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Published byJulius Logan Modified over 9 years ago
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Prominent AI Reseacher Colleague of Alan Turing at Bletchley Park 1992 Paper: ◦ Turing’s Test and Conscious Thought Turing’s Test and Conscious Thought ◦ Provides a critique of the test
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Solipsism and the “Charmed Circle” ◦ “…Turing underestimated the appeal of a more subtle form of solipsism generalized to groups.” ◦ The argument can be stated as: “the only way by which one could be sure that a machine thinks is to be a member of a charmed circle which has accepted that machine into its ranks and can collectively feel itself thinking.”
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Subarticulate Thought ◦ “The test can only detect only those processes that are susceptible to introspective verbal report.” Many thought processes that cannot be articulated by humans A machine might be able to articulate them, even when a human cannot. ◦ Most highly developed mental skills are of the verbally inaccessible kind (Hutchins) ◦ “Expert Systems” famously failed in knowledge extraction through dialog-acquisition.
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Consciousness and Human-Computer Interaction ◦ What story is assigned to a sequence of events? ◦ Cutaneous Rabbit 5 taps on the wrist 2 near the elbow 3 at the upper arm
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Chinese Room ◦ Consider a program that can appear intelligent in conversation in Chinese ◦ Suppose that someone who doesn’t speak Chinese executes the program “by hand” ◦ The non-Chinese speaker does not understand the conversation, just as a computer does not understand the conversation.
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A successful Turing Test could be accomplished through table lookup (given a large enough memory) Is this really intelligence?
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Turing’s test might not be passed in the foreseeable future, but that doesn’t really matter. Let machines make progress without the requirement that they imitate people Computers will provide their own contributions without the need for imitation.
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Weak AI ◦ How the task is accomplished doesn’t matter ◦ We can use a mechanism vastly different than what humans do ◦ Success is based strictly on performance Strong AI ◦ Tasks should use the same mechanisms used by humans ◦ We want to duplicate human intelligence ◦ We want machines to be conscious of what they are doing
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Defined by a set of problems that are generally considered to require intelligence in humans ◦ Knowledge Processing ◦ Natural Language Understanding ◦ Game Players ◦ Diagnostic/Classification Problems ◦ Machine Learning
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“Rules of Thumb” ◦ Methods that tend to work, but don’t guarantee success. Find a simpler problem you know how to solve and try to generalize to the larger problem Work backwards from the goal state
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In the 1970’s and 1980’s many people believed “expert systems” would replace many if not most experts “Knowledge Engineers” were tasked with extracting and encoding knowledge from experts. It didn’t work very well, largely because much if not most expertise is subarticulate.
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Puzzle solving Finding the best of a set of possible permutations
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Chess Checkers Go Chinese Chess Dots and Boxes
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Given a set of facts, deduce “useful” conclusions ◦ Representation of facts ◦ Method used for deduction ◦ Identification of “useful” facts
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If (some criteria) then some fact If (some criteria) then perform some action Expert Systems were often produced using production rules.
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Simplified model of basic building blocks of the brain Much smaller number of neurons Much simpler model of how neurons work Neural Networks are used in many pattern matching/classification/generalization problems.
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Simulate evolution Natural selection used as a form of search ◦ Genetic Algorithms A population of simulated genes evolves in an attempt to solve a problem ◦ Genetic Programming A population of programs evolves in an attempt to solve a problem
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