5 ms·

Deep learning is pattern finding, not pattern matching. "Pattern matching" obviously includes "pattern finding" already, in common parlance. By "deep learning

by 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.
space_fountain 5y ago
In addition to what others are saying I want to highlight that there's decent evidence that "all" we're doing is pattern finding and pattern matching. Definitely ML isn't at a human level yet, but I see no reason to think that we're missing some secrete sauce that's needed to make a true AI. When someday we do achieve human level intelligences in computers, it will be at least in large part with ML.
- Jensson 5y ago
  > In addition to what others are saying I want to highlight that there's decent evidence that "all" we're doing is pattern finding and pattern matching. Is there? Link? It is possible, I just don't think there is enough evidence to say anything about it. What evidence would show that humans are just pattern finding and matching machines?
- vanusa 5y ago
  We're getting into the territory of some external debates -- that is to say, into areas that have already been debate by others, so there isn't much knew I could tell you here. But basically I'm in the camp of Gary Marcus and others, who would probably respond by saying something along the lines of the following: "No, there's not particularly good evidence that all we're doing is pattern matching, and a lot of evidence to the contrary. For one thing, a lot of these ML algorithms (touted as near- or superhuman) are easily fooled, especially when you jiggle the environmental context by even just a little bit." "For another, and on a higher level, what algorithms lack -- but which sentient mammals have in spades -- is an ability to 'catch' themselves, an ability to look at the whole situation and say 'Woah, something just isn't right here'. (This is referred to as the 'elephant in the room' problem with modern AI)." "And then there's the lack of a genuine survival drive -- not to mention the fact that we don't see any inkling of evidence of some capacity for self-awareness in any currently existing system." Just as a starting point. But these are some huge differences between currently existing AI systems (however you want to define "AI here) and actual sentient cognition. Huge, huge differences... such that I don't see one can get the idea that "all" we're doing is pattern recognition. Even if it may cover roughly 90 percent of what our neural tissues do - that other 10 percent is absolutely crucial, and completely elusive to any current, working technology as such. When someday we do achieve human level intelligences in computers, it will be at least in large part with ML. No doubt it will, but still there's that ... remaining 10 percent. Which you won't get to with accelerated pattern finding any more than a faster airplane will get you to the moon.
jonas21 5y ago
I think most people would take "pattern matching" to mean matching against fixed patterns or engineered features, as opposed to learned features which you might call "pattern finding".
- vanusa 5y ago
  Although the distinctions you're drawing are valid -- from the bigger picture point of view, this is basically hair splitting. The more basic and important point is: technology that works on the basis of "pattern finding" (however you wish to call it) -- even if it performs exponentially better and faster than humans, in certain applications -- is still far different from, and falls far short of technology that actually mimics full-scale sentient (never mind if it needs to be human) cognition. Or that is to say; of any technology that can (meaningfully) be called AI.
  - fault1 5y ago
    I think what you are describing is the AI effect: https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect 'AI' has always almost been a marketing term, anyways. I would call all of this pattern detection btw. But so would i call the kernel perceptron (1964): https://en.wikipedia.org/wiki/Kernel_perceptron https://en.wikipedia.org/wiki/Kernel_perceptron
    - Jensson 5y ago
      I think the AI effect is reversed. It should be "any time a program can compete with humans at a new task it will get marketed as AI". I don't really get the original argument. I never thought "todays AI isn't smart, but if it could play Go, that would be an intelligent agent!". So "the AI effect" is just a strawman, I have seen no evidence that anyone actually made such a change of heart. AI research is important, but nothing so far has been anywhere remotely intelligent and I never thought "if it could do X it would be intelligent" for any of the X AI can do today. When an AI can pretty reliably hold a remote job and get paid without getting fired for like a year, that is roughly the point I'd agree we have an intelligent agent. Edit: That wouldn't require "much". If the AI can read and understand text, then it can read about backend programming and what makes a good server and build a mental model for stuff like code quality. Based on what it learned it can also device a strategy for how to get a job, so it creates a github account, writes some example projects and puts them on github and writes a CV based on those, then go look for jobs. Of course, this would only be simple if the agent was intelligent. Not like todays agents which are coded for extremely specific scenarios. Even the general gameplay AI they talked about is for very specific scenarios compared to what a human deals with. In order to move towards creating an intelligent agent we need to make progress in this direction. But nothing that has came out of AI research recently really do make any progress in that direction.
YeGoblynQueenne 5y ago
>> "Pattern matching" obviously includes "pattern finding" already, in common parlance. So, the terminology I recognise is "pattern matching" when referring to matching an object against a pattern, as in matching strings to regular expressions or in unification (where arbitrary programs can be matched); and "pattern recognition" when referring specifically to machine vision tasks, as an older term that as far as I can tell has fallen out of fashion. The latter term has a long history that I don't know very well and that goes back to the 1950's. You can also find it as "statistical pattern recognition". To be honest, I haven't heard of "pattern finding" before. Can you say what "common parlance" is that? What I'm asking is, whom is it common to, in what circles, etc? Note that a quick google for "pattern finding" gives me only "pattern recognition" results. To clarify, deep learning is not "pattern matching", or in any case you will not find anyone saying that it is.
gmadsen 5y ago
not sure how well acquainted you are with neuroscience, but that is the basis of human cognition. I really don't understand the attempt to belittle "pattern finding". State of the art neuroscience tells us, that we have thousands of thousands of mental systems that have developed useful models to interpret the world. Human intelligence is by and large pattern finding. I truly think all the criticism of deep learning is really a lack of understanding of neuroscience. It is not much different. We just currently do it much more inefficient than a brain

by vanusa 5y ago

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.

space_fountain 5y ago

In addition to what others are saying I want to highlight that there's decent evidence that "all" we're doing is pattern finding and pattern matching. Definitely ML isn't at a human level yet, but I see no reason to think that we're missing some secrete sauce that's needed to make a true AI. When someday we do achieve human level intelligences in computers, it will be at least in large part with ML.

