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by Xcelerate
757 days ago
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> What is the point of this work? Seriously? They say it right in the introduction. The goal is to learn how to infer algorithmic processes directly from data. Much like how MNIST was used in the early days of NNs, you have to start with small toy problems that are representative of the problem domain. Once you have success with that, you can scale up problem complexity. General algorithmic capability is one of the key traits that we think AGI should have, and it’s currently missing. If you have a better approach for getting there quicker than everyone else in the field, please share it. I would even appreciate seeing more papers on approaches that didn’t work very well so it saves other researchers from going in the wrong direction. That alone would be enough justification for publishing an article. |
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Yes, seriously.
>> The goal is to learn how to infer algorithmic processes directly from data.
And they demonstrated nothing like that. An "algorithmic process" is not finding the weights for a function given some carefully designed bias. An algorithm is a sequence of operations that calculates the result of a function. Nothing like that has been demonstrated in the linked paper at all.
>> General algorithmic capability is one of the key traits that we think AGI should have, and it’s currently missing. If you have a better approach for getting there quicker than everyone else in the field, please share it.
It's not missing at all, you just wont' find it in neural nets. And my PhD and post-doc research is exactly on that sort of thing, learning programs, algorithms and, currently, solvers for general planning problems.