> Are there no impactful computer science problems left outside of ML? Well, do Arden Koehler or Howie Lempel have 80,000 hours of CS Research experience? It l

> Are there no impactful computer science problems left outside of ML? Well, do Arden Koehler or Howie Lempel have 80,000 hours of CS Research experience? It looks like they have 80K hours of experience thinking about the best way to spend 80K hours. Woops. On the contrary, the massive influx of applied research driven by deep learning hype means there's probably a lot more high-impact stuff outside of ML than inside of ML. Quantum computing, bullet-proof crypto implementations, safety engineering for robots, and even hardening the electric grid/key compute infra against solar flares all seem much more important. Also much less "sexy". ...and even most of the AI/ML problems are scraped from a few papers from corporate AI labs with the best PR skills, with an eye toward AGI risk. But 1) AGI risk is massively overblown and 2) trying to solve those problems in general has a "silver bullet" vibe. All of the solutions to "AI Safety" questions are going to boil down to "do good software engineering", which is often more about processes and org incentives than technology or science. A really good automated HiL testing setup is going to be a lot more important than "preventing reward hacking". If you don't believe me, go ask anyone who's actually built a robot. So, I think maybe "AI Safety/Robustness broadly construed" could be one or two entries in a list of important problems in CS. But certainly not half the list as they are here. TBH, one of the highest impact problems in CS is convincing young PhDs to not waste their time applying deep learning to every god damn thing regardless of whether it's the right tool for the job.