5 ms·

We don’t write them in though. You can show a DNN a large sample of photos of animals, retain the lower sections of the network, and then show it a large sampl

We don’t write them in though. You can show a DNN a large sample of photos of animals, retain the lower sections of the network, and then show it a large sample of landscapes — and it will be able to recognize those faster precisely because a well-trained network picked up on the latent structure of shapes, and can reason about shapes in its internal language for describing pictures. It’s acquiring genuine semantic information — and learning about new experiences relative to that previous acquired understanding. We didn’t create that similarity of form in nature — and DNNs discover that in much the way we do: finding recurring, abstract patterns to utilize in our internal language.

mjburgess 5y ago
We labelled them -- their pixel space geometric structure has no inherent relevance to their character space structure --- and the only inferential contexts this will work in is where we rig the deployment to only require these sorts of coincidences. Animals acquire robust techniques which are not brittle to these sorts of thin contexts. We do so by not templating pregiven data -- but by coordinating our bodies in the world, and rearranging it, so as to determine how to regulate ourselves in response to it. Only a teen reading cosmo would think King-Man=Woman. The reason we can even interpret this thin formalism is because of our robust understanding of the concepts that these character symbols name. With these concepts we don't cross domains, we regulate our own imagination, and so on, so as to create representations which are thick across an infinity of domains. Talking in terms of feature domains has it backwards -- it assumes they're available for measurement. We don't transfer thin templates across domains. Our representations aren't within a feature space --- they're techniques for self-regulation. With these we can construct templates in an infinite number of different feature spaces -- ie., project by imagination from our techniques

mjburgess 5y ago

We labelled them -- their pixel space geometric structure has no inherent relevance to their character space structure --- and the only inferential contexts this will work in is where we rig the deployment to only require these sorts of coincidences. Animals acquire robust techniques which are not brittle to these sorts of thin contexts. We do so by not templating pregiven data -- but by coordinating our bodies in the world, and rearranging it, so as to determine how to regulate ourselves in response to it. Only a teen reading cosmo would think King-Man=Woman. The reason we can even interpret this thin formalism is because of our robust understanding of the concepts that these character symbols name. With these concepts we don't cross domains, we regulate our own imagination, and so on, so as to create representations which are thick across an infinity of domains. Talking in terms of feature domains has it backwards -- it assumes they're available for measurement. We don't transfer thin templates across domains. Our representations aren't within a feature space --- they're techniques for self-regulation. With these we can construct templates in an infinite number of different feature spaces -- ie., project by imagination from our techniques