youtube.com/watch?v=Aja0...

These DeepSeek results mostly just reflect the diminishing gap between open and closed models, such that any company with billions can start with llama as a baseline, make some tweaks, and appear like the next OpenAI. Going forward, data and scale won't be the decisive advantage.

It's not the size of your parameter space that matters, it's how you use it.

With interview season coming, don't despair. I conspicuously forgot the name of the place I was interviewing in a 1-1. I made sure to name drop the university a bunch in my job talk right after, just so my allies could be like "he really does know the name".

There's apparently another Andrew Wilson at NYU who teaches piano lessons. I get a lot of emails meant for him. Maybe I'll charge his rate minus $1.

📢 My team at Meta (including Yaron Lipman and Ricky Chen) is hiring a postdoctoral researcher to help us build the next generation of flow, transport, and diffusion models! Please apply here and message me: www.metacareers.com/jobs/1459691...

We're excited to announce the ICML 2025 call for workshops! The CFP and submission advice can be found at: icml.cc/Conferences/.... The deadline is Feb 10. Submit some creative proposals!

Happy New Year everyone! Excited for the year ahead.

Many of the greatest papers, now canonical works, have a story of resistance, tension, and, finally, a crucial advocate. It's shockingly common. Why is there a bias against excellence? And what happens to those papers, those people, when no one has the courage to advocate?

Research scientists using industry GPUs these days... "But Mr Garnier… we're scientists, we want to change the world. You have the finest GPUs that money can buy! You employ 3000 research staff." www.youtube.com/watch?v=hdHF...

This is your monthly reminder that understanding deep learning does not require rethinking generalization, and it never did.

So excited about this new work on Bayesian optimization for antibody design! It works by teaching a generative model how the human immune system evolves antibodies for strong and stable binders. Satisfying mix of ML+Bio. Check out the great thread from @alannawzadamin.bsky.social and the paper!

Excited for the #NeurIPS2024 workshops today! I'll be speaking at: (1) Science of DL (panel, 3:10-4:10, scienceofdlworkshop.github.io/schedule/) (2) "Time Series in the Age of Large Models" (talk, 4:39-5:14, neurips-time-series-workshop.github.io).

New model trained on new dataset of nearly a million evolving antibody families at AIDrugX workshop Sunday at 4:20 pm (#76) #Neurips! Collab between @andrewgwils.bsky.social and BigHatBio. Stay tuned for full thread on how we used the model to optimize antibodies in the lab in coming days!

It feels like _so_ much time has passed since NeurIPS in New Orleans last year. We're in a different universe.

Nice crowd and lots of engagement at our NeurIPS poster today, with Sanae Lotfi presenting on token-level generalization bounds for LLMs! arxiv.org/abs/2407.18158

Every year at NeurIPS, I get a sense of where the community is headed. I'm so happy that the era of larger language models on larger datasets is coming to an end.

Is wearing a scarf indoors a power move?

The logo needs more affirmation!

I wanted to make my first post about a project close to my heart. Linear algebra is an underappreciated foundation for machine learning. Our new framework CoLA (Compositional Linear Algebra) exploits algebraic structure arising from modelling assumptions for significant computational savings! 1/4

Will simply scaling up LLMs get us to AGI? My feature for Nature, w/ inputs from @fchollet.bsky.social @rao2z.bsky.social @yoshuabengio.bsky.social @melaniemitchell.bsky.social @dileeplearning.bsky.social @andrewgwils.bsky.social, Raia Hadsell, Keyon Vafa, Karl Friston www.nature.com/articles/d41...