Thank you for a level-headed and well thought-out response! I'm glad to see that
our communication isn't "ratcheting" to more and more forceful forms.

Now. I think the root of our disagreement was the necessity or not of AGI for
various kinds of program synthesis, which I think we 've probably pared down to
program synthesis from an informal specification, particularly a natural
language one.

I don't agree that AGI is necessary for that. I think that, as many AI tasks,
such a complete and uncompromising solution can be avoided and a more
problem-specific solution found instead.

In fact, we already had almost a full solution to the problem in 1968, with
SHRDLU [1], a program that simulated a robotic hand directed by a human user in
natural language to grasp and rearrange objects. This was in the strict confines
of a "blocks world" but its capability, of interpreting its user's intent and
translating it accurately to actions in its well-delineated domain remains
unsurpassed [2]. Such a natural language interface could well be implemented
for programming in bounded domains, for example to control machinery or run
database queries etc. This capability remains largely unexploited, because the
trends in AI have shifted and everybody is doing something else now. That's a
wider-ranging conversation though. My main point is that a fully-intelligent
machine is not necessary to communicate with a human in order to carry out
useful tasks. A machine that can correctly interpret statements in a subset of
natural language suffices. This is technology we could be using right now, only
nobody has the, let's say, political will to develop it because it's seen as
parochial, despite the fact that its capabilities are beyond the capabilities of
modern systems.

As to the other kind of informal specifications, by examples, or by
demonstration, what you say, that it won't scale etc, is not true. I mentioned
earlier one-shot learning of recursive hypotheses without an example of the base
case [3]. To clarify, the reason why this is an important capability is that
recursive programs can be much more compact than non-recursive ones and still
represent large sets of instances, even infinite sets of instances. In fact, for
some program learning problems, only recursive solutions can be complete
solutions (this is the case for arithmetic and grammar learning for example).
The ability to learn such solutions from a single example I think should
conclusively address concerns about scaling.

To be fair, this is a capability that was only recently achieved by Inductive
Logic Programming (ILP) systems, a form of logic program synthesis (i.e.
synthesis where the target language is a logic programming language, usually
Prolog, but not necessarily). The Gulwani survey I linked mentions this recent
advance in passing only. But ILP systems in general have excellent sample
complexities and can routinely generalise robustly from a handful of examples
(in the single digits), and have been doing this since the 1990's.

The cost of searching a large hypothesis space is, indeed, an issue. There are
ways around it however. Here, I have to tout my on horn and mention that my
doctoral research is exactly about logic program learning without search. I'd go
as far as to say that what's really been keeping program synthesis back is the
ubiquity of search-based approaches, and that program synthesis will be solved
conclusively only when we can construct arbitrary programs without searching. But you
can file that under "original research" (I mean, that's literally a description
of my job). Anyway, no AGI is needed for all of this.

So, I actually understand your earlier skepticism, along the lines of "if all
this is true, why haven't I heard of it?" (well, you said why do you still have
a job but it's more or less the same thing). The answer remains: because it's
not the trendy stuff. Trends drive both industry and research directions. Right
now the trend is for deep learning, so you won't hear about different approaches
to program synthesis, or even program synthesis in general. There's nothing to
do about it. Personally, I just grin and bear it.

_________________

[1] https://en.wikipedia.org/wiki/SHRDLU

[2] For example, in the demonstration conversation quoted by wikipedia, note the
"support supports support" sentence.

[3] This is already growing too long so I won't include examples, but let me
know if you're curious for some.