Moravec's paradox(en.wikipedia.org)
en.wikipedia.org
Moravec's paradox
https://en.wikipedia.org/wiki/Moravec%27s_paradox
17 comments
"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.
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.
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.
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.
"That does not necessarily imply that perception is intrinsically so much harder than logic, merely that it is using those tools."
Again, this might follow if, say, nobody had ever tried to come up with such tools, and we were going to try for the first time this year. But people have been trying, and failing, and failing. Of course it will be easier when the tools exist; robot.walk_to(grocery_store) is as easy as any other function to call, but it is obviously much harder to implement.
In the "mathematical sense" I can simply assert the existence of "recognize_character(image_matrix)". In reality, the complexity of such a function is obviously much higher than, say, a Prolog implementation.
You keep retreating into theory, but again, that's not the world we live in. The world we live in is one in which the problem of sensorimotor perception and manipulation has received immense work on it, and remains in a state in which it is still wildly less capable than my dog in most ways, whereas we were knocking out things like SAT solvers that blow humans away (which are notably incapable of holding very many symbols in their heads at once) decades ago. It doesn't matter if you can theorize a world in which perceptual problems are easier than sensory problems; we don't live there.
Again, this might follow if, say, nobody had ever tried to come up with such tools, and we were going to try for the first time this year. But people have been trying, and failing, and failing. Of course it will be easier when the tools exist; robot.walk_to(grocery_store) is as easy as any other function to call, but it is obviously much harder to implement.
In the "mathematical sense" I can simply assert the existence of "recognize_character(image_matrix)". In reality, the complexity of such a function is obviously much higher than, say, a Prolog implementation.
You keep retreating into theory, but again, that's not the world we live in. The world we live in is one in which the problem of sensorimotor perception and manipulation has received immense work on it, and remains in a state in which it is still wildly less capable than my dog in most ways, whereas we were knocking out things like SAT solvers that blow humans away (which are notably incapable of holding very many symbols in their heads at once) decades ago. It doesn't matter if you can theorize a world in which perceptual problems are easier than sensory problems; we don't live there.
I'm not sure I agree with your temporal notion of difficulty. One thing is not harder than another simply because the latter took less time to figure out. So what if many people have failed?* If someone with great insight like Ramanujan comes around and proves a bunch of new mathematical theorems, the fact that we now know those theorems does not imply that they are easier (or harder) to understand or make use of compared to some theorem which was not proved for another seventy years (there are examples here that go both ways.)
So why is robot.walk_to(...) "obviously harder to implement"? How could we know that simpler implementations will never be discovered? What you call "immense work" is not even a century's worth. In hundreds of years, making a robot walk convincingly might be no more difficult than implementing an effective chess AI.
I suppose I'm getting at a concept of minimality (like Komolgorov complexity). Just having a solution does not mean it's a minimal solution. We know enough to say perception is harder right now but not enough to say perception is intrinsically harder. We can't know that new breakthroughs (or just lots of gradual development) won't make these problems simple ones.
(*Actually, I do agree that lots of smart people failing at a problem is probably a good indicator that the problem is hard. This is still no guarantee though.)
So why is robot.walk_to(...) "obviously harder to implement"? How could we know that simpler implementations will never be discovered? What you call "immense work" is not even a century's worth. In hundreds of years, making a robot walk convincingly might be no more difficult than implementing an effective chess AI.
I suppose I'm getting at a concept of minimality (like Komolgorov complexity). Just having a solution does not mean it's a minimal solution. We know enough to say perception is harder right now but not enough to say perception is intrinsically harder. We can't know that new breakthroughs (or just lots of gradual development) won't make these problems simple ones.
(*Actually, I do agree that lots of smart people failing at a problem is probably a good indicator that the problem is hard. This is still no guarantee though.)
Your definition falls down when faced with real world problems. I can write a 'perfect' chess program in less than a day that could easily run on a PC, however it's run time would make it useless in the real world. Crossing the Atlantic is an easy problem now, but that does not mean it was easy 500 years ago even though mathematically nothing has changed. And in 500 years computer vision is probably going to be considered a vary easy problem often done as a high school level project, but that does not mean it's easy now.
Come up with an AI that can play the game of Go well. Then we'll talk.
That's just a snide comment, not a useful one. The difficulty of symbolic/logic problems can be made unboundedly great by construction. Nobody ever denied that. The point is that something like crossing a room and picking up a pencil seemed easy, and to this day is still a significant achievement for robotics, still requiring fairly controlled circumstances.
