AI Expert Roadmap

As to Wikipedia, see the second paragraph. The sections where it mentions, eg., symbolic or sub-symbolic approaches are prefixed with “Researchers in the 1960s and the 1970s” or “By the 1980s”. Kind'a telling.

Like, my point is not about whether you can find the odd person trying to solve intelligence with grammars, or what were GOFAI conferences still harbour GOFAI research in the corners, my point is that a) these approaches don't work as a way to actually tackle AI, the problem, b) the vast majority of the field does not take them as seriously as a method of doing so, regardless of other uses, and c) therefore it's natural, not ‘impossible’, to gain AI expertise without having much care for those parts of the field.

> Further, I can certainly point you to successes of symbolic AI, which you say don't exist. For one thing, the entire fields of automated theorem proving, planning, search, game playing, knowledge representation and reasoning, etc. that you say are "not AI", but are like I say still active and still state of the art in their respective tasks.

Yes, but there's a reason I suffixed that comment with “(again, in an AI context)”. GOFAI is great if you ignore the last two letters of the name, and how it failed almost all its major promises.

These used to be considered AI because it was thought that you could build a useful reasoning agent out of a combination of these techniques, given appropriate developments. Now (almost) nobody does that; Google Map's pathfinding is just a pathfinder, not a general reasoner.

> Below I've defined the problem in the format expected by Louise

Right, OK, I figured it'd be something like this, when you said it was trivial, but this is just another perspective on my original criticism. You wrote the program you wanted it to generate as the background knowledge.

It must be so, because your examples don't specify, even roughly approximately, the program you wanted generated. Another valid solution would be (among many)

    ordered(A,B,C):-shorter(A,B),shorter(A,C).

> I should point out that there exists no neural net approach that can learn the same (or a similar) program from a single positive example- not least because neural nets cannot make use of background knowledge (i.e. a library of programs from which to build other programs).

This is not true, but as you wanted to prune, I'll leave it there.

This comment addresses your concerns about me writing the program I wanted Louise to generate. I like to see background knowledge ("BK", e.g. shorter/2) as a library of sub-programs from which the learner can select the ones necessary to compose a target program. The example above is trivial because I've defined a BK predicate that is necessary and sufficient to learn, so the learner was indeed served the solution "on a plate".

However, as I said in my previous comment, Louise can learn its own background knowledge. This can be done by predicate invention, or more simply, by incrementally learning necessary sub-programs.

Below is a problem definition and learning session that first learns length/2 (renamed llength/2 to avoid name clashes with the built-in) and shorter/2 from list and numeric function primitives, before using the learned predicates as BK for ordered/2. Like I say in my previous comment, it's a little larger than the previous one:

  ?- list_mil_problem([llength/2,shorter/2,ordered/3]).
  Positive examples
  -----------------
  llength([],0).
  llength([a],s(0)).
  shorter([a],[b,c]).
  shorter([1,2],[3,4,5]).
  ordered([a],[b,c],[d,e,f]).
  
  Negative examples
  -----------------
  :-ordered([a],[b],[c]).
  :-ordered([a,b,c],[a,b],[c]).
  
  Background knowledge
  --------------------
  tail/2:
  tail([A|B],B).
  
  p/2:
  p(s(A),A).
  
  s/2:
  s(A,s(A)).
  
  Metarules
  ---------
  abduce metarule 'P(X,Y)'.
  list_rec_func metarule 'P(x,y):- Q(x,z),R(y,u),P(z,u)'.
  list_comp metarule 'P(x,y):- Q(x,z), R(y,u), S(z,u)'.
  triadic_chain metarule 'P(x,z,y):- Q(x,z), R(z,y)'.
  true.

  ?- time(learn_dynamic([llength/2,shorter/2,ordered/3])).
  llength([],0).
  llength(A,B):-tail(A,C),p(B,D),llength(C,D).
  shorter(A,B):-llength(A,C),llength(B,D),s(C,D).
  ordered(A,B,C):-shorter(A,B),shorter(B,C).
  % 20,928 inferences, 0.000 CPU in 0.007 seconds (0% CPU, Infinite Lips)
  true.

I've added the full source of the experiment file for the learning task on pastebin. It includes a few more detailed comments and a set of constraints to clean up the learned hypothesis, mostly for aesthetic reasons:

https://pastebin.com/nPYFKKpx

Of course this is still a toy problem and we know the solution. But I hope it demonstrates the principle. On the other hand, you'd still not be able to solve this with alternative approaches, e.g. I see that the benchmark suite you pointed to is used for genetic programming. I'm also not aware of neural approaches that build programs incrementally, from a couple of examples of each sub-program.

That is to say, this is a toy problem for ILP. For other approaches it's unsolvable.

    positive_example(ordered/3,ordered(S1,S2,S3)):-
        member([S1,S2,S3],[[[a],[b,c],[d,e,f]]
                          ,[[a,b],[c,d],[e,f,g]]
                          ,[[a],[c],[e,f,g,h,i]]
                          ]
              ).

    negative_example(ordered/3,ordered(S1,S2,S3)):-
        member([S1,S2,S3],[[[a],[],[c]]
     ,[[a,b,c],[a,b],[c]]
     ]
              ).

    ordered([a,b],[c,d],[e,f,g]).
    ordered([a],[c],[e,f,g,h,i]).
    ordered(A,B,C):-shorter(A,B),shorter(B,C).

    shorter(A,B):-llength(A,C),llength(B,D),s(C,D).

I know the literature. It's my job.

>> And it's not just my opinion; there's a reason AI conference attendance has shot up a factor of 10 or so in the last few years, why NeurIPS is the leading one (and even historically GOFAI conferences are majority NNs), why the big AI labs with big AI cash are all doing NNs, and why all of a sudden AI is a popular topic outside academia.

That's still an opinion- "it's not just my opinion, everyone says so". A.k.a. "It is known", in Dothraki. And of course it is of no consequence who's spending money on what and who's submitting papers where. The volume of research was never a criterion for its quality. Heed thee well the legend of our Lord Geoff Hinton's years in the academic wilderness and how he emerged victorious with the laws of deep learning in his hands.

I think what you've said so far has convinced me you're expressing a personal opinion that is strongly held without a good reason to do so. You make sweeping statements with great certainty, but you don't really seem to know how you know the things you know, so you end up "knowing" some things that you don't really know. For instance, you claimed that "GOFAI" successes just "aren't there" but I listed a few, like Deep Blue or MYCIN - and you didn't seem to have heard of these before (I'm more surprised about not knowning of Deep Blue than MYCIN).

You also claim that "these approaches are not AI". That's a "No True Scottsman" right there. Except there really is no True Scottsman (i.e. "AI" in the sense you use it)- ask Yoshua Bengio:

Bengio: In terms of how much progress we’ve made in this work over the last two decades: I don’t think we’re anywhere close today to the level of intelligence of a two-year-old child. But maybe we have algorithms that are equivalent to lower animals, for perception. And we’re gradually climbing this ladder in terms of tools that allow an entity to explore its environment.

Spectrum: Will any of these ideas be used in the real world anytime soon?

Bengio: No. This is all very basic research using toy problems. That’s fine, that’s where we’re at. We can debug these ideas, move on to new hypotheses. This is not ready for industry tomorrow morning.

https://spectrum.ieee.org/tech-talk/artificial-intelligence/...

Or, you know, ask any AI researcher :)

Edit: Which "GOFAI" conferences are majority NNs? What period are we talking about?