I'm happy to see we're still in healthy disagreement. However, I have to
apologise for confusing you by describing my field as "program learning" which
is admittedly vague, but I didn't want to go into the particulars. My field is
not program synthesis, which is constructing programs from complete, formal
specifications. Rather, it's Inductive Programming and more specifically Inductive
Logic Programming (ILP), which is learning programs from examples, i.e.
"incomplete specifications". I'm not familiar with the General Program
Synthesis Benchmark Suite, but the problem you list (test three strings are
ordered by length) is trivial for ILP approaches. Again, I don't want to point
to my own research, directly (I'm going through a bit of a modesty phase) (oh,
alright, it's just that the documentation of my project is crap). However, I
have to say that even so, if something is a difficult problem for program
synthesis approaches, then it's very unlikely that neural networks will do any
better at it. For instance, do you know how well deep neural nets perform on
this benchmark? I can't find any results with a quick search.

You make the point that one does not need to learn these "obsolete" AI
approaches because they are not relevant anymore. I don't understand why you
say that. These approaches are still state of the art for their respective
tasks and there is no other approach that has been shown to do any better,
including deep neural networks. In what sense are they "no longer more than
briefly and tangentially relevant" as you say?

Regarding the gold rush, the point of the analogy is that in a gold rush only
a very few people will ever strike gold. This is exactly the state of research
into deep learning currently. After a few initial big breakthroughs, like CNNs
and LSTMs, progress has stalled and the vast, vast majority of published
papers (or papers put on arxiv permanently) present incremental results, if
that. Literally thousands of deep learning papers are published each month and
the chance to have an impact is miniscule. From my point of view, as a
researcher, going into deep learning right now would be career suicide. Not to
mention that, while the first few successes were achieved by small academic
teams, who had typical academic motives (er, glory), the game has now passed
to the hands of big corporate teams that have quite different incentives, so
it's almost impossible for small teams or individual researchers to make a
dent.

As to the winter and whether GOFAI works, perhaps I haven't convinced you with
my sources, but in that case, I have to go back to my earlier question and ask
where your knowledge comes from. You clearly have a strong opinion on GOFAI
and the AI winter of the '80s, but what knowledge does this opinion come from?
Can you say? And if this sounds like a challenge, well, that's because it is.
I'm challenging you to re-examine the basis of your convictions, if you like.
Because to me, they sound like they are not well-founded and that you should
put some water in your wine. The things you say "don't work", work and the
things you say work, don't work as well as you say.

For my part, I certainly agree that GPT-3 or the next iteration of a large
transformer-built language model can be a useful tool, but such a tool will
always be limited by the fact that it's, well, a language model, and it can
only do what language models do, which does not include e.g. the ability for
reasoning (despite big claims to the contrary) or arithmetic (ditto) or
generation of novel programs. For instance, the append() example you
show above is clearly memorised: you haven't given the model any examples of
append(), so it can't possibly learn its definition from examples. It only
returns a correct result because it's seen the results of append() before. Not
the same result, but close enough. Like I say, this ability can definitely
be useful- but its usefulness is limited compared to the ability to learn
arbitrary programs, never before seen.

btw, why do you need to give it the list "a"? What happens if this is ommitted
from the prompt?