I'd like to suggest that sources which explicitly demand that readers not protect their privacy through incognito mode be banned from HN.
This is a demand which shows extreme contempt for the principles of personal privacy and choice.
Is AI Riding a One-Trick Pony?
181–190 of 219 posts
Re: Is AI Riding a One-Trick Pony?
#182> To continue reading this article, please exit incognito mode or log in. Eat shit and fucking die.
I had a slightly more diplomatic thought when I encountered the same thing. It looks like it's JS+CSS, so we should probably submit an AdBlock rule update for it.
That unfortunately does nothing on Chrome/Android, which doesn't support extensions. Or JS/CSS blocks.
Re: Is AI Riding a One-Trick Pony?
#183Here's an article by Gartner (credits to original poster ooOOoo): https://www.gartner.com/smarterwithgartner/top-trends-in-the... As per Gartner - Deep Learning and ML is near the peak of the Hype Cycle, nearing trough of disillusionment.
The Gartner Hype Cycle seems to produce a heck of a lot of standing waves. Technologies which remain at the same point for years, sometimes decades.
https://www.linkedin.com/pulse/8-lessons-from-20-years-hype-...
Though with all they hype AI/ML is getting, it would hardly surprise me if there were some great degree of disillusionment.
Re: Is AI Riding a One-Trick Pony?
#184The thing you have to figure out, is that our brains and consciousness are just a big collection of "thoughtless fuzzy pattern recognizers". There's nothing magic beyond that.
Re: Is AI Riding a One-Trick Pony?
#185I'm encouraged that so much fruitful work has come out of this one trick. If you can use the same basic framework for image labeling, playing Go, and translating natural languages, I'd say it's a powerful tool with broad applications. I think that there's a kernel of insight to "A real intelligence doesn’t break when you slightly change the problem." But human perception and intelligence are pretty brittle. The metho…
Of course, we're the product of an evolutionary history which results in such human "failure modes" being rare. If staring at a zebra made you hallucinate, you'd be unlikely to be the most successful member of your species, nor would your offspring thrive. So while we only tend to run into our obvious failing whens we do the unusual, computers fail at what we consider mundane.
Of course, our failure modes would be unusual to computers. Eg our short term memory is severely limited and imprecise.
Re: Is AI Riding a One-Trick Pony?
#186Earlier quoted context omitted.
The whole point of Deep Learning is that we don't want to describe math behind object recognition; it was the failed "classical" approach where people spent decades figuring out complex features which worked horribly. Deep Learning is actually pretty simple, well understood and parallelizable, and it's basically a billion-dimensional non-linear optimization. As optimization is infested with NP-hard problems, it's as…
Deep learning does not seem to abstract very well. Train on a data set then test with images that are simply upside down and the preformance can be significant. Feature extraction also works much better when you toss a lot of data and processing power behind it. So, a lot of progress is simply more data and computing power vs better approaches. Consider how poorly deep leaning works when using a single 286.
> Deep learning does not seem to abstract very well. Train on a data set then test with images that are simply upside down and the preformance can be significant.
But that's true of people too. How quickly can you read upside-down?
If you trained on a mixture of upside-down and right way up images, and tested on upside-down images, performance wouldn't take that much of a hit.
Re: Is AI Riding a One-Trick Pony?
#187I'd argue that the next problem to attack is manipulation in unstructured environments. Robots suck at that. There's been amazingly little progress in the last 40 years. DARPA had a manipulation project and the DARPA humanoid challenge a few years ago, and they got as far as key-in-lock and throwing a switch. Amazon is still trying to get general bin-picking to work. Nobody has fully automatic sewing that works well,…
Current ANNs aren't anywhere near squirrel brains, so of course robots using them won't perform as well as squirrels.
Take a single brain region specializing on one task, throw away all the integration and feedbacks from other regions, simplify it even further because we only need it to do one task, then run the whole thing on an emulation layer running on 2D hardware. And that's still neglecting the dissimilarities between artificial neurons and their natural role models.
Re: Is AI Riding a One-Trick Pony?
