Benjamin,
Thankyou for another really constructive response - and I think that I, at
any rate, am really starting to get somewhere. I didn't quite get to the nub
of things with my last post. I think I can do a better job this time &
develop the argument still more fully later.
Why are those ideas not crux ideas - those schools of programming not true
AGI? You almost hit the nail on the head with:
"My point is, however, that general purpose reasoning is possible -
I think there are plenty of signs of how it might actually work."
i.e. none of those approaches actually show true "general purpose
reasoning," you only hope and believe that some new ones will in the future
(and have some good suggestions about how).
What all those schools lack, to be a bit more precise, is an explicit
"generalizing procedure" - let's call it a "real world generalization
procedure." They don't tell you directly how they are going to generalize
across domains - how, having learnt one skill, they can move on to another.
The GA's, if I've understood, didn't generalize their skills - didn't
recognize that they could adapt their walking skills to water - their
minders did. An explicit real-world generalization procedure must tell you
how the system itself is going to recognize an unfamiliar domain as related
to the familiar one(s). How the lego construction system will recognize
irregular-shaped rocks as belonging to a larger class that includes the lego
bricks. How Ben's pet who, say, knows all about navigating neat, flat office
buildings will be able to recognize a very different, messy bomb site or
rocky hillside as nevertheless all examples of navigable.terrains. How a
football playing robot will recognize other games such as rugby, hockey etc
as examples of "ball games [I may be able to play]". How in other words the
AGI system will recognize unfamiliar (and not obviously classifiable)
problems as having something in common with familiar ones. And how those
systems will have general ways of adapting their skills/ solutions. How the
lego system will adapt its bricklaying movements to form rocklaying
movements, or the soccer player will adapt its arm and leg movements to
rugby.
I think you'll find that all the schools of programming only wave at this...
they don't offer you an explicit method. I'll take a bet, for example, that
Ben G cannot provide you with even a virtual world generalization procedure.
The AGI systems/agents, it must be stressed, have to be able to recognize
*independently* that they can move on to new domains - even though they will
of course also need to seek help to learn the rules etc, as we humans do.
And I think it's clear, if only in a very broad way, how the human mind
achieves this (which I'll expound in more detail another time) - it has what
you could call a "general activity language" - and learns every skill *as an
example of a general activity*. Every particular skill is learned in a
broad, general way in terms of concepts that can be and are applied to all
skills/ activities (as well as more skill-specific terminology). Very
general, "modular" concepts.
But such human powers of generalization are still way, way beyond current
computers. The human mind's ability to cross domains is dependent on the
ability, for example, to generalize from something as concrete as "taking
steps across a field" to something as abstract as "taking steps to solve a
problem in philosophy or formal logic".
And the reason that I classify all this as *real world* generalization is
that it cannot be achieved by logic or mathematics, which is what all the
schools you mention depend on, (no?) They can't help you classify the bricks
and rocks as alike, or rugby as like football, or a rocky bomb site as like
an office floor, let alone steps across a field as like steps in an
argument. They can only be brought into play *after* you've classified the
real world and predigested its irregularities into nice neat regular units
that they can operate on. That initial classification/ generalization
requires the general skill that is still beyond all AGI's - and actually, I
think, doesn't even have a name.
bENJAMIN: mt:>> I think your approach here *is* representative - &, as you
indicate,
the details of different approaches to AGI in this discussion, aren't
that important. What is common IMO to your and the thinking of others
here is that you all start by asking yourselves : what kinds of
programming will solve AGI? Because programming is what interests you
most and is your life.
Actually, that isn't necessarily accurate. I'm currently collaborating
with a cognitive scientist, and I've seen other people here hint at
drawing their own inspiration from cognitive science and other
non-programming disciplines.
I reason the problem like this:
1. I know intelligence is possible, by looking at the animal kingdom.
2. I don't believe that the animal kingdom is doing something that is
formally uncomputable (i.e., intelligence is computable).
3. I can see the things that intelligence can do, and have ideas about how
it may work.
4. I recognize that biological computing machinery is vastly different to
artificial computing machinery.
5. I assume that it is possible to build intelligence on current
artificial computing machinery (i.e., intelligence is computable on
current computers)
5. So, my goal is to translate those ideas about intelligence to the
hardware that we have available.
Programming comes into it not because we are obsessed with programming,
but because we have to make do with the computing machinery that is
available to us. We're attempting to exploit the strengths of computing
machinery (such as its ability to do fast search and precise logical
deduction) to make up for the weaknesses of the machinery (such as the
difficulty in analogizing or associative learning). I don't believe there
is only one path to intelligence, and we must be very conscious of the
platform that we are building on.
What you have to do in order to produce a true, crux idea, I suggest, is
not just define your approach but APPLY IT TO A PROBLEM EXAMPLE OR TWO of
general intelligence - show how it might actually work.
Well, that is what many of us are doing. We have these plausible crux
ideas, and we're now attempting to apply it to problems of general
intelligence. It takes time to build systems, and the more ambitious the
demonstration the longer it takes to build. I have my own challenge
problems in the pipeline (I have to start very small, and have been using
the commonsense problem page*), and I know most serious groups involved in
system building have their own problems too.
* http://www-formal.stanford.edu/leora/commonsense/
I've mentioned Semantic Web reasoning and General Game Playing. Even
something like the Weka toolkit could be seen as a kind of general
intelligence - you can run their machine learning algorithms on any kind
of dataset and it will discover novel patterns. I admit that those are
weak examples from an AGI perspective because they are purely symbolic
domains, but it seems that AGI comes in where those kind of examples end.
