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

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

#251

Post by the_omegist »

dreamcompiler wrote: post #219
ipiz0618 wrote: post #207

Earlier quoted context omitted.

"AI" is a very vague term. What you described aren't entirely "machine learning", but a combination of existing linguistic techniques and machine (deep) learning. People confuse what AI can do, and what is AI all the time. It also doesn't help when there are so many inexperienced data scientist making promises that they can't achieve. In your example, I'd argue that a human is not necessarily a better driver than a m…

Whether humans or AI are "better" drivers is completely beside the point. The point is that we can characterize human drivers. We know where they succeed and where they fail, both in a statistical sense and in an individual sense based on their age, attention, vision, chemical impairment, etc. But we cannot characterize ML networks. We take it on faith that they work and then we find (because somebody dies) that they…

Just playing the devil's advocate but : when you take a taxi, what do you know about the driver? You can vaguely see if he's sober and that's all. You EXPECT him to have a driver's license, to have a good eyesight, etc but you KNOW nothing about it. If he has an heart attack while he's driving on the highway, could it have been predicted (by you or the company)? No.

So i don't see why this distinction between AI and humans is made : both are black boxes. Perhaps humans have less "edge cases" but as long as the error level of AI is the same or lower than the one of humans, I don't care if the car crashed because the human driver looked at a sexy woman on an ad on a billboard or because a variable was poorly set in the car's code.

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

#252

Post by thu2111 »

perl4ever wrote: post #195
Judgmentality wrote: post #104

Earlier quoted context omitted.

Until GPT-3 can write something meaningful, it's really just a showcase of the technology and a gimmick of a product. Sure it's cool, but what problem is it solving? As far as I can tell the only useful function it has is polluting the internet with pseudo-intellectual comments to promote some agenda (likely political). So now that I think about it, it actually would be incredibly valuable for things like subverting…

"Sure it's cool, but what problem is it solving?" Isn't it good enough, or very nearly, to generate fake news? And if you can generate something that fools a large percentage of humans, even for a second or two, you can sell ads. It would be solving a problem for anyone who can profit from it. I thought the people who developed it stated that it was too dangerous to release widely? Dangerous = useful to bad people, n…

That was just PR fluff designed to play to the ideological biases of their Valley employee base. They released GPT-2 anyway some months later because other people were going to replicate it anyway, and guess what, the river of fake news we're flooded with daily is still not being generated by AI. It's being generated by journalists with an agenda, same as ever.

Don't get me wrong. You can absolutely generate news articles with these text generation models. But if you look at examples of the "fake news" generated by the monster GPT-3 model, it's not really any different to junk churned out by supposedly respectable news organisations i.e. grammatically correct but filled with logical contradictions, false statements that can be checked in 30 seconds with a search engine and so on. Most people already learned which news sources are trustworthy and which are pushing an agenda. If those outlets replace their journalists with GPT-3 it won't make any difference. The readers who don't trust them will still not trust them, and the readers who just want partisan cheerleading will continue to be satisfied.

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

#253

Post by thu2111 »

ImaCake wrote: post #111
laingc wrote: post #96

Earlier quoted context omitted.

I completely agree with this. Let me add a non-US perspective that may surprise some people. I think a big part of what is holding back many companies from making effective, genuine, real-world use of AI is that a significant majority of the individuals involved are bad at their jobs . On the business side, there is an widespread unwillingness to acknowledge that technical people may be better placed to make decision…

>On the business side, there is an widespread unwillingness to acknowledge that technical people may be better placed to make decisions than businesspeople. Maybe acknwledging this would mean losing their jobs. Buisness people have mortgages and children and they probably don't like the idea of having their job replaced by a robot. It is important to remember that the admin/money-side people are not playing the game…

They said technical people, not robots. Perhaps a subtle distinction ;)

Agree totally with the latter part though. In vast swathes of the business world, technology is not perceived as a way to gain a competitive advantage but rather something between an interesting novelty and an annoyance. The problem is there's a cultural expectation that you be "innovative" which tends to be treated as a synonym for deployment of computer-related technology upgrades (although of course it doesn't have to mean this).

But to people who have no intrinsic interest in technology, there's no real way for them to actually innovate. They just don't know where to start. So they latch on to trends they read about in the Economist or NYT on a truly massive scale. They spend days or weeks making PowerPoints about the transformative potential of blockchain, IoT or AI. They take a few programmers who are kicking around but seem bored and allocate them to an "innovation lab" where they putter around making prototypes and having fun but never impacting the business in any way.

