Ask HN: AI PhD workload and applications of research in real-world business?
I am currently a Master student in Math interested in discrete math and theoretical computer science, and I have submitted PhD applications in these fields as well. However, recently as we have seen advances of reasoning capacity of foundational models, I'm also interested in pursuing *ML/LLM reasoning and mechanistic interpretability*, with goals such as applying reasoning models to formalised math proofs (e.g., Lean) and understanding the theoretical foundations of neural networks and/or architectures, such as the transformer.
If I really pursue a PhD in these directions, I may be torn between academic jobs and industry jobs, so I was wondering if you could help me with some questions:
1. I have learned here and elsewhere that AI research in academic institutions is really cutting-throat, or that PhD students would have to work hard (I'm not opposed to working hard, but to working too hard). Or would you say that only engineering-focused research teams would be more like this, and the theory ones are more chill, relatively?
2. Other than academic research, if possible, I'm also interested in pursuing building business based on ML/DL/LLM. From your experience and/or discussions with other people, do you think a PhD is more like something nice to have or a must-have in these scenarios? Or would you say that it depends on the nature of the business/product? For instance, there's a weather forecast company that uses atmospheric foundational models, which I believe would require knowledge from both CS and atmospheric science.
Many thanks!