> From: [email protected]
> No. AGI is unsolved. I am saying that there are machine learning
> algorithms that build on previous learning. For example, the LZW
> compression algorithm builds its dictionary by extending the words it
> has already learned. Of course this is not AGI because it meets none
> of the requirements of solving language, vision, hearing, robotics,
> art, and predicting human behavior all with human level ability. But
> you don't claim to be trying to solve these problems either.
>
> But you haven't answered my questions. Exactly what will your program
> do? How will you demonstrate that your program "builds on previous
> learning"? What are the tests that you will give it?
Complexity is too great a problem.
1. What I have said is that if I could get my program to work with text based
IO then it could be modified to work with vision hearing robotics etcetera.
The idea that it could predict human behavior with human level ability is from
your definition, not mine.
2. What I have directly implied is that if I can't get it to work with
text-based IO I wouldn't be able to get it to work with vision hearing robotics
etcetera.
However, to continue to get nearer to answering your questions. Since there
are numerous algorithms (narrow AI) that can excel at tasks that even we would
not be able to do, this indicates that if I was able to write a simple AGI
program (which is much less able than the majority of human beings) then there
would be numerous AI things that my program would not be able to even try. So
I would have trouble demonstrating that my Simple AGI was better than other
programs on the kinds of tasks that they excelled in even if the other program
was narrow AI. If simple narrow AI algorithms that input numerical values
from an IO data environment and output values in a numerical range were mixed
together in the right way, we could use the old equivalency argument to argue
that even the simplest narrow AI programs could hypothetically be combined to
produce AGI. This argument suggests that machine learning algorithms are not
much more than a step up from the earlier AI methods. My interest then is in
finding the 'right way' to combine some narrow AI algorithms to produce AGI.
But many people in this group agree that narrow AI programs are not AGI and so
my goal is to build an simple AGI program that could be pitted against other
AGI programs. Even then, another AGI program that included some powerful
narrow methods could presumably beat my program in those particular challenges.
So I started describing conceptual integration and building on previous
learning as a means to begin finding a working definition of what is needed for
an AGI approach. Your responses have give me great hope that I am as far along
in my theories as I thought I was.
I started to answer your question by pointing to a number of ideas. Think
about the problem of an executive function (starting with meta-awareness) .
And look at the integration problem where integration involves more than
subsequent numerical or logical inclusion of a simple narrow kind. The
executive 'function' won't be involved in every detail but it will note some of
the characteristics of the algorithms as they relate to the context of the
application of the algorithm. The idea that an executive function should have
some greater awareness of the context of an application of an algorithm
sometimes seems to me as if it was a mandatory requirement of AGI. The
executive function will not be able to unerringly tell if an applied algorithm
works or not so it will have to rely on other methods like cross-analysis and
attention to structural integration methods. If an analysis leads to a strong
integration of a model that the program had been building than the application
of the algorithm in that situation will be explored further. But obviously,
there has to be a guard against self-confirming artificial delusion. So while a
narrow-AI algorithm could easily defeat my program at the tasks that it was
explicitly designed for, my program, if it works, will be able to place an idea
that it had learned about in a greater context of ideas. It will be able, if
it works, to talk about the idea that it had just learned about. Since my
program has got to be simple, every thing would be at a very primitive level
but it would still be better than a calculator function which does not know
much of anything about the contexts that it is used in. Even this simple
example, describing how an AGI program with some executive meta-awareness that
is aware of some of the characteristics of the context of an application of a
function is different than a calculator function is more subtle than it might
first seem. Depending on what you call awareness or meta-level executive
function one might argue (and wisely so in my opinion) that since certain
calculator functions can produce error remarks in certain situations that even
calculator functions have some meta-level executive artificial 'awareness' of
the application of the function. The only difference is that the calculator
'speaks' and is programmed in different kinds of languages. So then the
meta-awareness and executive functions of an AGI program must be shaped
partially on previous learning so that it can learn to speak sensibly about the
context and results of an application of a narrower AI method in ways that it
hadn't before and in ways that could be different for different deployments of
the native program. Of course, since my program has to be extremely simple,
this 'sensible conversation' is going to be very primitive and open to
criticism. Jim Bromer
> Date: Sat, 3 Aug 2013 19:53:31 -0400
> Subject: Re: [agi] A Very Simple AGI Project
> From: [email protected]
> To: [email protected]
>
> On Sat, Aug 3, 2013 at 6:07 PM, Jim Bromer <[email protected]> wrote:
> >
> > From: [email protected]
> >
> > "Building on previous learning" is kind of vague. Doesn't any machine
> > learning algorithm do that? How will you test your program, measure the
> > results, and compare it to other approaches to solving the same problems?
> > -----------
> >
> > Are you saying that there are machine learning algorithms that constitute
> > working AGI programs?
>
> No. AGI is unsolved. I am saying that there are machine learning
> algorithms that build on previous learning. For example, the LZW
> compression algorithm builds its dictionary by extending the words it
> has already learned. Of course this is not AGI because it meets none
> of the requirements of solving language, vision, hearing, robotics,
> art, and predicting human behavior all with human level ability. But
> you don't claim to be trying to solve these problems either.
>
> But you haven't answered my questions. Exactly what will your program
> do? How will you demonstrate that your program "builds on previous
> learning"? What are the tests that you will give it?
>
> --
> -- Matt Mahoney, [email protected]
>
>
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