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I’ve seen a million of such demos but simulations like these are nothing like the real world. Moravec’s paradox will make neural nets look like toddlers for a long time to come yet.
Well, that particular demo is more of a cockroach than a toddler, the neural network used seems to not have even a million weights.
Moravec’s paradox holds true because of two fronts:
Computing resources required
Lack of formal description of a behavior
But keep in mind that was in 1988, about 20 years before the first 1024-core multi-TFLOP GPU was designed, and that by training a NN, we’re brute-forcing away the lack of a formal description of the algorithm.
We’re now looking towards neuromorphic hardware on the trillion-“core” scale, computing resources will soon become a non-issue, and the lack of formal description will only be as much of a problem as it is to a toddler… before you copy the first trained NN to an identical body and re-training costs drop to O(0)… which is much less than even training a million toddlers at once.
I’ve seen a million of such demos but simulations like these are nothing like the real world. Moravec’s paradox will make neural nets look like toddlers for a long time to come yet.
Well, that particular demo is more of a cockroach than a toddler, the neural network used seems to not have even a million weights.
Moravec’s paradox holds true because of two fronts:
But keep in mind that was in 1988, about 20 years before the first 1024-core multi-TFLOP GPU was designed, and that by training a NN, we’re brute-forcing away the lack of a formal description of the algorithm.
We’re now looking towards neuromorphic hardware on the trillion-“core” scale, computing resources will soon become a non-issue, and the lack of formal description will only be as much of a problem as it is to a toddler… before you copy the first trained NN to an identical body and re-training costs drop to O(0)… which is much less than even training a million toddlers at once.