I didn't mean to say that using probability and weighted reasoning were wrong or something. I just meant that you cannot use probability without the supposition of a logically sound frame (or something). Jim Bromer
On Sun, Mar 17, 2013 at 9:44 AM, Jim Bromer <[email protected]> wrote: > 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
