How does a multi-agent AI system design experiments, challenge assumptions, and generate novel hypotheses—often outperforming experts in biomedical research tasks?
In this webinar, we break down the key findings from the recent AI Co-Scientist paper, which introduces a multi-agent system built on Gemini 2.0 that mimics the scientific method to generate, debate, and evolve research ideas.
We’ll walk through exactly what the paper demonstrates: how a multi-agent system simulates the scientific method (generate → debate → evolve), and how it performed on real biomedical case studies including drug repurposing, bacterial evolution, and protein function prediction. We’ll separate technical ambition from practical applicability—and explore what this means for your research today.
How does a multi-agent AI system design experiments, challenge assumptions, and generate novel hypotheses—often outperforming experts in biomedical research tasks?
In this webinar, we break down the key findings from the recent AI Co-Scientist paper, which introduces a multi-agent system built on Gemini 2.0 that mimics the scientific method to generate, debate, and evolve research ideas.
We’ll walk through exactly what the paper demonstrates: how a multi-agent system simulates the scientific method (generate → debate → evolve), and how it performed on real biomedical case studies including drug repurposing, bacterial evolution, and protein function prediction. We’ll separate technical ambition from practical applicability—and explore what this means for your research today.
Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.
Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.
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By attending this session, you’ll gain:
If you’re working with complex biological data, you may be asking:
Can generative AI truly assist in scientific reasoning, not just data analysis?
What does it mean for hypothesis generation, literature review, or even designing experiments?
Could this accelerate—not replace—my discovery pipeline?
Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.
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