The Artificial Intelligence community failed to grasp the power
of the mind, the most powerful intelligence in the universe,
because they used computational models. They wrongly believed
that intelligence was the achievement of life goals through
computation. The AI study was set in motion by the arrival of
computers in the 1940s, on the basic premise that the brain did
some kind of computation. Alan Turing was one of the first to
work on intelligent machines by programming computers.
Algorithmic procedures did enable programs to achieve striking
results. Computers could solve complex mathematical and
engineering problems. A few scientists even believed that a
large enough assembly of programs and collated knowledge could
achieve human level intelligence.
While there could be other possible ways, computer programs were
the best available resource for attempting to simulate a human
level intelligence. But, in the 1930s mathematical logicians,
including Turing and Godel, established that algorithms could
not be guaranteed to solve problems in certain mathematical
domains. Both the theory of computational complexity, which
defined the difficulty of general classes of problems and the AI
community failed to identify the properties of problems and
problem solving methods, which enabled humans to solve problems.
Every direction of search seemed to lead only to dead ends.
The AI community could not design a machine, which could learn
and become significantly intelligent. No program could learn
much by reading. Computers could use vast computational
capabilities to play chess at grandmaster level, but their
intelligence was limited. Parallel processing computers looked
promising, but proved difficult to program. Computer programs
could only solve domain specific problems. They could not
distinguish between problems, or be a “General Problem Solver.”
Since humans could solve problems in unique domains, Roger
Penrose argued that computers were intrinsically incapable of
achieving human intelligence. The philosopher Hubert Dreyfus
also suggested that AI was impossible. But, the AI community
continued its search, even though most researchers felt the need
for new fundamental ideas. In the end, the general consensus was
that computers were only “somewhat intelligent.” So, was the
basic definition of “intelligence” itself wrong?
Since much of human intelligence was little understood, it was
impossible to define a particular computational procedure as
being intelligent. Intelligence was clearly an ability to solve
problems. In nature, it was a matured intelligence,which
empowered the “homeostasis” of animals in the survival process.
Homeostasis was the ability of an entity to function normally,
achieving a relatively constant state within the body, in a
changeable, or even hostile, environment. It was an intelligent
process, internally maintained by the animals at many levels,
through various sensing, feedback and control systems,
supervised by a hierarchy of control centers. This process,
achieved by even the lowest animal was the ultimate “General
Problem Solver.” The process was not domain specific. It
recognized problems and responded with effective motor activity.
It applied to every aspect of survival.
The nervous system received a kaleidoscopic combination of
trillions of sensory inputs. A phenomenal memory enabled it to
remember and identify patterns. Intuition, an algorithmic
process, enabled it to isolate the context of a single pattern
from a galactic memory. The system could identify objects from
millions of received sensory inputs. That pattern recognition
ability was not limited to the identification of static objects.
It could identify problems. It recognized and interpreted
dynamic events to generate patterns of emotions. Emotions
clearly defined problems. Animals recognized the difference
between a friendly nudge and a deadly slither and responded.
Fear, anger, or jealousy motivated them. Each motor response had
a particular sequence of problem solving steps, which were,
again, remembered patterns of activities.
The environment presented the system with millions of enigmatic
phenomena. Many of these were caused by other phenomena. Most
problems were patterns of events, which had contextual links to
remembered successful problem solving strategies. Pattern
recognition enabled identification. The process was not domain
specific. It straddled the entire problem solving domain.
Pattern recognition merely identified the link between one
phenomenon and another. Intuition instantly identified the
contextual link. It did not identify the complex reasoning links
between the two. It did not use incremental logical steps to
solve problems. When primitive man took shelter when the storm
clouds advanced, he was merely responding to a perceived
pattern.
Across thousands of years, mankind responded adequately to much
of nature, without understanding underlying causes. That
intelligence was not computation, which reasoned its way through
life, by analyzing the logical and mathematically precise links
between particular causes and their effects. The reasons behind
causes were discovered only later, with advanced study and
research. Such analysis benefited only a minor segment of the
problem solving world. A group of symptoms related to a disease.
Physicians identified illnesses, without always knowing the
logical or reasoned links between the symptom and the disease.
Software code was logical. But, many quirks of complex code were
patterns of effects, related to particular programming events,
which could only be recognized by a pattern recognition
intelligence. Complex problem solving was achieved through
sensitive pattern recognition. True intelligence was this
powerful pattern recognition capability, which also,
incidentally, discovered logic, reasoning and mathematics.
About the author:
Abraham Thomas is the author of The Intuitive Algorithm, a book,
which suggests that intuition is a pattern recognition
algorithm. The ebook version is available at
www.intuition.co.in. The book may be purchased only in India.
The website, provides a free movie and a walk through to explain
the ideas.