Dmitry Kobak

@hippopedoid.bsky.social

PI at Ghent University and VIB.AI. Manifold learning, contrastive learning, self-supervised learning, genomics, transcriptomics, interpretability. Excess mortality, statistical forensics. Born but to die and reas'ning but to err. https://dkobak.github.io

While the Fields Medal celebrates the under-40s, the mathematician Joan Birman has, at the age of 99, solved a major open problem in representations of the Braid groups, a topic she has worked on for more than 60 years.

“I have done all of the things that one is supposed to do to earn a tenure-track position. And I have done approximately 85% of them by typing prompts into a large language model and then moderately editing the output.” 🤣🤣🤣 brilliant 👌🏽 open.substack.com/pub/inprepar...

Opinion: I Was Not Allowed To Type Prompts Into ChatGPT During My Chalk Talk And This Is Discrimination

By Dr. Rachel Simmons, Postdoctoral Fellow, Stanford University

open.substack.com

A slide I prepared for my introductory departmental seminar last week to illustrate my CV as a weighted graph. Node weights (shown as marker areas) are the number of years I lived in each place: 24, 3, 2, 4, 9, 0. Edge weights (not shown) are the number of people moving each time: 1, 2, 4, 5, 5.

Bild

I assigned random gender/ethnicity labels to scientific abstracts from the literature and then asked Claude to do a thematic analysis. Claude identified a clinical versus computational split for female/male authors and a DEI focus for Black/URM authors. All in completely random data.

BildBild

"An Erdős problem resolved by humans! One Abel prize winner was quoted as saying 'We knew the day would eventually come when humans could resolve Erdős problems, but we didn't know it would come this soon!' Several math departments now have plans for workshops on Human Alignment."

Humans Solve Erdos Problem!!

(In 2008 I wrote a survey of some of the known sum-product theorems, see  here . Avi Wigderson has a great slide-set on sum-product theorems...

blog.computationalcomplexity.org

Top-3 questions this month on MathOverflow are all about the math performance of frontier reasoning LLMs and what it implies for mathematical training and careers. Very interesting discussions with some very polarizing answers.

Bild

OpenAI's claim that this is a central conjecture in discrete geometry is not an exaggeration. This will I think be looked back on as the first time that AI solved a major mathematics problem (defined as a problem that all experts in some subfield had thought about). openai.com/index/model-...

An OpenAI model has disproved a central conjecture in discrete geometry

An OpenAI model solved the 80-year-old unit distance problem, disproving a major conjecture in discrete geometry and marking a milestone in AI-driven mathematics.

openai.com

This is a very cool work! Only saw it now. They could extract almost the entire (!) The Great Gastsby and 1984 from Claude (with some jailbreaking). But for some reason not Catch-22. I wonder why that is and what it tells us about the training data (or about Catch-22?). Catch-22 is great btw.

A. Feder Cooper@afedercooper.bsky.social · 7mo ago

We extracted (parts of) 12 books in experiments with 4 frontier-lab, production LLMs. We prompted the LLMs with a short prefix of a book and asked them to complete the rest. For Harry Potter and the Sorcerer’s Stone, we extracted 95.8% of the book from jailbroken Claude 3.7 Sonnet.

Screenshot of the paper title with authors listed