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Anthony Chang - Artificial Nerual Networks in Protein Secondary Structure Predictions
Anthony Chang - Artificial Nerual Networks in Protein Secondary Structure Predictions

... In a feed-forward neural network architecture, a unit will receive input from several nodes or neurons belonging to another layer. These highly interconnected neurons therefore form an infrastructure (similar to the biological central nervous system) that is capable of learning by successfully perfo ...
PDF - 1.4 MB - Massachusetts Institute of Technology
PDF - 1.4 MB - Massachusetts Institute of Technology

... Given a literal L, we look for a fact that unifies with L or a rule whose consequent (head) unifies with L. If we find a match, we push the antecedent literals (if any) onto the stack, apply the unifier to the entire stack and then rename all the variables to make sure that there are no variable con ...
Fromkin Rodman Hyams 403
Fromkin Rodman Hyams 403

... (Fromkin Rodman Hyams 397) • Explain why a theory for Artificial Intelligence must be rigorous and at the same time allow for language play. In AI, are rigor and language play compatible ...
Tutorial presentation
Tutorial presentation

... Define a disabling graph with actions as nodes and with an arc from a1 to a2 (a1 disables a2 ) if p1 ∪ p2 and e1 ∪ e2 are consistent and e1 ∪ p2 is inconsistent. The test for valid execution orderings can be limited to strongly connected components (SCC) of the disabling graph. ...
The Rise of Granular Computing - University of Regina
The Rise of Granular Computing - University of Regina

... of the same reasons” and there exists a set of common principles that underlies both human intelligence and artificial intelligence [14]. Although intelligent machines may be physically implemented different from brains, an understanding of the working principles of the brain is a prerequisite and i ...
TOWARDS A MENTAL PROBABILITY LOGIC Niki PFEIFER
TOWARDS A MENTAL PROBABILITY LOGIC Niki PFEIFER

... Subjective probability theory provides a tailor-made framework that perfectly fits the structure of logical inference problems. We consider a family of arbitrary conditional events F = (A1|B1, …, An|Bn), an associated probability assessment Pn = (p1, …, pn), and one further conditional event An+1|Bn ...
Three Alternative Scenarios of Work/Technology 2050 - PUC-SP
Three Alternative Scenarios of Work/Technology 2050 - PUC-SP

Hypothetical Pattern Recognition Design Using Multi
Hypothetical Pattern Recognition Design Using Multi

... Since our childhood, we have been seen different object in different patter around the world like flowers, animals, toys, different character and so on. So children can recognize a simple digit or letter and different type of complex character or handwritten character or partially occurred character ...
Artificial Intelligence: A Natural Pursuit
Artificial Intelligence: A Natural Pursuit

... Excerpt from the Monthly Intelligencer, 202:100, January 1857 : “M. Thomas, of Colmar, has lately made the finishing improvements in the calculating machine, called the arithmometer, at which he has been working for upwards of thirty years. Pascal and Leibnitz, in the seventeenth century, and Didero ...
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Document

... Solutions: Canadian Tire used MS-SharePoint to develop an information-sharing platform for its dealers but still had to revamp its employee intranet and improve processes Demonstrates IT’s role in making knowledge more accessible. Illustrates how an organization can become more efficient and profita ...
Practical Applications of Biological Realism in Artificial Neural
Practical Applications of Biological Realism in Artificial Neural

... Chapter 1: ...
randomizing the knowledge acquisition bottleneck
randomizing the knowledge acquisition bottleneck

Unit 1 : Computer Systems
Unit 1 : Computer Systems

... 14. Describe and give examples of pre-defined functions TOPIC 4 : Standard algorithms 15. Describe and give examples of the following standard algorithm in an appropriate high level language: input validation 16. Recognise appropriate use of the following standard algorithms: input validation, find ...
Combinations of Case-Based Reasoning with Other Intelligent Methods (short paper)
Combinations of Case-Based Reasoning with Other Intelligent Methods (short paper)

... basic types of such combinations and discuss future directions. ...
(IT) in Knowledge Management
(IT) in Knowledge Management

... AI methods used in KMS: Assist in and enhance searching knowledge  Help for knowledge representation (e.g., ES)  Help establish knowledge profiles of individuals and groups  Help determine the relative importance of knowledge when it is contributed to and accessed from the knowledge repository ...
Issues in Temporal and Causal Inference
Issues in Temporal and Causal Inference

4. Objectives for the Emotional Business Intelligence
4. Objectives for the Emotional Business Intelligence

... Why do we think that emotions have something to do with the decision-making? The nature of emotions is usually explained differently according to three main groups of theories: physiological, neurological and cognitive. The physiological theories suggest that responses within the body are responsib ...
Autonomous Units
Autonomous Units

... It is a way of imitating Nature in order to solve engineering problems. It includes simulation and emulation of living systems like plants or animals. It tries to achieve a new understanding of living systems, and of what is life. http://kal-el.ugr.es/pitis.html ...
Soft computing is an association of computing
Soft computing is an association of computing

... • Evolutionary computing uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. • Such processes are often inspired by biological mechanisms of evolution. • Evolutionary ...
Description Logics
Description Logics

... for speakers and PhD students to the conference knowledge base introduced so far, then this knowledge base would become inconsistent since it follows from the knowledge base that Frank is both a speaker and a PhD students, contradicting the stated disjointness of these two concepts. In order to ensu ...
The Singularity: A Philosophical Analysis
The Singularity: A Philosophical Analysis

... This intelligence explosion is sometimes combined with another idea, which we might call the “speed explosion”. The argument for a speed explosion starts from the familiar observation that computer processing speed doubles at regular intervals. Suppose that speed doubles every two years and will do ...
Uncertainty Handling for Sensor Location Estimation in Wireless
Uncertainty Handling for Sensor Location Estimation in Wireless

... GPS receiver. Nevertheless, this method does not seem to be feasible in many cases, which is due to the fact that most sensor nodes are battery operated and cannot be recharged because of deployment in harsh and remote environments [4]. To solve this constraint, researchers have developed many local ...
Information Technology and Knowledge Management
Information Technology and Knowledge Management

... For example, if we consider sustainable development, the role of subsystem Intelligent is prediction based on scientific knowledge. To achieve this task, this subsystem asks the lower system to develop a mathematical model, and then the subsystem Involvement of this lower system will collect necessa ...
Perspectives of Using Temporal Logics for Knowledge
Perspectives of Using Temporal Logics for Knowledge

... Temporal logic allows encoding both qualitative and quantitative temporal information, as well as relationships among events, therefore it is easy to express such relations, as “shorter”, “longer”, “simultaneously”, “earlier” etc. This in turn implies easiness of arranging phenomena in time, even if ...
A Cognitive Computation Fallacy?
A Cognitive Computation Fallacy?

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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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