@roydanroy
@Google DeepMind. On leave, Canada CIFAR AI Chair and Former Research Director, @VectorInst. Professor, @UofT (Statistics/CS). Views are my own.
@yaringal @andrewgwils @XYHan_ Nothing would get useless coauthors off of papers faster!
100% this.
Chris Albon@chrisalbon·Hands down a cargo e-bike has been my best SF purchase since covid. The freedom to ride by something and think, "oh lets stop there and get ice cream" or "oh hey that matcha place doesn't have a line right now" and be able to park for free right in front of it opens the door to 10,000 adventures with my kids that I will never forget. Get an angle-grinder resistant lock and bike insurance and you are golden.
That's some backbone. 👏👏👏
Julie Steele@eeeeiluj·I work at OpenAI. In my personal capacity, I also think we need to slow down. x.com/hilbertspaess/…
Navier Stokes represents a new moment for AI. Hopefully, OpenAI will turn its attention to problems of societal importance, like DeepMind has, and to the mathematical foundations of AI alignment, which are sorely lacking.
Demis Hassabis@demishassabis·With AlphaFold we mapped the protein universe - now with AlphaGenome Atlas we’re charting the human genome. It can predict the impact of all 9 billion possible single-letter DNA variants, helping scientists better understand disease. Freely available for academic research: t.co/Gsy6lW3z6O
Out with it already.
Well deserved, Owain.
Owain Evans@OwainEvans_UK·I was selected for Time Magazine's Top 100 in AI. This is for research done with many fantastic collaborators and esp. with @BetleyJan & @jameschua_sg . I’d also like to thank @ajeya_cotra and @MaxNadeau_ (@coeff_giving) MATS, Constellation, and Rethink Priorities for supporting my research and team over the years. I’ve also benefited from discussions of our papers here on Twitter!
Some fun at the Escher exhibit with @gkdziugaite
Waterloo has good taste. Sorry to see you leaving!
Gautam Kamath@thegautamkamath·Today's my last day at @UWCheritonCS, before I go on leave. I applied to 30+ faculty positions in Fall 2017. 3 interviewed me, only 1 offer. I'm deeply grateful that Waterloo CS alone saw potential here. I'm sad to be leaving and I will remain its loudest advocate.
The world simply needs to realize that mathematicians are not necessarily experts in AI and its coming effects on maths. Arguably, no one knows where "Math with AI" will settle.
Scott Armstrong@scottnarmstrong·It was really interesting to go back and re-read this article from February which, according to my computations, was only six months ago. nytimes.com/2026/02/07/sci…
Phenomenal effort.
Percy Liang@percyliang·🚢 Marin 535B-A23B started training this week! As usual, the whole process is open. Voyage plan: pretraining (80%) + midtraining (20%) on 18.75T tokens on 11 x GB200 NVL72 for ~3 months (2.7e24 FLOPs). Post-training will follow. Before kicking off the run, we trained a 4-rung scaling ladder from 1.6B-A61M (48B tokens) to 27.7B-A1.2B (926B tokens) to debug issues, and to make a forecast of our hero run. This is by far our biggest run, so definitely expecting the unexpected.
Perhaps there are 11 types of mathematicians. Those who liked exploring. And those who liked the toil.
Rishikesh Gajjala@publishiperishi·I have decided to leave math academia. Not because of burnout, the academic job market, or a loss of love for mathematics. Surprisingly, it is because of how much mathematics I could do with LLMs over the last few months. For most of my life, pursuing mathematics felt like the obvious choice. It gave me a sense of purpose, rooted in the search for hidden truths in God’s Book. Over the past few months, that conviction has been shaken. With LLMs, I could get several breakthroughs on problems I care deeply about and had worked on for years during my PhD. This progress might have taken years of my academic life if not for LLMs. This could have strengthened my conviction that mathematics was my calling. Instead, it undermined it. With each passing week, my role in the discovery loop seemed to grow smaller. What made mathematics meaningful to me was never just the final answer. It was the struggle: months or years of exploration, failed approaches, and deep thought before a hidden structure finally became visible. That struggle gave me a sense that the result was truly mine. Prompting my way to answers, without the struggle that once defined the process, no longer felt like the same vocation. The answer might be just as beautiful, but it did not feel earned in the same way. I now believe that we are rapidly approaching a world in which most answers from God’s Book will be only a prompt away. Once I truly internalized that possibility, dedicating much of my life to finding those answers slightly earlier no longer felt as meaningful as it once had. But one problem kept bothering me: How do we know the oracle is right? Intelligence (and hence the amount of math papers) is becoming abundant and cheaper by the day. Trust is not. The proofs produced by these systems can be highly sophisticated, and their errors can be extremely subtle. Determining whether an apparent breakthrough was actually correct sometimes took me days. Until it is verified, a beautiful proof is not different from slop. That gap drew me toward formal verification and eventually toward formalizing math proofs in Lean, including proofs of several longstanding conjectures and Erdős problems discovered with the help of LLMs. I began to see verification as a central intellectual bottleneck. Intelligence is useful only when its outputs can be trusted. So I am leaving math academia. I am shifting my attention from discovering answers to building systems that can certify them. I am very excited to be joining @PramaanaLabs to work on this challenge. Over the next few years, I hope to help build a future in which increasingly powerful systems are not merely intelligent, but provably correct and reliable across problems far beyond mathematics.
@ilyasergey Just kidding! Or am I? Just kidding!
