Mar 06, 2024

Progress on LLM-based agents that can autonomously plan out and execute sequences of actions has been rapid, and I continue to see month-over-month improvements.

This week's top AI news and research stories featured Mistral's new LLMs, a robot chemist, Google's open source LLMs, and a way to make LLMs better at math. But first:

The Batch AI News and Insights: Progress on LLM-based agents that can autonomously plan out and execute sequences of actions has been rapid, and I continue to see month-over-month improvements.

Large language models are not good at math. Researchers devised a way to make them better. Tiedong Liu and Bryan Kian Hsiang Low at the National University of Singapore proposed a method to fine-tune large language models for arithmetic tasks.

Google asserted its open source bona fides with new models. Google released weights for Gemma-7B, an 8.5 billion-parameter large language model intended to run GPUs, and Gemma-2B, a 2.5 billion-parameter version intended for deployment on CPUs and edge devices.

A robot outperformed human chemists at synthesizing chemicals. Researchers at University of Amsterdam built RoboChem, an integrated robotic system that learned to design light-activated chemical reactions while achieving optimal yields and throughput.

European AI champion Mistral AI unveiled new large language models and formed an alliance with Microsoft.