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Artificially Intelligent ‘Coscientist’ Automates Scientific Discovery

Is It Possible for AI to Advance Our Understanding of the Natural World?

In his welcoming remarks, National Academy of Medicine President Victor Dzau emphasized that AI is helping science in many ways, including by predicting outcomes from data, simulating complicated scenarios, and finding relevant trends in huge datasets.

According to a report in the Nature journal on December 21st, researchers from Carnegie Mellon University made history by creating the first ever non-organic intelligent system to conceive, plan, and carry out a chemistry experiment.

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Assistant Professor of Chemistry and Chemical Engineering Gabe Gomes, along with Daniil Boiko and Robert MacKnight, who were doctoral students in chemical engineering, created the system known as Coscientist. The complete spectrum of the experimental procedure is executed with a simple, plain English prompt using large language models (LLMs), such as OpenAI’s GPT-4 and Anthropic’s Claude.

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The research team proved in their Nature paper that Coscientist can do things like plan the chemical synthesis of known compounds, navigate hardware documentation, control liquid handling instruments, complete scientific tasks involving multiple hardware modules and diverse data sources, and solve optimization problems by analyzing previously collected data. They also used the documentation to execute high-level commands in an automated lab called a cloud lab.

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Cloud Lab

In early 2024, the first academic cloud lab will be opened by Carnegie Mellon in collaboration with ECL. More than 200 pieces of equipment will be available to academics and collaborators at Carnegie Mellon University through the Cloud Lab. Gomes intends to keep working on the technologies detailed in the Nature article so that they can be utilized by the Carnegie Mellon Cloud Lab and other autonomous research facilities down the road.

Additionally, a coscientist essentially unlocks the “black box” of research. By tracking and documenting each stage of the research process, the system ensures that the work can be easily reproduced.

[To share your insights with us, please write to sghosh@martechseries.com]

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