Tuhin Chakrabarty and Sam Rodriques reject claims that three recent Nature papers show AI systems fully replicating core scientific tasks and rendering scientists obsolete
Rodriques says human judgment remains essential for five-to-ten-year productivity gains.
@SGRodriques @sebkrier I hope you are right, but ten years at our current pace is a very long time.
Sam Rodriques@SGRodriquesI have spent my entire life working on this and thinking about this for the past 4 years. I don't know what will happen in 20 years, but I can promise you that on the 5-10 year timescale, scientists are not out of their jobs. AI is going to massively accelerate the pace of science, increase productivity, let individual scientists make way more discoveries way faster, and is going to make science overall more fun. But the model is going to be collaboration between humans and AI, not replacement. The key difference here between science and e.g. software engineering is that science is not verifiable in any rapid/convenient way (unlike software), unlike programming. We still need humans for their scientific taste.
#1007Sam Rodriques@SGRODRIQUESI have spent my entire life working on this and thinking about this for the past 4 years. I don't know what will happen in 20 years, but I can promise you that on the 5-10 year timescale, scientists are not out of their jobs. AI is going to massively accelerate the pace of science, increase productivity, let individual scientists make way more discoveries way faster, and is going to make science overall more fun. But the model is going to be collaboration between humans and AI, not replacement.
The key difference here between science and e.g. software engineering is that science is not verifiable in any rapid/convenient way (unlike software), unlike programming. We still need humans for their scientific taste.
I think the deep cruxes (beyond academic self-interest and defending self-image) are whether one cares whether knowledge is human-made, and whether academic structures (as they are and as traditions) have intrinsic value.
Anders Sandberg@anderssandbergAI also provides ungated access. I might not know the finer points of ophthalmology but I can ask a cheap expert. It is like asking a grad student in a bar: not entirely reliable, but if you don't have easy access to serious experts it sure beats the alternative. Competition!
#1675Anshul Kundaje@ANSHULKUNDAJEAgree completely. I'm very excited about these tools finally hitting a level of maturity that they are starting to be genuinely useful when used with a strong critical harness. 1/
Sam Rodriques@SGRodriquesI have spent my entire life working on this and thinking about this for the past 4 years. I don't know what will happen in 20 years, but I can promise you that on the 5-10 year timescale, scientists are not out of their jobs. AI is going to massively accelerate the pace of science, increase productivity, let individual scientists make way more discoveries way faster, and is going to make science overall more fun. But the model is going to be collaboration between humans and AI, not replacement. The key difference here between science and e.g. software engineering is that science is not verifiable in any rapid/convenient way (unlike software), unlike programming. We still need humans for their scientific taste.
One major challenge is making sure newer generations of scientists do not completely lose pretty fundamental knowledge about how all the automated stuff actually works, even if most of that time they can just automate it. There will continue to be a need for deep expertise. 2/
Anshul Kundaje@anshulkundajeAgree completely. I'm very excited about these tools finally hitting a level of maturity that they are starting to be genuinely useful when used with a strong critical harness. 1/
AI tools should amplify human expertise, not dilute it. It sounds trivial & obvious. But it may not turn out that way if we don't distinguish how to use AI for doing science vs truly amplifying our expertise & training budding scientists. 3/
Anshul Kundaje@anshulkundajeOne major challenge is making sure newer generations of scientists do not completely lose pretty fundamental knowledge about how all the automated stuff actually works, even if most of that time they can just automate it. There will continue to be a need for deep expertise. 2/
There is huge potential to improve both. But it's going to require careful restructuring of education, humility, honest reflection & critical thinking. 4/4
Anshul Kundaje@anshulkundajeAI tools should amplify human expertise, not dilute it. It sounds trivial & obvious. But it may not turn out that way if we don't distinguish how to use AI for doing science vs truly amplifying our expertise & training budding scientists. 3/