Performance not an unimportant question. I assume that AGI has necessarily
has costs which grow exponentially with the number of states and actions so
that AGI will always be interesting only for toy domains.

My assumption is that human intelligence is not truly general intelligence
and therefore cannot hold as a proof of existence that
AGI is possible. Perhaps we see more intelligence than  there really is.
Perhaps the human intelligence is to some extend overestimated and an
illusion as the free will.

Why? In truly general domains every experience of an agent only can be used
for the single certain state and action when the experience was made. Every
time when your algorithm makes generalizations from known state-action pairs
to unknown state-action pairs then this is in fact usage of knowledge about
the underlying state-action space or it is just guessing and only a matter
of luck.

So truly general AGI algorithms must visit every state-action pair at least
once to learn what to do in what state.
Even in small real world domains the state spaces are so big that it would
take longer than the age of the universe to go through all states. 

For this reason true AGI is impossible and human intelligence must be narrow
to a certain degree. 



-----Ursprüngliche Nachricht-----
Von: Pei Wang [mailto:[EMAIL PROTECTED] 
Gesendet: Sonntag, 27. April 2008 13:50
An: [email protected]
Betreff: Re: [agi] How general can be and should be AGI?

On Sun, Apr 27, 2008 at 3:54 AM, Dr. Matthias Heger <[EMAIL PROTECTED]> wrote:
>
>  What I wanted to say is that any intelligence has
>  to be narrow in a sense if it wants be powerful and useful. There must
>  always be strong assumptions of the world deep in any algorithm of useful
>  intelligence.

>From http://nars.wang.googlepages.com/wang-goertzel.AGI_06.pdf Page 5:
---
3.3. "General-purpose systems are not as good as special-purpose ones"

Compared to the previous one, a weaker objection to AGI is to insist
that even though general-purpose systems can be built, they will not
work as well as special-purpose systems, in terms of performance,
efficiency, etc.

We actually agree with this judgment to a certain degree, though we do
not take it as a valid argument against the need to develop AGI.

For any given problem, a solution especially developed for it almost
always works better than a general solution that covers multiple types
of problem. However, we are not promoting AGI as a technique that will
replace all existing domain-specific AI
techniques. Instead, AGI is needed in situations where ready-made
solutions are not available, due to the dynamic nature of the
environment or the insufficiency of knowledge about the problem. In
these situations, what we expect from an AGI system are not optimal
solutions (which cannot be guaranteed), but flexibility, creatively,
and robustness, which are directly related to the generality of the
design.

In this sense, AGI is not proposed as a competing tool to any AI tool
developed before, by providing better results, but as a tool that can
be used when no other tool can, because the problem is unknown in
advance.
---

Pei

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