I'm still not sure what successful AI implementations there have been. Stuff like Amazon/Spotify recommendations seem sensible. Is there anything else out there that is impressive?
Google Search?
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
#142Maybe it's because everyone talks/chats every day with "virtual assistants" at banks and every other organization, and never ever finds them useful. Their main purpose is to frustrate you enough so you give up trying to connect to a real person.
Judging by the number of times and different places I've heard the sentence "Please listen closely, as all our options have changed" even when they haven't changed in years suggests that this is new whine in old bottles.
Re: An understanding of AI’s limitations is starting to sink in
#143I'm still not sure what successful AI implementations there have been. Stuff like Amazon/Spotify recommendations seem sensible. Is there anything else out there that is impressive?
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.
Re: An understanding of AI’s limitations is starting to sink in
#144We 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…
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 mean squared error. But the more polynomial's you choose, two things will be true:
1. The line-fit will be far more likely to go through the points.
2. At points in the line where there was no data, the line will less approximate the underlying physical reality.
That same mathematical property is what is relevant here. There is nothing inherently evil about non-linearity, when the non-linear math model properly maps to the physical reality. But when you over fit a line, many of the functional solutions may be completely wrong.
Re: An understanding of AI’s limitations is starting to sink in
#145We 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…
https://youtu.be/hx7BXih7zx8?t=513
> complicated when you get to the long tail of it
Well, there's the problem right there.
Re: An understanding of AI’s limitations is starting to sink in
#146Earlier quoted context omitted.
Five years ago we would have said "the darn thing still can't write a cohesive paragraph".
That's still true. It can write a paragraph that's usually grammatical, and can stay on topic, but it's missing things like facts, or even the ability to remember which side of an argument it's taken previously.
I think over the course of several paragraphs that's true, but within one it tends to be pretty good.
Re: An understanding of AI’s limitations is starting to sink in
#147Re: An understanding of AI’s limitations is starting to sink in
#148Earlier 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…
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 case it has to get lucky twice, so it's that much less likely to happen randomly. If the author cannot respond to comments in a comprehensible manner, it's hard to defend the author.
Re: An understanding of AI’s limitations is starting to sink in
#149The 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…
> currently ML/AI requires us to know what the possible answers can be before we even begin training the network
Isn't that what unsupervised learning is for?
Re: An understanding of AI’s limitations is starting to sink in
#150Earlier 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'?
I assume they are using non-linear to mean non-continuous, which implies that there can be large, hard-to-understand changes in behavior when the input is changed only a small amount.
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 the range of the given points.