Hi, would you mind elaborating, or throwing some links my way regarding the physics connection in particular? I am a physics postdoc that has been interested in the field for some time (this becoming less of a unique characteristic as the technology develops and hype cycles peak). I was motivated by Stuart Russell's Reith Lectures in 2021 to pursue the field outright and had been working towards this, but am becoming increasingly aware that seeking direct research involvement in the field is a bit quixotic starting from where I am.
Thanks! The key point for me was that Russell put forward a strong case that the moral approach to the problems of artificial intelligence was to involve oneself with the evolution of these technologies where you can (8000hours style) and seek (in whatever limited capacity one can) to guide them away from the excesses and damages of the invisible hand (and the human condition it is a proxy for).
This would have been influential to me when I was a teenager and nihilistic about accelerating technology in the hands of distinctly-not-developing humankind (reading all about the Manhattan-project forebears to AI), but after devoting a decade to science and being fascinated yet boycotting of AI research and it's implications, I think it has ended up a bit too late for a change of heart and direction to have as much positive impact.
For those fortunate enough to be in a position of even trivial influence over the cutting-edge, and whose moral sophistication can therefore matter more than the next person, I hope you don't take the implications of whatever agency you may have lightly!
One connection I've seen is energy-based models. I suppose you could try applying ML to things like fluid dynamics?
In general, it is thought that physics knowledge transfers better to ML than math because it's less abstract and physicists are more likely to be used to dealing with large datasets and software.
Thanks for the insight! I have some friends that do computational fluid dynamics, with particle physics being similarly numerical, and was looking at physics-informed ML for my own particular area in quantum physics in a recent grant application in the hopes for funding to close the gap a bit myself. What is so powerful about ML and related statistical techniques is their versatility and genericity, so a project that can be benefited by that region of statistics tends not to be too far away. I will look into energy-based models, too.
His comment is quite good, I do work with physics informed ML for cfd and other dynamical systems (temperature, hydrology etc.), there is just a ton of opportunity and funding for this type of work in research. Coming from a typical physics education, where you’re learning quantum and Astro, and realizing that 90% of the physics funding from government is in the earth sciences and the related physics was eye opening. I felt shortchanged by my physics education not even including fluids etc.
It was the same here - fluid dynamics was an elective at my university as well (one I took, but still not core syllabus). I guess amount of funding for a domain depends strongly on impact, and in the earth sciences output is much more immediately tangible than uncovering another supremely true but at-the-time inapplicable pattern of physical behaviour in the quantum, or context to humanity in the astronomical or cosmological domains.