My point is, however, that general purpose reasoning is possible - I think
there are plenty of signs of how it might actually work.
You have to show how, for example, your GA might enable your
lego-constructing system to solve an unfamiliar problem about building a
dam of rocks in water. You must show that even though it had only learned
about regularly-shaped bricks, it could neverthless recognize
irregularly-shaped rocks as, say, "building blocks"; and even though it
had only learned to build on solid ground, it could nevertheless proceed
to build on ground submerged in water. [I think BTW, when you try to do
this, you will find that GA's *won't* work]
Why not?
Genetic algorithms have been used in robots that learn how to move. You
can connect a GA up to a set of motors and set up the algorithm so that
movement is rewarded. Attach the motors to legs and put it on land, and
the robot will eventually learn that walking maximizes its goals. Put the
motors into fins and a tail and put it in water, and the robot will
eventually learn that swimming maximizes its goals. Isn't this a perfect
example of how GAs can problem-solve across domains?
Or to address your specific (but more challenging) problem directly...
Lets say, instead, that we're using GAs to generate high level strategies,
plans and reasoning... the GA may evolve, on land, some wall-building
strategies:
1. Start with the base
2. Put lego blocks on top of other lego blocks
3. Make sure lego blocks are stacked at an even height
4. Make sure there are no gaps
When we give the robot the goal of building a dam, and it may then take
those existing strategies and evolve generalizations:
Here's one:
1. Start with the base
2. Put things on top of other things
3. Make sure things are stacked at an even height
4. Make sure there are no gaps
This could happen by a cross-over or mutation that generalizes categories
(Lego block -> Thing) -- and it may be the case that an AGI-optimized GA
would have a bias towards generalization and specialization mutations
because they are so useful in problem solving.
As for the recognition of irregular objects as building blocks, again, I
see no reason that genetic algorithms could not evolve classification or
categorization routines: the system would take low-level features supplied
by the vision processing code and evolve classifiers of interesting
objects in the vicinity.
In RoboCup, this process of learning (rather than hard-coding)
categorization is of interest to many groups. The group that I share a lab
with recently abstracted their object categorization code so that the
transformation of low-level visual features to high-level categories is
performed by a semantic web reasoner. The system would let you throw a
different color or shape ball on the field, and the robot would chase the
new ball simply after changing the ball declaration in the ontology. OWL
is a (fairly) general language, it seems reasonable to claim that it would
be possible to set up another version of the system where the declarations
in the ontology itself can be evolved with a GA (a GA searches strings in
a language to find an optimal solution, so surely it could search for OWL
statements that provide categorizations that maximize goal scoring).
You don't just have to tell me in general terms what your programming
approach can do, you have to apply it to specific true AGI END-PROBLEMS -
and invite additional tests.
I suggest you look again at any of the approaches you mention, as
formally outlined, and I suggest you will not find a single one, that is
actually applied to an end-problem, to a true test of its AGI
domain-crossing potential.
I thought I had already provided evidence that many approaches could
succeed on an "end-problem". Particularly in the sections on logic and
hybrid systems.
And I think if you go through the archives here you also won't find a
single attempt in relevant discussions to do likewise. On the contrary,
end-problems are shunned like the plague.
I don't believe end-problems are "shunned like the plague" - in fact, I
think it is the opposite case. We have all have our long term challenge
problems, but we also have pragmatic constraints: time, resources,
knowledge, algorithms that prevent us from being overly ambitious.
For those of us who are research students or professional researchers, we
know that in order to be taken seriously in academic circles we must find
ways of evaluting our claims: some work can appeal to computational or
mathematical arguments, but others, like my own, are faced with the
problem that there is no objective measurable definition of intelligence
and there's unlikely to be one anytime soon. This means that evaluation on
challenge problems actually plays an extremely crucial and important part
of our research programs.
Why are challenge problems not discussed much on this mailing list? I can
think of some examples of people who have discussed their goals, and when
I read behind the lines of work in the area I do see that people have
their ideas about what their aiming for, so I think you're being
pessimistic if you think it isn't discussed at all.
However, I can also think of good reasons why specific end goals aren't
discussed that much...
If I were to jump up and say I'm addressing some grand problem far beyond
the current state-of-the-art not only is this head-in-the-clouds dreaming,
there is a danger of coming across as a "kook". You have to be realistic.
Similarly, if I were to discuss "toy" problems, I would be dismissed as
thinking too small (even if I have a coherent idea about why the toy
problem is a crucial first step).
If I were to present a medium-sized problem, then it will (by necessity)
have flaws and holes that will be attacked for not being general enough. I
suspect that discussing research in the context of these challenge
problems may be better received in more significant publications like
theses, books or journal articles where you have time to lay out a
coherent argument and discuss the limitations.
Furthermore, an argument could be made for having a vision, but leaving
the specific end-problem until AFTER the system building. Exams are rarely
given out before the test - because if teachers were to hand out the exam
then students would only memorize the answers. Similarly an given formal
measure of intelligence could be easily gamed by an AGI builder. When you
proposed a "simple mathematical test of cog sci", many people responded by
pointing out that if you've got a clear mathematical definition of
intelligence, it really isn't hard to optimize for that specific
definition. For example, I suspect you could use something like a
randomized L-system - it could create aesthetically pleasing diagrams for
the imagination3 website that you would swear were highly original and
creative (themed and patterned in parts, but neither entirely random nor
entirely structured) if you had never seen an L-system before.
-Benjamin Johnston
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