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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

#161

Post by pwdisswordfish2 »

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 "AI" as a substitute for human judgment. It may "work" but that in itself may not be an adequate justification for its use.

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

#162

Post by EE84M3i »

cjhanks wrote: post #144
EE84M3i wrote: post #127

Earlier quoted context omitted.

Serious (and likely ignorant) question - what does linearity have to do with anything here? linear over what and why does non-linearity make something 'unpredictable'?

If you have ever opened up Excel or a similar program. One of the more useful options is to generate a regression line-fit on your data points. One option is to specify a polynomial function, you can specify how many coefficients you want. One of the measurements is the mean-squared-error between the line-fit and the points. You can add as many polynomial coefficients as you want, and you will be able to decrease the…

I'm confused. I agree that overfitting can lead to very bad models.

But, what I don't understand is that I thought that "linear" in ML contexts was normally used in the sense of 'linear transformations', which is a sense of linear that 'line-fit' from excel isn't -- it's affine.

Is a linear model with thousands/millions of weights/parameters (like deep learning models) really substantially simpler to understand? Can it do anything useful?

[1]: https://en.wikipedia.org/wiki/Linear_map

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

#163

Post by luisvictoria »

sixQuarks wrote: post #143
amelius wrote: post #141

Earlier quoted context omitted.

Google Search?

Google search has gotten worse over the years though. Perhaps that's because the amount of information is growing exponentially. So much of the search results seems spammy these days.

My guess would be because everyone's doing SEO, and since everyone's competing to be on the top of people's Google searches, they're often inadequate

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

#164

Post by wpietri »

p1esk wrote: post #125
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…

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 in communication.

And then there's the meaning that is even more internal. E.g., reading tarot cards or tea leaves, people generate meaning out of nothing. And then there's Pareidolia: https://en.wikipedia.org/wiki/Pareidolia

If you're saying machine-generated text is meaning in the sense of that last category then sure, it's something we loosely call meaning. But it's fundamentally the same as other sorts of cleromancy [1], just more elaborate.

[1] https://en.wikipedia.org/wiki/Cleromancy

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

#165

Post by quicklime »

pengaru wrote: post #154
gwern wrote: post #70

Earlier quoted context omitted.

Do you have NoScript or some other blocker on? As far as I can tell, Economist.com is using smallcaps for all acronyms but for some reason, the smallcaps are implemented solely through JavaScript, so if you read without that, you just see lower-case. The lowercase is because if you have uppercase, smallcaps does nothing - it can't become 'small capitals' because it's already large capitals, as it were. Letters need t…

Ah, yes, I am using noscript. That explains it, javascript is destroying the web.

I just disabled JavaScript by setting Firefox's "javascript.enabled" flag to false, and it still renders fine for me. Here's what the HTML source looks like when I grab the page using curl:

  predicts that artificial intelligence (AI) will add $16trn to the global economy by 2030
So it looks like it's coming through in caps from the server. I don't think a lack of JavaScript by itself is causing the problem...

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

#166

Post by abj »

ilaksh wrote: post #129
abj wrote: post #97

Earlier quoted context omitted.

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.…

There is a strong chance that the technology to create digital beings will be available before human society is able to integrate and adjust to the current generation of AI. So what may happen is that the way that AI really gets integrated is by actually replacing human beings who largely die off.

That's a really interesting perspective and a good point. Reminds of "Age of Em". Interested to see what the effects would be of digit beings before integrating current generation of AI would be.

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

#167

Post by pas »

neal_jones wrote: post #117
faitswulff wrote: post #81

Earlier quoted context omitted.

But it has also generated prodigious amounts of erotic fiction so that balances out some of those points, right?

I honestly don’t know if this is a joke or if there is a bunch of erotic fiction I’ve been missing

Deepfakes (faceswap but for porn). Decensored hentai. And of course the question came up again: how ethical are generated pictures depicting illegal content?

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

#168

Post by cjhanks »

EE84M3i wrote: post #162
cjhanks wrote: post #144

Earlier quoted context omitted.

If you have ever opened up Excel or a similar program. One of the more useful options is to generate a regression line-fit on your data points. One option is to specify a polynomial function, you can specify how many coefficients you want. One of the measurements is the mean-squared-error between the line-fit and the points. You can add as many polynomial coefficients as you want, and you will be able to decrease the…

I'm confused. I agree that overfitting can lead to very bad models. But, what I don't understand is that I thought that "linear" in ML contexts was normally used in the sense of 'linear transformations', which is a sense of linear that 'line-fit' from excel isn't -- it's affine. Is a linear model with thousands/millions of weights/parameters (like deep learning models) really substantially simpler to understand? Can…

I suppose from the perspective of someone implementing these models, yeah - it is linear, but it is not bijective. In a system with only one layer, that manifests as an alias (assuming the output dimensions are smaller). In a system with multiple layers of either `N->M` or `M->N`, those aliases tend to manifest as apparent "non-linearities".

So, I guess looking from the bottom up the system may look non-continuous and linear. But if you look from the top down, it would look continuous and non-linear.

Really, I am not sure which one is "true".

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

#169

Post by joe_the_user »

Kaibeezy wrote: post #116
cjhanks wrote: post #59

We have also been watching these machine learning models for 6 months: - increase the volatility in virtually every financial market they touched - be exploited by adversarial learning networks to amplify funded propaganda as news - use poorly contrived sentiment analysis to generate incomprehensibly meaningless news headlines These non-linear "function approximators" have absolutely unpredictable and insane non-line…

Where are stop signs hexagons? Am I being a pedantic numpty or am I illustrating a point about the many ways errors creep in, regardless of the natural- or artificial-ness of the intelligence?

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.

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

#170

Post by MiroF »

bsaul wrote: post #35
MiroF wrote: post #16

Earlier quoted context omitted.

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.

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 classifiers produce misclassifications, but so do humans - it is a very alien technology to us, so we view some of the machine's misclassifications as absurd, but at the same time the machine might view some of our misclassifications as obviously off-base as well.

None of what you said seems to explain the consistent negativity on HN to what has really been a transformative technology in a lot of ways.

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