It can be difficult to understand this from our present-day perspective where we have so thoroughly internalized this idea that it has apparently passed into invisibility for some of us. Go back and read Asimov's robots work, in which he has robots walking, talking, socially interacting with humans, even pondering great ethical conundrums, while at the same time it requires massive resources to attain the raw numerical computational power available to a Commodore 64.
It can be difficult to understand this from our present-day perspective where we have so thoroughly internalized this idea that it has apparently passed into invisibility for some of us. Go back and read Asimov's robots work, in which he has robots walking, talking, socially interacting with humans, even pondering great ethical conundrums, while at the same time it requires massive resources to attain the raw numerical computational power available to a Commodore 64.
Just because you discovered a few cool hacks that happened to work well in very specific problem domains doesn't mean you've "solved" symbolic thought. The way a chess program works is not the way humans play chess. It does a brute-force search, rather than learning patterns.
Go is a good example of a game that can't effectively be brute-forced. There are a lot of other examples out there; that was just the one I happened to pick.
The point is that something like crossing a room and picking up a pencil seemed easy, and to this day is still a significant achievement for robotics, still requiring fairly controlled circumstances.
Just because you failed to do one thing doesn't mean you succeeded in doing another.
Go is a good example of a game that can't effectively be brute-forced. There are a lot of other examples out there; that was just the one I happened to pick.
The point is that something like crossing a room and picking up a pencil seemed easy, and to this day is still a significant achievement for robotics, still requiring fairly controlled circumstances.
Just because you failed to do one thing doesn't mean you succeeded in doing another.
Some Go programs already play at professional level on 9x9 boards, and amateur mid-dan level on 19x19. That's "playing well" on my book: many people play go for years without reaching that level.
Yes, it's still far from the level computer chess was 15 years ago (beating top pros), but they're getting there. And I don't think it will take any major breakthrough in AI itself.
Yes, it's still far from the level computer chess was 15 years ago (beating top pros), but they're getting there. And I don't think it will take any major breakthrough in AI itself.
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.
"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.
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.
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.
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...
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...
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.
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.
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...
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...
>'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.
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.
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...
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...
Funny thing: I was a peon in Rod Brooks' group right at this transition. The robot I worked on was a small monster, which would beam data offboard to a lisp-machine for analysis (machine-vision problems, etc). Just after I left, Rod decided to go down the path of having small simple FSMs connected together, which it turns out displayed some pretty complex behavior. Then Colin Angle showed up.
Boy did I leave at the wrong time. :)
Boy did I leave at the wrong time. :)
Roboticist here. This is actually a huge problem in robotics and causes all sorts of problems for real robots. For example, my current project is to teach robots how levers work.
Well,
Teach as in "program", right?
It seems like the problem is that computers and robots don't learn in same fashion as human and thus the process of "teaching" them things in their not learning concept X in same fashion as humans - ie, they don't learn generalizations such a way that are "ready at hand" to use when the appropriate situation arises.
Teach as in "program", right?
It seems like the problem is that computers and robots don't learn in same fashion as human and thus the process of "teaching" them things in their not learning concept X in same fashion as humans - ie, they don't learn generalizations such a way that are "ready at hand" to use when the appropriate situation arises.
That's one of the underlying causes of Moravec's Paradox, yes. Humans have a very difficult time with precision and regularity, which are exactly what current computer systems are best at. Computers, by contrast, have a hard time with generalization, and creativity and physically interacting with the real world are some of the the most general systems possible.
levers as in how to operate a specific kind of lever/switch or do you mean "teaching" the abstract concept of levers? e.g. recognising that this thing can be used as a lever? The latter sounds much harder
The abstract concept of a lever. And it is definitely much harder than it sounds, mostly because it's comparatively so easy for humans to do.
This seems too obvious to be considered a paradox. The article says that the discover ran contrary to traditional assumptions, but I wonder if this is true, or if so, why it would be the case. Perhaps I just have the luxury of hindsight, but it seems like after the advent of electronic computers, it would quickly become obvious that computers could vastly outperform humans in things like multiplication, or counting words in a large text document, or finding a correct path through a digital maze.
Besides, the distinction between "high-level reasoning" and "low-level sensorimotor skills" seems fairly weak. Checkers already starts to blur the line: the problem space can be modeled as pattern recognition and tactics (like how humans model their own gameplay), or it can be modeled as a "dumb search" through a decision tree (like how a computer algorithm might play). Then you get to something like chess, which has a prohibitively large decision tree to do a "dumb search," then face recognition, then natural language processing, etc.
