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Fluid Concepts and Creative Analogies
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Fluid Concepts and Creative Analogies
Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought is a 1995 book by Douglas Hofstadter and other members of the Fluid Analogies Research Group exploring the mechanisms of intelligence through computer modeling. It contends that the notions of analogy and fluidity are fundamental to explain how the human mind solves problems and to create computer programs that show intelligent behavior. It analyzes several computer programs that members of the group have created over the years to solve problems that require intelligence.
It was the first book ever sold by Amazon.com.
The book is a collection of revised articles that appeared in precedence, each preceded by an introduction by Hofstadter. They describe the scientific work by him and his collaborators in the 1980s and 1990s. The project started in the late 1970s at Indiana University. In 1983 he took a sabbatical year at MIT, working in Marvin Minsky's Artificial Intelligence Lab. There he met and collaborated with Melanie Mitchell, who then became his doctoral student. Subsequently, Hofstadter moved to the University of Michigan, where the FARG (Fluid Analogies Research Group) was founded. Eventually he returned to Indiana University in 1988, continuing the FARG research there. The book was written during a sabbatical year at the Istituto per la Ricerca Scientifica e Tecnologica in Trento, Italy..
Upon publication, Jon Udell, a BYTE senior technical editor-at-large said:
Fifteen years ago, Gödel, Escher, Bach: An Eternal Golden Braid exploded on the literary scene, earning its author a Pulitzer prize and a monthly column in Scientific American. Douglas Hofstadter's exuberant synthesis of math, music, and art, and his inspired thought experiments with "tangled hierarchy," recursion, pattern recognition, figure/ground reversal, and self-reference, delighted armchair philosophers and AI theorists. But in the end, many people believed that these intellectual games yielded no useful model of cognition on which to base future AI research. Now Fluid Concepts and Creative Analogies presents that model, along with the computer programs Hofstadter and his associates have designed to test it. These programs work in stripped-down yet surprisingly rich microdomains.
On April 3, 1995, Fluid Concepts and Creative Analogies became the first book ordered online by an Amazon.com customer.
The first AI project by Hofstadter stemmed from his teenage fascination with number sequences. When he was 17, he studied the way that triangular and square numbers interleave, and eventually found a recursive relation describing it. In his first course on AI, he set to the students and to himself the task of writing a program that could extrapolate the rule by which a numeric sequence is generated. He discusses breadth-first and depth-first techniques, but eventually concludes that the results represent expert systems that incarnate a lot of technical knowledge but don't shine much light on the mental processes that humans use to solve such puzzles.
Instead he devised a simplified version of the problem, called SeekWhence, where sequences are based on very simple basic rules not requiring advanced mathematical knowledge. He argues that pattern recognition, analogy, and fluid working hypotheses are fundamental to understand how humans tackle such problems.
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Fluid Concepts and Creative Analogies
Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought is a 1995 book by Douglas Hofstadter and other members of the Fluid Analogies Research Group exploring the mechanisms of intelligence through computer modeling. It contends that the notions of analogy and fluidity are fundamental to explain how the human mind solves problems and to create computer programs that show intelligent behavior. It analyzes several computer programs that members of the group have created over the years to solve problems that require intelligence.
It was the first book ever sold by Amazon.com.
The book is a collection of revised articles that appeared in precedence, each preceded by an introduction by Hofstadter. They describe the scientific work by him and his collaborators in the 1980s and 1990s. The project started in the late 1970s at Indiana University. In 1983 he took a sabbatical year at MIT, working in Marvin Minsky's Artificial Intelligence Lab. There he met and collaborated with Melanie Mitchell, who then became his doctoral student. Subsequently, Hofstadter moved to the University of Michigan, where the FARG (Fluid Analogies Research Group) was founded. Eventually he returned to Indiana University in 1988, continuing the FARG research there. The book was written during a sabbatical year at the Istituto per la Ricerca Scientifica e Tecnologica in Trento, Italy..
Upon publication, Jon Udell, a BYTE senior technical editor-at-large said:
Fifteen years ago, Gödel, Escher, Bach: An Eternal Golden Braid exploded on the literary scene, earning its author a Pulitzer prize and a monthly column in Scientific American. Douglas Hofstadter's exuberant synthesis of math, music, and art, and his inspired thought experiments with "tangled hierarchy," recursion, pattern recognition, figure/ground reversal, and self-reference, delighted armchair philosophers and AI theorists. But in the end, many people believed that these intellectual games yielded no useful model of cognition on which to base future AI research. Now Fluid Concepts and Creative Analogies presents that model, along with the computer programs Hofstadter and his associates have designed to test it. These programs work in stripped-down yet surprisingly rich microdomains.
On April 3, 1995, Fluid Concepts and Creative Analogies became the first book ordered online by an Amazon.com customer.
The first AI project by Hofstadter stemmed from his teenage fascination with number sequences. When he was 17, he studied the way that triangular and square numbers interleave, and eventually found a recursive relation describing it. In his first course on AI, he set to the students and to himself the task of writing a program that could extrapolate the rule by which a numeric sequence is generated. He discusses breadth-first and depth-first techniques, but eventually concludes that the results represent expert systems that incarnate a lot of technical knowledge but don't shine much light on the mental processes that humans use to solve such puzzles.
Instead he devised a simplified version of the problem, called SeekWhence, where sequences are based on very simple basic rules not requiring advanced mathematical knowledge. He argues that pattern recognition, analogy, and fluid working hypotheses are fundamental to understand how humans tackle such problems.