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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

#331

Post by thu2111 »

saberience wrote: post #302
thu2111 wrote: post #254

Earlier quoted context omitted.

Disagree. GPT-2/GPT-3 are able to pass for humans when the reader isn't paying close attention. This article seems insightful to me: http://www.overcomingbias.com/2017/03/better-babblers.html A scarily large amount of human speech is essentially word prediction, especially in cases where someone wants to seem impressive without having actually done the work. We're all familiar with the problem of people "bullshitting…

We've been able to produce that kind of speech before though. I wrote programs using Markov Chains 10 years ago which could produce almost real sounding speech but I didn't think it was intelligent. The GPT models are essentially massive databases along with the use of probability models for how words are connected. Do you think that means intelligence? Can it generate new knowledge of things outside its database? Ca…

But at some level our own brains are just very large databases and very good prediction engines, at least when running on auto-pilot and lacking any deep understanding. Hence the phenomenon of BS artists. Sad to say but I've heard speeches that were less coherent and interesting than what GPT-3 produces.

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

#332

Post by thu2111 »

mellosouls wrote: post #300
thu2111 wrote: post #254

Earlier quoted context omitted.

Disagree. GPT-2/GPT-3 are able to pass for humans when the reader isn't paying close attention. This article seems insightful to me: http://www.overcomingbias.com/2017/03/better-babblers.html A scarily large amount of human speech is essentially word prediction, especially in cases where someone wants to seem impressive without having actually done the work. We're all familiar with the problem of people "bullshitting…

Where in the GPT models is the conscious (or even subconscious) thinking, comprehension, understanding, relating to experience, imagination etc, etc, you know: actual intelligence as a human would feel and use it, general or otherwise? Tic Tac Toe, Chess, Image Classifying, Translation, Go, Sentence Construction, etc.; We are creating phenomenally impressive calculating worm-equivalents, but nothing human-level, or e…

Where in the human mind are these things? We judge that they exist based primarily on what people say and do. GPT-style models can't do anything, but they can say. And for better or worse a whole lot of ordinary, everyday speech doesn't have a whole lot of thinking, comprehension or imagination to it. It's just people saying what other people seem to be saying in order to try and stay with the herd. GPT-3 is scarily close to matching that.

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

#333

Post by burntoutfire »

bcrosby95 wrote: post #55
bsaul wrote: post #48

Earlier quoted context omitted.

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 t…

Some of this would be considered a feature by companies. It's more defensible to have unknowable AI deciding to do illegal things than programmers hard coding illegal things. Which really boggles my mind. When my kid does something illegal I'm held liable. When an ML algorithm programmed by a team of people does, nothing we can do about that!

I'm not sure if the courts will see it this way. Anyway, in some fields some stuff (e.g. credit scoring in banka in my country) is regulated and it's the humans who have to make the final decisions - algoriths can only provide an input. In practice, the input is "approve/deny", but it's still a human who is making a "decision" based on this "input".

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

#334

Post by burntoutfire »

pwdisswordfish2 wrote: post #161
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…

Are there examples in history where things requiring great time and effort were built then abandoned? In recent times, due to a relative dearth of high yield investment opportunities, there is a lot of money with nowhere to go. Some believe much of it is "dumb" money. Overfunded startups are one possible symptom. Heavily-funded "AI" may be another. This is before we even consider the moral and ethical issues of using…

> Are there examples in history where things requiring great time and effort were built then abandoned?

The communist countries seen a huge misallocation of funds into giant projects that didn't make sense (e.g. huge steel mills in Poland that were always losing money).

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

#335

Post by burntoutfire »

calebkaiser wrote: post #64
neals wrote: post #33

Earlier quoted context omitted.

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

I work on a production ML platform, so I spend way too much time rabbit-holing on interesting looking projects. If you're looking for interesting startups/projects-not-from-big-tech: - Glisten.ai ( https://www.glisten.ai/ ). Recent YC startup, uses a combination of different models to parse product information (actually a huge manual problem in retail/ecommerce) and expose it as an api. - Wildlife Protection Solution…

> Maps: ETA Prediction

So THAT's why the ETA given is always too short! If it's based on how often a typical driver makes it, and typical driver is a speeding asshole, then no wonder that the estimates are unrealistic for someone who actually drives under the speed limit. It's a shame that Google is actually normalizing assholiness.

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

#336

Post by mjburgess »

machiaweliczny wrote: post #325
mjburgess wrote: post #319

Earlier quoted context omitted.

It isn't ever going to model our style of thinking. A "neural network" is just high-dimension linear regression; the idea it has anything to do with the brain is metaphorical nonesense. No algorithm running on digital hardware can emulate the biological process of animal intelligence.

Why not?

What algorithm running on a digital computer would make the computer transparent?

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

#337

Post by burntoutfire »

thu2111 wrote: post #332
mellosouls wrote: post #300

Earlier quoted context omitted.

