SAKARYA UNIVERSITY INDUSTRIAL ENGINEERING. Artificial Intelligence. HW5: Brief History of Artificial Intelligence

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1 SAKARYA UNIVERSITY INDUSTRIAL ENGINEERING Artificial Intelligence HW5: Brief History of Artificial Intelligence Instructor: Prof.Dr.Harun TAŞKIN By:Kerim GÖZTEPE 0650D06003 SAKARYA;FALL

2 1.History of Artificial Intelligence Artificial Intelligence (AI) is the key technology in many of today's novel applications, ranging from banking systems that detect attempted credit card fraud, to telephone systems that understand speech, to software systems that notice when you're having problems and offer appropriate advice. These technologies would not exist today without the sustained AI researchers over the past three decades. Experiments to build artificial intelligences go back a long way. Well before the modern age men have sought to build or endow intelligence onto machines. The classical Greek mythology is full of intelligent machines and devices and while men such as Hero and Daedalus constructed the hardware, philosophers like Aristotle invented the first formal deductive reasoning system for machines known as syllogistic logic. Moreover, AI has a long history of producing valuable spin-off technologies. AI researchers tend to look very far ahead, crafting powerful tools to help achieve the daunting tasks of building intelligent systems. Laboratories whose focus was AI first conceived and demonstrated such well-known technologies as the mouse, time-sharing, high-level symbolic programming languages (Lisp, Prolog, Scheme), computer graphics, the graphical user interface (GUI), computer games, the laser printer, object-oriented programming, the personal computer, , hypertext, symbolic mathematics systems (Macsyma, Mathematica, Maple, Derive), and, most recently, the software agents which are now popular on the World Wide Web. There is every reason to believe that AI will continue to produce such spin-off technologies. Early work in AI focused on using cognitive and biological models to simulate and explain human information processing skills, on "logical" systems that perform commonsense and expert reasoning, and on robots that perceive and interact with their environment. This early work was spurred by visionary funding from the Defense Advanced Research Projects Agency (DARPA) and Office of Naval Research (ONR), which began on a large scale in the early 1960's and continues to this day. Basic AI research support from DARPA and ONR -- as well as support from NSF, NIH, AFOSR, NASA, and the U.S. Army beginning in the 1970's -- led to theoretical advances and to practical technologies for solving military, scientific, medical, and industrial information processing problems. By the early 1980's an "expert systems" industry had emerged, and Japan and Europe dramatically increased their funding of AI research. In some cases, early expert systems success led to inflated claims and unrealistic expectations: while the technology produced many highly effective systems, it proved very difficult to identify and encode the necessary expertise. The field did not grow as rapidly as investors had been led to expect, and this translated into some temporary disillusionment. AI researchers responded by developing new technologies, including streamlined methods for eliciting expert knowledge, automatic methods for learning and refining knowledge, and common sense knowledge to cover the gaps in expert information. These technologies have given rise to a new generation of expert systems that are easier to develop, maintain, and adapt to changing needs. Today developers can build systems that meet the advanced information processing needs of government and industry by choosing from a broad palette of mature technologies. Sophisticated methods for reasoning about uncertainty and for coping with incomplete knowledge have led to more robust diagnostic and planning systems. Hybrid technologies that combine symbolic representations of knowledge with more quantitative representations inspired by biological information processing systems have resulted in more flexible, human- 2

3 like behavior. AI ideas also have been adopted by other computer scientists -- for example, "data mining," which combines ideas from databases, AI learning, and statistics to yield systems that find interesting patterns in large databases, given only very broad guidelines. 2. Some Important Events in AI History 13 th Century: Ramón Lull invented the Zairja, the first device that systematically tried to generate ideas by mechanical means. 17 th Century : Leibnitz and Pascal invented mechanical computing devices. Pascal was 19 years old in 1642 when he invented an eight-digit calculator, the Pascaline. In 1694, Gottfried Liebnitz invented the Liebnitz Computer, which multiplied by repetitive addition, an algorithm still in use today. Leibnitz also conceived of a 'reasoning calculator' for interpreting and evaluating concepts, but realized the problem was immense because of the great interconnectedness of concepts. 1847: George Boole developed a mathematical symbolic logic (later called Boolean algebra) for reasoning about categories (i.e., sets) of objects, which is also applicable to manipulating and simplifying logical propositions 1890: Herman Hollerith patented a tabulating machine to process census data fed in on punched cards. His company, the Tabulating Machine Company, eventually merged into what was to become IBM. 1921: Karel Capek, a Czech writer, invented the term robot to describe intelligent machines that revolted against their human masters and destroyed them. 1937: Alan Turing conceived of a universal Turing machine that could mimic the operation of any other computing machine. However, as did Godel, he also recognized that there exists certain kinds of calculations that no machine could perform. Even recognizing this limit on computers, Turing still did not doubt that computers could be made to think. 1945: ENIAC (Electronic Numerical Integrator and Calculator), which was to run 1,000 times faster than the relay-operated computers, was ready to run in late It was the first general purpose, fully electronic, programmable computer. John W. Mauchley and John Presper Eckert were its inventors. 1950: Turing proposed his test, the Turing test, to recognize machine intelligence. 1951: EDVAC, the first von Neumann computer, was built. 1956: A two-month summer conference on thinking machines was held at Dartmouth University. The attendees included John McCarthy, Marvin Minsky, Claude Shannon, Nathaniel Rochester, Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Herbert Simon, and Allen Newell. It did not result in a consensus view of AI. 1956: John McCarthy names the new discipline, "Artificial Intelligence." 1961: Mortimer Taube, an engineer, authored the first anti-ai book, "Computers and Common Sense: The Myth of Thinking Machines." It did not receive much attention. 3

