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AI - Formal Reasoning Group
AI - Formal Reasoning Group

... Usually languages of mathematical logic are used. inference From some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes, but new methods of nonmonotonic inference have been added to logic since the 1970s. The simplest kind of non-monotonic reasoning is defau ...
Minds & Machines Program
Minds & Machines Program

... www.mm.rpi.edu Director: Bram van Heuveln ...
Contact: Mary Ann Dawson 901.678.1592
Contact: Mary Ann Dawson 901.678.1592

... January 6, 2016 - As part of the University of Memphis’ push to develop strong research competencies in robotics, autonomous vehicles and drones, the FedEx Institute of Technology has granted research awards totaling $182,000 for 13 innovative research proposals. The recipients involved include rese ...
COMP 3710
COMP 3710

... Upon successful completion of the course, the student will demonstrate the ability to: 1. Understand the major areas and challenges of AI. 2. Identify problems that are amenable to solution by AI methods, and which AI methods may be suited to solving a given problem. 3. Formalize a given problem in ...
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PDF

Job Opening at the Palo Alto Research Center: Postdoctoral
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... A premier center for commercial innovation, PARC, a Xerox company, is in the business of breakthroughs. We work closely with global enterprises, entrepreneurs, government agencies and partners, and other clients to invent, co-develop, and bring to market game-changing innovations by combining imagin ...
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... Computer Science: Researchers in quest of artificial intelligence have created spin offs like dynamic programming, object oriented programming, symbolic programming, intelligent storage management systems and many more such tools. The primary goal of creating an artificial intelligence still remains ...
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FA08 cs188 lecture 1..
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The objective of this integrated strategic plan, supported by the
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... While artificial intelligence aims to make "machines do things that would require intelligence if done by humans", metacreation is a new field devoted to endow machines with creative behavior. The presentation will propose an overview of past and current works on computational creativty conducted at ...
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... heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment. ...
EIE557 - PolyU EIE
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...  fundamental principles and key concepts of the subject are delivered to students;  guidance on further readings, applications and implementation is given. The formal lectures will be accompanied by case studies of successful real-world engineering applications of intelligent systems technologies. ...
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... experimental techniques building fast computers design systems that maximize an objective function over time knowledge representation, grammar ...
Artificial Neural Network Quiz
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... 2. What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ‘computational’ rates a) (i) and (ii) are true b) (i) and (iii) are true c) O ...
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... To build systems that exhibit intelligent behavior To understand intelligence in order to model it ...
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AI winter

In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. The term was coined by analogy to the idea of a nuclear winter. The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later. There were two major winters in 1974–80 and 1987–93 and several smaller episodes, including: 1966: the failure of machine translation, 1970: the abandonment of connectionism, 1971–75: DARPA's frustration with the Speech Understanding Research program at Carnegie Mellon University, 1973: the large decrease in AI research in the United Kingdom in response to the Lighthill report, 1973–74: DARPA's cutbacks to academic AI research in general, 1987: the collapse of the Lisp machine market, 1988: the cancellation of new spending on AI by the Strategic Computing Initiative, 1993: expert systems slowly reaching the bottom, and 1990s: the quiet disappearance of the fifth-generation computer project's original goals.The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the ""American Association of Artificial Intelligence""). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research. At the meeting, Roger Schank and Marvin Minsky—two leading AI researchers who had survived the ""winter"" of the 1970s—warned the business community that enthusiasm for AI had spiraled out of control in the '80s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.Hypes are common in many emerging technologies, such as the railway mania or the dot-com bubble. An AI winter is primarily a collapse in the perception of AI by government bureaucrats and venture capitalists. Despite the rise and fall of AI's reputation, it has continued to develop new and successful technologies. AI researcher Rodney Brooks would complain in 2002 that ""there's this stupid myth out there that AI has failed, but AI is around you every second of the day."" In 2005, Ray Kurzweil agreed: ""Many observers still think that the AI winter was the end of the story and that nothing since has come of the AI field. Yet today many thousands of AI applications are deeply embedded in the infrastructure of every industry."" He added: ""the AI winter is long since over.""
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