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E-book InformationArtificial Intelligence And Intuition
by:
Abraham Thomas
The intuitive formula
Roger Penrose considered it impossible. Thinking could ne'er
imitate a computer process. He same
as more in his book, The Emperor's New Mind. But, a new book, The Intuitive Algorithm, (IA), recommended that intuition was a pattern recognition process. Intuition propelled information through many an neural regions like a lightning streak. Data emotional from input to output in a rumored
20 milliseconds. The mind saw, recognized, taken and acted. In the blink of an eye. Myriad processes regenerate
light, sound, touch and smell instantly into your nerve impulses. A dedicated region recognized those impulses as objects and events. The bodily structure system, another region, taken those events to generate emotions. A fourth region responded to those emotions with actions. The mind perceived, identified, evaluated and acted. Intuition got you off the hot stove in a fraction of a second. And it could be victimisation a simple algorithm.
Is instant holistic evaluation impossible?
The system, with over a hundred billion neurons, processed the information from input to output in simply half a second. All your psychological feature
was evaluated. Director Freeman, the far-famed neurobiologist, defined this amazing ability. "The psychological feature
guys think it's simply impossible to support throwing everything you've got into the computation every time. But, that is exactly what the brain does. Consciousness is simply about transfer your entire history to bear on your next step, your next breath, your next moment." The mind was holistic. It evaluated all its psychological feature
for the next activity. How could so more information be processed so quickly? Wherever
could such psychological feature
be stored?
Exponential growth of the search path
Unfortunately, the recognition of subtle patterns expose
formidable problems for computers. The difficulty was an exponential growth of the recognition search path. The problems in the identification
of diseases was typical. Normally, many an shared symptoms were given
by a multitude of diseases. For example, pain, or fever could be indicated for many an diseases. Each symptom pointed to some diseases. The problem was to recognize a single pattern among many an overlapping patterns. Once
searching for the target disease, the 1st elect ill with the 1st given
symptom could lack the second symptom. This meant back and forth searches, which enlarged exponentially as the information of diseases augmented in size. That ready-made the process absurdly long drawn – theoretically, even as years of search, for extensive databases. So, in spite of their dumfounding speed, rapid pattern recognition on computers could ne'er
be imagined.
The Intuitive Formula
But, industry strength pattern recognition was feasible. IA introduced an algorithm, which could instantly recognize patterns in extended databases. The relationship of each member of the whole information was coded for each question.
(Is pain a symptom of the disease?)
Disease1Y, Disease2N, Disease3Y, Sickness 4Y, Disease5N, Disease6N, Disease7Y, Disease8N, Disease9N, Disease10N, Disease11Y, Disease12Y, Disease13N, Disease14U, Disease15Y, Disease16N, Disease17Y, Disease18N, Disease19N, Disease20N, Disease21N, Disease22Y, Disease23N, Disease24N, Disease25U, Disease26N, Disease27N, Disease28U, Disease27Y, Disease30N, Disease31U, Disease32Y, Disease33Y, Disease34U, Disease35N, Disease36U, Disease37Y, Disease38Y, Disease39U, Disease40Y, Disease41Y, Disease42U, Disease43N, Disease44U, Disease45Y, Disease46N, Disease47N, Disease48Y,
(Y = Yes: N = No: U = Uncertain)
The key was to use elimination to assess the database, not selection. Every member of the information was one by one coded for elimination in the context of each answer.
(Is pain a symptom of the disease? Answer: YES)
Disease1Y, xxxxxxN, Disease3Y, Disease4Y, xxxxxx5N, xxxxxx6N, Disease7Y, xxxxxx8N, xxxxxx9N, xxxxxx0N, Disease11Y, Disease12Y, xxxxxx13N, Disease14U, Disease15Y, xxxxxx16N, Disease17Y, xxxxxx18N, xxxxxx19N, xxxxxx20N, xxxxxx21N, Disease22Y, xxxxxx23N, xxxxxx24N, Disease25U, xxxxxx26N, xxxxxx27N, Disease28U, Disease27Y, xxxxxx30N, Disease31U, Disease32Y, Disease33Y, Disease34U, xxxxxx35N, Disease36U, Disease37Y, Disease38Y, Disease39U, Disease40Y, Disease41Y, Disease42U, xxxxxx43N, Sickness 44U, Disease45Y, xxxxxx46N, xxxxxx47N, Sickness 48Y,
(All "N" Diseases eliminated.)
For sickness recognition, if an answer indicated a symptom, IA eliminated all diseases absent
of the symptom. Every answer eliminated, narrowing the search to reach diagnosis.
(Is pain a symptom of the disease? Answer: NO)
xxxxxx1Y, Disease2N, xxxxxx3Y, xxxxxx4Y, Disease5N, Disease6N, xxxxxx7Y, Disease8N, Disease9N, Disease10N, xxxxxx11Y, xxxxx12Y, Disease13N, Disease14U, xxxxxx15Y, Disease16N, xxxxxx17Y, Disease18N, Disease19N, Disease20N, Disease21N, xxxxxx22Y, Disease23N, Disease24N, Disease25U, Disease26N, Disease27N, Disease28U, xxxxxx27Y, Disease30N, Disease31U, xxxxxx32Y, xxxxxx33Y, Disease34U, Disease35N, Disease36U, xxxxxx37Y, xxxxxx38Y, Disease39U, xxxxxx40Y, xxxxxx41Y, Disease42U, Disease43N, Sickness 44U, xxxxxx45Y, Disease46N, Disease47N, xxxxxx48Y,
(All "Y" Diseases eliminated.)
If the symptom was absent, IA eliminated all diseases which always exhibited the symptom. Diseases, which at random given
the symptom were maintained in some
cases. So the process handled uncertainty – the “Maybe” answer, which normal computer programs could not handle.