Jensson 5y ago
> In addition to what others are saying I want to highlight that there's decent evidence that "all" we're doing is pattern finding and pattern matching. Is there? Link? It is possible, I just don't think there is enough evidence to say anything about it. What evidence would show that humans are just pattern finding and matching machines?
vanusa 5y ago
We're getting into the territory of some external debates -- that is to say, into areas that have already been debate by others, so there isn't much knew I could tell you here. But basically I'm in the camp of Gary Marcus and others, who would probably respond by saying something along the lines of the following: "No, there's not particularly good evidence that all we're doing is pattern matching, and a lot of evidence to the contrary. For one thing, a lot of these ML algorithms (touted as near- or superhuman) are easily fooled, especially when you jiggle the environmental context by even just a little bit." "For another, and on a higher level, what algorithms lack -- but which sentient mammals have in spades -- is an ability to 'catch' themselves, an ability to look at the whole situation and say 'Woah, something just isn't right here'. (This is referred to as the 'elephant in the room' problem with modern AI)." "And then there's the lack of a genuine survival drive -- not to mention the fact that we don't see any inkling of evidence of some capacity for self-awareness in any currently existing system." Just as a starting point. But these are some huge differences between currently existing AI systems (however you want to define "AI here) and actual sentient cognition. Huge, huge differences... such that I don't see one can get the idea that "all" we're doing is pattern recognition. Even if it may cover roughly 90 percent of what our neural tissues do - that other 10 percent is absolutely crucial, and completely elusive to any current, working technology as such. When someday we do achieve human level intelligences in computers, it will be at least in large part with ML. No doubt it will, but still there's that ... remaining 10 percent. Which you won't get to with accelerated pattern finding any more than a faster airplane will get you to the moon.

jonas21 5y ago

I think most people would take "pattern matching" to mean matching against fixed patterns or engineered features, as opposed to learned features which you might call "pattern finding".

vanusa 5y ago
Although the distinctions you're drawing are valid -- from the bigger picture point of view, this is basically hair splitting. The more basic and important point is: technology that works on the basis of "pattern finding" (however you wish to call it) -- even if it performs exponentially better and faster than humans, in certain applications -- is still far different from, and falls far short of technology that actually mimics full-scale sentient (never mind if it needs to be human) cognition. Or that is to say; of any technology that can (meaningfully) be called AI.
- fault1 5y ago
  I think what you are describing is the AI effect: https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect 'AI' has always almost been a marketing term, anyways. I would call all of this pattern detection btw. But so would i call the kernel perceptron (1964): https://en.wikipedia.org/wiki/Kernel_perceptron https://en.wikipedia.org/wiki/Kernel_perceptron
  - Jensson 5y ago
    I think the AI effect is reversed. It should be "any time a program can compete with humans at a new task it will get marketed as AI". I don't really get the original argument. I never thought "todays AI isn't smart, but if it could play Go, that would be an intelligent agent!". So "the AI effect" is just a strawman, I have seen no evidence that anyone actually made such a change of heart. AI research is important, but nothing so far has been anywhere remotely intelligent and I never thought "if it could do X it would be intelligent" for any of the X AI can do today. When an AI can pretty reliably hold a remote job and get paid without getting fired for like a year, that is roughly the point I'd agree we have an intelligent agent. Edit: That wouldn't require "much". If the AI can read and understand text, then it can read about backend programming and what makes a good server and build a mental model for stuff like code quality. Based on what it learned it can also device a strategy for how to get a job, so it creates a github account, writes some example projects and puts them on github and writes a CV based on those, then go look for jobs. Of course, this would only be simple if the agent was intelligent. Not like todays agents which are coded for extremely specific scenarios. Even the general gameplay AI they talked about is for very specific scenarios compared to what a human deals with. In order to move towards creating an intelligent agent we need to make progress in this direction. But nothing that has came out of AI research recently really do make any progress in that direction.

YeGoblynQueenne 5y ago

>> "Pattern matching" obviously includes "pattern finding" already, in common parlance. So, the terminology I recognise is "pattern matching" when referring to matching an object against a pattern, as in matching strings to regular expressions or in unification (where arbitrary programs can be matched); and "pattern recognition" when referring specifically to machine vision tasks, as an older term that as far as I can tell has fallen out of fashion. The latter term has a long history that I don't know very well and that goes back to the 1950's. You can also find it as "statistical pattern recognition". To be honest, I haven't heard of "pattern finding" before. Can you say what "common parlance" is that? What I'm asking is, whom is it common to, in what circles, etc? Note that a quick google for "pattern finding" gives me only "pattern recognition" results. To clarify, deep learning is not "pattern matching", or in any case you will not find anyone saying that it is.

gmadsen 5y ago

not sure how well acquainted you are with neuroscience, but that is the basis of human cognition. I really don't understand the attempt to belittle "pattern finding". State of the art neuroscience tells us, that we have thousands of thousands of mental systems that have developed useful models to interpret the world. Human intelligence is by and large pattern finding. I truly think all the criticism of deep learning is really a lack of understanding of neuroscience. It is not much different. We just currently do it much more inefficient than a brain