Reliant's paper-scouring AI takes on science's data drudgery
AI models have proven capable of many things, but what tasks do we actually want them doing? Preferably drudgery — and there's plenty of that in research
AI models have proven capable of many things, but what tasks do we actually want them doing? Preferably drudgery — and there’s plenty of that in research and academia. Reliant hopes to specialize in the kind of time-consuming data extraction work that’s currently a specialty of tired grad students and interns.
“The best thing you can do with AI is improve the human experience: reduce menial labor and let people do the things that are important to them,” said CEO Karl Moritz. In the research world, where he and co-founders Marc Bellemare and Richard Schlegel have worked for years, literature review is one of the most common examples of this “menial labor.”
Every paper cites previous and related work, but finding these sources in the sea of science is not easy. And some, like systematic reviews, cite or use data from thousands.
For one study, Moritz recalled, “The authors had to look at 3,500 scientific publications, and a lot of them ended up not being relevant. It’s a ton of time spent extracting a tiny amount of useful information — this felt like something that really ought to be automated by AI.”
They knew that modern language models could do it: one experiment put ChatGPT on the task and found that it was able to extract data with an 11% error rate. Like many things LLMs can do, it’s impressive but nothing like what people actually need.
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