Rough working notes, musing out loud.
Much effort in machine learning and AI research is focused on a few broad classes of problem. Three examples of such classes are:
Classifiers, which do things like classify images according to their category, generalizing from their training data so they can classify previously unseen data in the wild;
Generative models, which are exposed to data from some distribution (say, images of houses), and then build a new model which can generate images of houses not in the training distribution. In some very rough sense, such generative models are developing a theory of the underlying distribution, and then using that theory to generalize so they can produce new samples from the distribution;
Reinforcement learning, where an agent uses actions to explore some environment, and tries to learn a control policy to maximize expected reward.
These are old problem classes, going back to the 1970s or earlier, and each has seen tens of thousands of papers. Each of these problem classes is really beautiful: they’re hard, but not so hard it’s impossible to make progress; they’re precise enough that it’s possible to say clearly when progress is being made; they’re useful, and seem genuinely related to essential parts of the problem of AI.
I occasionally wonder, though, what’s the end game for these problem classes? For instance, what will it mean if, in some future world, we’re able to solve the classifier problem perfectly? How much would that help us achieve the goal of general artificial intelligence? What else would it let us achieve?
In other words, what happens if you skip over (say) the next few decades of progress in classifiers, or generative models, or reinforcement learning? And they become things you can just routinely do essentially perfectly, perhaps even part of some standard library, much as (say) sorting routines or random number generation can be regarded as largely solved problems today. What other problems then become either soluble, or at least tractable, which are intractable today?
Perfect solutions don’t obviously help, even with closely adjacent problems: One obvious point is that you can make a great deal of progress on one of these problems and it doesn’t necessarily help you all that much even with problems which seem closely adjacent.
For instance, suppose you can classify images perfectly.
That doesn’t necessarily mean that you can solve the image segmentation problem – identifying the different objects in some general image.
And even if you can solve the image segmentation problem for static images, that doesn’t mean you can solve it for video. I’ve watched (static) image segmentation algorithms run on video, and they can be remarkably unstable, with objects jumping in and out as we move from frame to frame. In other words, the identity of an object across frames is not obviously easy to track, even given perfect classifiers. For instance, something like one object obscuring another can cause considerable problems in making inferences about the identity of the objects in a scene.
AI-complete problems: The problem classes described above are in some sense very natural problems, the kind that would occur to anyone who thought about things like how humans recognize images, how they create new images, or how they play games. But you can ask a very different question, a much more top-down question, which is whether there is some class of problem which, if you could solve that, would enable you to build a genuinely artificially intelligent machine as a byproduct?
This notion is called AI-completeness (Wikipedia entry). According to Wikipedia the term was coined by the researcher Fanya Montalvo in the 1980s.
It’s interesting to read speculation about what problems would be AI-complete.
The classic Turing test may be viewed as an assertion that the problem of passing the Turing test – routinely winning the imitation game against competent humans – is AI-complete.
Another example which is sometimes given is the problem of machine translation. At first this seems ridiculous: the best machine translation services can now do a serviceable job translating many texts, and yet we’re very unlikely to be close to general artificial intelligence.
Of course, those services don’t yet do excellent translations. And some of the problems they face in order to do truly superb translations are very interesting.
For instance: very good translations of a novel or a poem may require the ability to track allusions, word-play, contrasts in mood, contrasts in character, and so on, across long stretches of text. It can require an understanding of quite a bit about the reader’s state of mind, and perhaps even very complex pieces of folk psychology – how the author thought the reader would think about the impact one character’s changing relationship with a second character would have on a third character. That sounds very complicated, but is utterly routine in fiction. Certainly, producing excellent translations is an extremely difficult problem which requires enormous amounts of understanding.
That said, I’m not sure machine translation is AI-complete. Even if a machine translation program did all those things, it’s not obvious you can take what is learned and use it to do other things. This is evident for certain tasks – learning to do machine translation, no matter how well, probably will only help a tiny bit with (say) robotics or machine vision. But I think it may be true even for problems which seem much more in-domain. For example, suppose your machine translation system can prepare first-rate translations of difficult math books. It might be argued that there is some sense in which they are truly understanding the mathematics. But even if that’s the case – and it’s not obvious – that understanding may be not be accessible in other ways.
To illustrate this point, let’s grant, for the sake of argument, that the putative perfect math-translation system really does understand mathematics deeply. Unfortunately, that doesn’t imply we can make use of that understanding to do other things. It doesn’t mean we can ask questions of the system. It doesn’t mean the system can prove theorems. And it doesn’t mean the system can conjecture new theorems, conjure up new definitions, and so on. Much of the relevant understanding of mathematics may well be available inside the system. But it doesn’t know how to utilize it. Now, it’s potentially the case that we can use some kind of transfer learning to make it significantly easier to solve those other problems. But that’d need to be established in any given context.
For these reasons, I’m skeptical that narrowly-scoped AI-complete problems exist.
A useful question: given the black-box ability to train a perfect classifier (or generative model or reinforcement learning system or [etc]), what other abilities would that give us? I am, I must admit, disappointed in my ability to give interesting answers to this question. Worth thinking more about.
The Turing Test as an assertion that the Imitation Game is AI-complete.
No narrowly-scoped problem can be AI-complete. The trouble is that if it’s narrowly scoped then while the system may in some sense have a deep internal understanding, that doesn’t mean that understanding can be used to solve other problems, even in closely-adjacent areas. Put another way: there is still a transfer learning problem, and it’s not at all obvious that problem will be easy. Put still another way: interface matters.