AI能否自主做研究？听听这些专家怎么说

TL;DR - Experts and companies at the 2026 Inclusion·Bund Conference examined how AI could evolve from assisting isolated research tasks to running iterative scientific-discovery workflows. They agreed that autonomous research depends on reliable data, domain knowledge, long-horizon agents, and closed loops between computational models and physical experiments.

• Materials and drug-development teams described systems combining generative models, simulations, automated experiments, and real-world feedback to propose and validate candidates.
• Scientific agents still struggle with open-ended evaluation, long task chains, incomplete memory, unreliable tool use, and distinguishing genuine mechanisms from correlations or good data fits.
• High-quality experimental data is a central constraint; simulations can supplement sparse wet-lab results but cannot replace mechanistic understanding and physical validation.
• Human scientists remain necessary for framing worthwhile questions, designing experiments, interpreting evidence, and overseeing consequential decisions.