Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

(YMMV)

The currently trendy "AI" looks more like massive data mining with powerful ML to me, that's very good for certain tasks but brings us nowhere near real AI. The knowledge representation problem has not yet been solved. In order to get even just a convincing simulation of AI, let alone real AI, we need a large common sense knowledge base / computational ontology.

One perhaps promising approach are geometric meaning theories/concept representations that allow for logical combinations. For example, Diederik Aerts works in this area. However, to be honest, I don't have the math skills to evaluate his approach. Generally speaking, logical modelling is too limited for good concept representations - especially classical 90s AI like in default reasoning and other nonmonotonic logics -, whereas traditionally geometric representations suffer from problems with representing logical inference and quantification. IMHO, that's a problem worth looking at. (Admittedly, I'm a bit biased towards symbolic AI like the people in this article.)

I was very pleasantly surprised this year to see the Symbolic Machine Learning course at Imperial College London well-attended by about 30 students each session. The course basically teaches the principles and practice of Inductive Logic Programming, a set of algorithms and techniques that learn logic programs from relational data [1]. It is an optional course at Master's level so I was originally expecting to see maybe half a dozen people attending (I assisted with a couple of the sessions and followed the rest to brush up on my background).

I discussed my surprise with my supervisor who is one of the tutors on the course. His opinion was that logic-based AI is much closer to the material that most Computer Science students are familiar with, than statistical and probabilistic AI. It's true that, although many CS courses have started to include statistics and probabilities in their curriculum (the one at Imperial sure does) the mainstay of computer science are logic-based, discrete maths, complexity and computation theory and language theory. The jump from there to GOFAI is just a short hop; in many ways, the two fields are really one subject.

My intuition is that the boom in deep learning is partially being fed by disciples of fields well outside CS -maths, physics, bio-sciences etc- jumping on to the, well, bandwagon, motivated by the deep learning frenzy in industry and not so much by any interest in AI or even computers. At the same time, there is plenty of traditional CS talent waiting in the wings. It may be that a revisiting of the good old ways of knowledge representation and inference may not be completely out of the question [2].

Of course, there's always the chance that the students only came to the course because it had "machine learning" in its name :)

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[1] More the point, both of the outputs and inputs of ILP are logic programs, i.e. binary relations. Most of the work uses Prolog but there also exist techniques to learn Answer Set Progarmming, Constraint Logic Progarmming and of course Probabilistic Logic Programming programs. In any case, the homoiconicity of logic programming languages allows a newly learned hypothesis to be immediately reused as data to learn a new concept, in a way that is probably impossible with statistical techniques. And of course, learning is extremly sample efficient and the resulting programs generalise vey well.

I bet you had never heard of any of this before now :)

[2] To be honest, my guess is that the way forward is a combination of the two approaches- perhaps, some monster combo of deep learning for sensory perception, logic for inference and probabilities for final decision making.

One way or another, I expect the AI researcher of the future will have to be a jack of all trades - and master of all of them at once. Learn to juggle.