Achievements of Artificial Intelligence (AI)

I thought it might be useful to create a thread to organize news on what Artificial Intelligence is able to achieve. Its applications might be useful for ME/CFS advocacy and research.

 

Noticed this recent Nature article where a large language model was able to help with solve or optimize the solutions of Math problems.

Mathematical discoveries from program search with large language models

• Bernardino Romera-Paredes,
• Mohammadamin Barekatain,
• Alexander Novikov,
• Matej Balog,
• M. Pawan Kumar,
• Emilien Dupont,
• Francisco J. R. Ruiz,
• Jordan S. Ellenberg,
• Pengming Wang,
• Omar Fawzi,
• Pushmeet Kohli &
• Alhussein Fawzi

Abstract
Large Language Models (LLMs) have demonstrated tremendous capabilities in solving complex tasks, from quantitative reasoning to understanding natural language. However, LLMs sometimes suffer from confabulations (or hallucinations) which can result in them making plausible but incorrect statements [1,2]. This hinders the use of current large models in scientific discovery. Here we introduce FunSearch (short for searching in the function space), an evolutionary procedure based on pairing a pre-trained LLM with a systematic evaluator. We demonstrate the effectiveness of this approach to surpass the best known results in important problems, pushing the boundary of existing LLM-based approaches [3]. Applying FunSearch to a central problem in extremal combinatorics — the cap set problem — we discover new constructions of large cap sets going beyond the best known ones, both in finite dimensional and asymptotic cases. This represents the first discoveries made for established open problems using LLMs. We showcase the generality of FunSearch by applying it to an algorithmic problem, online bin packing, finding new heuristics that improve upon widely used baselines. In contrast to most computer search approaches, FunSearch searches for programs that describe how to solve a problem, rather than what the solution is. Beyond being an effective and scalable strategy, discovered programs tend to be more interpretable than raw solutions, enabling feedback loops between domain experts and FunSearch, and the deployment of such programs in real-world applications.

Link: https://www.nature.com/articles/s41586-023-06924-6

 

Background article here:
Google DeepMind used a large language model to discover new math | MIT Technology Review