Moravec's paradox
en.wikipedia.org
Moravec's paradox
1–10 of 44 posts
Re: Moravec's paradox
#2It'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.
Re: Moravec's paradox
#3Roboticist 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.
Re: Moravec's paradox
#4Roboticist 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.
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
Re: Moravec's paradox
#5This 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.
Re: Moravec's paradox
#6It'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).
Re: Moravec's paradox
#7Funny 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. :)
Re: Moravec's paradox
#8Roboticist 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.
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.
Re: Moravec's paradox
#9This 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 w…
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.
Re: Moravec's paradox
#10It'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…
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.