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CIS 830 (Advanced Topics in AI) Lecture 2 of 45 - KDD
CIS 830 (Advanced Topics in AI) Lecture 2 of 45 - KDD

... • Is restricted to non-recursive, prepositional(i.e.. Variable-free) Horn clauses • May be misled given highly inaccurate domain theory • Is problematic to extract information from ANNs after learning because some weight settings have no direct Horn clause analog. • Blackbox method, which provide go ...
NAME: _______________________________ DECEMBER 12, 2013 ARTIFICIAL INTELLIGENCE 2
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... 4. John Searle has long been a critic of Strong AI. a. Name his counter theory to the Turing Test. b. Discuss how this theory works. 5. Identify the four sources that demonstrate Evidence of Human Intelligence. 6. There are 4 stages of development according to Piaget. Name them. 7. Identify the stat ...
The 24 Hour Midterm (PDF Version)
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... 4. John Searle has long been a critic of Strong AI. a. Name his counter theory to the Turing Test. b. Discuss how this theory works. 5. Identify the four sources that demonstrate Evidence of Human Intelligence. 6. There are 4 stages of development according to Piaget. Name them. 7. Identify the stat ...
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... it. AI in computers isn’t recent as we have been using the technology for digital devices for a while now from having GPS maps to talking Phone apps. This study investigates and critically assesses whether AI will advance on to becoming more intelligent than humans and to evaluate the repercussions ...
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... to a small set of target relations. In fact, the relations encountered when reading arbitrary text are not known in advance! Thus, it is impractical to generate a set of handtagged examples of each relation of interest. In contrast with many NLP tasks, MR is inherently unsupervised. ...
Knowledge Representation
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... First implementation of semantic networks in machine translation Quillian’s semantic network – Influential program – Define English words in a dictionary-like, but no basic axioms – Each definition leads to other definitions in an unstructured and sometimes circular fashion – When look up a word, tr ...
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... Up to now: Symbolic AI Benefits: - AI/KBs can be engineered and maintained like in software engineering - Behaviour can be predicted and explained eg using logic reasoning Problems: • Reasoning tends to be “brittle” – easily broken by incorrect / ...
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download

Evolving Robot Intelligence
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... mechanisms to make them more useful for our fictional system. Now, maybe we’ll reorganize the fictional system some more and go through the loop again. Once in a decade or so, and if our interests are broad, we might notice that the new AI concepts just invented could also be profitably used to desc ...
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... Buy a week's worth of groceries on the web? Buy a week's worth of groceries at Berkeley Bowl? Discover and prove a new mathematical theorem? Converse successfully with another person for an hour? Perform a surgical operation? Put away the dishes and fold the laundry? Translate spoken Chinese into sp ...
AI Technique in Diagnostics and Prognostics
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... An Expert System is an intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solution. Early diagnostic expert systems are rule-based and used empirical reasoning whereas new model-based ...
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CS 294-5: Statistical Natural Language Processing

...  1943: McCulloch & Pitts: Boolean circuit model of brain  1950: Turing's “Computing Machinery and Intelligence” ...
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History of artificial intelligence

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with ""an ancient wish to forge the gods.""The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true. Eventually it became obvious that they had grossly underestimated the difficulty of the project. In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again. This cycle of boom and bust, of ""AI winters"" and summers, continues to haunt the field. Undaunted, there are those who make extraordinary predictions even now.Progress in AI has continued, despite the rise and fall of its reputation in the eyes of government bureaucrats and venture capitalists. Problems that had begun to seem impossible in 1970 have been solved and the solutions are now used in successful commercial products. However, no machine has been built with a human level of intelligence, contrary to the optimistic predictions of the first generation of AI researchers. ""We can only see a short distance ahead,"" admitted Alan Turing, in a famous 1950 paper that catalyzed the modern search for machines that think. ""But,"" he added, ""we can see much that must be done.""
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