> The CSC team then prompted the model to scan 10,000 study abstracts published by U.K. researchers since 2010, looking for signs of commercial promise. I wish

> The CSC team then prompted the model to scan 10,000 study abstracts published by U.K. researchers since 2010, looking for signs of commercial promise. I wish they elaborated on how they measure commercial promise. I've seen papers that attempt to link grants to value via a 4 step chain: grants fund projects, projects make papers, papers make patents, patents create jumps in stock for US firms. Of course, this is a reductive way to measure progress, but if you want to use AI you'll need a reductive metric. > And so far, public funders are being cautious. In 2023, the U.S. National Institutes of Health banned the use of AI tools in the grant-review process, partly out of fears that the confidentiality of research proposals would be jeopardized. It sort of annoys me that this is framed as "fear" about a single issue. The NIH is increasingly criticized for funding low-risk, low-reward inefficient science. People are suggesting that they instead fund high-variance work, stuff that goes against the grain or lets the researcher chart a new path. Using AI would prevent this, because it tends to be a conventional wisdom machine. Its trained on our body of knowledge; how could it do otherwise?