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Artificial intelligence

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Artificial intelligence

  1. 1. ARTIFICIAL INTELLIGENCE PRESENTED BY :- SOURABH SHARMA
  2. 2. CONTENTS • INTRODUCTION • HISTORY OF A.I • GOALS • CATEGORIES OF AI • FIELDS OF A.I • APPLICATION • FUTURE SCOPE • CONCLUSION • BIBLIOGRAPHY
  3. 3. INTRODUCTION
  4. 4. INTRODUCTION ARTIFICIAL:- The simple definition of artificial is that objects that are made or produced by human beings rather than occurring naturally. INTELLIGENCE:- The simple definition of intelligence is a process of entail a set of skills of problem solving, enabling to resolve genuine problems.
  5. 5. INTRODUCTION OF A.I(CONT’D) Artificial intelligence is a branch of science which deals with helping machines find solution to complex problems in a more human like fashion. Artificial intelligence is generally associated with computer science, but it has many important links with other fields such as maths, psychology, cognition , biology and philosophy , among many others .
  6. 6. HISTORY
  7. 7. HISTORY (CONT’D) 1950s : -The Beginnings of Artificial Intelligence (AI) Research:- The person who finally coined the term artificial intelligence and is regarded as the father of the of AL is John McCarthy. In 1956 he organized a conference “the Darthmouth summer research project on artificial intelligence" to draw the talent and expertise of others interested in machine intelligence of a month of brainstorming.
  8. 8. HISTORY (CONT’D) 1960:- By the 1960’s, America and its federal government starting pushing more for the development of AI. AI's founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that "machines will be capable, within twenty years, of doing any work a man can do" .The rise of expert systems also became popular due to the creation of Edward Feigenbaum and Robert K. Lindsay’s DENDRAL. DENDRAL had the ability to map the complex structures of organic chemicals.
  9. 9. HISTORY(CONT’D) 1980:- In the early 1980s, AI research was revived by the commercial success of expert systems, a form of 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. In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence is used for logistics, data mining, medical diagnosis and many other areas throughout the technology industry
  10. 10. HISTORY (CONT’D) 1990 :- From 1990s until the turn of the century, AI has reached some incredible landmarks with the creation of intelligent agents. Intelligent agents basically use their surrounding environment to solve problems in the most efficient and effective manner. In 1997, the first computer (named Deep Blue) beat a world chess champion. In 1995, the VaMP car drove an entire 158 km racing track without any help from human intelligence. In 1999, humanoid robots began to gain popularity as well as the ability to walk around freely.
  11. 11. HISTORY (CONT’D) After 1990’s AI has been playing a big role in certain commercial markets and throughout the World Wide Web. The more advanced AI projects, like fully adapting commonsense knowledge, have taken a back-burner to more lucrative industries.
  12. 12. GOALS
  13. 13. GOALS The general problem of simulating (or creating) intelligence has been broken down into a number of specific sub- problems. These consist of particular traits or capabilities that researchers would like an intelligent system to display. The different types of obtaining goals are listed below are as follows.
  14. 14. GOALS(CONT’D) Deduction, reasoning, problem solving:- For difficult problems, most of these algorithms can require enormous computational resources most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. Knowledge representation:- Knowledge representation and knowledge engineering are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world.
  15. 15. GOALS(CONT’D) Planning:- Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future and be able to make choices that maximize the utility (or "value") of the available choices. Natural language processing:- Natural language processing gives machines the ability to read and understand the languages that humans speak.
  16. 16. GOALS(CONT’D) Motion and manipulation:- The field of robotics is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation and navigation. Perception:- Machine perceptions the ability to use input from sensors (such as cameras, microphones, sonar and others more exotic) to deduce aspects of the world. Computer vision is the ability to analyze visual input.
  17. 17. GOALS(CONT’D) Social intelligence:- Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. General intelligence:- Most researchers think that their work will eventually be incorporated into a machine with general intelligence (known as strong AI), combining all the skills above and exceeding human abilities at most or all of them. A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project.
  18. 18. CATEGORIES OF A.I 1. Conventional AI. 2. Computational Intelligence (CI). 1. Conventional AI :- Conventional AI mostly involves methods now classified as machine learning, characterized by formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI).
  19. 19. CATEGORIES OF A.I Methods include: • Expert systems • Case based reasoning • Bayesian networks • Behavior based AI
  20. 20. CATEGORIES OF A.I 2. Computational Intelligence (CI) :- Computational Intelligence involves iterative development or learning (e.g. parameter tuning e.g. in connectionist systems). Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing.
  21. 21. CATEGORIES OF A.I • Methods include: • Neural networks: • Fuzzy systems: • Evolutionary computation:
