On 11/3/02 4:26 AM, "Ben Goertzel" <[EMAIL PROTECTED]> wrote: > > Hutter's work draws on a long tradition of research into statistical > learning theory and algorithmic information theory, mostly notably > Solomonoff's early work on induction and Levin's work on computational > measure theory. At the present time, though, this work is more exciting > theoretically than pragmatically. The "constant factor" in his theorem may > be very large, so that in practice, AIXItl is not really going to be a good > way to create an AGI software program. In essence, what AIXItl is doing is > searching the space of all programs of length L, evaluating each one, and > finally choosing the best one and running it. The "constant factors" > involved deal with the overhead of trying every other possible program > before hitting on the best one!
I am very aware of these issues. The tractability issue isn't as bad as it seems, though it is implicit in the math. Hutter strongly implies a really ugly tractability problem, in no small part due to an exponential resource take-off, but it isn't as bad as it reads. In practice, the exponent can be sufficiently small (and much smaller than I think most people believe) that it becomes tractable for at least human-level AGI on silicon (my estimate), though it does hit a ramp sooner than later. > A simple AI system behaving somewhat similar to AIXItl could be built by > creating a program with three parts: > > . The data store > . The main program > . The metaprogram > > The operation of the metaprogram would be, loosely, as follows: > > . At time t, place within the data store a record containing: the complete > internal state of the system, and the complete sensory input of the system. > > . Search the space of all programs P of size |P|< L to find the one that, > based on the data in the data store, has the highest expected value for the > given maximization criterion > > . Install P as the main program There is a log(n) algorithm/structure that essentially does this, and it works nicely using maspar too. It does have a substantially more complex concept of "meta-program" though. > Conceptually, the main value of this approach for AGI is that it solidly > establishes the following contention: > > **If you accept any definition of intelligence of the general form > "maximization of a certain function of system behavior." > Then, the problem of creating AGI is basically a problem of dealing with the > issues of space and time efficiency** > > As with any mathematics-based conclusion, the conclusion only follows if one > accepts the definitions. If someone's conception of intelligence > fundamentally can't be cast into the form of a behavior-based maximization > criterion, then these ideas aren't relevant for AGI as that person conceives > it. However, we believe that the behavior-based maximization criterion > approach to defining intelligence is a good one, and hence we believe that > Hutter's work is highly significant. I agree with this. In complex environments, any usefully adaptive system will be balancing the time and space requirements of a bevy of maximization criterion, which themselves will be constantly adapting at the meta- level. > Well, I think their work is of limited practical value for the reasons I > mention above, but, you're obviously hinting at something else. But since > you won't tell us, it's not a very interesting topic of conversation huh ;) More to the point: I am involved in a commercial venture related to AGI, and the technology is substantially more developed and advanced than I can talk about without lawyers getting involved. It is sufficiently sexy that it has attracted quite a bit of smart Silicon Valley capital, which is no small feat for any company over the last year or two, never mind any outfit working with "AI". Cheers, -James Rogers [EMAIL PROTECTED] ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/