An understanding of AI’s limitations is starting to sink in • Hacker News Board

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

#351

Post by smabie » Mon, Jun 15, 2020, 9:17 PM UTC

burntoutfire wrote: post #335

calebkaiser wrote: post #64

Earlier quoted context omitted.

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.

What's wrong with speeding? People generally drive at a speed such that they're taking an appropriate level of risk. The speed limits usually set these risk limits too low. Moreover, the job of google's ETA is to be accurate for the most number of people. If you're in the minority, then too bad.

Moreover, I'm not sure how speeding makes you an asshole.

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

#352

Post by smabie » Mon, Jun 15, 2020, 9:18 PM UTC

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?

For the same reason you should care if another person is conscious or not.

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

#353

Post by HarHarVeryFunny » Mon, Jun 15, 2020, 9:53 PM UTC

dumb1224 wrote: post #345

HarHarVeryFunny wrote: post #342

Earlier quoted context omitted.

The best way to think of today's AI capability is: automation. You train it with a lots of examples of "given this input, this is the output i want", and hopefully it learns to get the "correct" (similar input => similar output) output for new inputs that you feed it. i.e. you've now automated the process of figuring the correct output for a given input. There is also the "reinforcement learning" AI paradigm where th…

In my opinion this type of tasks is the typical AI tasks that get most exposed to the general public. The AI/ML model is set to attempt a human task. The aspiration is to get as good as a human (or faster/better more precision etc). Classic statistical inference/ models usually don't perform very well in these scenarios(or do they?). The typical AI methods train a model that utilises features that don't make much sen…

The "AI" capabilities that are making headlines nowadays are mostly based on deep (multi-layer) neural networks with millions, or billions, or parameters. These nets do self-organize into a hierarchy of self-defined feature detectors followed by classifiers, but as you suggest for the most part they are best regarded as black boxes. You can do sensitivity analysis to determine what specific interval values are reacting to, and maybe glean some understanding that way, but by-nature the inner workings are not intended/expected to be meaningful - they are just a byproduct of the brute force output-error minimization process by which these nets are trained.

Neural nets are mostly dominant in perceptual domains such as image or speech recognition, where the raw inputs represent a uniform sampling of data values (pixels, audio samples) over space and/or time. For classical business problems where the inputs are much richer and more varied, and already have individual meaning, then decision tree techniques such as random forests may be more appropriate and do provide explainability.

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

#354

Post by kccqzy » Mon, Jun 15, 2020, 9:55 PM UTC

tylerhou wrote: post #157

kccqzy wrote: post #150

Earlier quoted context omitted.

Polynomials with large degrees are continuous. It's just that they can still change by a large amount (i.e. having a large derivative) when the input is changed by a small amount. I invite you to construct the Lagrange polynomial (i.e. interpolating polynomial) for points on a nice, simple curve with some noise. They will, by definition, pass through every point given, and yet it will likely behave very badly outside…

There is nothing wrong with using a non-linear model, though; x^2 or x^3 regressions make sense on many datasets. Non-continuous is also not the perfect terminology, but I argue that it is more precise than non-linear: the chief idea being that the model "changes unpredictably."

Sure you can argue things however you want, if you also decide to ignore hundreds of years of mathematical terminology.

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

#355

Post by perl4ever » Mon, Jun 15, 2020, 10:28 PM UTC

thu2111 wrote: post #252

perl4ever wrote: post #195

Earlier quoted context omitted.

"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 art…

The proportion of people who will spend 30 seconds with a search engine to check on something is practically infinitesimal. Even people who do it frequently don't do it most of the time. And it doesn't matter anyway, because it's too late - you only have to fool people for a second, or a fraction of a second.

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

#356

Post by etiam » Mon, Jun 15, 2020, 10:55 PM UTC

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…

Do you literally think these people are creating harmful technology because they have had a deficit of ethics courses?

I'm not convinced ethics course requirements do much for a moral living. (And a peculiarly large fraction of those curricula often seems to be concerned with agonizing over whether to push various small or large groups of people on to railway tracks to be run over, rather than trying to stop the frigging train)

The examples you link don't seem very difficult to assess if they're approached with high priority placed on not causing trouble for the innocent. Also, presumably the people purchasing and deploying systems like that have usually taken plenty of formal education about ethics (and possibly philosophy), and yet there seems to be plenty of demand. Why is that you reckon?

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

#357

Post by eanzenberg » Mon, Jun 15, 2020, 11:44 PM UTC

hamsterbooster wrote: post #187

tomp wrote: post #101

Earlier quoted context omitted.

Aside from possibly Google, all of these products / services would have just as much, if not more, value without any AI beyond basic statistics.

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..

More importantly, Netflix is using AI and data to determine where to invest next in acquiring content or producing original content.

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

#358

Post by wpietri » Tue, Jun 16, 2020, 12:34 AM UTC

p1esk wrote: post #341

wpietri wrote: post #322

Earlier quoted context omitted.

Different from what? Regular communication? Because the first involves trying to sync up two minds to have the same idea. The latter involves one mind, trying to generate an idea that ends up being useful. Useful in the George Box sense: "All models are wrong, some models are useful."

I meant how is knowledge extraction done by a language model different from knowledge extraction done by a human scientist?

Sorry, I don't understand the question.

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

#359

Post by p1esk » Tue, Jun 16, 2020, 1:06 AM UTC

wpietri wrote: post #358

p1esk wrote: post #341

Earlier quoted context omitted.

I meant how is knowledge extraction done by a language model different from knowledge extraction done by a human scientist?

Sorry, I don't understand the question.

GPT-3 does some form knowledge extraction, right?

You said above: "There's also the kind of meaning that scientists and researchers talk about when they extract knowledge from data."

So if we agree that in both cases some form of knowledge extraction is happening, I wonder how these two forms compare.

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

#360

Post by entha_saava » Tue, Jun 16, 2020, 2:15 AM UTC

blaser-waffle wrote: post #311

entha_saava wrote: post #225

Earlier quoted context omitted.

It sounds like some buzzword speak. Most things that are heralded as ML are nothing but data science idiots from python schools applying some basic math transformations and overselling them.

I was going to say "most ML these days nothing but chains of if-else statements" but yeah same idea. What's wrong with python?

Nothing wrong with python or JS. But there are many things wrong with bootcamp / workshop people who come out just learning Python with some ML library or JS with some web framework. They don't know even basics of CS and arguably produce bad, inefficient code.

There is a famous word for latter - 'webshit'. The former doesn't have such a title. But there is an obscure one originating from a Slashdot comment IIRC something like: 'you are one of those data science idiots from python schools eh?'

Edit: I didn't mean disrespect to those doing meaningful research. But that's not what most of things sold as 'ML' are.