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    Google and DeepMind release early research on how scientists use AI

    The research team says the study draws on 15 million Gemini interactions, an inventory of more than 2,600 specialized AI models and a survey of more than 600 scientists.

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    TLDR

    The researchers behind “AI in Science: Early Insights” report that scientists use AI more than most other occupations. They also describe specialized models as covering many disciplines and being highly cited. The Google–Google DeepMind team says it mapped its data to categories of scientific tasks and to publication and citation records. A project co-lead describes the work as an initial look and the beginning of a broader research agenda.

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    Combined views

    265.8K

    23 Sources, first seen 15d ago

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    Sentiment

    Positive62.1%37.9%Negative

    Summary

    Sentiment

    Positive62.1%37.9%Negative

    Many accounts welcomed Google's study showing AI delivers nearly 7 hours of weekly productivity gains for scientists, while others objected that added verification work and a shift to safer questions reduce genuine progress.

    Based on 32 sentiment-bearing replies from 29 accounts across 3 conversations.

    Summary

    Many accounts welcomed Google's study showing AI delivers nearly 7 hours of weekly productivity gains for scientists, while others objected that added verification work and a shift to safer questions reduce genuine progress.

    Based on 32 sentiment-bearing replies from 29 accounts across 3 conversations.

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    23 Sources

    @arthurturrellToday @Google, we published work led by @m_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...
    @danielrockCheck out our new paper (and Arthur's excellent thread) on how scientists are using AI! More to come here as well. @m_codreanu @alexolegimas @JMateosGarcia
    @Dr_AtoosaCheck out our new paper (and Arthur's excellent thread) on early empirical evidence for AI's impact on science ⭐️. Work led by the excellent @m_codreanu , @alexolegimas , @JMateosGarcia
    @wsisaacRT @danielrock: Check out our new paper (and Arthur's excellent thread) on how scientists are using AI! More to come here as well. @m_cod…
    @MIT_CSAILAI is already reshaping scientific research, according to an MIT & Google study. Scientists use LLMs & specialized AI models for different tasks, save ~7 hours a week, & reinvest much of that time in research — but new bottlenecks are emerging: https://bit.ly/3UT2G7Q
    @JMateosGarcia"AI in Science - early insights" I'm delighted to share the first output from project Zvi. In it, we combine Gemini logs, publications about specialized models, a survey, and a new scientific task taxonomy from @MITFutureTech to study how scientists are using AI.
    @alexolegimasThrilled to release AI in Science: Early Insights into the world. This was a project co-led with the excellent @JMateosGarcia and @m_codreanu and an incredible team of co-authors. This collaboration between @Google and @GoogleDeepMind teams is the first, initial look, representing the beginning of a research agenda for us. The promise of AI for economic growth and flourishing moves directly through its impact on science, and this paper is the start of a brand new effort in our group on the study of AI's impact on science. Lots of results, lots of insights, lots of questions still to answer. There is great excitement -but also concerns- about the impacts of AI on science, but so far little data. We provide early insights on this from three complementary data sources: 1) a sample of 15 million Gemini interactions, 2) an inventory of over *2,600* specialized AI models (e.g., AlphaFold) across disciplines, 3) a new survey of over 600 scientists. But what does this tell us about how scientists actually use AI in their workflow, and is the impact of AI? To answer these questions, we map these data to: a) a new taxonomy of scientific tasks from @ProfNeilT and his lab; b) bibliometric data tracking scientific publications, and citations (including to specialized AI models). Four main findings emerge. 1) We find broad adoption and coverage: scientists use AI more than most other occupations. Specialized AI models have huge disciplinary coverage and are highly cited. Nearly half of the scientists surveyed report using some form of AI every day. 2) There is evidence that LLMs and specialized models act as *economic complements*: LLMs are used for general analysis, coding, and manuscript preparation, while specialized models push the frontier through domain-specific predictions, data generation and classification. The figure below illustrates this nicely: Each model class reinforces the other in the scientific process. 3) Surveyed scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily reinvested in more research. Scientists also report having better access to interdisciplinary insights and increased capacity for synthesis. 4) But as some stages of scientific research become easier, bottlenecks shift downstream to non-automated tasks. Scientists report an increased backlog of untested hypotheses and substantial time spent on output verification. Half of scientists also report AI is pushing them towards safer, more incremental question, evidence of a potential “streetlight effect”. What do we make of this? Our findings suggest that AI holds significant potential to increase scientific productivity. However, as with other sectors, its ultimate impact will be governed by complex task interdependencies and investment into eliminating emerging bottlenecks. Investment into infrastructure and the organization of scientific production is necessary to realize AI’s full scientific potential for economic and societal gains. Finally, this is work in progress. There are many limitations, which we discuss in section 6 of the paper, at length. Link: https://ai.google/static/documents/AI-in-Science.pdf
    @sebkrierRT @alexolegimas: Thrilled to release AI in Science: Early Insights into the world. This was a project co-led with the excellent @JMateosGa…
    @TaylorLorenz"Surveyed scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily reinvested in more research."
    @HaydnBelfieldThis is excellent, fascinating work from my colleagues -- real early insights into how AI is being used by scientists

