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AiArtificial Itelligence


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  • 1. Report: Artificial IntelligenceSubmitted to: Submitted by:Prof. Jatinder Pal Singh Alisha Korpal David Kochar Nivia Jain Sharuti Jain 1|Page
  • 2. IndexSno Topic Page no 1 Introduction 3 2 AI includes 4 3 History 5 4 Applications 7 5 Positive points 12 6 Negative points 13 7 References 14 2|Page
  • 3. Introduction:Artificial intelligence (AI) is the intelligence of machines and the branch of computerscience that aims to create it. AI textbooks define the field as "the study and design of intelligentagents" where an intelligent agent is a system that perceives its environment and takes actionsthat maximize its chances of success. John McCarthy, who coined the term in 1956, defines it as"the science and engineering of making intelligent machines."The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by amachine. This raises philosophical issues about the nature of the mind and the ethics of creatingartificial beings, issues which have been addressed by myth, fiction and philosophy sinceantiquity. Artificial intelligence has been the subject of optimism, but has also sufferedsetbacks and, today, has become an essential part of the technology industry, providing the heavylifting for many of the most difficult problems in computer science.AI research is highly technical and specialized, and deeply divided into subfields that often failto communicate with each other. Subfields have grown up around particular institutions, thework of individual researchers, the solution of specific problems, longstanding differences ofopinion about how AI should be done and the application of widely differing tools. The centralproblems of AI include such traits as reasoning, knowledge, planning, learning, communication,perception and the ability to move and manipulate objects. General intelligence (or "strong AI")is still among the fields long term goals. 3|Page
  • 4. AI includesGames playingProgramming computers play games such as chess and checkers. Currently, no computers exhibitAI (that are able to stimulate human behavior), the greatest advances have occurred in the fieldof games playing. The best computer chess programs are now capable of beating humans. InMay 1997 an IBM super computer called Deep Blue defeated world chess champion GaryKasparov in chess match.Expert systemsProgramming computers to make decision in real life situations (for example, some expertsystem help doctors diagnose diseases based on symptoms)Natural languageProgramming computer understand natural languages. Natural language processing offers thegreatest potential rewards because it would allow people to interact with computer withoutneeding any specialized knowledge. You could simply walk up to a computer and talk to it.Neural networksSystems that simulate intelligence by attempting to reproduce the types of physical connectionsthat occur in brainsRoboticsProgramming computers to see hear and react to other stimuli.In the area of robotics, computersare now widely used in assembly plants, but they are capable only of very limited tasks. Robotshave great difficulty identifying objects based on appearance or feel and they still move andhandle objects clumsily. 4|Page
  • 5. History  15th century  Aristoltle invents first formal deductive reasoning system.  16th century  Rabbi invents an artificial man made out of clay.  17th century  Pascal creates first mechanical calculator  18th century  Wolfgang von invents fake chess playing machine  19th century  Charles Babbage and Lady Lovelace develop sophisticated programmable mechanical computer  20th century  Karel Kapek invents RobotsIn the early 1980s, AI research was revived by the commercial success of expert systems, a formof AI program that simulated the knowledge and analytical skills of one or more human experts.By 1985 the market for AI had reached over a billion dollars. At the same time, Japans fifthgeneration computer project inspired the U.S and British governments to restore funding foracademic research in the field. However, beginning with the collapse of the Lisp Machine marketin 1987, AI once again fell into disrepute, and a second, longer lasting AI winter began.In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behindthe scenes. Artificial intelligence is used for logistics, data mining, diagnosis and many otherareas throughout the technology industry. The success was due to several factors: the increasingcomputational power of computers (see Moores law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, anda new commitment by researchers to solid mathematical methods and rigorous scientificstandards. 5|Page
  • 6. On 11 May 1997, Deep Blue became the first computer chess-playing system to beat a reigningworld chess champion, Garry Kasparov In 2005, a Stanford robot won the DARPA GrandChallenge by driving autonomously for 131 miles along an unrehearsed desert trail.The leading-edge definition of artificial intelligence research is changing over time. Onepragmatic definition is: "AI research is that which computing scientists do not know how to docost-effectively today." For example, in 1956 optical character recognition (OCR) wasconsidered AI, but today, sophisticated OCR software with a context-sensitive spell checker andgrammar checker software comes for free with most image scanners. No one would any longerconsider already-solved computing science problems like OCR "artificial intelligence" today.Low-cost entertaining chess-playing software is commonly available for tabletcomputers. DARPA no longer provides significant funding for chess-playing computing systemdevelopment. The Kinect which provides a 3D-body-motion interface for the Xbox 360 usesalgorithms that emerged from lengthy AI research, but few consumers realize the technologysource.AI applications are no longer the exclusive domain of Department of defense R&D, butare now common place consumer items and inexpensive intelligent toys. In common usage, theterm "AI" no longer seems to apply to off-the-shelf solved computing-science problems, whichmay have originally emerged out of years of AI research. 6|Page
  • 7. Applications 1 Computer science 2 Finance 3 Medicines 4 Heavy industry 5 Online and telephone customer service 6 Transportation 7 Telecommunications 8 Toys and games 9 Music 10 Aviation 11 News and publishing 7|Page
