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Why AI is harder than we think

arxiv.org

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Re: Why AI is harder than we think

#51

Post by YeGoblynQueenne »

kthejoker2 wrote: post #38
YeGoblynQueenne wrote: post #21

Earlier quoted context omitted.

>> 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 w…

Symbolic AI is a well known topic. https://en.wikipedia.org/wiki/Symbolic_artificial_intelligen... As the article notes > Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the late 1980s. And, yes, Deep Blue was just "a bit of symbolic manipulation you can do in Lisp" at a very large hardware scale. It simply brute force calculated the value of as many position trees as it could up to ply…

>> And, yes, Deep Blue was just "a bit of symbolic manipulation you can do in Lisp" at a very large hardware scale.

Modern chess engines use the same approach as Deep Blue, on much smaller hardware. IBM certainly tried to position Deep Blue as a triumph of large hardware, but its success is widely recognised as being owed to its minimax with alpha-beta cutoff algorithm. I can't imagine anyone who would call minimax "brute force"- it's a search algorithm and alpha-beta is a heuristic pruning technique. It's very hard to see pruning of any kind as "brute force"; rather that's the whole point, you prune a search tree to avoid an exhaustive search.

The evaluation function was hard-coded, yes, according to human expertise and chess theory. Note that Deep Blue also had an extensive "opening book" which if I remember correctly made it possible to play a very strong early game.

In any case, it took a few decades to create a system like Deep Blue and it was far from "a bit of symbolic manipulation in Lisp" but scaled up. I recommend the Adversarial Search section in Russel and Norvig if you want to develop a more thorough understanding of the relevant approaches (probably fourth edition will do).

>> Creating Deep Blue's hardware in 1997 was the most impressive achievement.

Can you point to other achievements in symbolic AI that were not as impressive as Deep Blue? Do you know of any others that you could compare to Deep Blue?

Re: Why AI is harder than we think

#52

Post by YeGoblynQueenne »

teddyh wrote: post #36
YeGoblynQueenne wrote: post #21

Earlier quoted context omitted.

>> 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 w…

They are referring to “classic AI”, as exemplified by SHRDLU¹ and Cyc². 1. https://en.wikipedia.org/wiki/SHRDLU 2. https://en.wikipedia.org/wiki/Cyc

The capabilities of SHRDLU are still unsurpassed by modern NLP systems, unfortunately.

Cyc is a different matter. I don't really know how close the project is to achieveing its goals and it always seemed like a bit of a futile moonshot to me, but it's certainly not "a bit of symbolic manipulation you can do in lisp". If nothing else, the sheer scale of a project that has been going on for almost 40 years, should cause a reduction of the arrogance and brashness of proclamations about it.

Re: Why AI is harder than we think

#53

Post by someguyorother »

audunw wrote: post #43

I think another common fallacy is to assume that humans have more general intelligence than we actually do. This is manifest in three ways: 1. Assuming that we can build an AI than can do what a human does, without being embedded in the physical world and in constant communication with other humans in the same way we are. I think this is covered by "Intelligence is all in the brain" in the article 2. Not considering…

I like this refutation of the paperclip maximizer scenario, which ties in to your observation:

Let's say there's a runaway superintelligence equipped with an optimization function that says it should produce a maximal amount of paperclips. Then either its objective, "produce a maximal amount of paperclips" is defined literally or not.

If it's defined literally, e.g. "make your sensors return this data consistent with lots of paperclips having been produced", then it's far easier for the AI to corrupt its sensors with paperclip porn than actually destroy the Earth, and there's no problem.

On the other hand, if the objective is not defined literally, then the AI must be able to understand fuzzy instructions in the way the humans intended it to. In that case, it would be no problem to tell the AI to not be an ass either.

So the problem happens when the AI isn't intelligent enough, and uses heuristics instead of properly optimizing. The extreme case would be grey goo, which doesn't think at all, but just blindly consumes everything. Giving extreme power to something with limited intelligence is generally a bad move.

Re: Why AI is harder than we think

#54

Post by denton-scratch »

Borrible wrote: post #37

So... You're saying it isn't easy to build a system, that, after about five years of learning a myriad of other things, can lace its shoes after about a dozend tries and showing him just twice? That at the same time can explain why it is a good idea to lace them, if wearing them at all? That insists stubbornly, that it is much more fun to wear no shoes when walking in the wet sand at the beach? That about one or two…

It took me way more than "four or five tries" to learn to tie my laces. From childhood to about age 30, I was tying a pair of half-hitches, sometimes making a granny, rather than using the superior "bunny-in-a-hole" method. I only learned the better method when I had to teach my kids.

Re: Why AI is harder than we think

#55

Post by YeGoblynQueenne »

forgotpwd16 wrote: post #48
kthejoker2 wrote: post #38

Earlier quoted context omitted.

Symbolic AI is a well known topic. https://en.wikipedia.org/wiki/Symbolic_artificial_intelligen... As the article notes > Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the late 1980s. And, yes, Deep Blue was just "a bit of symbolic manipulation you can do in Lisp" at a very large hardware scale. It simply brute force calculated the value of as many position trees as it could up to ply…

Is symbolic AI dead nowadays? All AI papers I'm seeing is on machine learning.

