Understanding Moravec's Paradox
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Understanding Moravec's Paradox
1–10 of 14 posts
Re: Understanding Moravec's Paradox
#2Put all the bad robots in jail, for UBI.
EDIT: someone has to order the license plates
Re: Understanding Moravec's Paradox
#3Any attention on Moravec's paradox is good imo because it is important.
That said, the article starts with several problems.
1) Claims that it isn't a paradox, which is just silly. A paradox is a counter-intuitive result. The result is generally counter-intuitive whatever explanation you give. Zeno's paradox remains a paradox despite calculus essentially explaining it, etc.
2) Calls the article "Understanding Moravec's Paradox" when it should be called "My Explanation of Moravec's Paradox".
3) The author's final explanation seems kind of simplistic; "Human activities just have a large search space". IDK. Human activity sometimes does still in things that aren't walking also. I mean, "not enough data" is an explanation why neural networks can't do a bunch of things. But not all programs are neural networks. One of the things humans are really good at is learning things from a few examples. A serious explanation of Moravec's Paradox would have to explain this as well imo.
Re: Understanding Moravec's Paradox
#4Any attention on Moravec's paradox is good imo because it is important. That said, the article starts with several problems. 1) Claims that it isn't a paradox, which is just silly. A paradox is a counter-intuitive result. The result is generally counter-intuitive whatever explanation you give. Zeno's paradox remains a paradox despite calculus essentially explaining it, etc. 2) Calls the article "Understanding Moravec…
Indeed, also ideally, the 2 second rule.
Re: Understanding Moravec's Paradox
#5Why the name „Moravec“ is two times correct in this article, but it is misspelled if it is a link text.
Re: Understanding Moravec's Paradox
#6Any attention on Moravec's paradox is good imo because it is important. That said, the article starts with several problems. 1) Claims that it isn't a paradox, which is just silly. A paradox is a counter-intuitive result. The result is generally counter-intuitive whatever explanation you give. Zeno's paradox remains a paradox despite calculus essentially explaining it, etc. 2) Calls the article "Understanding Moravec…
> mean, "not enough data" is an explanation why neural networks can't do a bunch of things... One of the things humans are really good at is learning things from a few examples
I dispute the search space problem for something like folding clothes. Like a lot of human actions in space, folding clothes and other motor tasks are hierarchical sequences of smaller tasks that are strung together, similar to a sentence or paragraph of text.
We can probably learn things from each other from few examples because we are leaning on a large library of subtasks that all have learned or which are innate, and the actual novel learning of sequencing and ordering is relatively small to get to the new reward.
I expect soon we'll get AIs that have part of their training be unsupervised rl in a physics simulation, if it's not being done already.
Re: Understanding Moravec's Paradox
#7[dead]
Re: Understanding Moravec's Paradox
#8Any attention on Moravec's paradox is good imo because it is important. That said, the article starts with several problems. 1) Claims that it isn't a paradox, which is just silly. A paradox is a counter-intuitive result. The result is generally counter-intuitive whatever explanation you give. Zeno's paradox remains a paradox despite calculus essentially explaining it, etc. 2) Calls the article "Understanding Moravec…
> mean, "not enough data" is an explanation why neural networks can't do a bunch of things... One of the things humans are really good at is learning things from a few examples I dispute the search space problem for something like folding clothes. Like a lot of human actions in space, folding clothes and other motor tasks are hierarchical sequences of smaller tasks that are strung together, similar to a sentence or p…
> Like a lot of human actions in space, folding clothes and other motor tasks are hierarchical sequences of smaller tasks that are strung together
I disagree, you can model those tasks as hiearchical sequences of smaller tasks. But the terminal goal of folding clothes is to turn a pile of unfolded clothes into a neat pile of folded clothes.
The reason you would break down the task is because getting between those two states with the only reward signal being "the clothes are now folded" takes a lot of steps, and given the possible actions the robot can take, results in a large search space.
Re: Understanding Moravec's Paradox
#9Any attention on Moravec's paradox is good imo because it is important. That said, the article starts with several problems. 1) Claims that it isn't a paradox, which is just silly. A paradox is a counter-intuitive result. The result is generally counter-intuitive whatever explanation you give. Zeno's paradox remains a paradox despite calculus essentially explaining it, etc. 2) Calls the article "Understanding Moravec…
The human ability to learn from few examples can be explained with evolution (and thus search). We evolved to be fast learners as it was key to our survival. If you touched fire and felt pain, you better learn quickly not to keep touching it. This learning from reward signals (neurotransmitters) in our brain generalises to pretty much all learning tasks
Re: Understanding Moravec's Paradox
#10> At its core, Moravec's paradox is the observation that reasoning takes much less computation compared to sensorimotor and perception tasks. It's often (incorrectly) described as tasks that are easy for humans are difficult for machines and visa versa.
He just states that this description would be incorrect multiple times but never gives a reason why it would be incorrect.
Then he tries to simplify the paradox to a question of degree, e.g. "hard" problems for computers just have a larger search space and require more compute.
But wasn't a big part about the paradox also that we didn't even have insight as how the problems could be solved?
E.g. if you play chess or do math as a human, you're consciously aware if the patterns, strategies and "algorithms" you use - and there is a clear path to formalize them so a computer could recreate them.
However, with vision, walking, "thinking", etc, the process are entirely subconscious and we get very little information on the "algorithms" by introspection. Additionally, not just the environment and the input data is chaotic and "messy", but so is the goal of what we would want to archive in the first place. If you ever hand-labeled a classification corpus, you could experience this firsthand: If the classification criteria were even moderately abstract, labelers would often disagree how to label individual examples.
Machine learning didn't really solve this problem, it just sort of routed around it and threw it under a rug: Instead of trying to formulate a clear objective, just come up with a million examples and have the algorithm guess the objective from the examples.
I think this kind of stuff is meant with "the hard problems are easy and the easy problems are hard".