Charles, The notion of the probability of a prediction (an expectation in general human language) is nonsensical if you have ruled out assertions. Since you are not ruling out assertions you are unwittingly allowing the notion of "truth" in the front door even as you are chasing it out the back. An assertion is a notion of the truth of the assertion as a likely possibility. If you are saying that an AGI program was capable of somewhat reliably deducing the probability of a prediction then you are asserting that the process was based on the strength and the truth of the application of the methods used to derive those probabilities.
If it were easy for a computer program to attain the probability of an event based on observations of past events then this kind of discussion would not be relevant to AGI. The problem is that events are actually complexities which are not only composed of distinct 'kinds' of events and some background 'noise' but of different variations of 'kinds' of events and a lot of other events. The science of using probability and statistics is premised on the methodical actions of an agent who is not only intelligent but highly trained in the science of the applied statistical methods. The idea that intelligence can be founded on statistics is backwards. Jim Bromer On Tue, Feb 19, 2013 at 7:56 PM, Charles Hixson <[email protected]>wrote: > On 02/19/2013 11:03 AM, Piaget Modeler wrote: > > > I'm sure this topic has been discussed before. Sorry for rehashing it if > so. I have a specific question I'd like to answer. > > > In designing a cognitive system, someone made a criticism that utterly > confounded me. And got me thinking. > > The system receives sensory data sets from the world and transforms them > into percept propositions which it asserts to > its memory. Each percept proposition is activated when it is asserted. > Infereneces are made from these percepts. > These initial percepts and its inferences are called "Observables". All > observables can be activated, but there is only a > notion of activation. > > Next, the system can predict that these observables will recur at some > point. But the prediction refers only to predicting > the re-activation of observables. > > Then some one asked, where is the notion of TRUTH in your system. I was > flabbergasted. Speechless. Then I asked > well what is truth? I checked wikipedia. ( > http://en.wikipedia.org/wiki/Truth ) > > > It turns out that when someone says something is true, it means a very > many things: > > a) It means that the statement is logically consistent (validity), > b) that the statement corresponds, concurs, or conforms to reality > (verity), > c) that one is sure of the statement (certainty / confidence), > d) that the statement is likely to occur rather than unlikely > (Likelihood), and > e) that we agree with the statement (agreement). > > So my questions are: > > (1) Is truth necessary or important to a cognitive system? > (2) Which notion of truth should a cognitive system model? > (3) How do we ascribe truth (values) to sensory input or inferences > derived from sensory input? > > Your thoughts? > > ~PM. > > > ------------------------------------------------------------------------------------------------------------------------------------------------ > *Confidential *- *This message is meant solely for the intended > recipient. Please do not copy or forward this message without * > *the consent of the sender. If you have received this message in error, > please delete the message and notify the sender.* > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/232072-58998042> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > > Truth is an illusion. It is the belief that what you believe to be most > likely is, in fact, inevitable. > > An AI doesn't need the concept of truth...except to communicate with > people. Internally it can operate off of graded degrees of probability, > cost, benefit, etc. When communicating with people it needs to condense > that so that when something has more than a certain amount of probability, > and the benefit of asserting it is sufficiently large, and the cost of > being wrong is sufficiently small, then it synopsizes this as proclaiming > "truth". It's my belief that people operate in the same way, though this > is disguised because different people use different constraints on things > like "What is probable enough?". Also note that the cost and the benefit > are figured on the basis of the cost/benefit to the entity proclaiming a > truth rather than on those accepting it. > > So perhaps we would want a sufficiently capable AI to avoid talking about > truth, and instead talk about what the probabilities are, and what costs > and benefits can be expected. It's a bit harder to understand, but it > strikes me as much safer. > > -- > Charles Hixson > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/10561250-470149cf> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