(A sequence of questions narrows down to Disease29 - the answer.)
xxxxxx1Y, xxxxxx2N, xxxxxx3Y, xxxxxx4Y, xxxxxx5N, xxxxxx6N, xxxxxx7Y, xxxxxx8N, xxxxxx9N, xxxxxx10N, xxxxxx11Y, xxxxxx12Y, xxxxxx13N, xxxxxx14U, xxxxxx15Y, xxxxxx16N, xxxxxx17Y,xxxxxx18N, xxxxxx19N, xxxxxx20N, xxxxxx21N, xxxxxx22Y, xxxxxx23N, xxxxxx24N, xxxxxx25U, xxxxxx26N, xxxxxx27N, xxxxxx28U, Disease29Y, xxxxxx30N, xxxxxx31U, xxxxxx32Y, xxxxxx33Y, xxxxxx34U, xxxxxx35N, xxxxxx36U, xxxxxx37Y, xxxxxx38Y, xxxxxx39U, xxxxxx40Y, xxxxxx41Y, xxxxxx42U, xxxxxx43N, xxxxxx44U, xxxxxx45Y, xxxxxx46N, xxxxxx47N, xxxxxx48Y.
(If all diseases are eliminated, the sickness is unknown.)
Instant pattern recognition
IA was proven in practice. It had hopped-up Expert Systems acting with the speed of a simple computing
on a spreadsheet, to recognize a disease, identify a case law or diagnose the problems of a complex machine. It was instant, holistic, and logical. If some parallel answers could be presented, as in the multiple parameters of a power plant, recognition was instant. For the mind, wherever
millions of parameters were at the same time
presented, real time pattern recognition was practical. And elimination was the key.
Elimination = Shift
off
Elimination was shift
off - inhibition. Nerve cells were better-known to extensively inhibit the activities of else cells to highlight context. With access to millions of sensory inputs, the nervous system instantly reserved – eliminated trillions of combinations to zero in on the right pattern. The process stoutly used "No" answers. If a patient did not have pain, thousands of possible diseases could be ignored. If a patient could simply walk into the surgery, a doctor could overlook a wide range of illnesses. But, how could this process of elimination be applied to nerve cells? Wherever
could the wealth of psychological feature
be stored?
Combinatorial private secret writing
The mind received changeful
combinations of millions of sensations. Of these, smells were rumored
to be recognized through a combinatorial private secret writing process, wherever
nerve cells recognized combinations. If a nerve cell had nerve fibre
inputs, better-known as A, B, C and so on to Z, it could then fire, once
it received inputs at ABC, or DEF. It recognized those combinations. The cell could identify ABC and not ABD. It would-be be reserved for ABD. This recognition process was recently rumored
by science for modality neurons. In the experiment scientists rumored
that even as slight changes in chemical structure activated some combinations of receptors. Thus, octanol smelled like oranges, but the similar compound octanoic acid smelled like sweat. A Altruist Prize acknowledged that discovery in 2004.
Galactic nerve cell memories
Combinatorial codes were extensively used by nature. The four "letters" in the genetic code – A, C, G and T – were used in combinations for the production of a nearly infinite number of genetic sequences. IA discusses the deeper implications of this private secret writing discovery. Animals could differentiate between millions of smells. Dogs could quickly sniff a few footprints of a person and determine accurately which way the person was walking. The animal's nose could find the relative odour strength difference between footprints only a few feet apart, to determine the direction of a trail. Smell was better-known through remembered combinations. If a nerve cell had simply 26 inputs from A to Z, it could obtain millions of possible combinations of inputs. The average vegetative cell
had thousands of inputs. For IA, millions of nerve cells could give the mind galactic memories for combinations, sanctioning
it to recognize subtle patterns in the environment. Each cell could be a single member of a database, eliminating itself (becoming inhibited) for unrecognized combinations of inputs.
Elimination the key
Elimination was the special key, which evaluated immense combinatorial memories. Medical texts rumored
that the mind had a hierarchy of intelligences, which performed dedicated tasks. For example, there was an association region, which recognized a pair of scissors victimisation the context of its feel. If you black-and-blue this region, you could still feel the scissors with your eyes closed, but you would-be not recognize it as scissors. You still felt the context, but you would-be not recognize the object. So, intuition could modify
nerve cells in association regions to use perception to recognize objects. Medical research rumored
many an such recognition regions.
Serial process
A pattern recognition algorithm, intuition enabled the finite intelligences in the minds of living things to respond holistically inside
the 20 unit of time
time span. These intelligences acted serially. The 1st intelligence regenerate
the changeful
combinations of sensory perceptions from the environment into nerve impulses. The second intelligence recognized these impulses as objects and events. The third intelligence translated the recognized events into feelings. A fourth translated feelings into intelligent drives. Fear triggered an escape drive. A cervid delimited away. A bird took flight. A fish swam off. Spell the activities of running, flying and swimming differed, they achieved the same objective of escaping. Hereditary nerve cell memories hopped-up those drives in context.
The mind – seamless pattern recognition
Half a second for a 100 billion nerve cells to use context to eliminate irrelevancy and deliver motor output. The time between the shadow and the scream. So, from input to output, the mind was a seamless pattern recognition machine, hopped-up by the key private secret of intuition – discourse
elimination, from massive noninheritable and hereditary combinatorial memories in nerve cells.
Just simply about the author:
Ibrahim Thomas is the author of The Intuitive Algorithm, a book, which suggests that intuition is a pattern recognition algorithm. The ebook version is accessible at http://www.intuition.co.in.The book may be purchased only in India. The website, provides a free motion picture and a walk through to explain the ideas.
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