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Author Topic: Artificial Intelligence And Intuition  (Read 982 times)

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The intuitive algorithm

Roger Penrose considered it impossible. Thinking he could not imitate a computer process. He said both in his book, The Emperor's New Mind. However, a new book, The Intuitive Algorithm (IA), suggested that intuition is a process of pattern recognition. Intuition propelled information through many neural regions like lightning lightning. Data moved from input to output in a reported 20 milliseconds. The mind saw, recognized, interpreted and acted. In the blink of an eye. Myriad processes converted light, touch, sound and smell instantly into the nerve impulses. A dedicated region recognizes the impulses as objects and events. The limbic system, another region, interpreted the events to generate emotions. A fourth region responded to the emotions with actions. The mind perceives, identify, evaluate and act. The intuition that had the hot stove in a split second. And it could be using a simple algorithm.

Instant global assessment is impossible?

The system, with over one hundred billion neurons, processes information from input to output in just half a second. All his knowledge was assessed. Walter Freeman the famous neurobiologist, defined this amazing ability. "The cognitive guys think it's simply impossible to keep throwing everything you have in the calculation time. But that is exactly what the brain. The conscience is to bring its history to bring in the next step, the encouragement comes, the next moment. " The mind was holistic. We evaluated all its knowledge to the next activity. How much information can be processed so quickly? Where could such knowledge be stored?

The exponential growth of the search path

Unfortunately, the recognition of subtle patterns posed enormous problems for computers. The difficulty was an exponential growth in the recognition search path. Problems in the diagnosis of diseases was typical. Normally, many shared symptoms were presented by a multitude of diseases. For example, pain or fever may be indicated for many diseases. Each symptom pointed to several diseases. The problem was to recognize a unique pattern of many overlapping patterns. When searching for the target disease, the first selected ailment with the first symptom may occur lack the second symptom. This meant one side to another search engine, which grew exponentially as the database of the larger disease. That made the absurdly long process - in theory, even years of searching, large databases. Thus, despite its incredible speed, fast pattern recognition equipment could not imagine.

The intuitive algorithm

However, industry strength pattern recognition was feasible. IA introduced an algorithm that could instantly recognize patterns in extended database. The relationship of each member of the entire database was coded for each question.

(Is the pain is a symptom of the disease?)

Disease1Y, Disease2N, Disease3Y, 4Y disease 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 used to evaluate the elimination of the database, not selection. All members of the database was individually coded for elimination in the context of each response.

(Is the pain is 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, 44U disease, Disease45Y, xxxxxx46N, xxxxxx47N, 48Y disease ,

(All "N" Diseases deleted.)

For the recognition of the disease, if a response indicates a symptom, IA eliminated all diseases have no symptoms. Every answer eliminated, reducing the search to reach a diagnosis.

(Is the pain is 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, 44U disease, xxxxxx45Y, Disease46N, Disease47N, xxxxxx48Y,

(All "and" Diseases deleted.)

If the problem was absent, IA eliminated all diseases which always exhibited the symptom. Diseases, which randomly presented the symptom is maintained in both cases. So the process of managing uncertainty - "Maybe" answer, that the normal computer programs could not handle.

(A sequence of questions was reduced to -. Disease29 response)

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 you remove all disease, the disease is unknown.)

instant pattern recognition

IA was demonstrated in practice. Expert Systems was fed as the speed of a simple calculation in a spreadsheet, to recognize a disease, identify a case or diagnose the problems of a complex machine. Was instantaneous and global, and logical. If several parallel answers could be presented, as in the many parameters of a power plant, recognition was instant. For the mind, where millions of parameters were presented at the same time, real time pattern recognition was practical. And the removal of the key.

Elimination = Off

Clearance was off - inhibition. Nerve cells are widely known to inhibit the activities of other cells to highlight context. With access to millions of sensory stimuli, the nervous system instantly inhibited - eliminated trillions of combinations to zero in the right pattern. The process uses firmly "No" he replies. If a patient has no pain, thousands of possible diseases could be ignored. If a patient could only enter into the surgery, a doctor could overlook a wide range of diseases. But how could this process of elimination is applied to the nerve cells? Where could the wealth of knowledge stored?

Combinatorial coding

The mind kaleidoscopic combinations of millions received sensations. Of these, the odor was reported to be recognized through a combinatorial coding process, where nerve cells recognized combinations. If a nerve cell had dendritic inputs, identified as A, B, C and so on to Z, which could then fire, when it received inputs at ABC or DEF. Recognized combinations. The cell could identify ABC and not ABD. ABD might be impaired. This recognition process was recently reported by science for olfactory neurons. In the experiment scientists reported that even slight changes in chemical structure activated different combinations of receptors. Thus, octanol smelled like oranges, but the similar compound octanoic acid smell of sweat. A Nobel Prize recognized the discovery in 2004.

Galactic memories of nerve cells

Combinatorial codes widely used in nature. The four "letters" of genetic code - A, C, G and T - were used in combinations to create an almost infinite number of sequences. IA examines the deeper implications of this discovery of coding. The animals can differentiate between millions of smells. Dogs can quickly sniff a few footprints of a person and find out exactly how the person was walking. The animal's nose can detect the relative difference between odor strength traces only yards away, to determine the direction of a road. The smell was identified through remembered combinations. If a nerve cell had just 26 entries from A to Z, would receive millions of possible combinations of inputs. The average neuron, thousands of entries. For IA, millions of nerve cells could lead to galactic back memories for combinations, allowing you to recognize subtle patterns in the environment. Each cell can be a single member of a database, eliminating himself (being disabled) for unrecognized combinations of inputs.

Elimination of the key

Elimination was the special key, which evaluated combining great memories. Medical texts reported that the mind had a hierarchy of intelligences, which perform specific tasks. For example, there was an association region, which recognizes a pair of scissors in the context of their environment. If they get hurt in this region could still feel the scissors with your eyes closed, but not recognize it as scissors. I still felt the context, but does not recognize the object. So, intuition could enable nerve cells in regions of the association to use the perception to recognize objects. Medical research reported in many regions of this recognition.

serial processing

A pattern recognition algorithm, intuition enabled the finite intelligences in the minds of living things to respond holistically within the time frame of 20 milliseconds. These intelligences acted serially. The first intelligence converted the kaleidoscopic combinations of sensory perceptions of the environment into nerve impulses. The second intelligence recognized these impulses as objects and events. The third intelligence translated the recognized events in feelings. A fourth feelings translated into smart drives. The fear triggered an exhaust unit. A deer crashed. A bird flew. A fish swam off. While activities run, fly and swim differently, which achieves the same objective of escaping. Inherited nerve cell memories powered units in context.

The mind - seamless pattern recognition

Half a second of 100 billion nerve cells to use context to eliminate irrelevance and deliver motor output. The time between the shadow and the scream. So, from input to output, the mind was a machine pattern recognition seamlessly powered by the key secret of intuition - the elimination of context, acquired and inherited large combinatorial memories in nerve cells.




 

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