Practical AI for Science

Things that scientists can do to leverage AI right now

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There’s been a lot of talk about “AI for science” and “AI scientists” (including around here) but the majority of the conversation seems to revolve around some combination of “build a dataset and train a huge model,” “do a literature search and/or come up with a hypothesis,” or “imagine a world where a computer can do everything a scientist can do.” But the tools out there are getting good enough that there are many concrete things they unlock for how scientists do things day to day. So here are some things that scientists can (and are starting to) do to leverage AI right now. This being Reinvent Science, there are also some ideas about how doing this might change the structure of science (and as usual some might be good and some might be bad):

Custom software

It’s easier than ever to build custom software, even for people who have no software experience. This capability can unlock far more things for science than we can imagine, but some that come to mind

• Remotely monitor instruments; you can even install cameras to monitor experiments that don’t have a digital interface. These remote monitors could unlock new kinds of collaborations and share what actually happens in a lab.

• Build custom websites to share results or ideas. It used to require coding skills and extreme dedication to make things like Bret Victor’s Ladder of Abstraction or Ink&Switch’s project essays. Now scientists with zero coding skill can put together a compelling custom website more quickly than you can write something up in LaTeX. People have been predicting the demise of the pdf research paper for decades, but lowering the friction to creating something more informative and sharable may be what makes that prediction come true.

• Build a custom lab dashboard that, for example, pulls together experiment trackers, the lab’s calendar, and a custom paper newsfeed.

Statistics

For those that do statistical analyses, the tools have gotten good enough that you really can just say “run this analysis” and it will do a pretty good job. But that also means that “fishing expeditions” are effectively free. Statistics-based fields will need to account for this.

Cross-field understanding

Everybody knows that other fields often hold useful knowledge. (See the classic story about the biologist who reinvented integration) It’s also incredibly hard to figure out what that knowledge is without spending a lot of time with people from other fields. AI tools have gotten good enough that they can at least flag what an expert from another field might point out.

People

Science is secretly all about people. But it’s incredibly hard to know which people you should interact with. AI might be able to start creating what Michael Nielsen calls “designed serendipity.” For example, when you’re visiting another institution, it could help you figure out who would be relevant for you to talk to, even if you’ve never heard of them.

Administrivia

Doing science in the 21st century involves writing a lot of things that don’t need to be well written or opinionated. Scientists should probably be delegating most of it to LLMs. (Things that are meant to be compelling and read by people who care should still be written by people.)