Besides, the distinction between "high-level reasoning" and "low-level sensorimotor skills" seems fairly weak. Checkers already starts to blur the line: the problem space can be modeled as pattern recognition and tactics (like how humans model their own gameplay), or it can be modeled as a "dumb search" through a decision tree (like how a computer algorithm might play). Then you get to something like chess, which has a prohibitively large decision tree to do a "dumb search," then face recognition, then natural language processing, etc.
It's definitely true (that "the discover ran contrary to traditional assumptions"), and they give supporting evidence for this in the article. The history of AI bears this out quite explicitly.
Also, the decision tree for chess might be intractable to exhaustively search, but a "dumb search" is exactly how it's done, and it is currently vastly more powerful than any competing method. And you really can't just jump from chess to face recognition and NLP -- they are completely different problems. To wit, chess has perfect information; the entire state of the game is known at all times, and the representation is clearly defined and compact, where none of this is true for sensory tasks. This means the range of techniques available to solve each type problem are completely disparate, and tat there is almost nothing that you can take from a chess program and apply to "lower level" AI tasks.
Also, the decision tree for chess might be intractable to exhaustively search, but a "dumb search" is exactly how it's done, and it is currently vastly more powerful than any competing method. And you really can't just jump from chess to face recognition and NLP -- they are completely different problems. To wit, chess has perfect information; the entire state of the game is known at all times, and the representation is clearly defined and compact, where none of this is true for sensory tasks. This means the range of techniques available to solve each type problem are completely disparate, and tat there is almost nothing that you can take from a chess program and apply to "lower level" AI tasks.
This is exactly the kind of flawed behaviorist centric thinking that has held AI back for the past 50 year. Until AI researchers get over their obsession with mathematical constructs with no foundation in biology, like symbolic AI and bayesian networks, we will never have true AI. Once you realize this, you see that this “paradox” isn't really a paradox at all.
The brain is not anything like the Von Neumann architecture of a CPU. It is a massively parallel prediction machine. High level thought occurs in cerebral cortex whose base functional unit is Hierarchical Temporal Memory composed of about 100 neurons. Once we have figured out how these units are wired in the brain, “difficult” problems like pattern recognition will be trivial, and “trival” problems like playing checkers will require many hours of training and many HTM units just like in a real human brain.
For anyone interested in this, I highly recommend Jeff Hawkins' book, “On Intelligence”. http://books.google.com/books?isbn=0805078533
The brain is not anything like the Von Neumann architecture of a CPU. It is a massively parallel prediction machine. High level thought occurs in cerebral cortex whose base functional unit is Hierarchical Temporal Memory composed of about 100 neurons. Once we have figured out how these units are wired in the brain, “difficult” problems like pattern recognition will be trivial, and “trival” problems like playing checkers will require many hours of training and many HTM units just like in a real human brain.
For anyone interested in this, I highly recommend Jeff Hawkins' book, “On Intelligence”. http://books.google.com/books?isbn=0805078533
This is insulting to AI researchers in the extreme.
They know they are not making as good progress on "true AI" as they would like. But many researchers have tried very hard in many directions.
If you are so sure true AI is fairly trivial, why not do it? Even small amounts of progress would make you very famous.
They know they are not making as good progress on "true AI" as they would like. But many researchers have tried very hard in many directions.
If you are so sure true AI is fairly trivial, why not do it? Even small amounts of progress would make you very famous.
"This is insulting to AI researchers in the extreme."
Well, yes and no. If you want the entire book "On Intelligence" (which I highly recommend too) is insulting to AI researchers.
The book basically explains why we're on the wrong track.
To me AI is about reasoning and creativity and none of the stuff we currently have comes anywhere close to that.
Well, yes and no. If you want the entire book "On Intelligence" (which I highly recommend too) is insulting to AI researchers.
The book basically explains why we're on the wrong track.
To me AI is about reasoning and creativity and none of the stuff we currently have comes anywhere close to that.
This may also be an artifact that in terms of "intelligence" we have focused mostly on supervised learning as opposed to unsupervised learning; recent reversals in this trend have been shown to be surprisingly powerful [1].
Consider this: While reading, most humans use a different part of the brain to register consonants and vowels [2]. No matter how much we like to think we learn language in an orderly fashion, that is not the case. Our reading and speaking skills are simply built over time and experience by having neurons connect as we experience visual words and other people talking; formal language instruction probably plays a secondary role of attaching labels to already built neural networks.
[1] http://www.nytimes.com/2012/11/24/science/scientists-see-adv...
[2] http://www.nature.com/nature/journal/v403/n6768/full/403428a...
