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Is Uncertainty A Real World Problem?

Uncertainty can powerfully assist Expert Systems to diagnose problems by ignoring the “If, then..” routine and by following an elimination algorithm. This website suggests that intuition of the human mind follows such a routine. From its early beginnings, the Artificial Intelligence community sought to understand human intelligence by building computer programs, which exhibit intelligent behavior. AI researchers assume rightly that intelligence is essentially a problem solving ability. But, they make the mistake of assuming that such problems have clear logical, or mathematical solutions.

Unfortunately for them, problems in the real world do not present clear cut answers. The diagnosis of a disease does not follow simple logical paths; nor can it be calculated. If a patient has a group of symptoms, then she has a particular disease. But many of these symptoms are shared by other diseases. Occasionally, some of these symptoms may also be absent for the disease. The real world presents the problem of uncertainty.

  • In an application of artificial intelligence, Expert Systems manage goal oriented problem solving tasks by using the logic in the minds of experts in these fields.
  • One method is to search the knowledge base through "If, then..." rules.
  • But such searches paths increase exponentially, as the size of the knowledge base increases.
  • But, quick solutions are feasible, if the logic is used to eliminate unsuitable data from the entire knowledge base.
  • Using "fuzzy" logic concepts lead to unsatisfactory solutions.
  • The advantage of using elimination is that "uncertain" elements can be left in the knowledge base to be subsequently eliminated through "certain" considerations.
  • The elimination logic has proved to be effective in diagnostics.

An Astonishing Link Between AI And The Fascinating Nature Of Your Mind
This hypothesis
is unique
in accounting for the striking speed of human intuition; 
in offering simple new routines to control the mind; in revealing insights into the future of AI.  Where does the mind store its treasure trove of knowledge?  How does it retrieve solutions to topical problems from this almost infinite store?  These proposed explanations have been gathering millions of page views from around the world.  The 1989 beginning of this exciting mission was a revealing insight from a Prolog AI Expert System.  The Expert System could diagnose one out of 8 diseases hinged on the user entering answers to a long string of questions.  In contrast, a doctor could identify a disease out of 8000, without questions, with just a glance.  This is an unconventional hypothesis.  The idea stems from a single "Aha!" moment. That happened when the Expert System flashed its light on a single brilliant algorithm, which could be the secret behind the ability of the mind to recognize and act on a perceived pattern in milliseconds.

The Prolog Expert System could diagnose 8 diseases, which shared 13 symptoms. It used an algorithm, a step by step procedure, for the diagnosis. Out of curiosity, I began testing an alternate algorithm in a spreadsheet.  Its first step was to SELECT all diseases WITH a particular symptom. Contrary to my plan, the algorithm would DELETE all diseases WITHOUT the symptom. That reverse was caused by a chance double twist in its "if/then" logic.

So, when I clicked "Yes" for one particular symptom to test the first step, the spreadsheet DELETED 7 out of the 8 diseases, leaving behind just one disease.  Surprise!  That disease was indicated by that symptom.  In just one leap, it had proffered the correct diagnosis. As with the doctor, it was a split second verdict!  The algorithm had ELIMINATED all diseases without the symptom.  Was selective elimination from a known list the trick used by nature for its intuitions?

Could elimination provide a faster search strategy?  Since elimination shortened the steps, a programmer coded for me a new, more ambitious Expert System.  Instead of 8 diseases, it dealt with 225 eye diseases.  Its algorithm eliminated both irrelevant diseases and their connected questions, for each answer.  The Expert System was presented to a panel of doctors. "It identified Angular Conjunctivitis, without asking a single stupid question," said a doctor. The Expert System was satisfactorily diagnosing all the eye diseases in the textbook!  The algorithm was an impressive AI tool!  The year 1989 catalogued the premises, set out in these pages, explaining how the algorithm could be enabling the mind of a doctor to achieve split second diagnosis.

Could An Amazing Algorithm Have Stunning Control Over Your Mind?
This is what happens when an engineer researches the mind. Way back in 1989, the writer, an engineer, catalogued how the ELIMINATION approach of an AI Expert System could reveal a way by which the nervous system could store and retrieve astronomically large memories.  That historic insight is central to the six irresistible premises presented in this website. 

Behind the scenes, these premises conceal an eye-opening revelation.  About the incredible speed of intuition.  A physician is aware of thousands of diseases and their related symptoms.  How does he note a symptom and focus on a single disease in less than half a second?  How could he identify Disease X out of 8000 diseases with just a glance?  

First, the total born and learned knowledge available to the doctor could not exist anywhere other than as the stored/retrieved data within the 100 billion neurons in his brain.  The perceptions, sensations, feelings and physical activities of the doctor could only be enabled by the electrical impulses flowing through the axons of those neurons.  The data enabling that process could be stored as digital combinations.

Second, combinatorial decisions of neurons cannot be made by any entity other than the axon hillock, which decides the axonal output of each neuron.  The hillock receives hundreds of inputs from other neurons.  Each hillock makes the pivotal neuronal decision about received inputs within 5 milliseconds.  A
xon hillocks could be storing digital combinations.  It could be adding each new incoming digital combination to its memory store.  The hillock could fire impulses, if it matched a stored combination. If not, it could inhibit further impulses.  Using stored digital data to make decisions about incoming messages could make the axon hillocks intelligent.

