Artificial Intelligence And Intuition

The intuitive algorithm

Roger Penrose considered it impossible. Believing might never mimic a computer process. He said as much in his book, The Emperor's New Mind. However, a new book, The Intuitive Algorithm, (IA), suggested that intuition was a pattern acknowledgment process. Intuition moved details through lots of neural regions like a lightning streak. Data moved from input to output in a reported 20 milliseconds. The mind saw, recognized, translated and acted. In the blink of an eye. Myriad procedures converted light, sound, touch and odor instantly into your nerve impulses. A devoted region recognized those impulses as things and events. The limbic system, another region, interpreted those events to generate feelings. A 4th region responded to those feelings with actions. The mind perceived, determined, evaluated and acted. Intuition got you off the hot stove in a split second. And it could be utilizing an easy algorithm.

Is immediate holistic examination impossible?

The system, with over a hundred billion nerve cells, processed the info from input to output in just half a second.

All your understanding was evaluated. Walter Freeman, the well-known neurobiologist, defined this fantastic ability. "The cognitive men think it's simply difficult to keep throwing whatever you've entered into the computation each time. However, that is exactly what the brain does. Awareness is about bringing your entire history to bear upon your next action, your next breath, your next minute." The mind was holistic. It assessed all its knowledge for the next activity. How could a lot details be processed so rapidly? Where could such knowledge be kept?

Exponential development of the search path

Unfortunately, the recognition of subtle patterns presented powerful problems for computer systems. The difficulty was an exponential development of the acknowledgment search path. The issues in the medical diagnosis of diseases was normal. Typically, numerous shared signs were presented by a wide variety of diseases. For instance, discomfort, or fever might be suggested for numerous diseases.

Each symptom pointed to a number of illness. The problem was to acknowledge a single pattern among numerous overlapping patterns. When searching for the target illness, the very first selected condition with the first provided sign might lack the second symptom. This meant back and forth searches, which expanded greatly as the database of diseases increased in size. That made the process absurdly long drawn-- in theory, even years of search, for comprehensive databases. So, in spite of their incredible speed, quick pattern recognition on computers might never ever be envisioned.

The Intuitive Algorithm

But, market strength pattern recognition was possible. IA presented an algorithm, which might immediately acknowledge patterns in extended databases. The relationship of each member of the entire database was coded for each concern.

(Is pain a symptom of the illness?)

Disease1Y, Disease2N, Disease3Y, Disease 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 utilize elimination to evaluate the database, not selection.

Every member of the database was individually coded for removal in the context of each response.

(Is pain a symptom of the disease? Response: 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, Disease 44U, Disease45Y, xxxxxx46N, xxxxxx47N, Disease 48Y,

(All "N" Diseases removed.)

For disease recognition, if an answer indicated a sign, IA eliminated all illness without the sign. Every response gotten rid of, narrowing the search to reach medical diagnosis.

(Is pain a symptom of the disease? Response: 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, Disease 44U, xxxxxx45Y, Disease46N, Disease47N, xxxxxx48Y,

(All "Y" Diseases eliminated.)

If the sign was absent, IA got rid of all diseases which always showed the symptom. Diseases, which randomly presented the sign were kept in both cases. So the process managed uncertainty-- the "Maybe " response, which normal computer programs might not deal with.

(A sequence of concerns limits 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 illness are removed, the disease is unknown.)

Instantaneous pattern acknowledgment

IA was proved in practice. It had actually powered Expert Systems showing the speed of a basic recalculation on a spreadsheet, to recognize a disease, recognize a case law or detect the problems of an intricate machine.

It was immediate, holistic, and rational. If numerous parallel answers could be presented, as in the multiple criteria of a power plant, acknowledgment was instantaneous. For the mind, where countless parameters were simultaneously provided, actual time pattern acknowledgment was practical. And elimination was the key.

Removal = Switching off

Elimination was turning off - inhibition. Afferent neuron were understood to thoroughly prevent the activities of other cells to highlight context. With access to millions of sensory inputs, the nerve system instantly prevented-- eliminated trillions of combinations to no in on the best pattern. The procedure stoutly used "No" responses. If a client did not have pain, thousands of possible diseases could be ignored. If a patient might simply stroll into the surgical treatment, a physician could neglect a wide variety of illnesses. However, how could this process of elimination be used to afferent neuron? Where could the wealth of knowledge be stored?

Combinatorial coding

The mind got kaleidoscopic mixes of countless sensations.

Of these, smells were reported to be acknowledged through a combinatorial coding process, where afferent neuron recognized combinations. If a nerve cell had dendritic inputs, identified as A, B, C and so on to Z, it might then fire, when it received inputs at ABC, or DEF. It recognized those mixes. The cell could determine ABC and not ABD. It would be prevented for ABD. This recognition procedure was just recently reported by science for olfactory neurons. In the experiment researchers reported that even small changes in chemical structure activated various combinations of receptors. Thus, octanol smelled like oranges, however the similar substance octanoic acid smelled like sweat. A Nobel Prize acknowledged that discovery in 2004.

Stellar afferent neuron memories

Combinatorial codes were thoroughly used by nature. The 4 "letters" in the hereditary code-- A, C, G and T-- were used in combinations for the creation of a nearly infinite variety of hereditary sequences.

IA goes over the much deeper implications of this coding discovery. Animals might differentiate between countless smells. Canines could rapidly sniff a few footprints of an individual and determine properly which way the person was strolling. The animal's nose might find the relative smell strength difference in between footprints only a few feet apart, to identify the direction of a trail. Odor was recognized through remembered mixes. If an afferent neuron had simply 26 inputs from A to Z, it might receive millions of possible mixes of inputs. The average neuron had thousands of inputs. For IA, millions of afferent neuron might offer the mind galactic memories for mixes, enabling it to recognize subtle patterns in the environment. Each cell could be a single member of a database, eliminating itself (becoming hindered) for unacknowledged mixes of inputs.

Removal the essential

Removal was the unique secret, which evaluated vast combinatorial memories.

Medical texts reported that the mind had a hierarchy of intelligences, which performed dedicated tasks. For instance, there was an association area, which acknowledged a set of scissors utilizing the context of its feel. If you hurt this region, you could still feel the scissors with your eyes closed, however you would not acknowledge it as scissors. You still felt the context, however you would not acknowledge the things. So, intuition might make it possible for afferent neuron in association regions to utilize understanding to recognize items. Medical research study reported lots of such acknowledgment areas.

Serial processing

A pattern acknowledgment algorithm, intuition made it possible for the limited intelligences in the minds of living things to react holistically within the 20 millisecond time span. These intelligences acted serially. The very first intelligence transformed the kaleidoscopic mixes of sensory perceptions from the environment into nerve impulses. The second intelligence acknowledged these impulses as items and events.

The 3rd intelligence equated the acknowledged events into sensations. A fourth translated sensations into smart drives. Worry activated an escape drive. A deer bounded away. A bird took flight. A fish swam off. While the activities of running, flying and swimming differed, they achieved the same goal of getting away. Acquired afferent neuron memories powered those drives in context.

The mind-- seamless pattern recognition

Half a 2nd for a 100 billion nerve cells to utilize 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 seamless pattern acknowledgment machine, powered by the crucial secret of intuition-- contextual elimination, from huge obtained and inherited combinatorial memories in nerve cells.