Towards Robot Scientists for autonomous scientific discovery

Abstract

We review the main components of autonomous scientific discovery, and how they lead to the concept of a Robot Scientist. This is a system which uses techniques from artificial intelligence to automate all aspects of the scientific discovery process: it generates hypotheses from a computer model of the domain, designs experiments to test these hypotheses, runs the physical experiments using robotic systems, analyses and interprets the resulting data, and repeats the cycle. We describe our two prototype Robot Scientists: Adam and Eve. Adam has recently proven the potential of such systems by identifying twelve genes responsible for catalysing specific reactions in the metabolic pathways of the yeast Saccharomyces cerevisiae. This work has been formally recorded in great detail using logic. We argue that the reporting of science needs to become fully formalised and that Robot Scientists can help achieve this. This will make scientific information more reproducible and reusable, and promote the integration of computers in scientific reasoning. We believe the greater automation of both the physical and intellectual aspects of scientific investigations to be essential to the future of science. Greater automation improves the accuracy and reliability of experiments, increases the pace of discovery and, in common with conventional laboratory automation, removes tedious and repetitive tasks from the human scientist.

Conscious Sensation, Conscious Perception and Sensorimotor Theories of Consciousness

“…To avoid this problem, machine learning techniques could be used to infer the equations from recordings of cortical data. A computational approach to the discovery of scientific knowledge has shown promise in a number of areas [34][35][36], and it could be a good way of identifying potentially complex regularities in brain activity patterns that are correlated with conscious sensation and perception.…”

Omics Approaches to Meat Quality Management

“…The recent advent of "Robot Scientist," which harnesses artificial intelligence, has the ability to automate all aspects of the scientific process (including both "wet" benchwork and "dry" computational analysis), thereby enabling generation of reproducible and reusable datasets devoid of any potential biases (King et al 2009, Sparkes et al 2010. This stresses the need for a highly sophisticated computational analytic tool to normalize and compile the enormous amount of data generated across heterogeneous -omic experiments.…”

Measuring growth rate in high-throughput growth phenotyping

“…One extreme in scientific automation is the robot scientists Adam and Eve, which/who are designed to carry out automated microbial growth experiments and to use logic to independently evaluate growth data and plan further experiments [6,7]. The aim of automated large-scale screening approaches is to maximize throughput while preferably enhancing the accuracy and sensitivity of detection.…”