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1 year agoI normally try and do “fun” work. This largely depends on how autonomous your job is. I was a PhD student doing research for a company and I received very little oversight for 3 years.
The supervision I did receive was great though. They understood needing to take a break and slow down. At those point I would generally read papers, watch PyData talks (highly recommend them, like inspirational ted talks for data people), or contribute to open source to learn about new tools or design paradigms.
I feel this has been the case already for more time than people think. AI/ML has been its own subspecialty of SWE for years. There are some low hanging fruit that using sklearn or copy and pasting from stack overflow will let you do, but for the most part the advanced features require professional specialization.
One thing that bothers me is that subject matter expertise is often ignored. General AI researchers can be helpful, but often times having SME context AND and AI skillset will be way more valuable. For LLMs it may be fine since they produce a generalized solution to a general problem, but application specific tasks require relevant knowledge and an understanding of pros/cons within the use case.
It feels like a hot take, but I think that undergraduate degrees should establish a base knowledge in a domain and then AI introduced at the graduate-level. Even if you are not using the undergraduate domain knowledge, it should be transferable to other domains and help you to understand how to solve problems with AI within the context of a professional domain.