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It's an interesting observation and certainly something one needs to be aware of in thinking about intelligent systems but I'm not convinced that the notion tha

by tgflynn 14y ago

It's an interesting observation and certainly something one needs to be aware of in thinking about intelligent systems but I'm not convinced that the notion that perception is intrinsically harder than logic is quite true. I think the difficulty of a problem is highly dependent on the representations and models used. Computers were developed based on logical and mathematical principles so it makes sense that they are, in some sense, "good" at these kinds of problems. On the other hand traditional logic is incredibly inefficient at dealing with probabilistic and perceptual problems. It's also not clear that there's really a valid comparison here. In order for computers to recognize objects we need to program them to learn recognition on their own (because programming them explicitly to do it would be far too hard). When we program a computer to solve a logic problem the computer isn't learning to solve that problem, it's the programmer, not the program that "knows" how to solve it. Trying to teach a neural network to play chess is probably much harder than teaching it to recognize images (at least my very limited experiments suggest this to be true).

Breakthrough 14y ago
There's also the difficult task of real-world interaction (or as the article puts it, low-level sensorimotor skills), although I suppose this is what you mean by perceptual problems. Most things humans create are based on rules (e.g. games, markets, governments), and rules can be easily taught to computers as they're just abstract logical ideas. Even for probabilistic problems, you can certainly teach a neural network to perform than a naïve random selection. The problem comes back to lack of ability provide the network with enough, or in some cases, the proper inputs. "Trying to teach a neural network to play chess is probably much harder than teaching it to recognize images (at least my very limited experiments suggest this to be true)." That, sir, depends on your definition of "hard". The rules of chess are, after rules, so you could just set a neural network free within those constraints to play until it found a way to win, and continue to play until it reached a certain performance level.
- tgflynn 14y ago
  Well what I mean by "hard" is that I can train a neural network to do a reasonably good job of recognizing say handwritten characters given some amount of training data. My attempts to train similar networks to learn the rules of chess (just the rules, I'm not even talking about trying to win) have not led to good or steadily improving performance, despite the fact that there's effectively no limit to the amount of training data that can be generated for chess. There are a number of possible reasons for this of which I think 2 likely ones are that: 1) The networks I'm using have insufficient computational/storage capacity to learn the rules of chess. or 2) Even if (1) is not the case gradient descent is unlikely to find a sufficiently good near optimum because the response surface is too complex.
  - Breakthrough 14y ago
    Great response, I must say. As a conjecture, do you think splitting the logic up into several smaller neural networks with different objectives (i.e. for chess, one evaluates defensive maneuvers, one for attack, and so on) would alleviate the problem? Or at least improve the performance of the machine as a whole...
  - treePhase 14y ago
    You should also consider the possibility that the type of neural network you chose is not capable of learning the rules of chess, but may be sufficient for OCR.
  - simonster 14y ago
    I would bet heavily on (1). Non-human primates, with ~10 billion neurons (~3 billion neocortical), also cannot learn the rules of chess. Nearly all neural networks have far fewer.
jerf 14y ago
"I'm not convinced that the notion that perception is intrinsically harder than logic is quite true." But... where's the room for doubt? This isn't an article from 1930 about theoretical possibilities of what may happen someday when we have a lot of computational power. This is an observation about how perception has proved to be much more complicated than more pure reasoning, one quite old, robust, and well-established. The question is more about why that is true than whether it is true. In a further comment you reply about how you could not convince your neural net to play chess... but again, we are not theorizing that computers may be good at chess someday. We live in a world in which, if they are not already simply better than humans, we only a couple of years away. Certainly better than all but the absolute very best. Whereas we still get excited when we see a robot that can walk up or down a normal, rocky hill at all. Also, it baffles me how you think that explaining why logic is easier than perceptual problems is somehow disproof of that very fact. It doesn't matter that it's "really" the programmer that knows how to play chess (even if I'd observe the machine is still doing it better) when we still can't hardly make machines walk at all, regardless of whether the programmer or the computer is the one "knowing", a concept that in this context comes perilously close to a tautological assertion that if a computer can do it it must not be true "knowing". If we were really good at both sensorimotor and logic, but with two radically different toolkits, that might be an interesting point, but that's not the world we live in.
- tgflynn 14y ago
  The undeniable observation is that certain people spent a long time trying to do perception with a certain set of tools and found it to be very hard. That does not necessarily imply that perception is intrinsically so much harder than logic, merely that it is using those tools. It may turn out for example that there exist (in a mathematical sense) functions which are good at recognizing characters which are simpler than the simplest function which is good at playing chess. If that were the case it would make sense to say that chess is intrinsically harder than character recognition, even if humans have historically had a harder time discovering the recognition functions than the chess playing functions. On your last paragraph there's a huge difference between a program discovering a good solution to a problem, which is what happens in machine learning, and a programmer discovering such a solution then creating a program to implement it. In the first case we may begin to consider the program to be showing signs of intelligence while in the later it's just a really fast calculator which isn't doing anything the programmer couldn't do. Is there circularity here - yes because we can say the same thing of the learning program if we treat it at a lower level of abstraction and therein lies perhaps the core of the problem of understanding what we mean by intelligence.
twelvechairs 14y ago
I agree hugely that computers are based on hard logic and this makes them poor tools to deal with tasks like 'perception'. I'd also add that most people working in the field of AI are trained in hard logic, and not at all in the subtleties of the perceptual world (not that there's a university course for this), which probably doesn't help. I'm not so sure you start a good argument though. 'Harder' is all about the tools at hand. If you have the right tools, everything is easy. So to me this is all semantics. To me all the wikipedia page basically says is 'we haven't invented a good artificial tool for generalized, fast perception'. Of course the other way to think about it is that humans and other animals devote generally much larger amounts of physical space (in the brain and otherwise) to subconscious things like perception and subconscious memory than they do to conscious things like logic...
- simonh 14y ago
  >'Harder' is all about the tools at hand. If you have the right tools, everything is easy. >So to me this is all semantics. To me all the wikipedia page basically says is 'we haven't invented >a good artificial tool for generalized, fast perception' Ok I'll rephrase. "After 30+ years of intense research it has hard to develop the tools to generalised fast perception". Whereas in that time we have developed tools that make higher level reasoning computationally cheap in comparison. But as you yourself say that's just semantics. If you try to do something for over 30 years and make little progress, compared to another area of activity were many of the hard problems are solved in just a few years and the solutions to harder problems keep on coming, that's pretty good evidence that the first problem area is a hard one, however you try to finesse the description of the problems.
  - twelvechairs 14y ago
    You skew my words. What I was saying is that you can't say anything is hard if you have the right tools to do it. Not that this hasn't proved hard with our current technology/approach. This was in response to the above post using phrases like "intrinsically harder" and "highly dependent on the representations and models used". Apart from that I agree with you...

