Do they tell us what grief is doing when a loved one dies?
Well the "grief" that is felt when a loved one dies is similar to that of unreturned love. So you love them, and they don't love you back -- as they are dead. This causes a feeling of futility and eventually changes direction -- to focus more inwardly mu'a(in example) self-pity/self-love where you give yourself supporting beliefs rather than a different person. Do these inference system tell us why we get depressed when we keep failing to accomplish our goals? Why implies causation, which is something that is system specific and not an inherant property of the universe. So you'd have to ask yourself as the computer that created the rule set of "failing to achieve goals" causes depression. Personally I just choose not to fail. If I do, then I accept that it was I that set the standards -- perhaps to do something about it later. Do they give a model for understanding why we feel proud when we are encouraged by our parents? As a child you give power to your parents. So when your parents encourage you, they hold the belief that you will feel happy, and so you do -- being a child is giving others the responsibility for their environment. Many mortal Homo Sapiens can be considered children in that sense. So if you could imagine all mathematical expressions as a 3d fabric, where sentient creatures are "droplets" or "sets" of these mathematical expressions. You can envision two "parents" sharing a similar space in the "fabric" (at least time/location) and they form another "droplet" between the two of them. A sort of seeding of consciousness. It is possible to create this kind of mathematical "fabric". I think it would be very intersteing if we could figure out how, as then we would be able to map Homo Sapiens as well as other related conceptual species, maybe even figure out how to cross the belief barriers to access them. I'm not really sure what such a "belief fabric" would consist of. Though it is possible that we could just make a large database of beliefs in some logical language (Lojban) and have people describe their own beliefs, then we would be able to expand this if we got it onto a distributed network. If we get some people that believe they are aliens, or have significantly different beliefs and implications than we do, we could make a claim to first contact. *shrugs* it would be relatively simple to implement. Only concievable issue is lack of Lojban speakers. coding isn't useless, especially on the small scale where you grasp what is happening. When you can no longer grasp what is happening, things are "random" which is a sign of intelligence -- you couldn't predict my reply, and hence it was "random". Though you could just as easily control your reality by keeping a record of the things you believe and changing them when you want a change. An interesting thing to try out would be to have a set of beliefs/statements (perhaps that you want the computer to have) then you have a purely random number generator to select a belief at random to output. You could also add beliefs/statements to the file by saying them. Could probably have a relatively intelligent conversation with the computer. Typically will reply with what you expect it to. On 2/20/07, Bo Morgan <[EMAIL PROTECTED]> wrote:
On Tue, 20 Feb 2007, Richard Loosemore wrote: ) Chuck Esterbrook wrote: ) > On 2/19/07, John Scanlon <[EMAIL PROTECTED]> wrote: ) > > Language is the manipulation of symbols. When you think of how a ) > > non-linguistic proto-human species first started using language, you can ) > > imagine creatures associating sounds with images -- "oog" is the big hairy ) > > red ape who's always trying to steal your women. "akk" is the action of ) > > hitting him with a club. ) > > ) > > The symbol, the sound, is associated with a sensorimotor pattern. The ) > > visual pattern is the big hairy red ape you know, and the motor pattern is ) > > the sequence of muscle activations that swing the club. ) > ) > Regarding "imagine creatures associating sounds with images", I ) > imagine there being a "concept node" in between. The sound and the ) > image lead to this node and stimulation of the node stimulates the ) > associated patterns. My inspiration comes from this: ) > http://www.newscientist.com/article.ns?id=dn7567 ) ) Chuck, ) ) I'm glad you brought that article to my attention, I somehow missed it. Be ) warned: the result is extremely dubious, IMO. ) ) Just ask yourself what is the probability that the researchers just "happened" ) to come across the neurons that encoded the particular pictures they showed to ) their subjects..... ) ) The probability is ludicrously small. They were probably hitting something ) that was *part* of a temporary representation of "most recently seen things". ) Within the context of "most recently seen things" that neuron could easily ) have triggered only to (say) the Halle Berry concept. But if they had come ) back the next day, it would probably have triggered on something else. ) ) Haven't had a chance to read the original article yet, but on first look, this ) seems to be more of the same old neuroscience naivete that I complain about so ) frequently. ) ) More generally, what you say about concepts being formed as a result of ) associations must be something like the truth .... but the real story is ) vastly more complex that just co-occurence => new concept. Even what we know ) today, from regular old cognitive science studies, is huge. ) ) I could write a thousand-page book about the complex issues that branch off ) the paragraph you wrote above :-). Heck, I *am* writing it (not up to a ) thousand pages yet, but I wouldn't be surprised if it gets there eventually). ) ) ) Richard Loosemore. I agree that the pairing of co-occuring ideas is not going to amount to intelligence. But this is a certain type of A.I. that I've seen crop up pretty often as well. It amounts to the idea of inference combined with reinforcement learning. This is a pretty simple model. In regard to your comments about complexity theory: from what I understand, it is primarily about taking simple physics models and trying to explain complicated datasets by recognizing these simple models. These simple "complexity theory" patterns can be found in complicated datasets for the purpose of inference, but do they get us closer to human thought? Do they tell us what grief is doing when a loved one dies? Do these inference system tell us why we get depressed when we keep failing to accomplish our goals? Do they give a model for understanding why we feel proud when we are encouraged by our parents? These questions are trying to get at some of the most powerful thought processes in humans. ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
-- ta'o(by the way) We With You Network at: http://lokadin.blogspot.com .e http://lokiworld.org .i(and) more on Lojban: http://lojban.org irc: irc://irc.oftc.net/#ma'a mu'oimi'e lOkadin (Over, my name is lOkadin) ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
