Artificial Intelligence

Roger Penrose considered it impossible. Thinking could never

imitate a computer process. He said as much in his book, The

Emperor's New Mind. But, a new book, The Intuitive Algorithm,

(IA), suggested that intuition was a pattern recognition

process. Intuition propelled information through many neural

regions like a lightning streak. 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, sound, touch and smell

instantly into your nerve impulses. A dedicated region

recognized those impulses as objects and events. The limbic

system, another region, interpreted 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 using a simple algorithm.

Is instant holistic evaluation impossible?

The system, with over a hundred billion neurons, processed

the information from input to output in just half a second.

All your knowledge was evaluated. Walter Freeman, the famous

neurobiologist, defined this amazing ability. "The cognitive

guys think it's just impossible to keep throwing everything

you've got into the computation every time. But, that is

exactly what the brain does. Consciousness is about bringing

your entire history to bear on your next step, your next

breath, your next moment." The mind was holistic. It

evaluated all its knowledge for the next activity. How could

so much information be processed so quickly? Where could such

knowledge be stored?

Exponential growth of the search path

Unfortunately, the recognition of subtle patterns posed

formidable problems for computers. The difficulty was an

exponential growth of the recognition search path. The

problems in the diagnosis of diseases was typical. Normally,

many shared symptoms were presented by a multitude of

diseases. For example, pain, or fever could be indicated for

many diseases. Each symptom pointed to several diseases. The

problem was to recognize a single pattern among many

overlapping patterns. When searching for the target disease,

the first selected ailment with the first presented symptom

could lack the second symptom. This meant back and forth

searches, which expanded exponentially as the database of

diseases increased in size. That made the process absurdly

long drawn – theoretically, even years of search, for

extensive databases. So, in spite of their incredible speed,

rapid pattern recognition on computers could never be

imagined.

The Intuitive Algorithm

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 database was coded for each question.

(Is pain a symptom of the disease?)

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 use elimination to evaluate the database, not

selection. Every member of the database was individually

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, Disease 44U, Disease45Y, xxxxxx46N,

xxxxxx47N, Disease 48Y,

(All "N" Diseases eliminated.)

For disease recognition, if an answer indicated a symptom, IA

eliminated all diseases devoid 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, Disease 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 randomly

presented the symptom were retained in both 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 disease is unknown.)

Instant pattern recognition

IA was proved in practice. It had powered Expert Systems

acting with the speed of a simple recalculation 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 several parallel answers could be

presented, as in the multiple parameters of a power plant,

recognition was instant. For the mind, where millions of

parameters were simultaneously presented, real time pattern

recognition was practical. And elimination was the key.

Elimination = Switching off

Elimination was switching off - inhibition. Nerve cells were

known to extensively inhibit the activities of other cells to

highlight context. With access to millions of sensory inputs,

the nervous system instantly inhibited – 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 just 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? Where could the wealth

of knowledge be stored?

Combinatorial coding

The mind received kaleidoscopic combinations of millions of

sensations. Of these, smells were 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, it could then

fire, when it received inputs at ABC, or DEF. It recognized

those combinations. The cell could identify ABC and not ABD.

It would be inhibited for ABD. 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 smelled like sweat. A Nobel

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 creation of a nearly infinite number of

genetic sequences. IA discusses the deeper implications of

this coding 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 detect the relative odour

strength difference between footprints only a few feet apart,

to determine the direction of a trail. Smell was identified

through remembered combinations. If a nerve cell had just 26

inputs from A to Z, it could receive millions of possible

combinations of inputs. The average neuron had thousands of

inputs. For IA, millions of nerve cells could give the mind

galactic memories for combinations, enabling 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 vast

combinatorial memories. Medical texts reported 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 using the context of its feel.

If you injured this region, you could still feel the scissors

with your eyes closed, but you would not recognize it as

scissors. You still felt the context, but you would not

recognize the object. So, intuition could enable nerve cells

in association regions to use perception to recognize

objects. Medical research reported many such recognition

regions.

Serial processing

A pattern recognition algorithm, intuition enabled the finite

intelligences in the minds of living things to respond

holistically within the 20 millisecond time span. These

intelligences acted serially. The first intelligence

converted the kaleidoscopic 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 deer

bounded away. A bird took flight. A fish swam off. While the

activities of running, flying and swimming differed, they

achieved the same objective of escaping. Inherited nerve cell

memories powered those drives in context.

The mind – seamless pattern recognition

Half a second for a 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 seamless pattern recognition machine, powered

by the key secret of intuition – contextual elimination, from

massive acquired and inherited combinatorial memories in

nerve cells.