4 1962: The world's first industrial robots were marketed by a U.S. company : Tom Evans, under Marvin Minsky's supervision, created the program, ANALOGY. It was designed to solve problems that involved associating geometric patterns that occurred in a past case with the pattern in a current case. ANALOGY could solve shape problems of the kind, figure C is to which of several alternative figures as figure A is to figure B. 1969: A mobile robot called Shakey was assembled at Stanford, that could navigate a block world in eight rooms and follow instructions in a simplified form of English. Early 1970s : DARPA's Speech Understanding Research (SUR) program, for which Carnegie Mellon was the prime contractor was brought to an abrupt end. Although goals were met, the product, which has a limited grammar, was not considered practical. AI researchers turned from research into the control and expression of knowledge (such as was demonstrated in the Micro Worlds project) to the manipulation of large amounts of knowledge. This was done in recognition of the limitations of successful programs such as SHDLU and GPS to be extended to tackle the more complex problems of the real world in useful ways. The earlier work reflected overly simplistic approximations of the ways the human mind works, and better approximations were required. Manipulation of larger amounts of information was also enabled by the increasing power of computers. 1974: Paul J. Werbos invented the back-propagation algorithm, that enabled multilayer neural networks, that had the ability to perform classification operations beyond simple Perceptrons. Back-propagation was independently rediscovered in the early 1980s by David Rumelhat and David Parker. Late 1970s: First commercial expert system was developed. It was XCON (for expert CONfigurer), developed by John McDermott at Carnegie Mellon. He developed it for Digital Equipment Company, which started using it in January 1980 to help configure computer systems, deciding between all the options available for their VAX system. It grew from containing about 300 rules in 1979 to more than 3,000 and could configure more than 10 different computer systems. 1980s: Fuzzy logic was introduced in a fuzzy predictive system used to operate the automated subway trains in Sendai, Japan. This system, designed by Hitachi, reduced energy consumption by 10% and lowered the margin of error in stopping the trains at specified positions to less than 10 centimeters. 1984: GE built an expert system based on electric locomotive diagnosis knowledge of one expert, David Smith, who was close to retirement. Called the Diesel Electric Locomotive Troubleshooting Aid, it could diagnose 80% of breakdowns, and provide repair instructions. 1987: Etienne Wenger published his book, "Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge," a milestone in the development of intelligent tutoring systems. 1990s and 2000s:AI applications of many, seemingly unrelated kinds are quietly being commercialized in greater and greater numbers. Not all these applications work as well as desired, but they are continually improving. These include: 4

5 Automatic scheduling software to automatically create better project schedules faster Advanced learning software that works like human tutor in teaching one-onone with each student Continuous speech recognition programs that accurately turn speech into text Software to manage information for individuals, finding just the documents immediately needed from amongst million of documents, and automatically summarizing documents Face-recognition systems Washing machines that automatically adjust to different conditions to wash clothes better Automatic mortgage underwriting systems Automatic investment decision makers Software that improves the prediction of daily revenues and staffing requirements for a business Credit fraud detection systems Help desk support systems that help find the right answer to any customer's question faster 3. AI Applications in Engineering Episodic Memory System ICSI researchers, working with funding from DARPA's Perceptive Assistant that Learns program, are developing an episodic memory system for an enduring, personalized cognitive assistant. The goal of the DARPA project is to develop an enduring, personalized cognitive assistant in the form of a software system that can reason, learn from experience, follow instructions, explain its actions, reflect on its experience, and behave robustly in unexpected situations (CALO). SHRUTI: From Simple Associations to Systematic Reasoning Humans are capable of drawing a variety of inferences effortlessly, spontaneously, and with remarkable efficiency --- as though these inferences are a reflex response of our cognitive apparatus. This remarkable human ability poses a challenge for cognitive science and computational neuroscience: How can a system of slow neuron-like elements represent a large body of systematic knowledge and perform a wide range of inferences with such speed? SHRUTI attempts to address this challenge by demonstrating how a connectionist network can encode a large body of semantic and episodic facts, systematic rule-like mappings, knowledge about entities, and types, and yet perform a wide range of reflexive inferences within a few hundred milliseconds. 5

6 NTL The NTL project of the AI group works in collaboration with other units on the UCB campus and elsewhere. It combines basic research in several disciplines with applications to natural language processing systems. Basic efforts include studies in the computational, linguistic, neurobiological and cognitive bases for language and thought and continues to yield a variety of theoretical and practical findings. One ongoing applied effort (called EDU for Even Deeper Understanding) has been in operation since July 2000, with multi-year funding from the Klaus Tschira Foundation. A major aspect of this collaboration has been the interational workshops on Scalable Natural Language Understanding Systems (SCANALU). AQUAINT Researchers in ICSI's AI Group are participating in a project to study deep inferencing techniques and corpus based techniques for deriving the conceptual semantics needed to achieve this. This research is a collaboration with Stanford and the University of Texas at Dallas, and is sponsored by the ARDA AQUAINT Program. Our effort is being intergrated into an ambitious overall program to significantly advance the automated analysis of information. Video Event Taxonomy The ARDA sponsored Video Event Taxonomy project is a targeted multi-institutional effort comprised of researchers from the University of Southern California, SRI International, the University of Maryland, and ICSI. The project developed a Video Event Representation Language (VERL) to provide a common representational framework and ontology for describing video events. Furthermore, the project developed a common annotation language, VEML, to serve as a uniform data model to facilitate model codevelopment and interchange of annotated video data. 6

7 REFERENCES Ray Kurzweil, The Age of Spiritual Machines: When Computers Exceed Human Intelligence, Viking, Daniel Crevier, AI: The Tumultuous History of the Search for Artificial Intelligence, Basic Books,