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Intelligence tests and psychometrics have generally found high correlations between different aspects of intelligence, rather than the low correlations which Gardner's theory predicts, supporting the prevailing theory of general intelligence rather than multiple intelligences (MI). [1]
Gardner's theory of multiple intelligences can be seen as both a departure from and a continuation of the 20th century's work on the subject of human intelligence. Other prominent psychologists whose contributions variously developed or expanded the field of study include Charles Spearman, Louis Thurstone, Edward Thorndike, and Robert Sternberg.
Preface The third edition was significantly changed, adding a section on learning, including neural networks. The third edition was also updated to reflect changes in computer performance that had occurred since the second edition was published, and to address artificial intelligence at scale. [ 3 ] :
The book concludes with the statement that only some universes of the multiple universes (or multiverse) support life forms and that we are located in one of those universes. The laws of nature that are required for life forms to exist appear in some universes by pure chance [ clarification needed ] , Hawking and Mlodinow explain (see Anthropic ...
Soar [1] is a cognitive architecture, [2] originally created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University.. The goal of the Soar project is to develop the fixed computational building blocks necessary for general intelligent agents – agents that can perform a wide range of tasks and encode, use, and learn all types of knowledge to realize the full range of ...
It is unknown whether human-level artificial intelligence will arrive in a matter of years, later this century, or not until future centuries. Regardless of the initial timescale, once human-level machine intelligence is developed, a "superintelligent" system that "greatly exceeds the cognitive performance of humans in virtually all domains of interest" would most likely follow surprisingly ...
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The intermediate (thin) rectangles represent the hypotheses in the version space. Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis space is a disjunction [1]