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Ai2 outlines five AI‑agent tasks for scientific research On September 1, the research organization Ai2 published a discussion of systems that assist scientists. Participants identified five tasks for an AI agent: providing expert judgment, adjusting the agent’s course during work, varying how assignments are checked, monitoring the quality of source data, and linking analysis to laboratory experiments. The discussion used AutoDiscovery—a program that proposes hypotheses from scientific data and tests them via analysis—as a starting point. In an August case study of lobular cancer, an oncologist’s comments narrowed the program’s search, and the team then validated the found signal on independent data and tumor samples. This example illustrates scientific taste: a specialist selects results that could grow into the next question and sets the direction for further search. Research evolves as a project proceeds—unexpected outcomes, new papers, fresh datasets lead the researcher to change the agent’s instructions, context, and tools. Oncologist Kelly Paulson summed it up: “This is research, not search: we must discover something new and verify it.” Retrieving records, structuring information, and literature review can be predefined and checked against results, whereas a hypothesis about a novel mechanism or surprising experiment needs separate analysis and reproduction. Abraham Flaksman described a case where the AI spotted an error in the algorithms of a previously published paper; the researcher verified the comment, agreed, and asked the journal to retract the work. The speed of analysis depends on what the system receives—experiment design, data collection, and causal logic. Steven Salerno noted that AI amplifies both strong research and weak premises with methodological flaws. In projects involving hundreds of cell types and thousands of changing genes, the agent can gather literature and prioritize hypotheses for testing, with each lab result shaping the next hypothesis and the next experiment. 🔗 Read original →