Re:Measuring abstract reasoning in neural networks
by bhouston on Sunday July 15, 2018 @12:06PM
(#17535792)
> I think they roughly define what reasoning is. Deducing something from abstract information.
Reasoning is not prediction learned by seeing things happen before exactly in the same way. Moving a hand to catch a ball is not reasoning, it is recognition and prediction based on doing it a bunch of times.
Reasoning to know that 4 comes after 3 after seeing that happen a lot is not reasoning, it is prediction again, and even easier than predicting the motion of a ball.
Reasoning involves finding a pattern that you haven't seen before a thousand times but you use the elements you know and look to see how they combine to fit the existing problem. Reasoning by its nature is the ability to succeed in a novel situation that requires logic and exploration in search of a problem. Reasoning is successive cycles of experimental prediction with recognition and running an evaluator on top to see which way to explore. (I think this can be coded probably in the near term, but it is more than just a single NN.)
That said, if you provide enough situations to a NN it may be able to solve a lot of "reasoning" situations via recognition and prediction, but it will sort of be stupid if anything falls out of its training data -- similar to how NNs can be fooled completely by slightly modified pictures of animals.
Reasoning is not prediction learned by seeing things happen before exactly in the same way. Moving a hand to catch a ball is not reasoning, it is recognition and prediction based on doing it a bunch of times.
Reasoning to know that 4 comes after 3 after seeing that happen a lot is not reasoning, it is prediction again, and even easier than predicting the motion of a ball.
Reasoning involves finding a pattern that you haven't seen before a thousand times but you use the elements you know and look to see how they combine to fit the existing problem. Reasoning by its nature is the ability to succeed in a novel situation that requires logic and exploration in search of a problem. Reasoning is successive cycles of experimental prediction with recognition and running an evaluator on top to see which way to explore. (I think this can be coded probably in the near term, but it is more than just a single NN.)
That said, if you provide enough situations to a NN it may be able to solve a lot of "reasoning" situations via recognition and prediction, but it will sort of be stupid if anything falls out of its training data -- similar to how NNs can be fooled completely by slightly modified pictures of animals.