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Maybe we're talking about different mathematical formalisms.. When you say latent representational structure not present in the data, am I correct in thinking

by pizza 5y ago

Maybe we're talking about different mathematical formalisms.. When you say latent representational structure not present in the data, am I correct in thinking you mean: argmin theta: Loss = dist(data, model(data)) where model(data) = layerL( ... layer3 ( layer2 ... layer1(data) )) is actually obscuring some conditioning factor human_understanding(observed data) which actually goes into all the layers (by their choice of design, initialization), distance functions, optimization steps, data cleaning, splits, etc.? And furthermore, also that P(model simply memorizes observations + human provided inductive cues | model finds structure) >> P(model does something beyond memorization of observations + human cues | model finds structure) ? What would be your test to determine this? For instance, how would you measure the information content (under some pragmatic-enough encoding that could be realizable and built today) of the memorization + human cues, vs memorization + human cues + the extra bit that the model concludes? Not a gotcha question, curious about your answer. Though, I have to say something about the first bit. Suppose you were comparing the safety and probability of catastrophic failure of some of the earliest buildings made in human civilization compared to something made in 2022. And suppose you conditioned that probability on the sum total of human knowledge at the time. The same laws of physics apply, but different amounts of knowledge went into producing each artifact. Can we say that the latent structure (human theory of engineering) in the modern building (model) reveals more about the ground truth of the world (laws of physical reality) than is inside of the observations data set (does it collapse or not) than the old model? I think we can. And I don't feel that embarrassed to say that deep learning is just a part of the wider landscape of human experience. You may be interested in this article that proposes Rip van Winkle's Razor: if you apply a state of the art method to an old dataset, for a fair comparison, you need to measure and take into account the increase in tooling that has become available since the time the benchmark was first released: http://www.offconvex.org/2021/04/07/ripvanwinkle/ http://www.offconvex.org/2021/04/07/ripvanwinkle/

mjburgess 5y ago
Yes, I think that's largely right. What we require is a "bridge" from any particular feature space to the target. A compression of a feature space doesn't produce this bridge. When we look at a photo, its ink-space arragement can be bridged to its depicted situation via our ability to brigde. We see in the ink, a world. We do this via concepts which bridge. We get those by being in the world. We can "roughly accept" limited forms of inference which simply operate within a feature space, ie., we can be fooled by photos "made on photoshop" which appear to depict a situation which never really existed. A machine operates in "inkspace" and we rig it only ever to produce "human-bridgable" arragements of ink. The machine itself doesnt draw a situation -- as with an artist painting a room. A machine isnt in the room. It is, as-if it were in the room, and as if it had painted it. This illusion is created by us, we rig the system to average historical paintings of rooms, and only ever show the apes-at-home a good picture. They think, looking at it, that the machine has painted something. One formulation of this, I suppose, is to add "| HumanUnderstanding(Y-as-labelled, Y-the-actual-target)" to everything

by pizza 5y ago

mjburgess 5y ago

Yes, I think that's largely right. What we require is a "bridge" from any particular feature space to the target. A compression of a feature space doesn't produce this bridge. When we look at a photo, its ink-space arragement can be bridged to its depicted situation via our ability to brigde. We see in the ink, a world. We do this via concepts which bridge. We get those by being in the world. We can "roughly accept" limited forms of inference which simply operate within a feature space, ie., we can be fooled by photos "made on photoshop" which appear to depict a situation which never really existed. A machine operates in "inkspace" and we rig it only ever to produce "human-bridgable" arragements of ink. The machine itself doesnt draw a situation -- as with an artist painting a room. A machine isnt in the room. It is, as-if it were in the room, and as if it had painted it. This illusion is created by us, we rig the system to average historical paintings of rooms, and only ever show the apes-at-home a good picture. They think, looking at it, that the machine has painted something. One formulation of this, I suppose, is to add "| HumanUnderstanding(Y-as-labelled, Y-the-actual-target)" to everything