Some argue that bots should be entitled to ingest any content they see, because people can.

Avram Piltch is the editor in chief of Tom’s Hardware, and he’s written a thoroughly researched article breaking down the promises and failures of LLM AIs.

  • @barsoap@lemm.ee
    2•1 year ago

    They’re psychologists and I don’t see any LaTeX in there,

    Oh no that’s LaTeX alright. I can tell by everything from the font to the line breaking, some of it is hard to imitate with an office suite, the rest impossible. But I’ll totally roll with dunking on psychologists :)

    In this paper, we undercut these views and claims by presenting a mathematical proof of inherent intractability (formally, NP-hardness) of the task that these AI engineers set themselves

    Yeah I don’t buy it. If human cognition was inherently NP-hard we’d have brains the size of suns. OTOH it might be “close to NP” in the same sense as the travelling salesman is NP, but it’s quite feasible indeed to get answers guaranteed to not be X% (with user choice of X) worse than the actually shortest path which is good enough in practice. We do, after all, have to operate largely in real-time, there’s no time to be perfect when a sabre tooth tiger is trying to eat you.

    Or think about SAT solvers: They can solve large classes of problems ridiculously fast even though the problem is, in its full generality, NP. And the class they’re fast on is so large that people very much do treat solving SAT as tractable: Because it usually is. Maybe that is why we get headaches from hard problems.

    Unfortunately I can’t open your link, although it sounds interesting.

    Then let me throw citations at you. The first is for the underlying theory characterising the necessary cybernetic characteristics of human minds, the second one applies it to current approaches to AI. This comes out of German publicly-funded basic research (Max Planck / FIAS)

    Nikolić, Danko. “Practopoiesis: Or how life fosters a mind.” Journal of Theoretical Biology 373 (2015): 40-61.
    Nikolić, Danko. “Why deep neural nets cannot ever match biological intelligence and what to do about it?.” International Journal of Automation and Computing 14.5 (2017): 532-541.