Sorry about the meta-soliloquy, but to clarify.

I started out by saying that you have to test your ideas about AGI and then
you have to accept the results of your tests. But I realized those tests
cannot be fully defined in any detail beforehand. So using the example of
my planned project, I pointed out that if 5 months goes by and I did not
even start it, then that would be an indication that I did not really have
it figured out very well (unless there was a dramatic change in my life
which prevented me from working on it.) And if I started in on the project
but was not able to show that it even seemed to work after a year then that
would be an indication that I did not have it figured out. Yes you should
give an experimenter some leeway, but if he keeps kicking the can down the
road year after year then questions about the credibility about his claims
cannot be ignored. I also recognized that going through a trial and error
method of testing functions that you think would be needed is an important
step of the scientific method. I was just saying that you also need to test
simplified models of the essential qualities of the proposed project as
those kinds of tests became feasible. This is also an essential part of the
scientific method.  I pointed out that the program that I am going to work
on would be limited because AGI is something that is still on the frontier
of science, and I mentioned that some people would not be impressed with
anything that was so limited.  But other enthusiasts might be interested if
you could actually get your program to do something that seemed intelligent
near to or beyond what other people have already done.

I gave my planned project a classification label. I called it AGi where the
lower case "i" represented the idea that it would be limited.  I realized
that I needed to define some ways that I could actually define the results
of my experiments since I was saying that you had to accept the results of
your experiments.  I don't think that anything is absolutely falsifiable
but I do believe that there are ways we can gather empirical evidence on
how well our ideas work if we are willing to make that effort.  I have made
the claim that my program should be able to learn a rudimentary human-like
language.  This would be pretty easy for some enthusiasts to accept if it
actually occurred.  As I thought about what kind of tests I could use to
collect evidence if I was on the right track (or on a good track) I came up
with an insight about language that I now recognize is an objective which I
think is obviously correlated with some of the fundamental qualities of
intelligence.  I have often said that concepts can play different roles in
combination with other concepts. This theory can be extended to the use of
word-based representations of concepts.  I then thought of the simple
language (that I would want my program to learn) in terms of creating
relations between words as if this were occurring in a kind of simple
database program.  I realized that a very simple database instruction
language would not qualify as a simplified human-like language because
words in human languages play different kinds of roles.  For example, in a
simple database instruction language you might create a classification for
a record and then create definitions for each of the fields used in the
record.  With a human language your words can, to give a simple and obvious
example, be used in sentences that instruct someone to make a new category
and a new definition of a record and so on.  So human languages include the
ability to create new codes that can actually be used to motivate someone
to change the way he thinks about something.  I felt that this idea might
work as a definition for a minimal human-like language.  So I came up with
the insight that words should be able to invoke new ways of defining and
classifying the operations of a database on the collection of ideas that it
learned about.  For example, the language should not be defined to rely
only on special instruction keywords to define a new category of records
(in this database metaphor).  Words should be able to invoke procedural
modifications as well as simply add declarative values. So the same words
(or phrases or sentences) which have a declarative value can be used to
invoke a procedural action without relying on a pre-designated keyword that
is only used to represent an operation of the database.

There was some discussion about the difference between declarative and
procedural knowledge in the old days of AI, but I suspect that the concept
became bogged down in the traditional model of computer programming which
makes strong distinctions between data that refers to a procedure and data
that refers to a function call.

So I defined an objective which may be tested (or at least examined if I
start testing the AGi program I have in mind.)  A human-like language has
the ability to induce a novel encoding of symbols powerful enough to invoke
new ways of thinking about something.  This objective can be constructed to
be so simple that it could even be tested using a carefully designed test
where the abstract nature of the idea can be tested in a controlled
environment. So even if my program did not work, I could fall back on the
essential idea (of the objective) and work on that.

This idea makes a great deal of sense.  We want an AGI program to be able
to act on language in just this way.  Now perhaps there are other qualities
of intelligence that I haven’t thought of.  But in trying to design a
presentation in which I defined a method to test my progress on my project
I have managed to put certain ideas together in a slightly novel way which
I feel goes to the heart of what we think intelligence should be.  Because
I chose to take this route I serendipitously discovered a way that this
could be constructed in a highly controlled abstract test. If my
impressions are right about this it should be easy to demonstrate the
basics of how they work in my AGi program (even though that program would
be limited).  I hope this makes sense to someone.

Jim Bromer



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AGI
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