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An understanding of AI’s limitations is starting to sink in

economist.com

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Re: An understanding of AI’s limitations is starting to sink in

#41

Post by seibelj »

fxtentacle wrote: post #34
seibelj wrote: post #7

I’m a longtime AI skeptic who has been arguing passionately against the doom-sayers, many in my own family and in casual conversations with laymen, for several years. I stand by this article I wrote which summarizes my views https://medium.com/@seibelj/the-artificial-intelligence-scam... The hype on AI was absolutely astonishing. I’m glad it’s finally coming back to reality.

That article is well argued and I am inclined to agree. But then I noticed that we seem to strongly disagree on other topics. I find it fascinating that you can both see AI as what it truly is (a rebranding to solicit investment) yet still cheer for cryptocurrencies, which I personally believe to be not much more than a pyramid scheme to dupe those investors that buy in late. In my opinion, both AI and crypto are qui…

I am a longtime crypto guy, and totally 100% understand how you see the links. I have a very nuanced opinion on this, but it's difficult to explain briefly here. There are a lot of scams in crypto, but if you are a believer in Austrian economics and are not a fan of Keynesian theories, fiat currency, and the Federal Reserve, then Bitcoin / crypto is very attractive.

But that's a separate discussion, unrelated to my opinion of AI and is primarily a philosophical issue. Cryptocurrency / blockchain is fascinating but we are not promising robots cleaning your house.

Re: An understanding of AI’s limitations is starting to sink in

#42

Post by Animats »

mrfusion wrote: post #32

What’s the next big thing after deep learning?

That's a very good question.

Robot manipulation in unstructured situations is still not very good. (See the videos of the DARPA humanoid challenge) "Common sense", defined as the ability to predict the consequences of actions and to use that to plan, hasn't progressed much in years. Machine learning doesn't seem to have helped much with either, so far. Those are key areas for doing physical things in the real world. Humans are good at those.

So that's where to look for hard problems.

The payoff is low, though. Those skills are common to all healthy humans, so there's a huge pool of unskilled labor available with them. If you did a startup, and you solved, say, robotic shelf stocking, it would not be a huge win over low-wage people.

Re: An understanding of AI’s limitations is starting to sink in

#43

Post by tchaffee »

neals wrote: post #33
mtgp1000 wrote: post #11

I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…

Honest curiosity: do you have some examples of interesting applications? Large and small?

How about translation? Living in a foreign country I possibly find it useful more often than the average person.

Machine generated subtitles on YouTube is another example. I find those useful even in my native tongue, especially when watching at 2x speed.

If you make purchases on the web, there is a good chance that machine learning is behind the fraud detection that you don't even know is taking place.

Re: An understanding of AI’s limitations is starting to sink in

#44

Post by msla »

These days, you can translate text by pointing your phone at it and taking a picture. Thirty years ago, this would have been unambiguously AI, because it would have been not only impossible, but stupid impossible like something out of a soft SF novel where little self-flying robots deliver stuff to your house, or you can ask a computer a question in a natural voice and reasonably expect a civil, natural-language answer sourced from global databases.

Ah, but all that works. If AI is perpetually defined to be "that which does not work" then it's perpetually potential, perpetually postulated, perpetually possible perfection. It never has to be compared to a clunky translation, or a drone that gets shot down. Unwritten novels never have story problems in the third act, unwritten programs never give ludicrous output.

Re: An understanding of AI’s limitations is starting to sink in

#45

Post by effable »

mellosouls wrote: post #17
mtgp1000 wrote: post #11

I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…

There has been next to ZERO progress towards genuine AGI despite a never-ending deluge of AI articles; that's normally the cause of scepticism. After several decades and a much-hyped last few years we have fake cleverness - impressively so in both cases - but nothing more.

It's even hard to say how "genuine" cleverness in human beings works - and we've had a lot more time to study ourselves - granted that most of that time we've not had the tools to understand how our brains work at the algorithmic level. I am not claiming that achieving some version of "genuine" AGI necessarily involves understanding how human intelligence works, but it is reasonable to expect that knowing more about it will definitely reduce the search space of possible systems.

Re: An understanding of AI’s limitations is starting to sink in

#46

Post by Veedrac »

mtgp1000 wrote: post #11

I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…

> I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected.

They do worse than this; one of the articles in the section uses a bad Google translation in Yoruba, from 2018, and uses that to claim it “is therefore still baffled by the sorts of questions a toddler would find trivial”. I'm not sure Yoruba even used neural nets at that time!—‘massively multilingual’ training only really came about in 2019, and was productiviced June this year.

A disappointing show from The Economist, given how little effort it would have taken to check with the newest model.

Re: An understanding of AI’s limitations is starting to sink in

#47

Post by [deleted] »

mtgp1000 wrote: post #11

I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…

[deleted]

Re: An understanding of AI’s limitations is starting to sink in

#48

Post by bsaul »

mtgp1000 wrote: post #29
ManuelKiessling wrote: post #23

Earlier quoted context omitted.

Ok, consider me intrigued. What is that we are going to see/experience once you folks had some time? Genuine question! Can you give us a basic idea of the things that you are already sure by now will see the light of day?

Mathematical modeling that is 3-6 orders of magnitude faster, we are already talking deployment. Same for ML powered solutions to data management - I don't want to say enough to identify anything. My team has been working on a rudimentary humanlike reasoning engine based loosely on what AlphaGo proved: that machines can learn heuristics identical, equal to, or better than those of humans. And for perspective, AlphaGo…

since you’re working on the field : i’m still under the impression that modern ML (neural network in particular) are not producing any science, nor are they producing any level of understanding of the phenomenon it’s trying to model. And that as such, we can’t provide a reliable estimate of the limitations of the produced model, other than by feeding it tons of inputs and measure the result.

It would be like trying to understand gravity by throwing thousands of rocks, measuring the motions, and build an approximation on a huge matrix, without ever being able to get the « real » netwonian formula.

Am i correct ?

Re: An understanding of AI’s limitations is starting to sink in

#49

Post by criddell »

MiroF wrote: post #16
mtgp1000 wrote: post #11

I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…

Absolutely. To be perfectly honest, it surprises me the extent to which ML naysaying seems to be popular on HN. The evidence of enormous progress seems pretty obvious to me.

I wonder if it's because of the name machine learning? It seems a lot of applications boil down to some type of discriminator or pattern matcher.

Re: An understanding of AI’s limitations is starting to sink in

#50

Post by an_opabinia »

mtgp1000 wrote: post #29
ManuelKiessling wrote: post #23

Earlier quoted context omitted.

Ok, consider me intrigued. What is that we are going to see/experience once you folks had some time? Genuine question! Can you give us a basic idea of the things that you are already sure by now will see the light of day?

Mathematical modeling that is 3-6 orders of magnitude faster, we are already talking deployment. Same for ML powered solutions to data management - I don't want to say enough to identify anything. My team has been working on a rudimentary humanlike reasoning engine based loosely on what AlphaGo proved: that machines can learn heuristics identical, equal to, or better than those of humans. And for perspective, AlphaGo…

> And for perspective, AlphaGo was what...4 years ago? Just look at the growth of compute available cheaply in the cloud since then

How would we know the difference between a problem that was interesting to solve but was hard versus a problem that just needed Google's money to solve?

Besides, since when was cloud compute was getting cheaper? I'm pretty sure, considering how increasingly profitable it is despite the "discounts," cloud compute has gotten more expensive, in the sense of increasing margins for the seller.

I guess we'll all find out what OpenAI does with its Azure credits.

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