I think it would be beneficial, at least to me, to see a list of tasks. Not as a "defining" measure in any way. But as a list of work items that a general AGI should be able to complete effectively. I started on a list, and pulled some information off the net before, but never completed one.
My thoughts on a list like this is that is should be marked in increasing levels of difficulty, so an initial AGI should have the ability to complete the first level of tasks and so on.
One problem I am running into here and in other discussions is the seperation between online AI, and robotic AI, and I have had to restrict alot of the research I am doing to online AI, and only a small amount of simulated robotic AI.
There are many tasks that are not possible with non-robotic AI, and I would like to see the different classes of these tasks, so I could correctly model the system to handle the wide variety of the behaviors necessary.
Ex: One item of AI task is a simple question answering ability, that can respond with an answer currently in the Knowledge base of the system.
A more expansive item, would require the QA task to go and get outside information.
James Ratcliff
Ben Goertzel <[EMAIL PROTECTED]> wrote:
> I am happy enough with the long-term goal of independent scientific
> and mathematical discovery...
>
> And, in the short term, I am happy enough with the goals of carrying
> out the (AGISim versions of) the standard tasks used by development
> psychologists to study childrens' cognitive behavior...
>
> I don't see a real value to precisely quantifying these goals, though...
To give an example of the kind of short-term goal that I think is
useful, though, consider the following.
We are in early 2007 (if all goes according to plan) going to teach
Novamente to carry out a game called "iterated Easter Egg hunt" --
basically, to carry out an Easter Egg hunt in a room full of other
agents ... and then do so over and over again, modeling what the other
agents do and adjusting its behavior accordingly.
Now, this task has a bit in common with the game Hide-and-Seek. So,
you'd expect that a Novamente instance that had been taught iterated
Easter Egg Hunt, would also be good at hide-and-seek. So, we want to
see that the time required for an NM system to learn hide-and-seek
will be less if the NM system has previously learned to play iterated
Easter Egg hunt...
This sort of goal is, I feel, good for infant-stage AGI education....
However, I wouldn't want to try to turn it into an "objective IQ
test." Our goal is not to make the best possible system for playing
Easter Egg hunt or hide and seek or fetch or whatever....
And, in terms of language learning, our initial goal will not be to
make the best possible system for conversing in baby-talk...
Rather, our goal will be to make a system that can adequately fulfill
these early-stage tasks, but in a way that we feel will be
indefinitely generalizable to more complex tasks.
This, I'm afraid, highlights a general issue with formal quantitative
intelligence measures as applied to immature AGI systems/minds. Often
the best way to achieve some early-developmental-stage task is going
to be an overfitted, narrow-AI type of algorithm, which is not easily
extendable to address more complex tasks.
This is similar to my complaint about the Hutter Prize. Yah, a
superhuman AGI will be an awesome text compressor. But this doesn't
mean that the best way to achieve slightly better text compression
than current methods is going to be **at all** extensible in the
direction of AGI.
Matt, you have yet to convince me that seeking to optimize interim
quantitative milestones is a meaningful path to AGI. I think it is
probably just a path to creating milestone-task-overfit narrow-AI
systems without any real AGI-related expansion potential...
-- Ben
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