5 ms·

One technique in particular, called saliency maps, turned out to be especially helpful. It is often used in computer vision to identify which parts of an image

by vanusa 5y ago

One technique in particular, called saliency maps, turned out to be especially helpful. It is often used in computer vision to identify which parts of an image carry the most-relevant information. Saliency maps pointed to knot properties that were likely to be linked to each other, and generated a formula that seemed to be correct in all cases that could be tested. Lackenby and Juhász then provided a rigorous proof that the formula applied to a very large class of knots2. Kewl, and not to discount the result but sounds like ... pattern matching, right? So why do the editors of all these journals keep feeding off the ML == AI meme? Surely they know better by now.

gmadsen 5y ago
sounds more like pattern finding. and if you break down human cognition, it isn't much different..
- vanusa 5y ago
  Yep - that's the whole point, the only point. And again, I thought it was pretty simple and generally known.
Jensson 5y ago
Because pattern matching is the only significant new thing in AI research the past few decades. Every time they apply pattern matching to something new we will get articles like this and AI optimists will come and say that soon AI will take our jobs. Pattern matched images, pattern matched texts, reverse pattern matching to generate uncanny images and articles, pattern matched gameplay states etc.
- version_five 5y ago
  Deep learning is pattern finding, not pattern matching. That is the difference. There is a business narrative that's hyping up ML/AI, there's lots of retrospective tracing of modern ML's roots back to something older, but that doesn't change the fact that there has been a big explosion of demonstrated advances opened up by modern approaches over the past 10 years. Edit: just to expand a bit, the "intelligence" in modern AI is in the training, not inference. I think people see inference in a neural network as pattern matching or some kind of filter, and that's basically true, and "dumb" if you like. But learning the weights is a whole different thing. Stochastic Gradient Descent et al are using often only a comparative few examples to learn a pattern and embed it in a set of weights in a way that can generalize despite being highly underdetermined. It's not general intelligence, but it's a much different thing than the casual dismissals people like to post, usually directed at the inference part as if the weights just magically appeared
  - vanusa 5y ago
    Deep learning is pattern finding, not pattern matching. "Pattern matching" obviously includes "pattern finding" already, in common parlance. By "deep learning" one means, in effect, "hierarchical pattern finding" (which is basically what "feature" or "representation" learning means to a lay person). But still, at the end of the day ... pattern finding. Or that is to say: "just ML", not AI.
    - rackjack 5y ago
      I'm pretty sure they meant "pattern finding" as "discovering new patterns that can be used for matching", not "finding a pattern that matches an existing known one".
      - Jensson 5y ago
        But currently humans do that "pattern finding". If you want it to learn to recognize animals you give it thousands of images of different animals in all sorts of poses, angles and environments and tells it what those animals are, basically the "pattern" is created by the humans who compiles the dataset with enough examples to catch every situation, and then the program uses that pattern to find matches in other images. However if you want a human to recognize animals it is enough to show this picture and then they will be able to recognize most of these animals in other photos and poses, humans creates the pattern from the image and don't just rely on having tons of data: https://i.pinimg.com/originals/35/78/47/35784708f8cc9ef2345cf0670816a317.png https://i.pinimg.com/originals/35/78/47/35784708f8cc9ef2345c... Edit: In some cases you can write a program to explore a space. For example, you can write a program that plays board games randomly and notes the outcome, that is a data generator. Then you hook that to a pattern recognizer powered by a data centre, and you now have a state of the art gameplay AI. It isn't that simple, since writing the driver and feedback loop is extremely hard work that an AI can't do, humans has to do it.
    - visarga 5y ago
      > But still, at the end of the day ... pattern finding. And programming at the end of the day ... if's and for's. See how ridiculous it is? A fuzzy high level concept explaining away the complexity of getting there.
atty 5y ago
I get that when you boil it down to pattern matching it sounds less impressive, but we are starting to see superhuman pattern matching algorithms, and algorithms that display logic through advanced “pattern matching”. And it’s quite obvious now that pattern matching is far more useful than pure rule based systems for any problem of sufficient complexity. And AI is a very broad field, including pattern matching (supervised ML or otherwise), logic/rule based systems, etc. Is your complaint that they use AI/ML interchangeably? Because in this case, while AI is a more term for the technology than ML, it is not a strictly incorrect title.
- vanusa 5y ago
  Is your complaint that they use AI/ML interchangeably? Yes, that's all I'm saying. And it seems like such a known point that they're not the same and not interchangeable, or so I thought (that I'm kind of astonished at the downvotes at my original comment). Because in this case, while AI is a more term for the technology than ML, it is not a strictly incorrect title. I get what you're saying at the product level -- and the fact that the vast bulk of the public subjected to these technologies couldn't tell you the difference, nor could they begin to care. But to practitioners, the basic facts remain: ML ≠ AI, it's a proper subset, and we're doing the public a genuine disservice (and arguably causing substantial harm) by pretending to tell them that we're making good progress developing AI as distinct from hypercharged ML, that any day now they'll have self driving cars ... and all that crap the industry has basically been telling people.
TulliusCicero 5y ago
> So why do the editors of all these journals keep feeding off the ML == AI meme? Your comment is a good example of AI meaning whatever we haven't solved yet. Playing chess well is AI until we have programs that can regularly defeat professionals, then it suddenly doesn't count. Accurately labeling the contents of a photo is AI until we have services that do this for anyone, and then it's not AI anymore. Pretty soon self-driving cars will also not count as AI, because we'll have solved self-driving and therefore it's not hard enough anymore.
- vanusa 5y ago
  Your comment is a good example of AI meaning whatever we haven't solved yet. Yes, I'm aware of the Rodney Brooks quote. I think it's kind of glib and misses the mark. Take chess, for example. When algorithms were developed to beat humans at chess -- it wasn't, like the quote implies, out of some sense of envy or schadenfreude that people said, "Oh, but that's not real AI". But rather -- from the intrinsic features of the system that first met this challenge. Which, at the end of the day, were basically about generalized tree search. Okay, there was more to it than that -- but in 3 word or less, that's gist of the AlphaZero algorithm, as described in the original paper. So people said "Oh -- it turns out you don't need human-like cognition to beat humans at chess/go/shogi. You just need good enough tree search, and some decent hardware." Pretty soon self-driving cars will also not count as AI, because we'll have solved self-driving and therefore it's not hard enough anymore. That's one of the weird things about the current moment in AI: people keep talking about self-driving cars as if they're a straight-line extrapolation the breakthroughs we've seen so far (give or take a few details). They're not. If you just think about if for a minute: nearly all of the challenges met thus far -- taking the two you just mentioned as particularly shining examples -- as significant as they have been, have been pretty darn one-dimensional. "Here's a function; optimize it" (basically). The reason self-driving cars are floundering at the moment is that the intrinsic problem they're dealing with is an entirely different beast from "solve this symbolic game faster than a human" or "label these photos better than a roomful of monkees". It is vastly more complex. And the emerging consensus (whatever some companies may say to the contrary) is that solution is not lurking just around the corner, and not coming our way anytime soon -- certainly not "pretty soon".

