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Artificial Intelligence And Intuition
By Abraham Thomas, Fri Dec 9th
The intuitive algorithm Roger Penrose considered it impossible. Thinking could neverimitate a computer process. He said as much in his book, TheEmperor's New Mind. But, a new book, The Intuitive Algorithm,(IA), suggested that intuition was a pattern recognitionprocess. Intuition propelled information through many neuralregions like a lightning streak. Data moved from input to outputin a reported 20 milliseconds. The mind saw, recognized,interpreted and acted. In the blink of an eye. Myriad processesconverted light, sound, touch and smell instantly into yournerve impulses. A dedicated region recognized those impulses asobjects and events. The limbic system, another region,interpreted those events to generate emotions. A fourth regionresponded to those emotions with actions. The mind perceived,identified, evaluated and acted. Intuition got you off the hotstove in a fraction of a second. And it could be using a simplealgorithm. Is instant holistic evaluation impossible? The system, with over a hundred billion neurons, processed theinformation from input to output in just half a second. All yourknowledge was evaluated. Walter Freeman, the famousneurobiologist, defined this amazing ability. "The cognitiveguys think it's just impossible to keep throwing everythingyou've got into the computation every time. But, that is exactlywhat the brain does. Consciousness is about bringing your entirehistory to bear on your next step, your next breath, your nextmoment." The mind was holistic. It evaluated all its knowledgefor the next activity. How could so much information beprocessed so quickly? Where could such knowledge be stored? Exponential growth of the search path Unfortunately, the recognition of subtle patterns posedformidable problems for computers. The difficulty was anexponential growth of the recognition search path. The problemsin the diagnosis of diseases was typical. Normally, many sharedsymptoms were presented by a multitude of diseases. For example,pain, or fever could be indicated for many diseases. Eachsymptom pointed to several diseases. The problem was torecognize a single pattern among many overlapping patterns. Whensearching for the target disease, the first selected ailmentwith the first presented symptom could lack the second symptom.This meant back and forth searches, which expanded exponentiallyas the database of diseases increased in size. That made theprocess absurdly long drawn – theoretically, even years ofsearch, for extensive databases. So, in spite of theirincredible speed, rapid pattern recognition on computers couldnever be imagined. The Intuitive Algorithm But, industry strength pattern recognition was feasible. IAintroduced an algorithm, which could instantly recognizepatterns in extended databases. The relationship of each memberof 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, notselection. Every member of the database was individually codedfor 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, IAeliminated all diseases devoid of the symptom. Every answereliminated, 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 whichalways exhibited the symptom. Diseases, which randomly presentedthe symptom were retained in both cases. So the process handleduncertainty – the “Maybe” answer, which normal computer programscould not handle. (A sequence of questions narrows down to Disease27 - the answer.) xxxxxx1Y, xxxxxx2N, xxxxxx3Y, xxxxxx4Y, xxxxxx5N, xxxxxx6N,xxxxxx7Y, xxxxxx8N, xxxxxx9N, xxxxxx10N, xxxxxx11Y, xxxxxx12Y,xxxxxx13N, xxxxxx14U,
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