The hard parts of AI-assisted science
Allen Institute (AI2)
Ai2 and the Paul G. Allen Research Center held an August 27 event to review what modern AI still cannot do for steering live scientific research as evidence and hypotheses change. The discussion centered on five recurring challenges, including steerable agents and delegation choices, rather than improvements to day-to-day analysis. The takeaway was that future scientific AI work should focus on adaptability, human steering and verification, and tighter AI-to-lab feedback loops instead of only generating hypotheses or accelerating computation.
Why it matters
At an Ai2 event marking our expanded collaboration with Providence Swedish, researchers explored the hardest problems in AI-assisted science: keeping systems steerable, grounded in human judgment and sound methods, and responsive to new evidence and experiments.
