Stefan Pernar wrote:


On 10/26/07, *Richard Loosemore* <[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>> wrote:

    Stefan can correct me if I am wrong here, but I think that both yourself
    and Aleksei have misunderstood the sense in which he is pointing to a
    circularity.

    If you build an AGI, and it sets out to discover the convergent desires
    (the CEV) of all humanity, it will be doing this because it has the goal
    of using this CEV as the basis for the "friendly" motivations that will
    henceforth guide it.

    But WHY would it be collecting the CEV of humanity in the first
    phase of
    the operation?  What would motivate it to do such a thing?  What exactly
    is it in the AGI's design that makes it feel compelled to be friendly
    enough toward humanity that it would set out to assess the CEV of
    humanity?

    The answer is:  its initial feelings of friendliness toward humanity
    would have to be the motivation that drove it to find out the CEV.

    The goal state of its motivation system is assumed in the initial state
    of its motivation system.  Hence: circular.


Interesting point and I guess asking if a programmers CEV would be to let an AGI find the CEV of humanity is another aspect of finding circularity in the concept.

Yup.

What really matters is finding out how to dispose the AGI to have friendliness from the outset, so that it can then seek the specific needs of humanity.

The way out of the circularity is to understand how to build motivational systems in an AGI, and how to give global feelings of (e.g.) empathy to the AGI.

As I have said before, I have an approach to that problem. I also believe that the standard AI understanding of motivation, in which goals are represented in the same semantic format as the rest of the system's explicit knowledge, are such a bad way to drive an AGI that it will (a) not actually work if you want the system to be generally intelligent, and (b) would in any case be a disastrous way to ensure the motivations of an AGI.


Richard Loosemore

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