AI in Science: Boosting Productivity and Innovation | Arthur Turrell posted on the topic | LinkedIn

Today at Google & Google DeepMind, we published work led by Mihai Codreanu on AI in Science, drawing on 15 million Gemini interactions, 2,600 specialised AI models & a 600-scientist survey. What did we find about how AI is changing science? Read on... First, why does this matter? If AI is a tech boost we get a one-off economic growth boost. But if AI is an "innovation in the method of innovation", we get long run higher expected future growth rates! Compound interest makes those scenarios very different for future prosperity. Where might we see innovation in the method of innovation first? Well, science of course—the engine of innovation and one of the main drivers behind the huge increase in prosperity over the last 200 years. So what do we find? 1) In our data, scientists are about 2.7x overrepresented in AI use compared to what you'd expect from employment share. Scientists are using AI a lot! 2) Science convos are longer (tokens), more multimodal, and involve more domain expertise 3) Computer science has highest use 4) specialised models and LLMs are complements: LLMs do code, stats, literature, drafting, teaching, admin while specialised models predict, generate, classify — eg protein structures, weather. 5) within LLMs, analysis & modelling + communicating research findings are top 2 tasks But are we getting *improvements*? 6) 74% of scientists surveyed report net time savings; (6% report losses). Average saving of ~7 hours/week. This is mostly going back into more research. 47% report an increase in outputs of 10% or more. There's another effect too though—as some things (hypothesis generation, modelling) become 'cheap', science gets bottlenecked by other tasks... 7) 89% of those who save time spend more than a tenth of it checking AI outputs & 41% report a growing backlog of untested hypotheses Looking a bit beyond, we expect time spent on bottleneck tasks to grow as a %, more papers that recombine ideas from across disciplines, & publication volume increases for fields that don't need wet lab work. Unfortunately, not all of this volume may be high quality ("sci-slop") So what's our early take on AI as an innovation in the method of innovation? There's significant potential to increase scientific productivity, & 7 hours/week (big!) is *already* being saved. But it's early days & bottlenecks (inc. verification) limit sci-productivity take off Huge team involved in this work, as you can imagine—see the paper for more! https://lnkd.in/g55s4MtC | 38 comments on LinkedIn

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The rapid advancement of Artificial Intelligence (AI) is reshaping the landscape of scientific inquiry. This article examines the challenges confronting modern science, including knowledge fragmentation, diminishing returns of current paradigms, and the disconnect between science and society. It explores how AI offers solutions enhancing collaboration and integrating knowledge—as well as considers the possible “darker side” of using AI in research. However, the advent of AI in science not only enhances scientific research but also redefines the role of human researchers and fosters a new paradigm of human understanding. By embracing these transformations, a more cohesive and responsive scientific enterprise can emerge.