Google’s Gemini LLMs (Large Language Model) made new math discoveries that solve real-world problems. This is a significant step to AGI. The AI is speeding up progress in math and science.
This is the first time that challenging open problems in science or mathematics have been solved using LLMs. FunSearch discovered new solutions… its solutions could potentially be slotted into a variety of real-world industrial systems to bring swift benefits… the power of these models can be harnessed not only to produce new mathematical discoveries, but also to reveal potentially impactful solutions to important real-world problems.
An Australian mathematician said the open math problem (best bounds for cap sets) was his favorite open question. Six months ago, Aussie Prof Terry Tao, said would take three years for LLM to reach this level of capability.
AI expert, Alan Thompson has moved up his conservative estimate of AI reaching AGI (Artificial general intelligence from 61% to 64%.
[Published in Nature] 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. 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. 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.
Here is 38 pages of supplemental information.
Alan Thompson will be giving about 30% more to his AGI tracker when we have capable humanoid robots with large language model AIs.
Tesla released the Optimus Gen 2 Teslabot with huge advances in capabilities. The fusion of advancing AI and mass produced humanoid robots is very near.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog.
