Re:Is There a Smarter Path to Artificial Intelligence? Some ...
by YeGoblynQueenne on Thursday June 21, 2018 @06:11AM
(#17363605)
>> If the reach of deep learning is limited, too much money and too many fine
minds may now be devoted to it, said Oren Etzioni, chief executive of the
Allen Institute for Artificial Intelligence. “We run the risk of missing other
important concepts and paths to advancing A.I.,” he said.
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 :)
__________________
[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.
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 :)
__________________
[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.