This ticks the box labelled "we are innovative" without requiring anyone to think too hard, learn anything new or take any risks, all things that the generic unskilled graduate managerial class in our society hate doing. And of course technology upgrades are risky. When run by non-technical people they tend to go wrong in spectacularly expensive and mysterious ways, so a lot of business executives would rather pull their own teeth out than plan a major IT upgrade. This is partly why tech firms exist as a concept.

As for AI, you can't replace most of these jobs with AI because their outputs are undefined to begin with. And to be frank, my observations over the years has been that a lot of these jobs appear to be undefined in order to enable a form of cultural stuffing. If you're a non-technical executive you wouldn't want your nice business to end up filled with geeks playing board games and aggressively proving other people wrong now, would you? Better keep them diluted by hiring lots of PMs, "operations specialists", "customer analysts", "business insight teams" and so on. That way you can meet your diversity numbers and be surrounded by like-minded people.

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

#254

Post by thu2111 »

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.

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" about technical topics, especially in the business realm where there are lots of people with no technical background who want to learn and have authoritative opinions on technology, like in this article. The GPT models are quite capable of producing this kind of "sounds legit" speech. And if that isn't a form of intelligence, then what is?

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

#255

Post by p1esk »

wpietri wrote: post #164
p1esk wrote: post #125

Earlier quoted context omitted.

Until GPT-3 can write something meaningful What is "meaningful"? Honest question. Isn't meaning assigned by a reader? If I'm reading poetry generated by GPT-3 and I like it just as much as poetry written by a human poet, does it make it meaningful? What if I finetune GPT-3 (or the bigger next gen version) on every scientific paper ever written, and as a result it generates a novel idea that turns out to be valid and…

In communication, meaning is a collaboration between writer and reader. The writer does their best to convey something; the reader does their best to understand. There's also the kind of meaning that scientists and researchers talk about when they extract knowledge from data. That's pretty different from communication; it's more a process of internal generation of notions and explanations that could later be conveyed…

a process of internal generation of notions

How is this "knowledge extraction from data" process different?

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

#256

Post by YeGoblynQueenne »

perl4ever wrote: post #190
YeGoblynQueenne wrote: post #183

Earlier quoted context omitted.

Linear models have more bias, so they represent current data less well and are more predictive of future, unseen data (think of a straight line through a point cloud). Non-linear models have more variance so they represent current data better and are less predictive of future, unseen data (think of a line snaking around a point cloud). An added complication is that deep neural net models are, in practice, vectors (or…

I'm not good at math, but I'm confused by the association of AI with non-linear stuff, setting aside the association of non-linear with "bad". I thought ML involved linear algebra or something (says xkcd!) which would presumably be...linear?

I'm not sure where the "linear" in "linear algebra" comes from. You hear about linear algebra in relation with machine learning a lot because training a neural net (with the backpropagation algorithm and friends) requires some matrix arithmetic. Inputs to neural nets are vectors or matrices, their weights are (arrayed in) vectors or matrices, their outputs are - well, usually scalars but can also be vectors or matrices.

Also, the use of linear/ nonlinear in machine learning is a bit misleading. A "line" is not necessarily a "straight line", but usually when we say "linear" we mean "straight" and so when we want to say "not straight" we use "nonlinear".

In any case, when we say "line" in machine learning we mean a function, the function of a line. So a "nonlinear" function is a function that curves and turns, e.g. a sigmoid, whereas a "linear" function is straight as a rod.

Why a line? Classifiers er classify by drawing a line through space. "Space" means a Cartesian space where our training examples are represented as points (hence, "data points"). Data points are located in Cartesian space according to coordinates that represent their attributes, or features (these coordinates are the "feature vectors" that are input to neural nets). We classify data points by drawing a line between those that belong to one class and those that belong to other classes. More to the point, when we train a classifier, we find the parameters of a function of a line that separates the points of separate classes and when we want to classify a new point, we look at where it falls with relation to that line.

So that's where all that stuff about lines and "linear" and "nonlinear" models comes from. A "linear model" or "linear classifier" can only draw straight lines. A "nonlinear model" can go twirling around madly.

Finally, "non-linear" doesn't mean "bad". There are tradeoffs- in particular, the "bias variance tradeoff" that I hint at in my earlier comment. A linear model is more limited in what it can represent, but a nonlinear model is less likely to represent data that it hasn't seen in training.

