Towards Language-driven Scientific AI

TL;DR

This paper explores designing scientific AI systems that leverage natural language as the primary medium for understanding, reasoning, and collaboration with humans to tackle complex scientific challenges.

Contribution

It proposes a vision for language-driven scientific AI and discusses key research challenges to realize this approach.

Findings

Identifies natural language as central to scientific AI systems

Highlights challenges in reasoning and knowledge integration

Suggests pathways for human-AI scientific collaboration

Abstract

Inspired by recent and revolutionary developments in AI, particularly in language understanding and generation, we set about designing AI systems that are able to address complex scientific tasks that challenge human capabilities to make new discoveries. Central to our approach is the notion of natural language as core representation, reasoning, and exchange format between scientific AI and human scientists. In this paper, we identify and discuss some of the main research challenges to accomplish such vision.