Push-button science

Konrad Hinsen's blog

The advent of AI agents based on large language models (LLMs) has put the idea of automating the intellectual and cognitive work of researchers on the table. A lively, sometimes even heated discussion is already going on. A frequently missing piece in this debate is the question why we, individually and as a society, actually do science. I will examine this question first, and then consider what it implies for introducing automation into science.

Tech companies are cutting jobs and betting on AI. The payoff is far from guaranteed

AI experts say we’re living in an experiment that may fundamentally change the model of work

What technology takes from us – and how to take it back | Rebecca Solnit

The long read: Decisions outsourced, chatbots for friends, the natural world an afterthought: Silicon Valley is giving us life void of connection. There is a way out – but it’s going to take collective effort

Science Must Decentralize

Knowledge production doesn’t happen in a vacuum. Every great scientific breakthrough is built on prior work, and an ongoing exchange with peers in the field. That’s why we need to address the threat

The Current Crisis: What's Happening to Science in America

Care at the Edge of Automation – Topos Institute

Technologies don’t just solve problems, they change us. We invent technologies, and they invent us in turn, shaping our lives and worlds. This is the phenomenon that Terry Winograd and Fernando Flores, in Understanding Computers and Cognition (1986), called “ontological design.” It matters now more than ever—along with a second lesson they saw clearly. Technological research is always guided (and sometimes misguided) by deep ontological assumptions about, e.g., the nature of cognition, agency, and communication. If we are to create technologies that truly serve human flourishing and care, we must bring these hidden assumptions into the open and question them at their roots.

Artificial intelligence and illusions of understanding in scientific research

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

AI in scientific publishing: Slower, worse, and more expensive

There’s a saying in the management world, popularized by NASA administrator Daniel Goldin in the 1990s, that the goal of technological improvements is to make products faster, better, and cheaper. Although this strategy had some success in the aerospace industry, the zealots of artificial intelligence (AI) have been making the same argument regarding how it will transform work, claiming that so little human effort will be required that humanity will enter an era of radical abundance, free from disease, drudgery, and danger, among other benefits, leaving society with more time for creative pursuits. But history tells a different story. When machines began to increase productivity during the second industrial revolution, American engineer Frederick Winslow Taylor’s The Principles of Scientific Management encouraged corporations to use surveillance to get employees to work harder and longer, an approach that exhausted and discouraged workers and led to the transfer of knowledge and any decision-making from workers to management, while enriching the profits for only those at the top. Yet, it remains foundational to the American economic enterprise. Indeed, scientific publishing is starting to experience some Taylorism with the insertion of AI. Rigorous human checking of AI-generated research papers is creating bottlenecks as publishers strive to maintain the integrity of the scientific record. The challenge is requiring even more human effort, making the whole endeavor slower and more expensive.

AI as the Catalyst of the New Paradigm of Science? | Cadmus Journal

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.

No. 009: AI agents p-hacking, submission surge data, Gemini for Science - The Hallway Track

The Hallway Track No. 009 - How AI is affecting the practice of science

How AI Agents are transforming scientific discovery

AI agents are starting to reshape science, from proposing novel hypotheses to writing code. Learn what comes next for scientists and policymakers.

No. 023: Co-Scientist, Terminal-Bench Science, AI in peer review - The Hallway Track

The Hallway Track No. 023 - How AI is affecting the practice of science

The Whale and the Reactor: A Search for Limits in an Age of High Technology, Second Edition

“In an age in which the inexhaustible power of scientific technology makes all things possible, it remains to be seen where we will draw the line, where we will be able to say, here are possibilities that wisdom suggest we avoid.” First published to great acclaim in 1988, Langdon Winner’s groundbreaking exploration of the political, social, and philosophical implications of technology is timelier than ever. He demonstrates that choices about the kinds of technical systems we build and use are actually choices about who we want to be and what kind of world we want to create—technical decisions are political decisions, and they involve profound choices about power, liberty, order, and justice. A seminal text in the history and philosophy of science, this new edition includes a new chapter, preface, and postscript by the author.

AI, peer review and the human activity of science

When researchers cede their scientific judgement to machines, we lose something important.

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