| >> AI as a field relied mostly on 'understanding' based approaches for 50 years
without much success. These approaches were too brittle and ungrounded. Why
return to something that doesn't work? To begin with, because they do work and much better than the new approaches in
a range of domains. For example, classical planners, automated theorem provers
and SAT solvers are still state-of-the-art for their respective problem
domains. Statistical techniques can not do any of those things very well, if
at all. Further, because the newer techniques have proven to also be brittle in their
own way. Older techniques were "brittle in the sense that they didn't deal
with uncertainty very well. Modern techniques are "brittle" because they are
incapable of extrapolating from their training data. For example see the
"elephant in the room" paper [1] or anything about adversarial examples
regarding the brittleness of computer vision (probably the biggest success in
modern statistical machine learning). Finally, AI as a field did not rely on "understanding based approaches for 50
years"; there is no formal definition of "understanding" in the context of AI.
A large part of Good, Old-Fashioned AI studied reasoning, which is to say,
inference over rules expressed in a logic language, e.g. this was the approach
exemplified by expert systems. Another large avenue of research was that on
knowledge representation. And of course, machine learning itself was part of
the field from its very early days, having been named by Arthur Samuel in
1959. Neural networks themselves are positively ancient: the "artifical
neuron" was first described in 1938, by Pitts & McCulloch, many years before
"artificial intelligence" was even coined by John McCarthy (and at the time it
was a propositional-logic based circuit and nothing to do with gradient
optimisation). In general, all those obsolete dinosaurs of GOFAI could do things that modern
systems cannot - for instance, deep neural nets are unrivalled classifiers but
cannot do reasoning. Conversely, logic-based AI of the '70s and '80s excelled
in formal reasoning. It seems that we have "progressed" by throwing out all
the progress of earlier times. ____________ [1] https://arxiv.org/abs/1808.03305 P.S. Image, speech and text generation are cute, but a very poor measure for
the progress of the field. There are not even good metrics for them so even
saying that deep neural nets can "generate surprisingly good text" doesn't
really say anything. What is "surprisingly good text"? Surprising, for whom?
Good, according to what? etc. GOFAI folk were often accused of wastig time
with "toy" problems, but what exactly is text generation if not a "toy
problem" and a total waste of time? |