Consider this: While reading, most humans use a different part of the brain to register consonants and vowels [2]. No matter how much we like to think we learn language in an orderly fashion, that is not the case. Our reading and speaking skills are simply built over time and experience by having neurons connect as we experience visual words and other people talking; formal language instruction probably plays a secondary role of attaching labels to already built neural networks.
[1] http://www.nytimes.com/2012/11/24/science/scientists-see-adv...
[2] http://www.nature.com/nature/journal/v403/n6768/full/403428a...
What does it take to have a program come up with the theory of relativity, given the knowledge that was available in 1904?
I think there are all kinds of reasoning skills that we've never been able to test, because they depend on perception and motor skills. It seems possible those would take many more computational resources. I find it hardly surprising that feeding a program abstractions and allowing it to reason about those abstractions is simple. It's the interacting with the real world, correlating abstractions with the real world and coming up with useful new abstractions, that's hard.
I don't think we'll ever have an AI until something is built that can freely interact with the world, freely gather data and freely modify itself to enhance all its abilities. An AI without pressure sensors that ever touched sand will never understand the universe.
I think there are all kinds of reasoning skills that we've never been able to test, because they depend on perception and motor skills. It seems possible those would take many more computational resources. I find it hardly surprising that feeding a program abstractions and allowing it to reason about those abstractions is simple. It's the interacting with the real world, correlating abstractions with the real world and coming up with useful new abstractions, that's hard.
I don't think we'll ever have an AI until something is built that can freely interact with the world, freely gather data and freely modify itself to enhance all its abilities. An AI without pressure sensors that ever touched sand will never understand the universe.
I would say that this is true only as long as this "adult intelligence" pretty much remains within a single "logical frame" (http://en.wikipedia.org/wiki/Frame_%28artificial_intelligenc...).
I would claim that while a single logical frame is easy to simulate on a digital computer, creating and balancing multiple, not-necessarily consistent frames is very difficult and requires as much computer activity or brain activity as the also difficult activities of raw input processing.
One might argue that neural system began as very different systems from digital computers but the evolution of the large human has allowed them emulate discreet, including a computer's digital logic while still doing the balancing of multitudes of environmental constraints which neural system have excelled at for millions of years. And letting "us" conceive-of and to even build computers perfecting this discreet logic. Pretty amazing.
I would claim that while a single logical frame is easy to simulate on a digital computer, creating and balancing multiple, not-necessarily consistent frames is very difficult and requires as much computer activity or brain activity as the also difficult activities of raw input processing.
One might argue that neural system began as very different systems from digital computers but the evolution of the large human has allowed them emulate discreet, including a computer's digital logic while still doing the balancing of multitudes of environmental constraints which neural system have excelled at for millions of years. And letting "us" conceive-of and to even build computers perfecting this discreet logic. Pretty amazing.
Restatement in pictures: http://abstrusegoose.com/496
This doesn't seem very paradoxical to me, and at a layman's level I think one good parallel between a human's "operation" and a computer's operation with regard to this question is as follows . . .
Even as a non-programmer (I am a programmer) I might relate well to an ordinary desktop/laptop running my Excel spreadsheets. I can create a spreadsheet, enter data in cells, enter formulae, format the content beautifully, specify and view charts of the data I'm entering and information I'm computing. I might be able to respect and appreciate the beauty and complexity of how the spreadsheet program was implemented in an abstract sense. I might describe to another person my ideas about how the spreadsheet program was created, its major features and concerns, and its obvious complexity. What I'd be missing though, likely, is (a) the complex interface between what I see and what supports that experience behind the scenes; and (b) the 50+ years of computing technology under the hood that has evolved to support my narrow and visible relations with my Excel spreadsheets.
From the user's view, the Excel spreadsheets, Windows Explorer, the Start button, etc., are the aspects of the computer analogous to a human's thought processes. They're visible and explainable. The user might have some vague notion that files are stored on disk, that there's something called a CPU, that does the computer thing, etc. The user has no clue, though, that the Excel spreadsheet program itself contains but a very small portion of the effort to make its visible manifestations happen. There's an enter support system from file system, CPU, memory, buses all over the place, GPU, video display, chips, specialized interfaces to I/O and other subsystems, ASIC's, semiconductor physics, electricity, magnetism, etc. The hardware, firmware, and software for the latter have had 50 years to evolve and mature. To a normal user these aspects aren't understandable. They understand Excel.
And so for us, we can understand and describe human thought and cognition in an abstract way. But most thought is below the level we're conscious of, and supporting that thought is an entire interface with the physical elements of the body, its nervous system and autonomous function, and the interface of these with the brain.
