64
AI Lie: Machines Don’t Learn Like Humans (And Don’t Have the Right To)
(www.tomshardware.com)
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 13 Sep 2023
64 points (100.0% liked)
Technology
37758 readers
810 users here now
A nice place to discuss rumors, happenings, innovations, and challenges in the technology sphere. We also welcome discussions on the intersections of technology and society. If it’s technological news or discussion of technology, it probably belongs here.
Remember the overriding ethos on Beehaw: Be(e) Nice. Each user you encounter here is a person, and should be treated with kindness (even if they’re wrong, or use a Linux distro you don’t like). Personal attacks will not be tolerated.
Subcommunities on Beehaw:
This community's icon was made by Aaron Schneider, under the CC-BY-NC-SA 4.0 license.
founded 2 years ago
MODERATORS
That is not what van Rooij et al. said, which is who was cited in here. They published their essay here, which I haven't really read, but which appears to make an argument about any possible computer. They're psychologists and I don't see any LaTeX in there, so they must be missing something.
Unfortunately I can't open your link, although it sounds interesting. A feedforward network can approximate any computable function if it gets to be arbitrarily large, but depending on how you want to feed an agent inputs from it's environment and read it's actions a single function might not be enough.
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 :)
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.
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.
Arxiv link for the first one: https://arxiv.org/abs/1402.5332
Also, TIL people use LaTeX for normal documents with no formulas.