<p>Based on this critical insight, this essay presents a hypothesis concerning the mind. It suggests where human memory may be stored, how memory can be recalled, how objects and events may be recognised, and how the mind may control the body. The thesis suggests how emotions, judgement and will may finally manipulate the system.
<p>&quot; .............. The concept of an intuitive algorithm may provide us a key to the mechanisms and working of the human brain and the concept of &quot;MIND&quot;. Dr.K.Jagannathan, MD DTM FAMS, Consultant Neurologist.
<p>&quot;........... The tenet of <i>The Intuitive Algorithm</i> raises innovative and interesting questions on the very basis of intuitive thinking.&quot; Dr.Prithika Chary MD DM(Neuro) PhD (Neuro) MNAMS (Neuro) MCh (Neurosurgey) Neurologist &amp; Neurosurgeon. Recipient - Indian Council of Medical Research Award for Outstanding Woman Scientist of The Year 1982.<p>&quot;............ A highly commendable intellectual endeavour, which can provide leads to researchers in artificial intelligence, cognitive sciences and advanced computer systems&quot;. Dr.K.Sundaram, PhD., Head of the Department of Computer Science, University of Madras, Principal Contributions in Bio-Physics and Computer Science at the University, at the All India Institute of Medical Sciences, New Delhi and at NASA, U.S.A.
<p><b>CONTENTS:</b>
<p><b><A HREF="#1">Barriers to understanding the mind</A>. </b>How does the mind internally represent information? How does it instantly isolate a single pattern from a mass of interveawing patterns? How does it handle &quot;uncertainty&quot;? How does it achieve this in an astronomically large search space? How is such speed achieved despite slower neuronal transmissions? Does it use a reasoning process? Where is memory stored? A brief survey of these issues.
<p><b><A HREF="#2">A new algorithm</A>. </b>Describes an algorithm, which successfully diagnoses diseases. Essentially, it reverses the logic of the search process from selection to elimination, to achieve remarkably speedy results.
<p><b><A HREF="#3">Instant recognition</A>.</b> When presented with unique links, the algorithm achieves instant recognition in massive search spaces. It logically handles uncertainty, avoids stupid questions and is holistic. It also ignores the age old reasoning chains of science, travelling a new avenue in the application of inductive logic.
<p><b><A HREF="#4">The nerve cell and recognition</A>. </b>Currently, nerve cells are believed to be computational devices. A new recognition role is suggested for neurons. They may recognise incoming patterns. Recognition may explain such phenomena as the modification of pain, the focus of attention, awareness and consciousness.
<p><b><A HREF="#5">Memory</A>. </b>Recognition is &quot;the establishment of an identity&quot;. It may be achieved by comparing the features of an entity to those in memory. Recognition may mandate memory. Nerve cells may carry such memory. Feelings may be nerve impulses, the recognition of which may provide context for the recall of memory.
<p><b><A HREF="#6">Recognition of objects</A>. </b>Nerve channels project from point to point, observing neighbourhood relationships. Such mapping may suggest a matrix type transmission. Intuition may be the instant recognition of such cyclic transmitted pictures. Cortical association regions recognise objects and may transmit pictures, for recognition by the system.
<p><b><A HREF="#7">Motor control</A>. </b>Instant intuitive recognition of pictures may empower motor control functions. Persisting iterating patterns may form the basis for achieving objectives. Such goal patterns may be triggered by feelings. Habitual activities may be recalled through intuitive and iterative pattern recognition by the cerebellum.
<p><b><A HREF="#8">Event recognition</A>. </b>Intuitive iterating patterns are suggested as enabling the recognition of events. Event recognition may be the key to complex thought processes. Event recognition may automatically trigger feelings.
<p><b><A HREF="#9">The goal drive</A>. </b>Iterating goal patterns may provide basic drives and long term goals and may represent the &quot;purpose&quot; of the system. Purpose is set by the current feeling. The will of the system may be decided by the limbic system which may determine the &quot;current feeling&quot;.