by tgflynn 14y ago

Breakthrough 14y ago

There's also the difficult task of real-world interaction (or as the article puts it, low-level sensorimotor skills), although I suppose this is what you mean by perceptual problems. Most things humans create are based on rules (e.g. games, markets, governments), and rules can be easily taught to computers as they're just abstract logical ideas. Even for probabilistic problems, you can certainly teach a neural network to perform than a naïve random selection. The problem comes back to lack of ability provide the network with enough, or in some cases, the proper inputs. "Trying to teach a neural network to play chess is probably much harder than teaching it to recognize images (at least my very limited experiments suggest this to be true)." That, sir, depends on your definition of "hard". The rules of chess are, after rules, so you could just set a neural network free within those constraints to play until it found a way to win, and continue to play until it reached a certain performance level.

tgflynn 14y ago
Well what I mean by "hard" is that I can train a neural network to do a reasonably good job of recognizing say handwritten characters given some amount of training data. My attempts to train similar networks to learn the rules of chess (just the rules, I'm not even talking about trying to win) have not led to good or steadily improving performance, despite the fact that there's effectively no limit to the amount of training data that can be generated for chess. There are a number of possible reasons for this of which I think 2 likely ones are that: 1) The networks I'm using have insufficient computational/storage capacity to learn the rules of chess. or 2) Even if (1) is not the case gradient descent is unlikely to find a sufficiently good near optimum because the response surface is too complex.
- Breakthrough 14y ago
  Great response, I must say. As a conjecture, do you think splitting the logic up into several smaller neural networks with different objectives (i.e. for chess, one evaluates defensive maneuvers, one for attack, and so on) would alleviate the problem? Or at least improve the performance of the machine as a whole...
- treePhase 14y ago
  You should also consider the possibility that the type of neural network you chose is not capable of learning the rules of chess, but may be sufficient for OCR.
- simonster 14y ago
  I would bet heavily on (1). Non-human primates, with ~10 billion neurons (~3 billion neocortical), also cannot learn the rules of chess. Nearly all neural networks have far fewer.

