7 ms·

Moravec's paradox

188 points by kristiandupont 14y ago

Retric 14y ago
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.
saulrh 14y ago
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.
- phpnode 14y ago
  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
  - saulrh 14y ago
    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.
- joe_the_user 14y ago
  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.
  - saulrh 14y ago
    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.
baddox 14y ago
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.
- shmageggy 14y ago
  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.
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.
jpdoctor 14y ago
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. :)
msandford 14y ago
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.
c8ion 14y ago
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.
justhw 14y ago
Certainly true, but perception and mobility as basic as they are, are a lot complex in nature than intelligence.
Confusion 14y ago
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.
thyrsus 14y ago
Restatement in pictures: http://abstrusegoose.com/496 http://abstrusegoose.com/496
osetinsky 14y ago
an interesting example of this (machine listening): http://en.wikipedia.org/wiki/Source_separation http://en.wikipedia.org/wiki/Source_separation http://en.wikipedia.org/wiki/Cocktail_party_effect http://en.wikipedia.org/wiki/Cocktail_party_effect
tathastu 14y ago
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-advances-in-deep-learning-a-part-of-artificial-intelligence.html http://www.nytimes.com/2012/11/24/science/scientists-see-adv... [2] http://www.nature.com/nature/journal/v403/n6768/full/403428a0.html http://www.nature.com/nature/journal/v403/n6768/full/403428a...
joe_the_user 14y ago
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_intelligence%29 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.
ilaksh 14y ago
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.
- martinced 14y ago
  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" ; )
slacka 14y ago
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 http://books.google.com/books?isbn=0805078533
- CJefferson 14y ago
  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.
  - martinced 14y ago
    "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.
tocomment 14y ago
I'd say programming is a high level skill that computers can't do. Counter example?
- kragen 14y ago
  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.
minoru 14y ago
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?

188 points by kristiandupont 14y ago

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.

saulrh 14y ago

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.

phpnode 14y ago
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
- saulrh 14y ago
  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.
joe_the_user 14y ago
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.
- saulrh 14y ago
  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.

baddox 14y ago

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.

shmageggy 14y ago
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.

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.

jpdoctor 14y ago

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. :)

msandford 14y ago

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.

c8ion 14y ago

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.

justhw 14y ago

Certainly true, but perception and mobility as basic as they are, are a lot complex in nature than intelligence.

Confusion 14y ago

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.

thyrsus 14y ago

Restatement in pictures: http://abstrusegoose.com/496 http://abstrusegoose.com/496

osetinsky 14y ago

an interesting example of this (machine listening): http://en.wikipedia.org/wiki/Source_separation http://en.wikipedia.org/wiki/Source_separation http://en.wikipedia.org/wiki/Cocktail_party_effect http://en.wikipedia.org/wiki/Cocktail_party_effect

tathastu 14y ago

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-advances-in-deep-learning-a-part-of-artificial-intelligence.html http://www.nytimes.com/2012/11/24/science/scientists-see-adv... [2] http://www.nature.com/nature/journal/v403/n6768/full/403428a0.html http://www.nature.com/nature/journal/v403/n6768/full/403428a...

joe_the_user 14y ago

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_intelligence%29 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.

ilaksh 14y ago

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.

martinced 14y ago
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" ; )

slacka 14y ago

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 http://books.google.com/books?isbn=0805078533

CJefferson 14y ago
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.
- martinced 14y ago
  "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.

tocomment 14y ago

I'd say programming is a high level skill that computers can't do. Counter example?

kragen 14y ago
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.

minoru 14y ago

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?