Ayaan Hossain

A parts list of promoters and gRNA scaffolds for mammalian genome engineering and molecular recording go.nature.com/49eTPCu

Some aspects of AI discourse seem to come from a different planet, oblivious to basic realities on Earth. AI for science is one such area. In this new essay, @sayashk and I argue that visions of accelerating science through AI should be considered unserious if they don't confront the production-progress paradox. ========= AI leaders have predicted that it will enable dramatic scientific progress: curing cancer, doubling the human lifespan, colonizing space, and achieving a century of progress in the next decade. Given the cuts to federal funding for science in the U.S., the timing seems perfect, as AI could replace the need for a large scientific workforce. It’s a common-sense view, at least among technologists, that AI will speed science greatly as it gets adopted in every part of the scientific pipeline. But many early common-sense predictions about the impact of a new technology on an existing institution proved badly wrong. The Catholic Church welcomed the printing press as a way of solidifying its authority by printing Bibles. The early days of social media led to wide-eyed optimism about the spread of democracy worldwide following the Arab Spring. Similarly, the impact of AI on science could be counterintuitive. Even if individual scientists benefit from adopting AI, it doesn’t mean science as a whole will benefit. When thinking about the macro effects, we are dealing with a complex system with emergent properties. That system behaves in surprising ways because it is not a market. It is better than markets at some things, like rewarding truth, but worse at others, such as reacting to technological shocks. So far, on balance, AI has been an unhealthy shock to science, stretching many of its processes to the breaking point. Any serious attempt to forecast the impact of AI on science must confront the production-progress paradox. The rate of publication of scientific papers has been growing exponentially, increasing 500 fold between 1900 and 2015. But actual progress, by any available measure, has been constant or even slowing. So we must ask how AI is impacting, and will impact, the factors that have led to this disconnect. Our analysis in this essay suggests that AI is likely to worsen the gap. This may not be true in all scientific fields, and it is certainly not a foregone conclusion. By carefully and urgently taking actions such as those we suggest, it may be possible to reverse course. Unfortunately, AI companies, science funders, and policy makers all seem oblivious to what the actual bottlenecks to scientific progress are. They are simply trying to accelerate production, which is like adding lanes to a highway when the slowdown is actually caused by a toll booth. It’s sure to make things worse. Contents 1. Science has been slowing — the production-progress paradox 2. Why is progress slowing? Can AI help? 3. Science is not ready for software, let alone AI 4. AI might prolong the reliance on flawed theories 5. Human understanding remains essential 6. Implications for the future of science 7. Final thoughts Full essay (about 6,500 words) aisnakeoil.com/p/could-ai-slo…

Engineered bacteria are great for making biofuels and other bioproducts, but you don’t want those changes escaping to the wild. Scientists @Harvard & @WUSTLmed engineered the E. coli genome to keep useful edits from spreading in an uncontrolled way: energy.gov/science/ber/ar…