  22. 22. CATEGORIES OF A.I Typical problems to which AI methods are applied :- • Pattern recognition • Optical character recognition • Handwriting recognition • Speech recognition • Face recognition • Natural language processing, Translation and Chatter bots • Non-linear control and Robotics • Computer vision, Virtual reality and Image processing • Game theory and Strategic planning
  23. 23. FIELDS OF A.I
  24. 24. FIELDS OF A.I 1. Automation:- Automation is the use of machines, control systems and information technologies to optimize productivity in the production of goods and delivery of services.
  25. 25. FIELDS (CONT’D) 2. Cybernetics:- Cybernetics is that of artificial intelligence, where the aim is to show how artificially manufactured systems can demonstrate intelligent behavior. 3. Hybrid intelligent system :- Hybridization of different intelligent systems is an innovative approach to construct computationally intelligent systems consisting of artificial neural network, fuzzy inference systems, rough set, approximate reasoning and derivative free optimization methods such as evolutionary computation, swarm intelligence, bacterial foraging and so on.
  26. 26. FIELDS(CONT’D) 4. Intelligent agent:- In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals. 5. Intelligent control:- Intelligent Control or self- organising/learning control is a new emerging discipline that is designed to deal with problems. Rather than being model based, it is experiential based.
  27. 27. FIELDS(CONT’D) 6. Automated reasoning:- The study of automated reasoning helps produce software that allows computers to reason completely, or nearly completely, automatically. 7. Data mining:- The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. 8. Behavior-based robotics:- Behavior-based robotics is a branch of robotics that bridges artificial intelligence (AI), engineering and cognitive science.
  28. 28. FIELDS(CONT’D) 9. Developmental robotics:- Developmental Robotics (DevRob), sometimes called epigenetic robotics, is a methodology that uses metaphors from neural development and developmental psychology to develop the mind for autonomous robots. 10. Evolutionary robotics:- Evolutionary robotics (ER) is a methodology that uses evolutionary computation to develop controllers for autonomous robots.
  29. 29. FIELDS(CONT’D) 11. Chatbot:- Chatterbot, a chatter robot is a type of conversational agent, a computer program designed to simulate an intelligent conversation with one or more human users via auditory or textual methods. 12. Knowledge Representation:- Knowledge representation (KR) is an area of artificial intelligence research aimed at representing knowledge in symbols to facilitate inferencing from those knowledge elements, creating new elements of knowledge.
  30. 30. American Association for Artificial Intelligence (AAAI) :-
  31. 31. APPLICATIONS OF A.I 1. Hospitals and medicine:- A medical clinic can use artificial intelligence systems to organize bed schedules, make a staff rotation, and provide medical information. 2. Heavy industry:- Robots have become common in many industries. They are often given jobs that are considered dangerous to humans. 3. Game Playing :- This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of Artificial Intelligence
  32. 32. APPLICATIONS OF A.I 4. Speech Recognition :- It is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient. 5. Understanding Natural Language :- The computer has to provide with an understanding of the domain the text is about and this is presently possible only for very limited domains.
  33. 33. APPLICATIONS OF A.I 6. Computer Vision :- The world is composed of three-dimensional objects, but the inputs to the human eye and computer’s TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two- dimensional views. 7. Expert Systems :- A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task.
  34. 34. APPLICATIONS OF A.I 8. Heuristic Classification :- One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information.
  35. 35. FUTURE SCOPE OF A.I  In the next 10 years technologies in narrow fields such as speech recognition will continue to improve and will reach human levels.  In 10 years AI will be able to communicate with humans in unstructured English using text or voice, navigate (not perfectly) in an unprepared environment and will have some rudimentary common sense (and domain-specific intelligence).  However the field of artificial consciousness remains in its infancy.  The early years of the 21st century should see dramatic strides forward in this area however.
  36. 36. CONCLUSION We conclude that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. AI systems are now in routine use in various field such as economics, medicine, engineering and the military, as well as being built into many common home computer software applications, traditional strategy games etc.
  37. 37. BIBLIOGRAPHY • Programs with Common Sense :- John McCarthy, In Mechanization of Thought Processes, Proceedings of the Symposium of the National Physics Laboratory, 1959. • Artificial Intelligence, Logic and Formalizing Common Sense :- Richmond Thomason, editor, Philosophical Logic and Artificial Intelligence. Klüver Academic, 1989. • Logic and artificial intelligence :- • Richmond Thomason. In Edward N. Zalta, editor, The Stanford Encyclopedia of Philosophy. Fall 2003. http://plato.stanford.edu/archives/fall2003/entries/logic-ai/. LINKS:-  www.google.com  www.wikipedia.com  http://www.aaai.org/  http://www-formal.stanford.edu/  http://insight.zdnet.co.uk/hardware/emergingtech/
  38. 38. THANKYOU

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