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Comment by pavlov | Hacker News Reader

The problem is that this network-contained concept of "cat face" is still a symbolic representation. It's a much more complex algorithmic symbol than the rules found in something like 1960s Eliza, but its understanding of the world is on the same level.

You can't ask the "cat face" neural network anything about cats. It has no idea what they actually are in relation to the world. A two-year-old human can usually tell you more about cats than you'd care to listen.

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I think the important part is that it "learned" to distinguish this concept of cat face without that being the direct intent provided through labeling. Given the set of images, it learned that "concept" which we can then retroactively label to make it useful in the typical human sense by associating it with cats. If we had to compose computer "knowledge" by training it in everything by manually specifying what it was training for, that process would surely be insurmountable in a general sense. Yet this seems to provide the possibility of automating this process.

A separate network of connections between "knowledge" bits could maybe be used to associate related knowledge like you talk about in terms of their relation to the world. This could also probably be formed in a similar manner giving the algorithm the ability to distinguish "important" concepts contained in the data. The thing I find most odd and interesting about this is that the network tends to identify different important concepts than humans do.

You can't ask a child's visual cortex to tell you anything about cats, either. But connect that visual recognition ai with something like Watson, and you have what you're looking for, no?

I just don't know. I'd be happy to see that be the case...

But I'm afraid it's going to be the equivalent of this cake recipe: "Take an egg and a packet of sugar. Break the egg over the packet."

You certainly need eggs and sugar to make a cake, but on their own and combined without understanding of the whole, you're not getting very far.

The human brain is mostly a bunch of ugly wetware hacks, not a single coherent 'intelligence' that does all the thinking. If you attach enough single purpose AIs together you might get something much more human like than trying to create a single neural network that does everything.

If word embedding can produce things like "king - man + woman = queen", then concept embedding on images might be able to achieve a similar level of "understanding" (i.e. very low, but probably better than nothing).

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