by vanusa 5y ago

gmadsen 5y ago

sounds more like pattern finding. and if you break down human cognition, it isn't much different..

vanusa 5y ago
Yep - that's the whole point, the only point. And again, I thought it was pretty simple and generally known.

Jensson 5y ago

Because pattern matching is the only significant new thing in AI research the past few decades. Every time they apply pattern matching to something new we will get articles like this and AI optimists will come and say that soon AI will take our jobs. Pattern matched images, pattern matched texts, reverse pattern matching to generate uncanny images and articles, pattern matched gameplay states etc.

version_five 5y ago
Deep learning is pattern finding, not pattern matching. That is the difference. There is a business narrative that's hyping up ML/AI, there's lots of retrospective tracing of modern ML's roots back to something older, but that doesn't change the fact that there has been a big explosion of demonstrated advances opened up by modern approaches over the past 10 years. Edit: just to expand a bit, the "intelligence" in modern AI is in the training, not inference. I think people see inference in a neural network as pattern matching or some kind of filter, and that's basically true, and "dumb" if you like. But learning the weights is a whole different thing. Stochastic Gradient Descent et al are using often only a comparative few examples to learn a pattern and embed it in a set of weights in a way that can generalize despite being highly underdetermined. It's not general intelligence, but it's a much different thing than the casual dismissals people like to post, usually directed at the inference part as if the weights just magically appeared
- vanusa 5y ago
  Deep learning is pattern finding, not pattern matching. "Pattern matching" obviously includes "pattern finding" already, in common parlance. By "deep learning" one means, in effect, "hierarchical pattern finding" (which is basically what "feature" or "representation" learning means to a lay person). But still, at the end of the day ... pattern finding. Or that is to say: "just ML", not AI.
  - rackjack 5y ago
    I'm pretty sure they meant "pattern finding" as "discovering new patterns that can be used for matching", not "finding a pattern that matches an existing known one".
    - Jensson 5y ago
      But currently humans do that "pattern finding". If you want it to learn to recognize animals you give it thousands of images of different animals in all sorts of poses, angles and environments and tells it what those animals are, basically the "pattern" is created by the humans who compiles the dataset with enough examples to catch every situation, and then the program uses that pattern to find matches in other images. However if you want a human to recognize animals it is enough to show this picture and then they will be able to recognize most of these animals in other photos and poses, humans creates the pattern from the image and don't just rely on having tons of data: https://i.pinimg.com/originals/35/78/47/35784708f8cc9ef2345cf0670816a317.png https://i.pinimg.com/originals/35/78/47/35784708f8cc9ef2345c... Edit: In some cases you can write a program to explore a space. For example, you can write a program that plays board games randomly and notes the outcome, that is a data generator. Then you hook that to a pattern recognizer powered by a data centre, and you now have a state of the art gameplay AI. It isn't that simple, since writing the driver and feedback loop is extremely hard work that an AI can't do, humans has to do it.
  - visarga 5y ago
    > But still, at the end of the day ... pattern finding. And programming at the end of the day ... if's and for's. See how ridiculous it is? A fuzzy high level concept explaining away the complexity of getting there.

