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Why AI is harder than we think

arxiv.org

21–30 of 77 posts

Re: Why AI is harder than we think

#21
post #7

"AI is harder than we think" of course depends a bit on who "we" is. The paper seems to focus a bit on the types who thought it's a bit of symbol manipulation you can do in lisp which has always seemed a bit dumb. Then the "it'll never happen" crowd seems dumb as well given people do think and seem to be built from atoms and stuff obeying normal physics. The more sensible view it seems to me is to compare neural comp…

>> The paper seems to focus a bit on the types who thought it's a bit of symbol manipulation you can do in lisp which has always seemed a bit dumb.

About half of the paper is about modern deep neural networks, but can you please give a few examples of the kind of AI you say is "a bit of symbol manipulation you can do in lisp"? Because I would say this is an extreme oversimplification borne by terminal unfamiliarity with the subject, beyond what may be commented on twitter.

For instance, remember that Deep Blue, despite its name, had nothing to do with deep learning or machine learning of any kind, and was a manually programmed system, yet I doubt anyone would sensibly describe it as "a bit of symbol manipulation you can do in lisp".

But please give examples of what you mean.

Re: Why AI is harder than we think

#22
post #14

This paper is written by a non-technical author, criticising AI based on predictions made by non-technical pundits. By concentrating on binary outcome predictions ( Do we have "full self driving cars" available to the general public? ) it misses the real progress made in a huge number of areas. For example, one of the claims this paper claims to be false is Zuckerberg's 2015 declaration that: One of [Facebook’s] goal…

Just wanted to point out that I don't think she's a non-technical author https://en.wikipedia.org/wiki/Melanie_Mitchell

Also the paper mainly comments on the work and sayings of AI and CS researchers like John McCarthy, Stuart Russel, Andrew Ng, Geoff Hinton, Hans Moravec, Drew McDermot, Claude Shannon, Alan Turing etc. i.e. hardly "non-technical pundits".

Re: Why AI is harder than we think

#23

i personally believe it's do-able. But it's not going to happen using machine learning and certainly not with quantum computers. I think u just need plain old code. It's just really complex to understand how a brain works. I mean, words, actions, experiences and concepts have to all work together. Let alone emotions, perception of others, projecting the future and a value system. But even that said, im sure it's do-a…

Just a heads-up, all of your comments seem to get shadow banned. I'm not sure why, maybe you should contact dang.

Re: Why AI is harder than we think

#24
post #20

Link to paper: https://arxiv.org/pdf/2104.12871.pdf TLDR: Fallacy 1: Narrow intelligence is on a continuum with general intelligence (solving an easy AI problem doesn't immediately lead to being able to solve a hard AI problem) Fallacy 2: Easy things are easy and hard things are hard (ie AI is hard) Fallacy 3: The lure of wishful mnemonics (ie people tend to give parts of a computer program anthropomorphic labels, eg…

Fallacy 4 is wrong. There are good reasons to believe intelligence can be "disembodied". The primary one is that signal mappers decouple. https://www.overcomingbias.com/2011/01/signal-processors-dec...

(Part of) intelligence is about applying acquired knowledge to a new situation. Can a disembodied "intelligence" even encounter new situations?

Re: Why AI is harder than we think

#25
post #23

i personally believe it's do-able. But it's not going to happen using machine learning and certainly not with quantum computers. I think u just need plain old code. It's just really complex to understand how a brain works. I mean, words, actions, experiences and concepts have to all work together. Let alone emotions, perception of others, projecting the future and a value system. But even that said, im sure it's do-a…

Just a heads-up, all of your comments seem to get shadow banned. I'm not sure why, maybe you should contact dang.

Thanks for the heads-up. I was already suspecting this for a while. Who/what is dang? U mean the HN user named dang?

Re: Why AI is harder than we think

#26
post #14

This paper is written by a non-technical author, criticising AI based on predictions made by non-technical pundits. By concentrating on binary outcome predictions ( Do we have "full self driving cars" available to the general public? ) it misses the real progress made in a huge number of areas. For example, one of the claims this paper claims to be false is Zuckerberg's 2015 declaration that: One of [Facebook’s] goal…

The paper criticises the benchmarks that are typically used to show that "AI systems are already better than humans in vision and [speech processing]" etc.

For example, given state of the art in current benchmarks on language understanding, if those benchmarks really did measure language understanding, we could all be AIs debating the paper that itself could have been written by AI. Suffice it to say, this is not likely given the current level of "understanding" in modern systems.

The paper refers to various pieces of work within natural language processing and other sub-fields of AI that analyse the weaknesses of such benchmarks and investigate the ability of modern systems to beat benchmarks by finding shortcuts or exploiting surface statistical regularities etc. I recommend, for example:

T. McCoy, E. Pavlick, and T. Linzen. Right for the wrong reasons: Diagnosing syntactic heuristics innatural language inference. InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), pages 3428–3448, 2019.

https://www.aclweb.org/anthology/P19-1334.pdf

Re: Why AI is harder than we think

#27

Link to paper: https://arxiv.org/pdf/2104.12871.pdf TLDR: Fallacy 1: Narrow intelligence is on a continuum with general intelligence (solving an easy AI problem doesn't immediately lead to being able to solve a hard AI problem) Fallacy 2: Easy things are easy and hard things are hard (ie AI is hard) Fallacy 3: The lure of wishful mnemonics (ie people tend to give parts of a computer program anthropomorphic labels, eg…

Fallacy #1 is mostly just a proposition/conjecture.

The current trend is for these "narrow" AI systems to become more and more general as the technology gets better.

So far, we haven't seen the end of that trend. It's anyone's guess whether we're going to run into the proposed hypothetical barrier.

Re: Why AI is harder than we think

#28

Link to paper: https://arxiv.org/pdf/2104.12871.pdf TLDR: Fallacy 1: Narrow intelligence is on a continuum with general intelligence (solving an easy AI problem doesn't immediately lead to being able to solve a hard AI problem) Fallacy 2: Easy things are easy and hard things are hard (ie AI is hard) Fallacy 3: The lure of wishful mnemonics (ie people tend to give parts of a computer program anthropomorphic labels, eg…

I think fallacy 2 is the big one, and not just in the AI field. Sometimes a simple lookup table (or some other 'dumb' model) works well on tasks that humans generally consider 'hard'.

Re: Why AI is harder than we think

#29
post #23

Earlier quoted context omitted.

Just a heads-up, all of your comments seem to get shadow banned. I'm not sure why, maybe you should contact dang.

Thanks for the heads-up. I was already suspecting this for a while. Who/what is dang? U mean the HN user named dang?

Yes, dang is a (the?) moderator on HN. Send a mail to hn@ycombinator.com and he'll (hopefully) sort it out.

Re: Why AI is harder than we think

#30
post #7

"AI is harder than we think" of course depends a bit on who "we" is. The paper seems to focus a bit on the types who thought it's a bit of symbol manipulation you can do in lisp which has always seemed a bit dumb. Then the "it'll never happen" crowd seems dumb as well given people do think and seem to be built from atoms and stuff obeying normal physics. The more sensible view it seems to me is to compare neural comp…

It's true that those two approaches are "a bit dumb", but the statement

> The more sensible view it seems to me is to compare neural computation to what we can do with silicon and then project on a Moore's law type assumption as to when it would be likely

is no less dumb imo. What makes you say this? What's the evidence? What neural network system has achieved something even close to being on a path to AI?

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