Where in the GPT models is the conscious (or even subconscious) thinking, comprehension, understanding, relating to experience, imagination etc, etc, you know: actual intelligence as a human would feel and use it, general or otherwise? Tic Tac Toe, Chess, Image Classifying, Translation, Go, Sentence Construction, etc.; We are creating phenomenally impressive calculating worm-equivalents, but nothing human-level, or e…

Where in the human mind are these things? We judge that they exist based primarily on what people say and do. GPT-style models can't do anything, but they can say. And for better or worse a whole lot of ordinary, everyday speech doesn't have a whole lot of thinking, comprehension or imagination to it. It's just people saying what other people seem to be saying in order to try and stay with the herd. GPT-3 is scarily…

So, they're getting pretty close to matching us in mindlessness and stupidity. Call me when they can match us in our highs, not in our lows.

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

#338

Post by coverup »

Veedrac wrote: post #326
coverup wrote: post #288

Earlier quoted context omitted.

Yep, you and the user you're replying to are both right in different ways. One thing's for sure - machines don't generate "insights" on their own. Let's define an "insight" as "new meaningful knowledge", just for fun. We could talk about what comprises "new" and "meaningful" but it would be beside the point I'm making. In a supervised learning problem, the range of possible outputs is already known, meaning the model…

This is demonstrably false; AlphaGo made significant new discoveries, for example.

Yeah this is where it would have helped if I had discussed what I meant by "new".

AlphaGo is a supervised learner that outputs optimal Go moves given opposing play. It yields new discoveries in the same sense that a model designed to predict mechanical failures from labeled sensor data would: I didn't know what the model was going to predict until it predicted it, and now I know.

But what the factory owners want is a machine that can take raw, unlabeled sensor data and predict mechanical failures from that. They want insights. "Why not just feed all our data into the model and just see what comes out?" they ask. "I don't see why we need to hire at all if we have this neural net."

The reason you need a human somewhere in the system if you want insights is because someone needed to program AlphaGo specifically to try to win at Go. At the factory, someone needs to tell the machine what a mechanical failure is, in terms of the data, before it can successfully predict them.

Then, neither "winning at Go" nor "mechanical failure" are states that the system hasn't already been programmed to recognize. That's what I mean when I say a supervised learner cannot generate "new" output.

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

#339

Post by randcraw »

FiberBundle wrote: post #61
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…

Well of course you say that, you're an ML researcher who likely went into the field expecting further steady progress comparable to what we saw between 2012 and 2016. If actual progress in the field would be slower than what is currently still expected by the majority of people this would have dramatic consequences for future research investment, which is why you see most ML researchers reinforcing the hype or at lea…

I think a major problem in judging the advancement of AI is that its successes are not general purpose -- it works great for narrow tasks and not well for broad tasks that we humans do every day, especially those that require what we call "understanding" (as in modeling and employing semantics or relationships among objects like causal inference).

Deep learning's big success is its ability to attach a label to a complex signal -- an image or a sound. That's pattern recognition: speech in, speech out, and image recognition. These tasks were largely beyond computers until 2012 but something that a child or a squirrel does very well, so it's been long known that skills like these are not useful signs of intelligence or rising levels of cognition.

Other tasks DL does well are those that benefit from memorization of results form death-by-search and from vast amounts of RL simulation, like playing of board games and the transforming of patterns (images and sounds) in fun ways using GANs. But ever since Deep Blue's win over Kasparov using clever pattern matching of past chess games (by memorization), we've known that narrow skills like game play also are not useful signs of intelligence.

Yet pattern matching skills are 99% of what deep learning hath wrought. Yes, that's useful, but it's not really intelligent. It shows no signs of thinking / cognition aside from probabilistic association / clustering. So there's no reason to imagine that techniques like deep nets will take us all the way to thinking like a human. Today, because of DL, we're much better at pattern matching. But in terms of what's essential to cognition, pattern matching achieves surprisingly little if your real goal is to THINK.

As far as "thinking" tasks go, cognitive tasks like machine interpretation of intent in written text and machine translation between languages still suck, despite the impressive advances in semantic-surface associators like BERT and newer transformer-based NLP engines. To do more than answer basic questions about nouns and verbs, you need a model for deeper semantics and an understanding of logic and relations between actors. Until deep nets can model semantics that are not present in the test data, and employ logical inference, it can't be said to think, much less intelligently.

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

#340

Post by levitatorius »

abj wrote: post #97
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's value, the tech works, but applying it is surprisingly hard. I met someone who dedicates their life to using machine learning to replace/aid/automate pathologists 6+ hour days searching for cancer tumors in lungs. They have been at it for 5 years. There is an insane amount of approvals, red tape, knowing the right people, convincing the hospital to use it - all tasks not related to the tech actually working.…

I am a practicing pathologist and I have seen many attempts and publications to use ML in pathology, which all lack in these aspects: 1. ML is trained on simplified sets (preselected ROIs, limited choice of diagnoses), 2. ML is biased by the experts who labeled learning sets, 3. there is no obvious process of learning from failures after initial training, 4. who is responsible in case of ML error with substantial consequences for patient? The first point is especially for the lack of better word.. wishful. In the daily practice we are used to account for "things unexpected" - non-representative biopsies, parasites in tissue where tumor was suspected, foreign body reaction from previous operations, laboratory accidents (such as swapped paraffin blocks of two patients), and so on (the list is much longer). We deal with it. That ML can discern between 5 most common diagnoses is fine, but it is rather narrow problem to solve.
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