<p><b><A HREF="#10">The mind</A>. </b>Consciousness may be an independent intelligence, which expresses judgment and will, and resides in a restricted group of nerve channels. The limbic system may over rule will to determine the current feeling and hence set goals for the system.
<p><b><A HREF="#11">An expert system shell</A>.</b> Details of the design of an AI shell program, which can be utitlised to create expert systems. Explains simple method of knowledge input. Suggests areas in which expert systems can be helpful.
<p><b><A HREF="#12">References</A>.</b>
<A NAME="1"><p align="center"><H1>Barriers to Understanding the Mind</H1>
<p><b>Artificial Intelligence awaits a breakthrough. </b>This essay concerns Artificial Intelligence, pattern recognition and the concept of mind. The first of these, the term &quot;Artificial Intelligence&quot; (AI) originated in the early sixties, representing, at the time, an ambitious effort to define human intelligence for simulation by machines. The AI effort has succeeded in solving many problems which were believed to require intelligence, including those in information processing, pattern recognition, game playing and medical diagnostics. Yet, several decades later, as continuing research unravels the awesome complexity of the mind, the scientific community has serious doubts as to whether true AI can ever be developed. AI faces a series of hurdles in defining human intelligence. A new view from a different perspective may overcome some of these restraints.
<p><b>The problem of internal representation. </b>The primary restraint is the mystery surrounding the internal language of the mind. An information processing system may receive data as language, formulae, or even digital readouts. The system must translate these into its own internal representation. Computers manage with the digital format. These are stored in memory, recalled, processed and then translated into an acceptable output mode. In AI, problems are translated into specialised languages. Problem specific languages assist programs to play chess, or diagnose diseases. This need for specialised languages partitions AI solutions into compartments. There is no single way in which problems can be represented in AI to tackle chess, diagnostics, chemical analysis and banking. While the ultimate goal of AI may be to become a single equivalent to human intelligence, its own languages fail to communicate with each other. As opposed to this, the internal language used by the mind appears to fathom the whole world as we know it. This mystery is sought to be addressed in this essay, using the logic of a new algorithm. The logic may point to a single internal representation, for use by the mind. This may be its own interior language of communication.
<p><b>Pattern Recognition. </b>The second issue that has baffled AI researchers is the problem of how to identify a problem as belonging to the field of mathematics, vision, or game playing, even before attempting to solve it. With its abstract qualities, one can see difficulties in identifying a problem. Let alone identify a problem, AI efforts have failed to even identify a tangible physical object, such as a face. Today, in spite of huge advances in technology, a computer cannot identify a particular face as belonging to a particular person. The difficulty is that all recognisable objects and events in our environment have innumerable shared qualities. For a computer, they form trillions of patterns, which overlap each other. Establishing the identity of a single pattern among a range of overlapping patterns is called pattern recognition. The recognition of a known face is a pattern recognition task. In AI, a computer algorithm may follow a logical procedure to solve this problem. A pattern recognition algorithm may attempt to establish the identity of a seen pattern through a sequence of logical steps. It may seek to identify a seen face as one belonging to a known person.
<p><b>An exact match an impossibility. </b>Current AI algorithms attempt to identify a pattern by matching its characteristics strictly with that of a known pattern. The characteristics of known patterns can be stored in the memory of computers for recall. Consider the problems in the recognition of a face. There are billions of faces in the world. They share thousands of common features. The characteristics of colour, skin texture, facial features and makeup overlap each other on a virtually infinite scale. People age, grow beards or change appearances with moods. The changes caused by light and shade add further complexity. In such an environment, where patterns themselves have millions of shifting characteristics, it is virtually impossible to find an exact match even if patterns are matched at the microscopic level of detail. This essay suggests an algorithm which can establish the identity of a pattern in such a complex and changing environment.