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I remember an AI researcher (I forget who) recently said something to the effect that early AI research produced all sorts of planning algorithms. E.g. the top

I remember an AI researcher (I forget who) recently said something to the effect that early AI research produced all sorts of planning algorithms. E.g. the top down camp of AI. But they weren't capable of working with the real world because we didn't have very good low level perception. E.g. this complicated algorithm for planning the robot's actions, but it depended on getting input about where objects are. Now we have decent low level perception from the bottom-up camp of AI, but they are limited by a lack of high level stuff like planning and reasoning. But you are right that there is no obvious way to just combine these wildly different algorithms without lots of human guidance.

malkia 12y ago
Well, it's basically the same with me and reading math expressions filled with symbols out of nowhere, and you need to know the context, history and how they are used... If someone translated this to me as lisp/scheme or some other form of symbols, with full-meaningful names I might have better idea of what's going on. Okay, not exactly the same... but won't mind AI algorithm that sorts it out for me.
TylerJay 12y ago
I think you hit the nail on the head here. That's one of the most interesting parts of thinking about GAI for me. Which parts will end up being top-down and which parts will end up being bottom-up? And even if we have evidence that a certain part is TD or BU in humans, do we even want machine intelligence to work the same way? The article says something to the effect of "no matter how much you advance this strategy, you never get a toddler out of it." And that makes sense because, presumably, certain parts of the human brain exercise some sort of top-down control over the sensory-data-processing and other parts. For example, it seems like the human mind is built to see things as things. Does the human mind reallY start off seeing "pixels" and then learn by itself to think of the word as solid, whole objects instead of collections of similarly-colored photons/pixels or atoms? It seems like this is a universal use-case and it would make sense if our tendency to see the world in terms of "things" instead of patches of color is built-in (gestalt psychology seems to suggest this as well). It sounds like the AI in the article starts off from pixels and then builds up some sort of model of blocks, the ball, paddle, game physics, etc, (but then again, maybe it doesn't have those models at all and is just doing statistical analysis on patterns of pixels). Either way, it likely doesn't have any higher, context-independent model of objects/things like humans do. I suspect this may be one of the hurdles in transfer learning. Humans think of objects as having certain properties. When other objects in other contexts appear to have similar properties, we guess that they may have other properties in common which gives at least a rough model of the new object. So I guess what I'm trying to say is: Humans have hierarchical models of the world that let us think separately about patterns of light, atoms/molecules, whole physical objects/things, systems, etc. They are all first-class citizens and we ascribe properties to each of them. We already have a rough-model of anything at the same level, but a different context, and with similar-enough properties to something we already know. It seems to me like this is fundamentally connected to humans' ability to do transfer-learning. Could this effect be achieved through bottom-up algorithms, or are we going to have to figure out some top-down way of developing transferrable, generalizable, hierarchical models?

malkia 12y ago

Well, it's basically the same with me and reading math expressions filled with symbols out of nowhere, and you need to know the context, history and how they are used... If someone translated this to me as lisp/scheme or some other form of symbols, with full-meaningful names I might have better idea of what's going on. Okay, not exactly the same... but won't mind AI algorithm that sorts it out for me.

TylerJay 12y ago

I think you hit the nail on the head here. That's one of the most interesting parts of thinking about GAI for me. Which parts will end up being top-down and which parts will end up being bottom-up? And even if we have evidence that a certain part is TD or BU in humans, do we even want machine intelligence to work the same way? The article says something to the effect of "no matter how much you advance this strategy, you never get a toddler out of it." And that makes sense because, presumably, certain parts of the human brain exercise some sort of top-down control over the sensory-data-processing and other parts. For example, it seems like the human mind is built to see things as things. Does the human mind reallY start off seeing "pixels" and then learn by itself to think of the word as solid, whole objects instead of collections of similarly-colored photons/pixels or atoms? It seems like this is a universal use-case and it would make sense if our tendency to see the world in terms of "things" instead of patches of color is built-in (gestalt psychology seems to suggest this as well). It sounds like the AI in the article starts off from pixels and then builds up some sort of model of blocks, the ball, paddle, game physics, etc, (but then again, maybe it doesn't have those models at all and is just doing statistical analysis on patterns of pixels). Either way, it likely doesn't have any higher, context-independent model of objects/things like humans do. I suspect this may be one of the hurdles in transfer learning. Humans think of objects as having certain properties. When other objects in other contexts appear to have similar properties, we guess that they may have other properties in common which gives at least a rough model of the new object. So I guess what I'm trying to say is: Humans have hierarchical models of the world that let us think separately about patterns of light, atoms/molecules, whole physical objects/things, systems, etc. They are all first-class citizens and we ascribe properties to each of them. We already have a rough-model of anything at the same level, but a different context, and with similar-enough properties to something we already know. It seems to me like this is fundamentally connected to humans' ability to do transfer-learning. Could this effect be achieved through bottom-up algorithms, or are we going to have to figure out some top-down way of developing transferrable, generalizable, hierarchical models?