Amazing stuff! Very impressive! And this is only the beginning!
"An AI agent synthesizes novel scientific research hypotheses. It's already making an impact in biomedicine.
What’s new: Google introduced AI co-scientist, a general multi-agent system designed to generate in-depth research proposals within constraints specified by the user. The team generated and evaluated proposals for repurposing drugs, identifying drug targets, and explaining antimicrobial resistance in real-world laboratories. ...
How it works: AI co-scientist accepts a text description of a research goal, including relevant constraints or ideas. In response, it generates research proposals and reviews, ranks, and improves them using seven agents based on Google’s Gemini 2.0 family of large language models. The completed proposals include sections that explain background, unmet needs, a proposed solution, goals, hypotheses, reasoning, study steps, and relevant articles. The agents take feedback and outputs from other agents to perform their prompted task simultaneously. ...
The supervisor agent periodically determines how often to run the other six agents, how important their output is, and whether the system is finished. To accomplish this, it computes statistics that represent the number of proposals generated so far, how many have been reviewed, and so on.
The generation agent generates a list of proposals. It searches the web for relevant research articles, identifies testable assumptions, and debates with itself to improve ambiguous statements and adhere to constraints.
The reflection agent filters the generated proposals according to correctness, quality, safety, and novelty. First, it reviews a proposal without web search and discards obviously bad proposals. Then it reviews each proposal against literature it finds online. It breaks down and checks the proposal’s assumptions, checks whether the proposal might explain some observations in previous work, and simulates the proposed experiment (via text generation, similar to how a person performs a thought experiment).
The proximity agents compute similarity between proposals to avoid redundancy.
The ranking agent determines the best proposals according to a tournament. It examines one pair of proposals at a time (including reviews from the reflection agent) and debates itself to pick the better one. To save computation, it prioritizes comparing similar proposals, new proposals, and highest-ranking proposals.
The evolution agent generates new proposals by improving existing ones. It does this in several different ways, including simplifying current ideas, combining top-ranking ideas, and generating proposals that are very different from current ones.
The meta-review agent identifies common patterns in the reflection agent’s reviews and the ranking agent’s debates. Its feedback goes to the reflection and generation agents, which use it to address common factors in future reviews and avoid generating similar proposals, respectively. ...
Google researchers generated proposals for experiments that would repurpose drugs to treat acute myeloid leukemia. They shared the 30 highest-ranked proposals with human experts, who chose five for lab tests. Of the five drugs tested, three killed acute myeloid leukemia cells.
Experts selected three among 15 top-ranked generated proposals that proposed repurposing existing drugs to treat liver fibrosis. Two significantly inhibited liver fibrosis without being toxic to general cells. ...
AI co-scientist invented a hypothesis to explain how microbes become resistant to antibiotics. Human researchers had proposed and experimentally validated the same hypothesis, but their work had not yet been published at the time, and AI co-scientist did not have access to it. ..."
From the abstract:
"Scientific discovery relies on scientists generating novel hypotheses that undergo rigorous experimental validation. To augment this process, we introduce an AI co-scientist, a multi-agent system built on Gemini 2.0. The AI co-scientist is intended to help uncover new, original knowledge and to formulate demonstrably novel research hypotheses and proposals, building upon prior evidence and aligned to scientist-provided research objectives and guidance.
The system's design incorporates a generate, debate, and evolve approach to hypothesis generation, inspired by the scientific method and accelerated by scaling test-time compute.
Key contributions include:
(1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling;
(2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute, improving hypothesis quality.
While general purpose, we focus development and validation in three biomedical areas: drug repurposing, novel target discovery, and explaining mechanisms of bacterial evolution and anti-microbial resistance.
For drug repurposing, the system proposes candidates with promising validation findings, including candidates for acute myeloid leukemia that show tumor inhibition in vitro at clinically applicable concentrations.
For novel target discovery, the AI co-scientist proposed new epigenetic targets for liver fibrosis, validated by anti-fibrotic activity and liver cell regeneration in human hepatic organoids.
Finally, the AI co-scientist recapitulated unpublished experimental results via a parallel in silico discovery of a novel gene transfer mechanism in bacterial evolution.
These results, detailed in separate, co-timed reports, demonstrate the potential to augment biomedical and scientific discovery and usher an era of AI empowered scientists."
Accelerating scientific breakthroughs with an AI co-scientist "We introduce AI co-scientist, a multi-agent AI system built with Gemini 2.0 as a virtual scientific collaborator to help scientists generate novel hypotheses and research proposals, and to accelerate the clock speed of scientific and biomedical discoveries."
Towards an AI co-scientist (open access)
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