sparc
Posts: 2089
Joined: April 2007
|
| Quote (The whole truth @ Nov. 08 2012,06:34) | | Quote (GaryGaulin @ Nov. 07 2012,19:02) | | Quote (OgreMkV @ Nov. 07 2012,19:36) | Gary, so now we add complexity to the list of things you have no clue on?
Just out of curiosity, have you researched your paper's failure on the concept of natural selection, the Cambrian explosion and the rather inexplicable graphs?
One would think that things in one's life's work that are wrong would be high on the discussion topic list.
Do you have a definition of "intelligence"? If so, what is it? No, I'm NOT going to troll through your paper for it. |
Here is the operational definition for intelligence, from the Introduction:
| Quote | Intelligence is here operationally defined by how it works, as an autonomous sensory-feedback (confidence) guided sensory addressed memory system that through trial-and-error learns new successful actions to be taken in response to environmental conditions. In addition to something to CONTROL and MEMORY there must be one or more CONFIDENCE levels gauging failure or success of its motor actions towards reaching the goal and a way to GUESS motor actions when a learned response does not yet exist but it must try something. A good-guess is based upon existing knowledge. A random-guess is the last resort and only has to be "random" to the intelligence. For example where one must think up "random numbers" for another to guess they may use their phone number, which the other person does not know so to them they are indeed a random string of numbers. What is most important for something to be "random" is that the intelligence perceives it as such.
Confidence gauges whether it is getting closer to its goal or not, if not then Guess is taken by changing direction to produce tumble/guess where to next go. In a most simple chemotaxis system Guess and Motor are combined, are the molecules that act as a switch to change motor direction where only a single memory location is required, instead of two as shown here that takes the same concept to self-learning as in the human brain and other intelligent living things where Motors (muscles) are separate from the Guess mechanism requiring two memory locations with In0/Out0 a 4 state (0-3) or more analog to recall confidence level that increases each time the action worked, decreases when it failed then upon reaching 0 produces a guess. Like us we know when we need to take a guess or have an action response we are confident will work. And as when first born, almost everything is a new experience. No memory at all of what to do is then sensed by Out0 being 0 which likewise produces a guess. What works is stored with increasing confidence, for as long as it keeps working, but confidence level does not need to increase past 3 for a good model. In bacteria the interoceptive sensors would simply be metabolic pathway molecules reporting motor condition back to the sensory end of the system to provide time delay that through Confidence being restored by that action switches motor back to swimming after tumble has been completed.
There should always be an easily recognizable circuit where each part works with others in a certain way. This includes motors/muscles where there are expected to be two connections to the memory/brain. The input connects to the data action outputs of a Random Access Memory controlling it. The output is a sensory feedback signal to RAM addressing that adds (usually subconscious) awareness of the muscle action. This sensory output can be from other sensors not directly connected to, such as touch sensors on skin that “feel” muscles moving or eye sensing travel direction. Without at least indirect sensory feedback of motor actions addressing RAM the system has no way to know whether the motor has in turn produced the expected action, or not.
Although not a circuit requirement (as in the four above) there should be the production of regular detectable synchronized cycles, as the algorithm/system keeps repeating the one thought at a time process. Where these cycles are no longer present then the intelligence is nonfunctional.
Where a system is missing one or more requirements we have a system that may appear to be intelligent but would only qualify as a protointelligent behavior. This is true where the sensor(s) connect directly to the motors in a way that keeps the system on course, but does not learn how to control itself. There must be a memory system between sensors and motors being controlled. An example so simple it is almost cheating is the E.coli chemotaxis system where chemoreceptors address a single memory location that increases or decreases according to the amount of chemical being sensed, and when it is going the wrong way tumbles to try another direction.
Being self-learning, intelligence will produce the next emergent level of intelligence when it learns how to achieve it. Large numbers of rudimentary intelligences are predicted to have a tendency to spontaneously produce easily detectable and measurable emergent intelligence at the next level. No computer code is needed, entities learn how to on their own. Demonstrating this intelligent cause/causation would require many intelligent entities with rudimentary intelligence which self-assemble (at higher complexity is also called self-organize) to produce an emergent intelligence, much the same way a molecular genome produces a living cell, or living cells produced us.
http://theoryofid.blogspot.com/....pot....pot.com
|
There is more detail in following sections, but that's a summary. Not needing a "natural selection" variable is the result of not needing a 1200+ year old generalization. This is a cognitive theory, that explains how intelligence works, through time... |
Gary, I'm trying to understand your definition of "intelligence". After reading it several times I get the impression that you're kinda sorta describing evolution but have added something along the line of molecules and/or cells having the ability to consciously think about what they want or need to do. Is that what you're saying, or close to it?
Some other questions I have are:
Do you agree with joe g and most or all other ID pushers that "ID" is all about 'origins'? If so, can and will you tell me where, when, and how "intelligence" originated and how you figured that out, if you have?
What is the "goal" of "intelligence", whether the alleged "intelligence" is in a molecule, a cell, a flower, a cat, a human, or anything else?
When a baby is born (and even before that) it has a heartbeat, brain activity, and other body functions (unless there's something wrong with it). Is it "intelligence" that causes those things? If so, does the "intelligence" reside in the baby's molecules, cells, heart, brain, or what?
Is "intelligence" the same as knowledge?
Do you see "intelligence" as something that was front-loaded (initially programmed) into atoms, molecules, cells, and/or something else by a 'designer/creator'?
Do you see human beings as the pinnacle of "intelligence"?
Are the molecules and/or cells in a spider, a mushroom, a tree, or a gorilla less or more intelligent than the ones in a human or are they equally intelligent?
Do all molecules everywhere contain "intelligence"? |
Gary, from what I know about him you may want to meet gpuccio of UD. It seems likely that he will uderstand you: | Quote | 38 gpuccio July 30, 2008 at 1:50 am
sparc:
First of all, I really believe that my assumptions are right, and I invite you to tell where they should be wrong. In a sense, they are not assumptions at all, but very simple facts about probability.
Regarding the immune system, the scenario is completely different. Primary antibody diversification is a process which uses random variation very intelligently targeted to generate a repertoire of basic antibody specificities to cover, at a low specificity level, a search space which is very big, but not immense, referring to possible epitopes in nature (an epitope is a very small aminoacid sequence, usually a few aminoacids, or up to ten -fifteen). Even so, the basic repertoire is very unspecific, and can ensure only a low level interaction with possible epitopes. Antibody maturation “after” primary response, instead, is a typical process which utilizes random variation very intelligently targeted plus very intelligent selection to increase the specificity of the immune response. Indeed, the process utilized here is the same as used in modern protein engineering: the results of targeted random variation are “measured” against the original epitope, and intelligent selection takes place (obviously, here selection includes very specific informatioon about the target, that is the epitope itself, and is therefore very efficient).
So, as you can see, there is nothing in what we know about antibody generation which is inconsistent with my “assumptions”. Antibody generation is a perfect example of intelligent engineering using the realistic resources of probability. It is therefore perfectly natural and reasonable that the immune system of birds or mammals can “produce antibodies against antigens that they or their ancestors never encountered before”. |
--------------
"[...] the type of information we find in living systems is beyond the creative means of purely material processes [...] Who or what is such an ultimate source of information? [...] from a theistic perspective, such an information source would presumably have to be God."
- William Dembski -
|
