My experience with applied machine learning is strictly undergraduate-level modulo a little tinkering and a little industry experience, so these impressions might be quite unlike those of an actual specialist, but my sense is that while it comes up with a lot of interesting stuff that might potentially be useful in making a hypothetical AGI, it ultimately isn’t that interested in generalizing outside domain-specific approaches and that limits its bandwidth to a large extent.
Machine-learning algorithms are treated as—not exactly a black box, but pretty well distinguished from the task-level inputs and outputs. For example, you might have a pretty clever neural-network variation that no one’s ever used before, but most of the actual work in the associated project is probably going to go into highly specialized preprocessing to render down inputs into an easily digestible form. And that’s going to do you exactly no good at all if you want to use the same technique on a different class of inputs.
(This can be a little irritating for non-AI people too, by the way. An old coworker of mine has a lengthy rant about how all the dominant algorithms for a particular application permute the inputs in all kinds of fantastically clever ways but then end with “and then we feed it into a neural network”.)
I would agree that the specific applications that machine learning generally pursues are useless for general AI, but the general theory that they develop and use (e.g. probabilistic networks, support vector machines, various clustering techniques, etc. etc....) seems like something that AGI would eventually be built on. Of course, the narrow applications get more funding than the general theory, but that’s how it always is. My knowledge/experience of ML is probably even less than yours, though.
I have this (OpenCog-influenced) mental image of a superintelligent AGI equipped with a huge arsenal of various reasoning and analysis techniques, and when it encounters a novel problem which it doesn’t know how to solve, it’ll just throw everything it has on it (prioritizing techniques that have worked on similar problems before) until it starts making progress. (For an “artistic” depiction of the same, see “AI thought process visualization, part II” here.) The hard part of such an AGI would then be mostly in finding a good data format that could efficiently represent the outputs of all those different thought mechanisms, and to balance the interactions of various modules together. (I have no idea of how realistic this vision is, and even less of an idea about how to make such an AGI Friendly.)
I think it’s largely true: the narrow AI “arsenal” currently being developed often comes up with results that seem to be transferable between fields. For example, there is a recent paper that applies the same novel strategy both for image understanding and natural language sentence parsing, both with success. Although you often need lots of tinkering to get state-of-art results, producing the same quality just using a general method without any parameters seems to make a good paper.
And while the problem of how to build an AGI is not directly solved by these, we certainly get closer to it using them. (You still need a module to recognize/imagine/process visual data, unless the solution is something really abstract like AIXI...)
My experience with applied machine learning is strictly undergraduate-level modulo a little tinkering and a little industry experience, so these impressions might be quite unlike those of an actual specialist, but my sense is that while it comes up with a lot of interesting stuff that might potentially be useful in making a hypothetical AGI, it ultimately isn’t that interested in generalizing outside domain-specific approaches and that limits its bandwidth to a large extent.
Machine-learning algorithms are treated as—not exactly a black box, but pretty well distinguished from the task-level inputs and outputs. For example, you might have a pretty clever neural-network variation that no one’s ever used before, but most of the actual work in the associated project is probably going to go into highly specialized preprocessing to render down inputs into an easily digestible form. And that’s going to do you exactly no good at all if you want to use the same technique on a different class of inputs.
(This can be a little irritating for non-AI people too, by the way. An old coworker of mine has a lengthy rant about how all the dominant algorithms for a particular application permute the inputs in all kinds of fantastically clever ways but then end with “and then we feed it into a neural network”.)
I would agree that the specific applications that machine learning generally pursues are useless for general AI, but the general theory that they develop and use (e.g. probabilistic networks, support vector machines, various clustering techniques, etc. etc....) seems like something that AGI would eventually be built on. Of course, the narrow applications get more funding than the general theory, but that’s how it always is. My knowledge/experience of ML is probably even less than yours, though.
I have this (OpenCog-influenced) mental image of a superintelligent AGI equipped with a huge arsenal of various reasoning and analysis techniques, and when it encounters a novel problem which it doesn’t know how to solve, it’ll just throw everything it has on it (prioritizing techniques that have worked on similar problems before) until it starts making progress. (For an “artistic” depiction of the same, see “AI thought process visualization, part II” here.) The hard part of such an AGI would then be mostly in finding a good data format that could efficiently represent the outputs of all those different thought mechanisms, and to balance the interactions of various modules together. (I have no idea of how realistic this vision is, and even less of an idea about how to make such an AGI Friendly.)
I think it’s largely true: the narrow AI “arsenal” currently being developed often comes up with results that seem to be transferable between fields. For example, there is a recent paper that applies the same novel strategy both for image understanding and natural language sentence parsing, both with success. Although you often need lots of tinkering to get state-of-art results, producing the same quality just using a general method without any parameters seems to make a good paper.
And while the problem of how to build an AGI is not directly solved by these, we certainly get closer to it using them. (You still need a module to recognize/imagine/process visual data, unless the solution is something really abstract like AIXI...)