artificial intelligence

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artificial intelligence

  1. 1. BY –mynk
  2. 2.  INTODUCTION OF AI  EVOLUTION OF A.I. BRANCHES AND APPLICATIONS OF A.I  WHAT WE ACHIEVED IN A.I.  CONCLUSION
  3. 3.  Artificial- Not natural Intelligence- Capability to learn and take decisions A.I. is a branch of computer science that studies the computational requirements for tasks such as perception, reasoning and learning and develop systems to perform those tasks.
  4. 4.  In the beginning the focus of AI research was on modelling the human brain. (This was impossible). John McCarthy term first artificial intelligence. Research shifted to using games like noughts and crosses, drafts etc to create “AI” systems.  The games had a number of rules that were easy to define.
  5. 5.  In 1965 Researchers agreed that game playing programs could not pass the Turing test The focus shifted to language processing ELIZA (1966)  1st language processing program  Responded to users inputs by asking questions based on previous responses PARRY (1972)  Parry modelled a conversation with a paranoid person  This seems odd but the program was created by a psychiatrist
  6. 6.  The Turing test is a test of a machines ability to exhibit intelligent behavior. In Turings original illustrative example, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test. The test does not check the ability to give the correct answer; it checks how closely the answer resembles typical human answers. The conversation is limited to a text-only channel such as a computer keyboard and screen so that the result is not dependent on the machines ability to render words into audio.
  7. 7.  ARTIFICIAL NUERAL SYSTEM COMPUTER VISION NATURAL LANGUAGE PROCESSING (N.L.P) MACHINE LEARNING ROBOTICS
  8. 8.  ANS is an approach to AI where the developer attempts to model the human brain Simple processors are interconnected in a way that simulates the connection of nerve cells in the brain Advantages & Disadvantages of ANS- Advantages They can learn without needing to be reprogrammed Disadvantages Time consuming and requires a lot of technical expertise to set up Can’t tell the reason behind the decision.
  9. 9.  Stages  Difficulties with 1. Input Image using Digital Camera Vision Systems 2. Detect Edges of Object 3. Compare to Knowledge  Shadows on Objects Base – Pattern Matching  Identifying the Edge of the Uses Image  Security systems, recognizing  Glare faces at airports  Objects hiding other parts  Inspection of manufactured of the Image goods judging quality of  Viewing from different production angles  Vision systems on automated cars  Interpretation of Satellite photos for military use
  10. 10. TRADITIONAL VISION- LATEST(NEURAL) VISION-
  11. 11. TRDITIONAL VISION- LATEST(NEURAL) VISION-
  12. 12.  NLP or Speech Recognition is where an AI system can be controlled and respond to verbal commands Examples  Speech-driven word processors  Military weapon control  Mobile phones(SIRI)  Customer query lines
  13. 13. TRADITIONAL N.L.P. LATEST(NEURAL) N.L.P.
  14. 14.  What is learning- “To gain knowledge or understanding of, or skill in by study, instruction or experience  Learning a set of new facts  Learning HOW to do something  Improving ability of something already learned What is machine learning- ``Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more effectively the next time
  15. 15.  Rote learning – One-to-one mapping from inputs to stored representation. “Learning by memorization.” Association-based storage and retrieval. Induction – Use specific examples to reach general conclusions Clustering – Unsupervised identification of natural groups in data Analogy – Determine correspondence between two different representations Discovery – Unsupervised, specific goal not given Genetic algorithms – “Evolutionary” search techniques, based on an analogy to “survival of the fittest” Reinforcement – Feedback (positive or negative reward) given at the end of a sequence of steps
  16. 16.  Robots can be considered intelligent when they go beyond simple sensors and feedback (dumb robots), and display some further aspect of human-like behaviour  Vision Systems  The ability to learn and improve performance  Robot that can walk rather than on wheels  NLP response Examples  The delivery of goods in warehouses  The inspection of pipes  Bomb Disposal  Exploration of Ocean floor or space
  17. 17.  ASIMO has the ability to recognize moving objects, postures, gestures, its surrounding environment, sounds and faces, which enables it to interact with humans also determine distance and direction. This feature allows ASIMO to follow a person, or face him or her when approached. The robot interprets voice commands and human hand movements, enabling it to recognize when a handshake is offered or when a person waves or points, and then respond accordingly. ASIMOs ability to distinguish between voices and other sounds allows it to identify its companions. ASIMO is able to respond to its name and recognizes sounds associated with a falling object or collision. This allows the robot to face a person when spoken to or look towards a sound. ASIMO responds to questions by nodding or providing a verbal answer and can recognize approximately 10 different faces and address them by name.
  18. 18. • Stanley is an autonomous vehicle created by Stanford Universitys Stanford Racing Team in cooperation with the Volkswagen Electronics Research Laboratory (ERL). It competed in, and won, the 2005 DARPA Grand Challenge, earning the Stanford Racing Team the 2 million dollar prize, the largest prize money in robotic history. Stanley was characterized by a machine learning based approach to obstacle detection. To process the sensor data and execute decisions, Stanley was equipped with six low-power 1.6 GHz Intel Pentium M based computers in the trunk, running different versions of the Linux operating system.
  19. 19.  Stanfords Autonomous Helicopter project pushes the limits of autonomous flight control by teaching a computer to fly a competition-class remote controlled (RC) helicopter through a range of aerobatic stunts. The only helicopter that can hover inverted. Our apprenticeship learning approach learns to fly the helicopter by observing human demonstrations and is capable of a wide variety of expert maneuvers. In many cases, it can even exceed the performance of the human expert from which it learned.
  20. 20.  Watson is an artificial intelligence computer system capable of answering questions posed in natural language, developed in IBMs Deep QA project by a research team led by principal investigator David Ferrucci. Watson was named after IBMs first president, Thomas J. Watson. In 2011, as a test of its abilities, Watson competed on the quiz show Jeopardy!, in the shows only human-versus-machine match- up to date. In a two-game, Watson beat Brad Rutter, the biggest all-time money winner on Jeopardy!, and Ken Jennings, the record holder for the longest championship streak (74 wins).Watson received the first prize of $1 million, while Ken Jennings and Brad Rutter received $300,000 and $200,000, respectively
  21. 21.  The European research project ALEAR (Artificial Language Evolution on Autonomous Robots), carried out by Dr. Manfred .Myon is an 1.25 meters humanoid robot. It was revealed to the public for the first time at the International Design Festival DMY and the Institute for Advanced Study Berlin (Wissenschaftskolleg Berlin) and it caused an extremely high interest. autonomous robots move.
  22. 22.  Kismet is a robot made in the late 1990s at Massachusetts Institute of Technology by Dr. Cynthia Breazeal. The robots auditory, visual and expressive systems were intended to allow it to participate in human social interaction and to demonstrate simulated human emotion and appearance.
  23. 23.  Finally we can say that Artificial intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents“ where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines

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