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.