  • 8. Computer scienceAI researchers have created many tools to solve the most difficult problems in computer science.Many of their inventions have been adopted by mainstream computer science and are no longerconsidered a part of AI.  Time sharing  Interactive interpreters  Graphical user interfaces and the computer mouse  Rapid development environments  The linked list data type  Automatic storage management  Symbolic programming  Functional programming  Dynamic programming  Object-oriented programmingFinanceBanks use artificial intelligence systems to organize operations, invest in stocks, and manageproperties. In August 2001, robots beat humans in a simulated financial trading competition.Financial institutions have long used artificial neural network systems to detect charges or claimsoutside of the norm, flagging these for human investigationMedicalA medical clinic can use artificial intelligence systems to organize bed schedules, make a staffrotation, and provide medical information.Artificial neural networks are used as clinical decision support systems for medical diagnosis,such as in Concept Processing technology in EMR software. 8|Page
  • 9. Heavy IndustryRobots have become common in many industries. They are often given jobs that are considereddangerous to humans. Robots have proven effective in jobs that are very repetitive which maylead to mistakes or accidents due to a lapse in concentration and other jobs which humans mayfind degrading. Japan is the leader in using and producing robots in the world. In 1999,1,700,000 robots were in use worldwide. For more information, see survey about artificialintelligence in business.TransportationTelecommunicationMany telecommunications companies make use of heuristic search in the management of theirworkforces, for example BT Group has deployed heuristic search in a scheduling application thatprovides the work schedules of 20,000 engineers.Toys and GamesThe 1990s saw some of the first attempts to mass-produce domestically aimed types of basicArtificial Intelligence for education, or leisure. This prospered greatly with the DigitalRevolution, and helped introduce people, especially children, to a life of dealing with varioustypes of AI, specifically in the form of Tamagotchis and Giga Pets, the Internet (example: basicsearch engine interfaces are one simple form), and the first widely released robot, Furby. A mereyear later an improved type of domestic robot was released in the form of Aibo, a robotic dogwith intelligent features and autonomy. AI has also been applied to video games. 9|Page
  • 10. AviationThe Air Operations Division AOD, uses AI for the rule based expert systems. The AOD has usefor artificial intelligence for surrogate operators for combat and training simulators, missionmanagement aids, support systems for tactical decision making, and post processing of thesimulator data into symbolic summaries.The use of artificial intelligence in simulators is proving to be very useful for the AOD. Airplanesimulators are using artificial intelligence in order to process the data taken from simulatedflights. Other than simulated flying, there is also simulated aircraft warfare. The computers areable to come up with the best success scenarios in these situations. The computers can also createstrategies based on the placement, size, speed, and strength of the forces and counter forces.Pilots may be given assistance in the air during combat by computers. The artificial intelligentprograms can sort the information and provide the pilot with the best possible maneuvers, not tomention getting rid of certain maneuvers that would be impossible for a sentient being toperform. Multiple aircraft are needed to get good approximations for some calculations socomputer simulated pilots are used to gather data. These computer simulated pilots are also usedto train future air traffic controllers. 10 | P a g e
  • 11. Positive points  Tireless  Copying  Accurate decisions  Not human bias 11 | P a g e
  • 12. Negative points"Can a machine act intelligently?" is still an open problem. Taking "A machine can actintelligently" as a working hypothesis, many researchers have attempted to build such a machine.The general problem of simulating (or creating) intelligence has been broken down into anumber of specific sub-problems. These consist of particular traits or capabilities that researcherswould like an intelligent system to display. The traits described below have received the mostattention.Deduction, reasoning, problem solvingEarly AI researchers developed algorithms that imitated the step-by-step reasoning that humanswere often assumed to use when they solve puzzles, play board games or make logicaldeductions. By the late 1980s and 90s, AI research had also developed highly successfulmethods for dealing with uncertain or incomplete information, employing concepts fromprobability and economics.For difficult problems, most of these algorithms can require enormous computational resources— most experience a "combinatorial explosion": the amount of memory or computer timerequired becomes astronomical when the problem goes beyond a certain size. The search formore efficient problem solving algorithms is a high priority for AI research. Human beings solvemost of their problems using fast, intuitive judgments rather than the conscious, step-by-stepdeduction that early AI research was able to model. AI has made some progress at imitating thiskind of "sub-symbolic" problem solving: embodied agent approaches emphasize the importanceof sensor motor skills to higher reasoning; neural net research attempts to simulate the structuresinside human and animal brains that give rise to this skill.Knowledge representationOntology represents knowledge as a set of concepts within a domain and the relationshipsbetween those concepts.Knowledge representation and knowledge engineering are central to AI research. Many of theproblems machines are expected to solve will require extensive knowledge about the world.Among the things that AI needs to represent are: objects, properties, categories and relationsbetween objects; situations, events, states and time; causes and effects; knowledge aboutknowledge (what we know about what other people know); and many other, less well researched 12 | P a g e
  • 13. domains. A representation of "what exists" is an ontology (borrowing a word fromtraditional philosophy), of which the most general are called upper ontologism.References  a=v& Chapter11.ppt+ppt+future+of+artificial+intelligence&hl=en&gl=in&pid=bl&srcid=ADG EESiFCTrV2bsrOZ1VJQt6SY1uarV9NfzHdG5jpt_K-BJ7AH1aO- fCxrSFdEBRkPgpz2OcdnlAzcNrckqu6cR41mshgxPKuYYWDTiJnfGNPskufuQtKdiqM qlw6KnoBJVpCOjCYW3C&sig=AHIEtbRN-O2LlQ8vSmAJFRkotUz6uFSL1A&pli=1     q=ppt+positive+and+negative+of+artificial+intelligence&hl=en&pwst=1&prmd=ivns&e i=LhhLTqbZEofUiALQBw&start=10&sa=N&biw=1280&bih=869&cad=cbv 13 | P a g e