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

#257

Post by imtringued »

bufferoverflow wrote: post #217
joe_the_user wrote: post #169

Earlier quoted context omitted.

You're being a pedantic. Human beings are tremendously better at driving than machines despite sometimes saying hexagonal rather than octagonal. Humans and current AIs both make mistakes but humans manage a kind of robustness, ability to deal gracefully with unexpected situations, that current AIs don't seem to be progressing towards.

> Human beings are tremendously better at driving than machines Human drivers: 1 death per 88 million miles traveled (in the US) [1] Tesla Autopilot: 5 deaths per 3 billion miles [2] [1] https://www.iihs.org/topics/fatality-statistics/detail/state... [2] https://electrek.co/2020/04/22/tesla-autopilot-data-3-billio... and https://en.wikipedia.org/wiki/List_of_self-driving_car_fatal...

Tesla autopilot is just a very fancy form of cruise control. Without corrections by human drivers it will happily run into stationary obstacles.

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

#258

Post by YeGoblynQueenne »

phreeza wrote: post #232
YeGoblynQueenne wrote: post #183

Earlier quoted context omitted.

Linear models have more bias, so they represent current data less well and are more predictive of future, unseen data (think of a straight line through a point cloud). Non-linear models have more variance so they represent current data better and are less predictive of future, unseen data (think of a line snaking around a point cloud). An added complication is that deep neural net models are, in practice, vectors (or…

The bias/variance trade-off is not really related to extrapolation. Think of a point cloud following a quadratic shape. A linear model will extrapolate terribly.

Well, "more predictive" doesn't mean it's a perfect fit. Every model has error. A line through a point cloud curving upwards will still represent some of the points in the cloud. So it will have high error, but it's still a representation of the data.

And yes, the bias-variance tradeoff is about generalisation (i.e. the ability to extrapolate to unseen data). But this is more related to the fact that in the real world, problem spaces don't have nice, friendly, regular shapes nor do their shapes stay put after we've trained a model.

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

#259

Post by p1esk »

joe_the_user wrote: post #159
p1esk wrote: post #125

Earlier quoted context omitted.

Until GPT-3 can write something meaningful What is "meaningful"? Honest question. Isn't meaning assigned by a reader? If I'm reading poetry generated by GPT-3 and I like it just as much as poetry written by a human poet, does it make it meaningful? What if I finetune GPT-3 (or the bigger next gen version) on every scientific paper ever written, and as a result it generates a novel idea that turns out to be valid and…

The thing is, if we could write a specific, closed-end, prescriptive definition of "meaningful" or "understanding" or whatever, then we'd be able to program it. And we can't, so we have to settle for something else, usually how a thing fails to be what we (indeed subjectively) consider meaningful. Still, it's not arbitrary. The way that something like GPT-3 tends to fail basically is that you 2-3 paragraphs where par…

the thing has no fixed world-model

What is "world-model"? What makes you think GPT-3 does not have some kind of a world model? It's clearly not a very good one, but at the same time it does not mean it can't get better. A 3 year old also does not have a very good world model, what's the difference between his world model and one of GPT-3? Again, clearly there's a big difference, I'm just not sure we know enough about what's going on inside 175B model to make any dismissive statements about it. I'm also not sure what will happen if you train a much bigger model on much bigger data. Things might start to emerge at some scale.

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

#260

Post by p1esk »

Judgmentality wrote: post #148
p1esk wrote: post #125

Earlier quoted context omitted.

Until GPT-3 can write something meaningful What is "meaningful"? Honest question. Isn't meaning assigned by a reader? If I'm reading poetry generated by GPT-3 and I like it just as much as poetry written by a human poet, does it make it meaningful? What if I finetune GPT-3 (or the bigger next gen version) on every scientific paper ever written, and as a result it generates a novel idea that turns out to be valid and…

Well you can randomly string words together and occasionally get lucky and make a meaningful argument, but that doesn't mean you've created a good method for constructing new ideas. It seems to me the simplest way to decide whether or not something is meaningful (in this context) is whether or not the author (which is the algorithm GPT-3) can respond to criticisms against its own argument in a coherent way. In which…

can respond to criticisms against its own argument in a coherent way

I'm not sure about GPT-3, but let's imagine GPT-4 next year will be able to do this. It just does not strike me as a particularly high bar to clear. Let's go further, and assume GPT-5 in 2022 will pass the Turing Test (you personally will not be able to tell). What would you say then?

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