I believe the central problem of the 21st century is how civilization co-evolves with science and technology. As our understanding of the world deepens, it enables technologies that confer ever more power to both improve and damage the world. There's currently much public discussion of this in the context of artificial superintelligence (ASI). However, it's a fundamental recurring challenge, affecting areas including climate change, genetic engineering, nanotechnology, geoengineering, brain-computer interfaces, and many more. How can we best benefit from science and technology, while supplying safety sufficient to ensure a healthy, flourishing civilization?
B. Scot Rousse: "Language, Technology, & Care"
Appropriate Measures — Real Life
Changing the tech we use is not enough to mitigate the environmental and social harm of mass technology
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
Technology is Not Values Neutral: Ending the Reign of Nihilistic Design
We fail to take tech seriously when we do not grasp its full impact on humans
AI for Science & Safety Nodes - Request for Proposals
Artificial intelligence is accelerating the pace of discovery across science and technology. But today’s AI ecosystem risks centralizing compute, talent, and decision-making power – concentrating capabilities in ways that could undermine both innovation and safety.
Pioneering Scientific Superintelligence
Pioneering Scientific Superintelligence to solve humankind’s greatest challenges.
Will AI spark a scientific renaissance — or a diffuse monoculture?
Artificial intelligence’s ability to enrich science will depend not only on model capability, but also on whether researchers, reviewers and funders reward originality over speed.
From Genius Science to Scenius Science - Cosmik Labs
Why personal AI tools might not be all we need to revolutionize 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, I tried to love you – Writings and rehearsals by Nathan Schneider
Among my peers who study the cultures and economies surrounding technology, I often find myself more tech-positive than most. On the whole I like computers, like the internet, and like in general the emergent human creativity that these things have enabled and revealed. A source of my appreciation is that I expend a lot of effort on the practice of “slow computing,” which is to say, choosing tools and technical communities that I can feel in right relationship with. I tend to approach new socio-technologies (like gig economies, blockchains, and social media) not from a perspective of why is this so bad? so much as asking how could we do this right?
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.
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.
BBC Earth | Environment, Climate Change, AI, Food, Health, Social, & Technology
As we face the world’s greatest environmental challenges, BBC Earth brings you solutions in psychology, food, climate change, health, social trends, and technology that can make the world a more sustainable place.
BBC Earth | Environment, Climate Change, AI, Food, Health, Social, & Technology
As we face the world’s greatest environmental challenges, BBC Earth brings you solutions in psychology, food, climate change, health, social trends, and technology that can make the world a more sustainable place.
BBC Earth | Environment, Climate Change, AI, Food, Health, Social, & Technology
As we face the world’s greatest environmental challenges, BBC Earth brings you solutions in psychology, food, climate change, health, social trends, and technology that can make the world a more sustainable place.
Bridging the gap: inequalities that divide those who can and cannot create sustainable outcomes with AI
The widespread use of AI technologies impacts individuals, organisations, and societies in ways that warrant more comprehensive investigation. Inequalities in benefiting from AI advancements may le...
