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AI language models learn to control biological systems offline

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to control biological systems using language models, specifically by training a vision-language model on existing intervention-outcome data. This approach allows for the creation of a natural-language interface for biological interventions without requiring new wet-lab experiments or human validation. The system demonstrated an 80% accuracy in generalizing to new instructions for controlling xenobots, a synthetic multicellular construct. AI

IMPACT Enables new avenues for biological research and control through natural language interfaces.

RANK_REASON The cluster contains an academic paper detailing a new research methodology in AI applied to biology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI language models learn to control biological systems offline

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13 / 100
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The cluster contains an academic paper detailing a new research methodology in AI applied to biology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nam H. Le, Douglas Blackiston, Michael Levin, Josh Bongard ·

    Toward Controlling Biology with Language:Offline Learning of Prompt-Conditioned Interventions for Cells, Organoids, and Biobots

    arXiv:2610.02247v1 Announce Type: cross Abstract: Artificial intelligence increasingly serves as a natural-language interface to complex technical systems, letting people accomplish sophisticated tasks by describing what they want rather than specifying how to do it. Extending th…

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