    23 Sources

    @arthurturrellToday @Google, we published work led by @m_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...
    @danielrockCheck out our new paper (and Arthur's excellent thread) on how scientists are using AI! More to come here as well. @m_codreanu @alexolegimas @JMateosGarcia
    @Dr_AtoosaCheck out our new paper (and Arthur's excellent thread) on early empirical evidence for AI's impact on science ⭐️. Work led by the excellent @m_codreanu , @alexolegimas , @JMateosGarcia
    @wsisaacRT @danielrock: Check out our new paper (and Arthur's excellent thread) on how scientists are using AI! More to come here as well. @m_cod…
    @MIT_CSAILAI is already reshaping scientific research, according to an MIT & Google study. Scientists use LLMs & specialized AI models for different tasks, save ~7 hours a week, & reinvest much of that time in research — but new bottlenecks are emerging: https://bit.ly/3UT2G7Q
    @JMateosGarcia"AI in Science - early insights" I'm delighted to share the first output from project Zvi. In it, we combine Gemini logs, publications about specialized models, a survey, and a new scientific task taxonomy from @MITFutureTech to study how scientists are using AI.
    @alexolegimasThrilled to release AI in Science: Early Insights into the world. This was a project co-led with the excellent @JMateosGarcia and @m_codreanu and an incredible team of co-authors. This collaboration between @Google and @GoogleDeepMind teams is the first, initial look, representing the beginning of a research agenda for us. The promise of AI for economic growth and flourishing moves directly through its impact on science, and this paper is the start of a brand new effort in our group on the study of AI's impact on science. Lots of results, lots of insights, lots of questions still to answer. There is great excitement -but also concerns- about the impacts of AI on science, but so far little data. We provide early insights on this from three complementary data sources: 1) a sample of 15 million Gemini interactions, 2) an inventory of over *2,600* specialized AI models (e.g., AlphaFold) across disciplines, 3) a new survey of over 600 scientists. But what does this tell us about how scientists actually use AI in their workflow, and is the impact of AI? To answer these questions, we map these data to: a) a new taxonomy of scientific tasks from @ProfNeilT and his lab; b) bibliometric data tracking scientific publications, and citations (including to specialized AI models). Four main findings emerge. 1) We find broad adoption and coverage: scientists use AI more than most other occupations. Specialized AI models have huge disciplinary coverage and are highly cited. Nearly half of the scientists surveyed report using some form of AI every day. 2) There is evidence that LLMs and specialized models act as *economic complements*: LLMs are used for general analysis, coding, and manuscript preparation, while specialized models push the frontier through domain-specific predictions, data generation and classification. The figure below illustrates this nicely: Each model class reinforces the other in the scientific process. 3) Surveyed scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily reinvested in more research. Scientists also report having better access to interdisciplinary insights and increased capacity for synthesis. 4) But as some stages of scientific research become easier, bottlenecks shift downstream to non-automated tasks. Scientists report an increased backlog of untested hypotheses and substantial time spent on output verification. Half of scientists also report AI is pushing them towards safer, more incremental question, evidence of a potential “streetlight effect”. What do we make of this? Our findings suggest that AI holds significant potential to increase scientific productivity. However, as with other sectors, its ultimate impact will be governed by complex task interdependencies and investment into eliminating emerging bottlenecks. Investment into infrastructure and the organization of scientific production is necessary to realize AI’s full scientific potential for economic and societal gains. Finally, this is work in progress. There are many limitations, which we discuss in section 6 of the paper, at length. Link: https://ai.google/static/documents/AI-in-Science.pdf
    @sebkrierRT @alexolegimas: Thrilled to release AI in Science: Early Insights into the world. This was a project co-led with the excellent @JMateosGa…
    @TaylorLorenz"Surveyed scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily reinvested in more research."
    @HaydnBelfieldThis is excellent, fascinating work from my colleagues -- real early insights into how AI is being used by scientists