AI for Science Curation
Knowing exactly who to talk to or what to read
Let’s talk about AI for scientific curation. Not scientific communication, which has been discussed to death, but how individual scientists go about figuring out who the right people to talk to are and what the state of scientific knowledge is.
The default approach to curation today revolves around conferences and papers. It is downstream of a world that no longer exists: one in which there were a small number of scientists who all knew each other, the fastest way to send a detailed report was through the mail, and the only way to learn deep technical knowledge was directly from other scientists. Yes, there have been changes around the edges like preprints and now AI literature surveys.
This still works fairly well for small, well-defined, relatively slow moving fields. These are the fields where you can meet all the relevant people at the “Floopion acceleration conference” (to avoid calling out any specific field) and the equipment is so expensive and specialized that you can be reasonably confident that nobody is going to do interesting work in a cave with scraps.
But as science becomes faster, more international, and less institutionalized, scientists will need new curation approaches. New tools like AI agents, the myriad of feeds, and group chats can enable these approaches. We are not actually sure what these approaches look like exactly but we can imagine some possibilities:
A tool that ingests the firehose of twitter, bluesky, mastodon, threads, linkedin, and whatever other social media on which people are talking about science and flags the things that you should actually pay attention to instead of whoever is demanding the most attention. A tool that pays attention to what you’re writing and automatically reaches out to the right person for you to talk to about a certain place you’re stuck. A service that creates a custom journal delivered weekly that is the new preprints that you specifically should read.
If we’re successful we can move towards what Michael Nielsen has called “designed serendipity” – knowing exactly who to talk to or what to read at the exact moment that you need it.
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> A tool that ingests the firehose of twitter, bluesky, mastodon, threads, linkedin, and whatever other social media on which people are talking about science and flags the things that you should actually pay attention to instead of whoever is demanding the most attention.
A subset of this is being built on atprotoscience, with Semble, Margin, Paper Skygest, etc.!
See https://semble.so/
And https://atproto.science/
Enabled by an open protocol, tough do to this on top of twitter/linkedin et al
And made better by lightweight, shared standards over modular science components:
https://continuousfoundation.org/
https://mira-science.github.io/landing-page/#top