Have you looked at recent publications in AAAI and IJCAI? Those are the big conferences of AI without specific focus on machine learning.

You may also want to have a look at KRR (Knowledge Representation and Reasoning) and one of the robotics conferences, whose names unfortunately I don't remmeber by heart. Again, those don't tend to focus on machine learning.

Finally, there is such a thing as symbolic machine learning. For example, see the IJCLR conference (unifying a bunch of conferences from the symbolic machine learning field, like ILP, STAR, NeSy etc).

Re: Why AI is harder than we think

#56

Post by Borrible »

pitspotter wrote: post #47
Borrible wrote: post #37

So... You're saying it isn't easy to build a system, that, after about five years of learning a myriad of other things, can lace its shoes after about a dozend tries and showing him just twice? That at the same time can explain why it is a good idea to lace them, if wearing them at all? That insists stubbornly, that it is much more fun to wear no shoes when walking in the wet sand at the beach? That about one or two…

Worth emphasising that the knowledge of how to do these things like tying shoelaces and writing topology papers is contained in the surrounding culture. So if a machine can learn this culture, much of the complexity is outsourced. The ability to learn just one piece of existing culture conveys the ability to learn any other part of it, if one is inclined to do so. >And that with just 20 Watt? It is amazing; however t…

'emphasising that knowledge...is contained in the surrounding culture'

This! And some more that is not in the brain. Not to say what is in the brain, but isn't considered as intelligence like emotions, that control large parts of the planetary biomass. Fear for example is a simple automatism that for the most part you don't need to replace with more 'intelligent' approaches and still has much more influence on human behaviour than abstract intelligence, whatever that may be. If it exists at all.

Personally, and I emphazise I am thrilled by what the AI crowd has done the last years, kudos to that, they look to me like someone quite bright, that pretends or even believes to understand the inner workings of a computer by simulation its GUI. If I'm right, my critisism would be, that's great and you're making really fun toys and gadgets and tools, but don't sell it to me as intelligence. That's just a marketing ploy.

That energy optimising theory of the brain you're mentioning sounds interesting, have to ponder that.

Many thanks!

Re: Why AI is harder than we think

#57

Post by sgt101 »

JackMorgan wrote: post #46
sgt101 wrote: post #42

Earlier quoted context omitted.

GPT2?

I'm not sure what you mean by this comment. GPT2 is a precomputed web of associations between words that can produce a simulacrum of human speech. It's not used for actual propagation of anything. It's a Looney Tunes painting of a train tunnel, not an actual train tunnel. GPT2 "AI" isn't trying to "tell us anything" any more than a magic 8 ball is trying to tell us anything.

JackMorgan, I meant that your comments read like those generated by GPT2.

Re: Why AI is harder than we think

#58

Post by nil-sec »

While I agree with the general point of this paper I don’t think it’s quite right to compare the current situation with the last AI spring. It’s not AGI but it’s very good narrow AI that has real commercial value right now. The systems back then did not to the same extent, and for this reason I don’t see funding dry up for current ML approaches.

Re: Why AI is harder than we think

#59

Post by nil-sec »

audunw wrote: post #43

I think another common fallacy is to assume that humans have more general intelligence than we actually do. This is manifest in three ways: 1. Assuming that we can build an AI than can do what a human does, without being embedded in the physical world and in constant communication with other humans in the same way we are. I think this is covered by "Intelligence is all in the brain" in the article 2. Not considering…

This is a good point which I think stems from wrongly equating human level intelligence to AGI in the popular literature. It’s not at all clear what a general intelligence should be and it’s much less clear that humans have general intelligence. In my view we have a set of very good innate priors (e.g. space/time continuity, intuitive physics) that have been optimized by evolution for thousands of years. These priors in turn allow us to learn fast from unlabeled data, but would anyone call such a system general intelligence? I’m not sure.

Re: Why AI is harder than we think

#60

Post by Borrible »

denton-scratch wrote: post #54
Borrible wrote: post #37

So... You're saying it isn't easy to build a system, that, after about five years of learning a myriad of other things, can lace its shoes after about a dozend tries and showing him just twice? That at the same time can explain why it is a good idea to lace them, if wearing them at all? That insists stubbornly, that it is much more fun to wear no shoes when walking in the wet sand at the beach? That about one or two…

It took me way more than "four or five tries" to learn to tie my laces. From childhood to about age 30, I was tying a pair of half-hitches, sometimes making a granny, rather than using the superior "bunny-in-a-hole" method. I only learned the better method when I had to teach my kids.

Wether the "bunny-in-a-hole" method is superior, is quite debatable, sir! ;-)

Funny thing is, I had to invent the two-loop shoelace knot/bunny-ears/bowknot myself, because I couldn't be bothered to understand the bunny-rabbit/loop-swoop-and-pull.

Of course you have to do it right or you end up with an unbalanced granny knot, which is of course not acceptable ;-)))

Until my personal groundbreaking knot invention I used velcro and a double simple knot sometimes for three or four years. Since velcros came out of fashion for inexplicable reasons, I was under pressure to change my modus operandi...

Ah, and of course the influence and dynamics of raising children over a long period of time on intelligence is something seldom considered in AI.

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