atty 5y ago

I get that when you boil it down to pattern matching it sounds less impressive, but we are starting to see superhuman pattern matching algorithms, and algorithms that display logic through advanced “pattern matching”. And it’s quite obvious now that pattern matching is far more useful than pure rule based systems for any problem of sufficient complexity. And AI is a very broad field, including pattern matching (supervised ML or otherwise), logic/rule based systems, etc. Is your complaint that they use AI/ML interchangeably? Because in this case, while AI is a more term for the technology than ML, it is not a strictly incorrect title.

vanusa 5y ago
Is your complaint that they use AI/ML interchangeably? Yes, that's all I'm saying. And it seems like such a known point that they're not the same and not interchangeable, or so I thought (that I'm kind of astonished at the downvotes at my original comment). Because in this case, while AI is a more term for the technology than ML, it is not a strictly incorrect title. I get what you're saying at the product level -- and the fact that the vast bulk of the public subjected to these technologies couldn't tell you the difference, nor could they begin to care. But to practitioners, the basic facts remain: ML ≠ AI, it's a proper subset, and we're doing the public a genuine disservice (and arguably causing substantial harm) by pretending to tell them that we're making good progress developing AI as distinct from hypercharged ML, that any day now they'll have self driving cars ... and all that crap the industry has basically been telling people.

TulliusCicero 5y ago

> So why do the editors of all these journals keep feeding off the ML == AI meme? Your comment is a good example of AI meaning whatever we haven't solved yet. Playing chess well is AI until we have programs that can regularly defeat professionals, then it suddenly doesn't count. Accurately labeling the contents of a photo is AI until we have services that do this for anyone, and then it's not AI anymore. Pretty soon self-driving cars will also not count as AI, because we'll have solved self-driving and therefore it's not hard enough anymore.

vanusa 5y ago
Your comment is a good example of AI meaning whatever we haven't solved yet. Yes, I'm aware of the Rodney Brooks quote. I think it's kind of glib and misses the mark. Take chess, for example. When algorithms were developed to beat humans at chess -- it wasn't, like the quote implies, out of some sense of envy or schadenfreude that people said, "Oh, but that's not real AI". But rather -- from the intrinsic features of the system that first met this challenge. Which, at the end of the day, were basically about generalized tree search. Okay, there was more to it than that -- but in 3 word or less, that's gist of the AlphaZero algorithm, as described in the original paper. So people said "Oh -- it turns out you don't need human-like cognition to beat humans at chess/go/shogi. You just need good enough tree search, and some decent hardware." Pretty soon self-driving cars will also not count as AI, because we'll have solved self-driving and therefore it's not hard enough anymore. That's one of the weird things about the current moment in AI: people keep talking about self-driving cars as if they're a straight-line extrapolation the breakthroughs we've seen so far (give or take a few details). They're not. If you just think about if for a minute: nearly all of the challenges met thus far -- taking the two you just mentioned as particularly shining examples -- as significant as they have been, have been pretty darn one-dimensional. "Here's a function; optimize it" (basically). The reason self-driving cars are floundering at the moment is that the intrinsic problem they're dealing with is an entirely different beast from "solve this symbolic game faster than a human" or "label these photos better than a roomful of monkees". It is vastly more complex. And the emerging consensus (whatever some companies may say to the contrary) is that solution is not lurking just around the corner, and not coming our way anytime soon -- certainly not "pretty soon".