I just finished a PhD in stats, and before that, studied computer science. I had planned to go back to industry to do software engineering or be a data scientist afterwards.

However, it seems that in the meantime, vibe coding has taken over the software industry and swallowed most data scientist roles. Few open positions, and the ones that do exist are just pushing a button instead of thinking and solving problems. Is this a correct assessment? Should I try to get back in or just find a new career? I am not too keen on staying in academia either.

  • evujumenuk@lemmy.world
    2·
    5 days ago

    That’s what’s gotta sting the most about this. Imagine you worked a problem for a long time… and then The Giant Pattern Matcher With Zero Fluid Intelligence actually finds something.

    If it did find something, that implies the solution already existed in bits and pieces in the published literature. You would “just” have had to have read about it, and remember it, for successful application.

    But suppose you had succeeded in that. You would have published a paper that enumerates a bunch of results some other people had reached, then plugs them together for a supposedly new theorem. Sort of like importing a dozen Python libraries, adding around fifty lines of glue code around them and then going, yeah, here’s a “new” thing, Fields Medal plz!

    How much remaining interestingness did your chosen problem really have if the new math you had to create for it fits on the placemat buttressing the campus cafeteria sandwich you had for lunch?

    • ArbitraryValue@sh.itjust.worksEnglish
      2·
      5 days ago

      What I’ve seen appears to indicate some genuinely new approaches by the AI, rather than putting together existing information. That blog post, for example, quotes some running commentary from a mathematician studying the proof of the theorem that she had been working on for years:

      It feels like something written by someone who’s on psychedelics. So much unclear and doesn’t make sense. Lots of name dropping of previous work without discussing why it can be used despite impossibility results

      Basically the paper is so horribly written that it’s impossible to read it without AI help

      I asked Astra for reasonable completeness and soundness claims of the noise gadget and it gave them by combining claims from all over the paper

      They also have direct optimal NP hardness of approximation proofs for the main applications of the UGC (Max Cut and all CSP) that bypass the UGC.

      The UGC proof invents a completely new bizarre code with a noise test. It’s some crazy recursive construction.

      It’s not the long code, not the short code – some alien craziness

      I still think that there maybe is a proof that uses the half space code (which is natural)

      The citations are often irrelevant and confusing

      A possible future is a math world that’s heavenly if you have vision/creative ideas that AI could help check and implement.

      And of course there’s a lot for us to learn from the aliens

      Note that this particular proof is correct, according to that same mathematician.

      • evujumenuk@lemmy.world
        1·
        5 days ago

        The issue with longstanding problems such as these is that yes, they can be used to establish evidence of plagiarizing if your LLM is simply regurgitating stuff, but suppose you cannot come up with any… you don’t really know if it’s because this particular line of thought is new, or because you just hadn’t read about it.

        That’s true even if you’re an expert practitioner; LLMs have been trained on All Of The Text and have superhuman recall after all.