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that's not the task we trained it for! Yes, exactly. We're not training consciousness networks. That's not even a goal of all this research. We're training

by seiji 11y ago

that's not the task we trained it for! Yes, exactly. We're not training consciousness networks. That's not even a goal of all this research. We're training dumb high-speed classifiers. How might we train an ANN to explain its reasoning? There are networks that exist today that'll happily explain an entire scene to you (the whole "this picture contains a pizza sitting on an oven in a kitchen and there is a dog in the corner"), which is closer, but still just pairwise training. The "thought" process isn't recurrent or turing complete so it can't make progress on its own.

poizan42 11y ago
> There are networks that exist today that'll happily explain an entire scene to you (the whole "this picture contains a pizza sitting on an oven in a kitchen and there is a dog in the corner"), which is closer, but still just pairwise training. The "thought" process isn't recurrent or turing complete so it can't make progress on its own. Aren't those generated by RNNs, which are Turing complete?
chriswarbo 11y ago
> There are networks that exist today that'll happily explain an entire scene to you (the whole "this picture contains a pizza sitting on an oven in a kitchen and there is a dog in the corner") I was thinking more along the lines of: "This picture contains pizza. This is because there is an ellipse which appears to be covered with cheese and pepperoni. I say that because the dominant colour is yellow and there are elliptical patches of a more reddish colour. ..." and so on, down to the pixel level. We can kind of get this by running the networks backwards, but it's not really deductive reasoning; for example, we can't correct the network by saying "that yellow ellipse covered with red ellipses is actually a pile of vomit", and watch that knowledge propagate through the weights. Instead, we have to re-train with more examples of pizzas and vomit.

by seiji 11y ago

poizan42 11y ago

> There are networks that exist today that'll happily explain an entire scene to you (the whole "this picture contains a pizza sitting on an oven in a kitchen and there is a dog in the corner"), which is closer, but still just pairwise training. The "thought" process isn't recurrent or turing complete so it can't make progress on its own. Aren't those generated by RNNs, which are Turing complete?

chriswarbo 11y ago

> There are networks that exist today that'll happily explain an entire scene to you (the whole "this picture contains a pizza sitting on an oven in a kitchen and there is a dog in the corner") I was thinking more along the lines of: "This picture contains pizza. This is because there is an ellipse which appears to be covered with cheese and pepperoni. I say that because the dominant colour is yellow and there are elliptical patches of a more reddish colour. ..." and so on, down to the pixel level. We can kind of get this by running the networks backwards, but it's not really deductive reasoning; for example, we can't correct the network by saying "that yellow ellipse covered with red ellipses is actually a pile of vomit", and watch that knowledge propagate through the weights. Instead, we have to re-train with more examples of pizzas and vomit.