>> Actually this is another nice feature of universal intelligence: An agent
>> can't
>> really get a better score by having a large database of prior knowledge.
Hmmm. That seems *really* counter to my intuition.
>> The problem is that if you put some information into the agent's database,
>> "the grass is green and the sky is blue" then this will help the agent in a
>> world where this is true. However, there will be another world with about
>> the
>> same complexity where the sky is green and the grass blue.
But the grass is green and the sky is blue for a reason (chlorophyll has
evolved to be the most effective color given the sun-characteristics and
atmospheric composition dictates the blue) and similarly, if the sky is green
and the grass blue, it will also be for a reason and one small piece of
information will tell you a tremendous amount about the situation you are in
(because there truly are *NOT* infinite combinations of possibilities that
occur with *reasonable* probability. You've allowed the wonder of infinity to
blind you to reality. Things are *never* totally without correlation yet this
is what you are implying (and enforcing with your overly general test).
- - - -
Nice conversation though.
Mark
----- Original Message -----
From: Shane Legg
To: [email protected]
Sent: Wednesday, May 02, 2007 6:43 PM
Subject: Re: [agi] rule-based NL system
Mark,
I was objecting to the fact that your paper did not even mention, much less
stress, the fact that prediction of previously unseen things is critical to
intelligence.
It's true that I don't talk about the prediction of "previously unseen"
things.
I suppose I just take it for granted. I mean, if I predictor can't
generalise
from past experience to predict patterns that have not been seen yet, but
are in some way related to the past, then it's a pretty poor predictor!
Perhaps this illustrates something more important however: One of the
problems with defining intelligence is that it seems to involve an endless
range of abilities. Some argue that perception is very important. Some that
language is key. Others think that mathematical thinking, or probabilistic
reasoning, or rational thought are very important. Perhaps planning is
very important. Maybe the ability to abstract. Emotions? And so on...
The beauty of the approach that we have taken is that we abstract above
all this and just talk in terms of goal achieving performance. Thus things
such as planning, reasoning or prediction are important to the extent that
they enable the agent to achieve goals. If they don't enable the system
to work better in some environment, then they aren't a part of intelligence.
Not only does this allow us to not have to name what each of these things
are, it also means that the intelligence test measures cognitive abilities
that may enhance an agent's performance that we have not yet thought of.
I agree with the first sentence of your second paragraph entirely but point
out that a "by rote" machine with virtually infinite experience will test as if
it had high universal intelligence unless the test manages to hit upon some
area where it didn't have experience -- and I feel that this is entirely
incorrect.
Actually this is another nice feature of universal intelligence: An agent
can't
really get a better score by having a large database of prior knowledge.
The problem is that if you put some information into the agent's database,
"the grass is green and the sky is blue" then this will help the agent in a
world where this is true. However, there will be another world with about
the
same complexity where the sky is green and the grass blue. The agent has
no idea which of these two worlds it is going to face. This makes a database
of prior experience is useless. The only way that the agent can do well is
by
quickly learning from its experience and adapting to deal with the
uncertainties
in its environment.
Unfortunately, in the 8 page Benelearn paper there wasn't space to get into
many of these interesting aspects of this intelligence test.
Cheers
Shane
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