#188There is a bit of "can't see the forest for the trees" failure in the article. AI is spearheading a paradigm shift in how we write programs. Or rather, we don't write programs. We write much much shorter programs that search the program space for programs that satisfy some desiderata. The programs we get as the output of the search process are extremely flexible, work very well, are very homogeneous in compute (e.g.…
>So sure, backprop (the credit assignment scheme that gives us a good search direction in program space, one of multiple techniques that could do so) is pervasive, but AI is starting to work primarily as a result of a deeper epiphany - that we are not very good at all at writing code.
Isn't it applicable to a class of programs only? Best example of which is Computer Vision. Or do you imply your argument to hold for a wider set of programs. I can think of a large set of programs in which direct coding of logic, rather than discovery, is more suited.
For example take sorting. I guess, sorting could also be taught to the machine, by having a training set. But what about the latency of the discovered program. Also what about the proof of such a sorting program, which is discovered by Machine learning?
Must add, that I largely agree to your excellent point regarding discovery of programs. But I am not sure about its wide applicability. In fact, I contend that it applies only to a subset of all programs. Particulary those which have been traditionally difficult to code.
So in that sense, now making a tangential point here, it is good that more complex applications are now possible, by combining both kind of programs. And there will be more programming work in the future.
Edit: minor
Re: Is AI Riding a One-Trick Pony?
#189Earlier quoted context omitted.
OTOH, we are merely at circa Year Five into deep reinforcement learning research. It started as a cluster of 16M CPUs having taught itself to recognize a cat 95% of the time after training on 1B google images. And we are now at One-Shot Imitation Learning, "a general system that can turn any demonstrations into robust policies that can accomplish an overwhelming variety of tasks". One Shot Imitation Learning https://…
It's easy to recognize a cat 95% of the time. I can write a program in 30 seconds that will recognize a cat 95% of the time. No, wait, this just in! My program will recognize a cat 100% of the time! The program has just one line: Print "It's a cat!"
Tutorial: So, with that program, whenever the picture is a cat, the program DOES recognize it. So the program DOES recognize a cat 100% of the time. The OP only claimed 95% of the time.
Uh, we need TWO (2), that's TWO numbers:
conditional probability of recognizing a cat when there is one (detection rate)
conditional probability of claiming there is a cat when there isn't one.
The second is the false alarm rate or the conditional probability of a false alarm or the conditional probability of Type I error or the significance level of the test or the p-value, the most heavily used quantity in all of statistics.
One minus the detection rate is the conditional probability of Type II error.
Typically we can adjust the false alarm rate, and, if we are willing to accept a higher false alarm rate, then we can get a higher detection rate.
With my little program, the false alarm rate is also 100%. So, as a detector, my little program is worthless. But the program does have a 100% detection rate, and that's 5% better than the OP claimed.
If focus ONLY on detection rate, that is, recognizing a cat when there is one, then it's easy to get a 100% detection rate with just a trivial test -- just say everything is a cat as I did.
What's tricky is to have the detection rate high and the false alarm rate low. The best way to do that is in the classic Neyman-Pearson lemma. A good proof is possible using the Hahn decomposition from the Radon-Nikodym theorem in measure theory with the famous proof by von Neumann in W. Rudin, Real and Complex Analysis.
My little program was correct and not a joke.
Again, to evaluate a detector, need TWO, that's two, or 1 + 1 = 2 numbers.
What about a detector that is overall 95% correct? That's easy, too: Just show my detector cats 95% of the time.
If we are to be good at computer science, data science, ML/AI, and dip our toes into a huge ocean of beautifully done applied math, then we need to understand Type I and Type II errors. Sorry 'bout that.
Are we learning yet?
Re: Is AI Riding a One-Trick Pony?
#190Earlier quoted context omitted.
Then your point is wrong. What's so novel about CPUs? We've had sand since the Earth was first formed! I swear the tech industry has more luddites than the Amish.
The Amish are not luddites.
They are not capital-L official Luddites, but they could be classified as colloquial lowercase-L luddites.