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Digital Steganalysis: Computational Intelligence Approach
Digital Steganalysis: Computational Intelligence Approach

... art of using natural language to conceal secret message [27]. It focuses on hiding information in text by using steganography [28-34] and linguistic steganography [35-39]. The crucial requirement for steganography is perceptual and algorithmic undetectability. In any case, once the presence of hidde ...
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Computer Vision and Remote Sensing – Lessons Learned

... and building an internal map of the outside world. His “primal sketch” stimulated work on image feature extraction, especially feature based 3D reconstruction methods. It took two decades until this view was overcome, and vision – at least by parts of the community – was understood as part of a syst ...
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Unit 2 - Department of Correctional Services, Jamaica
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Artificial Participation: An Interview with Warren Sack

... me, or whether they were coaxing the same words out of me. Of course the conversation stopped being the same the moment I froze, and I've never been able to repeat the conversation. What follows is a reconstruction of an interview I conducted with Warren Sack, a software designer and media theorist ...
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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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