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BY –mynk
 INTODUCTION OF AI


        EVOLUTION   OF A.I.

 BRANCHES AND APPLICATIONS OF A.I


     WHAT WE ACHIEVED IN A.I.


           CONCLUSION
 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.
 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.
 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
 The Turing test is a test of a machine's ability to exhibit
  intelligent behavior. In Turing's 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 machine's ability to
  render words into audio.
 ARTIFICIAL NUERAL SYSTEM


 COMPUTER VISION


 NATURAL LANGUAGE PROCESSING (N.L.P)


 MACHINE LEARNING


 ROBOTICS
 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.
artificial intelligence
        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
TRADITIONAL VISION-   LATEST(NEURAL) VISION-
TRDITIONAL VISION-   LATEST(NEURAL) VISION-
 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
TRADITIONAL N.L.P.   LATEST(NEURAL) N.L.P.
 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''
 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
 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
 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. ASIMO's 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.
• Stanley is
  an autonomous vehicle created
  by Stanford University's 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.
 Stanford's 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.
 Watson is an artificial intelligence computer
  system capable of answering questions posed
  in natural language, developed in IBM's Deep
  QA project by a research team led
  by principal investigator David Ferrucci.
  Watson was named after IBM's first
  president, Thomas J. Watson.
 In 2011, as a test of its abilities, Watson
  competed on the quiz show Jeopardy!, in the
  show's 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
 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.
 Kismet is a robot made in
 the late 1990s
 at Massachusetts Institute
 of Technology by
 Dr. Cynthia Breazeal. The
 robot's auditory, visual and
 expressive systems were
 intended to allow it to
 participate in human social
 interaction and to
 demonstrate simulated
 human emotion and
 appearance.
 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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artificial intelligence

  • 2.  INTODUCTION OF AI  EVOLUTION OF A.I.  BRANCHES AND APPLICATIONS OF A.I  WHAT WE ACHIEVED IN A.I.  CONCLUSION
  • 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.  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.  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.  The Turing test is a test of a machine's ability to exhibit intelligent behavior. In Turing's 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 machine's ability to render words into audio.
  • 7.  ARTIFICIAL NUERAL SYSTEM  COMPUTER VISION  NATURAL LANGUAGE PROCESSING (N.L.P)  MACHINE LEARNING  ROBOTICS
  • 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.
  • 10. 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
  • 11. TRADITIONAL VISION- LATEST(NEURAL) VISION-
  • 12. TRDITIONAL VISION- LATEST(NEURAL) VISION-
  • 13.  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
  • 14. TRADITIONAL N.L.P. LATEST(NEURAL) N.L.P.
  • 15.  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''
  • 16.  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
  • 17.  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
  • 18.  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. ASIMO's 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.
  • 19. • Stanley is an autonomous vehicle created by Stanford University's 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.
  • 20.  Stanford's 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.
  • 21.  Watson is an artificial intelligence computer system capable of answering questions posed in natural language, developed in IBM's Deep QA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first president, Thomas J. Watson.  In 2011, as a test of its abilities, Watson competed on the quiz show Jeopardy!, in the show's 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
  • 22.  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.
  • 23.  Kismet is a robot made in the late 1990s at Massachusetts Institute of Technology by Dr. Cynthia Breazeal. The robot's auditory, visual and expressive systems were intended to allow it to participate in human social interaction and to demonstrate simulated human emotion and appearance.
  • 24.  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