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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

#271

Post by inimino »

p1esk wrote: post #260
Judgmentality wrote: post #148

Earlier quoted context omitted.

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?

Please don't wildly speculate about technologies you clearly don't understand. "Does not strike me as a particularly high bar" means you're unfamiliar with how these systems work at a deep level (by which I mean, you personally cannot sit down at a terminal and build one). So rather than -ahem- making things up and asking "what then?" -- please ask for textbook recommendations on these topics if you'd like to know more.

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

#272

Post by arethuza »

Carpetsmoker wrote: post #208
hamsterbooster wrote: post #187

Earlier quoted context omitted.

Many AI systems are used in the backend to increase revenue. Netflix has a very complex recommendation algorithm based on deep learning/statistics. Amazon uses a lot of machine learning to optimize transportation (NP-hard problem!) , sales, etc..

A while ago I watched a 4-part documentary about the every-day lives of ancient Egyptians. Pretty interesting.[1] For weeks after that my recommendations were filled with bullshit such as "PROOF ALIENS BUILT THE PYRAMIDS!" and such. So yeah, maybe those "very complex recommendation algorithm based on deep learning/statistics" is perhaps not always such a great idea. In this particular case, it's just a mere annoyance…

My biggest issue with YouTube is that it shows me adverts for the same product for a about 2 months at a time and I therefore end up hating those products and would never buy them.

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

#273

Post by shivsinghh900 »

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

#274

Post by hyperbovine »

richk449 wrote: post #28
mellosouls wrote: post #17

Earlier quoted context omitted.

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.

> After several decades and a much-hyped last few years we have fake cleverness - impressively so in both cases - but nothing more. Why should I care if my fridge is fake clever or real clever?

Why do I want my fridge to be clever at all. Just keep things cold please.

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

#275

Post by papeda »

AlexandrB wrote: post #220
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 far more to do with ML and AI than self driving cars and shitty ad recommendations. Yeah, there's also shitty sentencing recommendations[1], new-age phrenology[2], and high-tech redlining[3]. I think your entire field needs to take a year off and take some ethics and philosophy courses before going any further. Otherwise we're all going to end up much worse off. [1] https://www.nytimes.com/2017/10/26/opinio…

I agree that plenty of people have been too cavalier about slapping together some models, predicting something, and calling it a day. On the other hand, it's not like fair sentencing or fair loan recommendation is a solved problem for humans either. There is evidence that, when carefully designed, algorithms can produce more equitable outcomes than humans, for example when deciding who and how to release on bail [1].

So I think we don't want to hand over sentencing decisions to a neural network that nobody understands, but I also think careful use of (simpler) machine learning can still improve a lot of our decision-making. The question is how much these decisions improve over what we have now. There's not exactly a surfeit of wise, highly trained ethicists who are happy to make consequential decisions all day. Many human decision-makers are quite flawed too, they just get to hide behind the opacity of being a human rather than an algorithm.

To that end, a whole "fair machine learning" field has sprung up over the past few years to study this. There are like a dozen papers in the area at NeurIPS and ICML every year. There's some progress.

[1] https://www.nber.org/papers/w23180

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

#276

Post by pmezard »

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.

> To be perfectly honest, it surprises me the extent to which ML naysaying seems to be popular on HN.

Maybe because there is a larger fraction of people who know or see how the sausages are made.

There are cool things, but is not magical nor transformative. At least not yet.

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

#277

Post by tgv »

dreamcompiler wrote: post #216

Here we are in 1989 again. The cycle keeps repeating. A new advancement in computing power, networking, or algorithms means there's a new batch of low-hanging fruit for AI to pick, so we pick it. Investors say "What about the high-hanging fruit?" and we say "No problem. We just need a slightly longer ladder." Two years later everybody finally realizes the high-hanging fruit is on the moon.

My first AI teacher (even before '89) compared solving AI with neural nets to teaching pigs to fly by throwing them from a tower. Improvements come from building higher towers.

There's a recent NLP model that was trained on a trillion words. It would take us 10,000 years to read or listen (no breaks, no sleep) to that many words. Problems like attention, and the relation between memory and sequential thinking haven't been cleared up at all. Even semantics, i.e. basic understanding of a utterance or a scene, is in its infancy.

Large neural nets can help with interesting problems, but it's not going to mimic our style of thinking any time soon.

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

#278

Post by TrackerFF »

mattlondon wrote: post #53

The trouble is that people have been sold this idea that ML/AI can do amazing things, without properly being told that really the things it can do are quite narrowly-scoped. They've been sold the Star Trek computer idea. For example, years ago I was working on a prototype/proof-of-concept thing for instrumenting industrial machinery with stick-on small computers. Simple stuff - attach accelerators, temperature, humid…

That's actually something ML is incredibly useful at, when it comes to machines with sensors - failure prediction / anomaly detection, etc.

In the industry, (preventive) maintenance takes up a pretty huge chunk of resources. It's something techs need to do often, and it's often a laborious task, but it's obviously done to reduce downtime.

So the business insight, as they like to call it, is to reduce costs tied up to repairs and maintenance.

All critical applications have multiple levels of redundancy, so that a complete breakdown is very unlikely, but it's still a very expensive process if you're dealing with contractors. If you can get techs to swap out parts before the whole unit goes to sh!t, then that's often going to be a much cheaper alternative.

But, in the end, it comes down to the quality of data, and the models being built. A lot of industrial businesses hire ML / AI engineers for this task alone, but expect some magic black-box that will warn x days / hours / minutes ahead that a machine/part is about to break down, and it's time to get it fixed. And they unfortunately expect a near-perfect accuracy, because someone in sales assured them that this is the future, and the future is now.

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

#279

Post by foldr »

MiroF wrote: post #170
bsaul wrote: post #35

Earlier quoted context omitted.

Siri still isn’t able to understand « do NOT set the alarm to 3pm », and many image classifier produces aberrations that no human would ever commit. Many people feel that ML has so far only produced « tricks », but still doesn’t show any sign of « understanding » anything. As in, provide meaning. It may be unfair, but i think that at this point people would be more impressed by a program « smiling » at a good joke th…

Sure, ML is not at human-level intelligence - it is very much a tool-AI where we give a task to the machine and let it get very good at that. Nonetheless, it seems like progress on that front has been made incredibly quickly. Sure, Siri might not always "understand" what you're asking but the ML is able to very accurately transcribe your voice to text, something not really possible a few decades ago. Image classifier…

Progress in speech recognition has been slow and incremental, not fast and impressive. Hidden Markov Models did a decent job in the 90s. Now we have more data and more computing power.

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

#280

Post by imtringued »

0xBABAD00C wrote: post #202
Olreich wrote: post #188

Earlier quoted context omitted.

If models actually were able to tell you what they know and don’t know, then sure. But instead they just give you an output for any input you give them, whether they have a clue or not.

This is such a simplistic view of a vast and evolving field of scientific research, it's too cartoonish of an argument to even warrant a real response. Which is why the conversation around ML gets negatively selected against actual researchers and practitioners, who get headaches from opinions like the one above.

Honestly last time I tried to get into machine learning the huge limitations made me lose interest. The idea that we can just pretend that these limitations don't exist is absurd. ML lets us do a lot of very interesting things but it's just a stepping stone. It's something we are stuck with rather than something that we want to keep.
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