jerf 14y ago

"I'm not convinced that the notion that perception is intrinsically harder than logic is quite true." But... where's the room for doubt? This isn't an article from 1930 about theoretical possibilities of what may happen someday when we have a lot of computational power. This is an observation about how perception has proved to be much more complicated than more pure reasoning, one quite old, robust, and well-established. The question is more about why that is true than whether it is true. In a further comment you reply about how you could not convince your neural net to play chess... but again, we are not theorizing that computers may be good at chess someday. We live in a world in which, if they are not already simply better than humans, we only a couple of years away. Certainly better than all but the absolute very best. Whereas we still get excited when we see a robot that can walk up or down a normal, rocky hill at all. Also, it baffles me how you think that explaining why logic is easier than perceptual problems is somehow disproof of that very fact. It doesn't matter that it's "really" the programmer that knows how to play chess (even if I'd observe the machine is still doing it better) when we still can't hardly make machines walk at all, regardless of whether the programmer or the computer is the one "knowing", a concept that in this context comes perilously close to a tautological assertion that if a computer can do it it must not be true "knowing". If we were really good at both sensorimotor and logic, but with two radically different toolkits, that might be an interesting point, but that's not the world we live in.

tgflynn 14y ago
The undeniable observation is that certain people spent a long time trying to do perception with a certain set of tools and found it to be very hard. That does not necessarily imply that perception is intrinsically so much harder than logic, merely that it is using those tools. It may turn out for example that there exist (in a mathematical sense) functions which are good at recognizing characters which are simpler than the simplest function which is good at playing chess. If that were the case it would make sense to say that chess is intrinsically harder than character recognition, even if humans have historically had a harder time discovering the recognition functions than the chess playing functions. On your last paragraph there's a huge difference between a program discovering a good solution to a problem, which is what happens in machine learning, and a programmer discovering such a solution then creating a program to implement it. In the first case we may begin to consider the program to be showing signs of intelligence while in the later it's just a really fast calculator which isn't doing anything the programmer couldn't do. Is there circularity here - yes because we can say the same thing of the learning program if we treat it at a lower level of abstraction and therein lies perhaps the core of the problem of understanding what we mean by intelligence.

twelvechairs 14y ago

I agree hugely that computers are based on hard logic and this makes them poor tools to deal with tasks like 'perception'. I'd also add that most people working in the field of AI are trained in hard logic, and not at all in the subtleties of the perceptual world (not that there's a university course for this), which probably doesn't help. I'm not so sure you start a good argument though. 'Harder' is all about the tools at hand. If you have the right tools, everything is easy. So to me this is all semantics. To me all the wikipedia page basically says is 'we haven't invented a good artificial tool for generalized, fast perception'. Of course the other way to think about it is that humans and other animals devote generally much larger amounts of physical space (in the brain and otherwise) to subconscious things like perception and subconscious memory than they do to conscious things like logic...

simonh 14y ago
>'Harder' is all about the tools at hand. If you have the right tools, everything is easy. >So to me this is all semantics. To me all the wikipedia page basically says is 'we haven't invented >a good artificial tool for generalized, fast perception' Ok I'll rephrase. "After 30+ years of intense research it has hard to develop the tools to generalised fast perception". Whereas in that time we have developed tools that make higher level reasoning computationally cheap in comparison. But as you yourself say that's just semantics. If you try to do something for over 30 years and make little progress, compared to another area of activity were many of the hard problems are solved in just a few years and the solutions to harder problems keep on coming, that's pretty good evidence that the first problem area is a hard one, however you try to finesse the description of the problems.
- twelvechairs 14y ago
  You skew my words. What I was saying is that you can't say anything is hard if you have the right tools to do it. Not that this hasn't proved hard with our current technology/approach. This was in response to the above post using phrases like "intrinsically harder" and "highly dependent on the representations and models used". Apart from that I agree with you...