On Mon, Oct 13, 2008 at 11:30 PM, Matt Mahoney <[EMAIL PROTECTED]> wrote:
> Ben,
> Thanks for the comments on my RSI paper. To address your comments,
You seem to be addressing minor lacunae in my wording, while ignoring my
main conceptual and mathematical point!!!
>
>
> 1. I defined "improvement" as achieving the same goal (utility) in less
> time or achieving greater utility in the same time. I don't understand your
> objection that I am ignoring run time complexity.
>
OK, you are not "ignoring run time completely" ... BUT ... in your
measurement of the benefit achieved by RSI, you're not measuring the amount
of run-time improvement achieved, you're only measuring algorithmic
information.
What matters in practice is, largely, the amount of run-time improvement
achieved. This is the point I made in the details of my reply -- which you
have not counter-replied to.
I contend that, in my specific example, program P2 is a *huge* improvement
over P1, in a way that is extremely important to practical AGI yet is not
captured by your algorithmic-information-theoretic measurement method. What
is your specific response to my example??
>
> 2. I agree that an AIXI type interactive environment is a more appropriate
> model than a Turing machine receiving all of its input at the beginning. The
> problem is how to formally define improvement in a way that distinguishes it
> from learning. I am open to suggestions.
>
> To see why this is a problem, consider an agent that after a long time,
> guesses the environment's program and is able to achieve maximum reward from
> that point forward. The agent could "improve" itself by hard-coding the
> environment's program into its successor and thereby achieve maximum reward
> right from the beginning.
>
Recursive self-improvement **is** a special case of learning; you can't
completely distinguish them.
>
> 3. A computer's processor speed and memory have no effect on the
> algorithmic complexity of a program running on it.
Yes, I can see I didn't phrase that point properly, sorry. I typed that
prior email too hastily as I'm trying to get some work done ;-)
The point I *wanted* to make in my third point, was that if you take a
program with algorithmic information K, and give it the ability to modify
its own hardware, then it can achieve algorithmic information M > K.
However, it is certainly true that this can happen even without the program
modifying its own hardware -- especially if you make fanciful assumptions
like Turing machines with huge tapes ... but even without such fanciful
assumptions.
The key point, which I did not articulate properly in my prior message, is
that: ** by engaging with the world, the program can intake new information,
which can increase its algorithmic information **
The new information a program P1 takes in from the **external world** may be
random with regard to P1, yet may not be random with regard to {P1 + the new
information taken in}.
As self-modification may cause the intake of new information causing
algorithmic information to increase arbitrarily much, your argument does not
hold in the case of a program interacting with a world that has much higher
algorithmic information than it does.
And this of course is exactly the situation people are in.
For instance, a program may learn that "In the past, on 10 occasions, I have
taken in information from Bob that was vastly beyond my algorithmic
information content at that time. In each case this process helped me to
achieve my goals, though in ways I would not have been able to understand
before taking in the information. So, once again, I am going to trust Bob
to alter me with info far beyond my current comprehension and algorithmic
information content."
Sounds a bit like a child trusting their parent, eh?
This is a separate point from my point about P1 and P2 in point 1. But the
two phenomena intersect, of course.
-- Ben G
This intake
-------------------------------------------
agi
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