Yea, so the way this was built causes regions of density (core machine learning topics) to be good and sparse areas like biology to be worse. There are some areas of intersection like Medical Coding, which anecdotally Joseph Paul Cohen (was a mentor of mine and a postdoc with Yoshua Bengio back in the day) reviewed while we were at COLM and he thought was good enough to share around his research lab. All that said, research proposals will be hit or miss.

The reason that biology is sparse is because we are processing full papers from open access venues. Machine learning papers have a long history of open access publication, whereas other areas still have a lot of concepts locked up. I suspect our method would probably not work very well if you only provided abstracts (like AI2 ScholarQA).

Our clusters and therefore our sampling of new ideas is based on density in concept space, and a lot of the key concepts don't end up in the abstract.

Go open science! All that said, the right partnership with a commercial publisher would probably make this work well in that regime.