Ross D. King started his PhD at the Turing Institute forty years ago, the subject was the first application of machine learning to predict protein structure. As a postdoc he developed the first application of machine learning to drug design. He is now one of the most experienced AI researchers in Europe. He spent 15 years at The University of Wales, Aberystwyth. He now has joint positions at the University of Cambridge (Biotechnology), and is Professor of Machine Intelligence at Chalmers Institute of Technology, Sweden. His main research interest is AI for science. He originated the idea of a ‘Robot Scientist’ (aka self-driving lab): integrating AI and laboratory robotics to physically implement closed-loop scientific discovery. His Robot Scientist ‘Adam’ was the first machine to autonomously discover scientific knowledge. ‘Eve’ is currently searching for drugs against neglected tropical diseases. He is building ‘Genesis’ a Robot Scientist designed to automate eukaryotic systems biology. He is a founder of the Nobel Turing Challenge: to build a machine able to do Nobel prize quality scientific research autonomously.
The Automation of Science: Past, Present, and Future
A Robot Scientist (AI scientist, self-driving lab) is a physically implemented robotic system that applies techniques from AI to execute cycles of automated scientific experimentation. The motivation for developing Robot Scientists is to both to better understand the scientific method, and to make scientific research more efficient. I am now working on Genesis, a third-generation Robot Scientist designed to work on yeast systems biology. Genesis will be able to run 100 cycles of hypothesis-led experiment in parallel per day. Large Language Models (LLMs) are transforming AI, however their output cannot be trusted. One very promising way forward is to utilise LLMs to output formalised scientific knowledge that can be tested and trusted. In the future I believe that it is likely that advances in AI and lab automation will drive the development of ever-smarter Robot Scientists. Therefore, I am co-organising the ‘Nobel Turing Challenge’ to develop: AI systems capable of making Nobel-quality scientific discoveries highly autonomously at a level comparable, and possibly superior, to the best human scientists by 2050 or sooner.
