CVPR 2022, the largest and most prestigious conference in computer vision and one of the most important ML venues in general, has just finished in New Orleans. With over 2000 accepted papers, reviewing the contributions of this year’s CVPR appears to be a truly gargantuan task. Over the next series of blog posts, we will attempt to go over the most interesting papers directly related to our main topic: synthetic data. Today, I present the first but definitely not the last installment devoted to papers from CVPR 2022.
One of the most interesting AI-related news for me recently was a paper by DeepMind researchers that presented a new mathematical result found by large language models: new constructions for the cap set problem. In this post, we take a step back and discuss the general relation between math and AI. A mathematical proof is easy to verify but may be very hard to find. But there are AI-shaped holes in looking for a proof: math involves multi-step reasoning and planning, hard theorems need to be decomposed into lemmas, there are search strategies involved… However, mathematics has turned out to be unexpectedly difficult for AI. In this post we discuss what people have been doing with AI in math and how LLMs can help mathematicians right now.