Third, combinations are reported to enable a powerful coding mode for axon hillocks.  Olfactory combinatorial data is known (Nobel Prize 2004) to store memories for millions of smells.  Each one of 100 billion axon hillocks have around a 1000 links  to other neurons.  The hillocks can mathematically store more combinations than there are stars in the sky. Each new digital combination could be adding a new relationship link.  In this infinite store, specific axon hillocks could be storing all the symptom = disease (S=D) links known to the doctor as digital combinations.

Fourth, instant communication is possible in the nervous system.  Within five steps, information in one hillock can reach all other relevant neurons.  Just 20 Ms for global awareness.  Within the instant the doctor observes a symptom, 
feedback and feed forward links could inform every S=D link of the presence of the symptom. Only the S=D link of Disease X could be recalling the combination and recognizing the symptom.

Fifth, on not recognizing the symptom, all other S=D hillocks could be instantly inhibiting their impulses. The S=D links of Disease X could be continuing to fire. Those firing S=D link would be recalling past complaints, treatments and signs of Disease X, confirming the diagnosis.  This could be enabling axon hillocks to identify Disease X out of 8000 in milliseconds.  Eliminating improbable (unrecognized) prospects to arrive at a possible (recognized in the past) solution powers the powerful inductive logic of the mind!

Worldwide interest in this website acknowledges its rationale. Not metaphysical theories, but processing of digital memories in axon hillocks could be explaining innumerable mysteries of the mind.  Over three decades, this website has been assembling more and more evidence of the manipulation of emotional and physical behaviors by narrowly focused digital pattern recognition.  It has also received over 2 million page views from over 150 countries.

What Are Expert Systems?

The AI community was faced with the problem of ambiguity. In many fields, knowledge was found to be vague. Only the specialist knew when a particular symptom was applicable. There were experts in each field, with extensive prior knowledge. The AI objective was to transfer the logic of the decisions in the minds of experts into diagnostic programs. Expert Systems were expected to yield intelligent answers to the problems in the world. Expert Systems managed goal oriented problem solving tasks including diagnosis, planning, scheduling, configuration and design. In every case, the AI community avoided the blurred boundaries of problems.

How Do Rule based Expert Systems Work?
Expert Systems operated on certainties. One method of knowledge representation was through “If, then...” rules. When the “If” part of a rule was satisfied, then the “Then” part of the rule was concluded. If a particular symptom was present, then a specific disease could be selected. But knowledge was sometimes factual and at other times, vague. Factual knowledge had clear cause to effect relationships, where conclusions could be drawn from concrete rules.

Pain was one symptom of a disease. If the disease always exhibited pain, then pain pointed to the disease. But vague and judgmental knowledge presented a problem, when pain was present only occasionally. Such ambiguity was useless information for rule based Expert Systems. Many diseases also shared many symptoms. So, Expert Systems conducted back and forth searches, till they located a particular disease, which presented all the symptoms.

What Is The Exponential Growth Problem?
The back and forth searches of the Expert Systems faced problems with large databases. In theory, the search process took twice the time for each newly added symptom. If a search for a single symptom takes 1 second, 2 symptoms require 2 seconds and 12 symptoms will take more than 4 HOURS! Any doctor can evaluate thousands of symptoms within milliseconds. But this problem can be solved if the whole database is evaluated on an elimination basis. Any diseases, which are clearly unrelated to the presence, or absence of particular symptoms are eliminated from the database. If a symptom is present, all diseases, which positively do not present the symptom are eliminated. If a symptom is absent, all diseases, which positively present the symptom are eliminated. This process can swiftly evaluate any reasonable database and effectively handle uncertainty.

Can Uncertainty Become An Advantage?

The AI community tried to solve the ambiguity problem by suggesting a statistical, or heuristic analysis of uncertainty. The possibilities were represented by real numbers or by sets of real-valued vectors. The vectors were evaluated by means of different “fuzzy” concepts. The components of the measurements were listed, giving the basis of the numerical values. Variations were combined, using methods for computing combination of variances. The combined uncertainty and its components were expressed in the form of “standard deviations.”

Uncertainty was given a mathematical expression, which was hardly useful in the diagnosis of a disease. Instead of delivering a mathematical formula as diagnosis, ambiguity can become powerfully useful information for other members of the database. Pain may be only occasionally present for some diseases, but will be certain for some and completely absent for many others. The search process eliminates the "certain" diseases. Only an elimination routine can take advantage of uncertainty.

How Does Intuition Work?
The human mind does not compute mathematical relationships to solve problems. The mind knows that a particular symptom points to a possibility, because it uses 
intuition, a process of elimination, to instantly identify patterns. Vague information is powerfully useful to an elimination process, since it eliminates myriad other possibilities. If the patient lacks pain, all diseases, which always exhibit pain, are eliminated. Diseases, which sometimes exhibit pain are retained. Further symptoms help identification from a greatly reduced database. The final choice is easier from a smaller group. Uncertainty can be powerfully useful for an elimination process.

Can Elimination Deliver An Expert System?
Walter Freeman, the famous neurobiologist outlined the process: “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.” An elimination algorithm can evaluate the whole database, eliminating every context which does not fit. This algorithm has been proved. It has powered Expert Systems which speedily recognize a disease, identify a case law or diagnose the problems of a complex machine. These systems are swift, holistic, and logical. If several parallel answers are presented, as in the multiple parameters of a power plant, recognition can even be instant. For the mind, where millions of parameters are simultaneously presented, real time pattern recognition is practical. And elimination is the key, which can conclusively handle uncertainty, without delivering a perplexing statistical calculations as the diagnosis.

This page was last updated on 12-Sep-2016.

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