A robot equipped with sophisticated AI has successfully simulated the creation of artificial lifeforms, in a key first step towards the eventual goal of creating true artificial life.
The robot, which was developed by scientists at the University of Glasgow, was able to model the creation of artificial lifeforms using unstable oil-in-water droplets. These droplets effectively played the role of living cells, demonstrating the potential of future research to develop living cells based on building blocks that cannot be found in nature.
Significantly, the robot also successfully predicted their properties before they were created, even though this could not be achieved using conventional physical models.
The robot, which was designed by Glasgow University’s Regius Chair of Chemistry, Professor Lee Cronin, is driven by machine learning and the principles of evolution.
It has been developed to autonomously create oil-in-water droplets with a host of different chemical makeups and then use image recognition to assess their behaviour.
Using this information, the robot was able to engineer droplets to have different properties. Those which were found to be desirable could then be recreated at any time, using a specific digital code.
“This work is exciting as it shows that we are able to use machine learning and a novel robotic platform to understand the system in ways that cannot be done using conventional laboratory methods, including the discovery of ‘swarm’ like group behaviour of the droplets, akin to flocking birds,” said Cronin.
“Achieving lifelike behaviours such as this are important in our mission to make new lifeforms, and these droplets may be considered ‘protocells’ – simplified models of living cells.”
The research, which is published today in the journal PNAS, is one of several research projects being undertaken by Cronin and his team within the field of artificial lifeforms.
While the overarching goal is moving towards the creation of lifeforms using new and unprecedented building blocks, the research may also have more immediate potential applications.
The team believes that their work could also have applications in several practical areas, including the development of new methods for drug delivery or even innovative materials with functional properties.
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