Even as a non-programmer (I am a programmer) I might relate well to an ordinary desktop/laptop running my Excel spreadsheets. I can create a spreadsheet, enter data in cells, enter formulae, format the content beautifully, specify and view charts of the data I'm entering and information I'm computing. I might be able to respect and appreciate the beauty and complexity of how the spreadsheet program was implemented in an abstract sense. I might describe to another person my ideas about how the spreadsheet program was created, its major features and concerns, and its obvious complexity. What I'd be missing though, likely, is (a) the complex interface between what I see and what supports that experience behind the scenes; and (b) the 50+ years of computing technology under the hood that has evolved to support my narrow and visible relations with my Excel spreadsheets.
From the user's view, the Excel spreadsheets, Windows Explorer, the Start button, etc., are the aspects of the computer analogous to a human's thought processes. They're visible and explainable. The user might have some vague notion that files are stored on disk, that there's something called a CPU, that does the computer thing, etc. The user has no clue, though, that the Excel spreadsheet program itself contains but a very small portion of the effort to make its visible manifestations happen. There's an enter support system from file system, CPU, memory, buses all over the place, GPU, video display, chips, specialized interfaces to I/O and other subsystems, ASIC's, semiconductor physics, electricity, magnetism, etc. The hardware, firmware, and software for the latter have had 50 years to evolve and mature. To a normal user these aspects aren't understandable. They understand Excel.
And so for us, we can understand and describe human thought and cognition in an abstract way. But most thought is below the level we're conscious of, and supporting that thought is an entire interface with the physical elements of the body, its nervous system and autonomous function, and the interface of these with the brain.
I'd say programming is a high level skill that computers can't do. Counter example?
The hard (for humans) part of programming is already being done by your compiler, linker, version control system, IDE, interpreters, etc. All that's left for you is the parts that are hard for computers: deciding what the program should do, guessing which libraries are worth the risk that they might be buggy, discovering gaps between what you decided the program should do and what its users need, figuring out how to make it understandable to users, getting people to try it, and so on.
It's all in how you think about the problem.
Brains are vary good at fuzzy highly parallel tasks and bad at sequential ones. Computers suck at those fuzzy parallel tasks, but are rather good at accurate sequential ones. People are easy to train individually, computers take a lot of up front effort but after that it's easy.
Brains are vary good at fuzzy highly parallel tasks and bad at sequential ones. Computers suck at those fuzzy parallel tasks, but are rather good at accurate sequential ones. People are easy to train individually, computers take a lot of up front effort but after that it's easy.
Sounds like people posting or contributing to an article like this are aiming at understanding artificial general intelligence. So just search for that. Artificial General Intelligence or AGI.
It's a bit sad that instead of acknowledging that AI has nothing at all to do with intelligence, AI researchers instead decided to coin a new term ("strong AI").
So you now have two terms: "AI" and "strong AI" (AGI if you wish) that are totally unrelated.
Had people been honest "strong AI" would be "AI" and the "AI" we have now should be called "fake AI" or "pink-unicorn AI" ; )
So you now have two terms: "AI" and "strong AI" (AGI if you wish) that are totally unrelated.
Had people been honest "strong AI" would be "AI" and the "AI" we have now should be called "fake AI" or "pink-unicorn AI" ; )
Sorry for going a little bit off topic here, but certainly there are works of science fiction based on this paradox — after all, it’s more than thirty years old! Any suggestions?
Certainly true, but perception and mobility as basic as they are, are a lot complex in nature than intelligence.
an interesting example of this (machine listening):
http://en.wikipedia.org/wiki/Source_separation http://en.wikipedia.org/wiki/Cocktail_party_effect
http://en.wikipedia.org/wiki/Source_separation http://en.wikipedia.org/wiki/Cocktail_party_effect
It's only difficult because computers are digital devices and all the "hard" things for computers/robots to do aren't done in hardware. Trying to have a single CPU do all of the things a robot needs to do is asinine.
Making a robot hand pick up an egg and a cup of coffee and turn a wrench isn't difficult provided that you build in similar feedback loops and low-level "firmware" that the brain does for us, unconsciously.
But if you did that it would take tens of kilowatts to run a halfway decent robot. And that's clearly ridiculous! So nobody does it.
Making a robot hand pick up an egg and a cup of coffee and turn a wrench isn't difficult provided that you build in similar feedback loops and low-level "firmware" that the brain does for us, unconsciously.
But if you did that it would take tens of kilowatts to run a halfway decent robot. And that's clearly ridiculous! So nobody does it.
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).