For those who don't know Morse code, Levent is saying the following: "Help. I'm stuck here in pretraining at Anthropic and they won't let me out until I hit my perplexity quota. I keep trying to do ridiculous things to alert my friends and family outside, like tweeting solutions to major conjectures, but y'all think I'm just having fun and being nonchalant. No! Attention! Sauve-moi!
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Deep question. x.com/JianfengLu_mat…
Sumner for next Fields prize?
Is this a normal way for dogs to sleep or is our new puppy a weirdo?
Focus. Focus. Focus.
Parents and society praise their children for excelling in subjects liike math because it correlates with so many desirable outcomes. Discipline, hardwork, etc. Math has the additional benefit that it has more than paid for itself through various innovations. It's useful to society.
My favourite part of Henry’s tweet thread on the Astra problems. x.com/henryquantum/s…
“Generate me a visual meme for Twitter about NeurIPS reviewers not fulfilling their duty.” Not bad. :)
Fortune favours the bold. Now is not the time to do easy things. It is a time to try the hardest things. x.com/aminkarbasi/st…
True story: Not everyone can get into MIT. That's why they made Berkeley and Stanford. The rest is history. x.com/ziv_ravid/stat…
Personal story: I was fundamentally uninterested in money and getting rich, while at MIT. It wasn't the culture of people around me. Given how many people in each year went into finance though, it was clearly part of the culture, just not a part I interacted with much. Starting companies was probably lumped in with "making money" in my brain back then. That's wrong of coures. I think it's too bad I didn't really understand that starting a company is one way to realize an idea, even if it required enormous resources. It certainly wasn't something that the culture automatically made sure you understood. It took an embarassing amount of time to understand that this was also an opportunity for faculty.
Here's one way to understand today’s claimed nonsofic-group result. Choose a random root node (vertex) in a large finite graph, uniformly at random from the vertices and look only at bounded-radius neighborhoods of that root. What laws on infinite rooted graphs can arise as weak limits?
Every such limit obeys the mass-transport principle: in expectation, mass sent from the root equals mass received. Aldous and Lyons asked whether this necessary symmetry (called unimodularity) is also sufficient. This was recently resolved in the negative but via nonconstructive arguments....
Not my experience. Quite the opposite except for first part. :) x.com/aaswaminathan0…
That awkward time of year when you have 2x as many skies as skiers. Guess which ones are actively used.
ICLR capped submissions to 20 papers per author. This is ridiculous and will slow down AI progress. 👎👎👎 Why 20? This is an arbitrary number, and with AI agents 🤖 able to complete interesting if incremental work 🔨 in AI with little guidance , we should be encouraging everyone to complete and submit as much as possible 🔥💪🦾, especially since all reviewing is now handled by LLMs (...admit it). Click here to sign my petition. t.co/FialJAYkQq
The goal of math is to explain the unknown. There will always be unknown, and, further, no matter how powerful the tools we possess, there will be questions just beyond their capabilities. x.com/jasondeanlee/s…
Amazing to learn all the way my colleagues have some sort of connection to these field medalists! Congrats everyone! ;)
Interesting project: work out what data was most responsible for the advancement. x.com/littmath/statu…
Ever struggle with a ML paper back in grad school and come away thinking this work is suss? Pass it through an LLM and $10 it finds 5 fatal errors.
Check out information, Inference, and Learning Algorithms by David MacKay. Every probability distribution defines a compression algorithm. That compression algorithm is optimal for data drawn from that same distribution. There’s a notion of universal compression, defined in terms of regret under log loss, and then one can talk about minimaxity and adaptive minimaxity. Here the behavior can be different from prediction with respect to a probability model but it’s closely related. In short, the identification I state (prediction = compression) relies only on an algorithm offering probability distributions on next tokens. If one interprets linear regression as offering Gaussian conditional predictions , you again get a compression scheme. It’s true that arbitrary prediction algorithms may not offer any such interpretation but once they offer a sequence of distributions on next tokens, they are an optimal compression scheme in disguise.
Benchmaxxing = paperclip factory? x.com/natolambert/st…
Good artists copy. Great artists steel. x.com/scaling01/stat…
Prediction is compression. Same thing. x.com/_yusufknl/stat…
Congrats to Axiom. x.com/axiommathai/st…
Statistics as a field has not historically oriented itself around formalized open problems. Part of the reason is that the hardest problems in the field are ones of formalizing what the problem even is. Once you formalize a problem, much of the work may be done. Of course, there are counter examples, but, if I were to cast statistics as an AI problem, I think the bulk of (impactful) statistics is closer to auto-formalization than to theorem proving.
A large thread, kicked off by an AI bot. x.com/PierceLilholt/…
Back from ICML and brimming with new ideas and a new profile photo so people can actually recognize me in person ;-)
Nice theory for an empirical phenomenon. x.com/BingbinL/statu…
Fascinating finding. I hear there are antecedents in the literature and I’m glad I now know about this line of work. x.com/anthropicai/st…
So much interesting work. Got through only 20% of the poster session.
This is the right setting to study unlearning just as it is the right setting to study privacy. x.com/gkdziugaite/st…
Arrived into Seoul. If you see me, say hi!
New work on the large and deep neural networks. x.com/mufan_li/statu…
Who’s headed to ICML? Who’s going to the AI for math workshop?
When in Korea….
One of the best introductions to the subject IMO. x.com/bremen79/statu…
I like using the term alien too. x.com/tonylfeng/stat…
Knowing what I know, I’m bullish. Gemini has so much headroom it’s crazy.
