Artificial Intelligence.pptx by R Darshan
- 1. ARTIFICIAL INTELLIGENCE R . Darshan AIML 1st year , Section –’A’
- 2. CONTENTS Introduction History of Artificial Intelligence Why AI Limitations of Human Mind Applications Advantages Disadvantages
- 3. INTRODUCTION Artificial Intelligence is a branch of science which deals with helping machines finds solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics rom human intelligence, and applying them as algorithms in a computer friendly way.
- 4. HISTORY OF AI Greek Mythology-Talos. Talos was a giant animated bronze warrior programmed to guard the island of Crete , created by Hephaestus. 1950 - Alan Turning. He published a landmark paper in which he speculated about the possibility of creating machines that think. 1951 – Game AI. Christopher Strachey wrote a checkers program and Dietrich Prinz wrote one for chess.
- 5. 1956 – The Birth of AI. John McCarthy first coined the term “Artificial Intelligence” in 1956 at the Dartmouth Conference 1959 – First AI Laboratory. MIT AI Lab was first set up in 1959. The research on AI began. 1960 – General Motors Robot. First robot was introduced to General Motors assembly line. 1961 – First chatbot. The first AI chatbot called ELIZA was introduced in 1961
- 6. 1997 – IBM Deep Blue. IBM’s Deep Blue beats world champion Garry Kasparov in the game of chess. 2005 – DARPA Grand Challenge. Stanford Racing Team’s autonomous robotic car , Stanley wins the 2005 DARPA Grand Challenge. 2011 – IBM’s questions answering system , Watson defeated the two greatest Jeopardy Champions , Brad Rutter and Ken Jennings.
- 7. WHY AI ? Computers are fundamentally well suited to perform mechanical computations, using fixed programmed rules. This allows artificial machines to perform simple monotonous tasks efficiently and reliably, which humans are ill-suited to.
- 8. LIMITATIONS OF HUMAN MIND Object recognition. People cannot properly explain how they recognize objects. Face recognition. Cannot be passed on to another person by explanation. Naming of colours. Based on learning, not on absolute standards.
- 9. APPLICATIONS OF AI Expert systems. Natural Language Processing (NLP). Speech recognition. Computer vision. Robotics.
- 10. EXPERT SYSTEMS An Expert System is a computer program designed to act as an expert in a particular domain (area of expertise). Phases in Expert System Expert Systems currently are designed to assist experts, not to replace them, they have been used in medical diagnosis, chemical analysis, geological explorations etc.
- 11. NATURAL LANGUAGE PROCESSING The goal of NLP is to enable people and computers to communicate in a natural (humanly) language (such as, English) rather than in a computer language. The field of NLP is divided in 2 categories: • Natural Language understanding. • Natural Language generation.
- 12. SPEECH RECOGNITION The primary interactive method of communication used by humans is not reading and writing, it is speech. The goal of speech recognition research is to allow computers to understand human speech. So that they can hear our voices and recognize the words we are speaking. It simplifies the process of interactive communication between people and computers, thus it advances the goal of NLP.
- 13. COMPUTER VISION People generally use vision as their primary means of sensing their environment, we generally see more than we hear, fell or smell or taste. The goal of computer vision research is to give computers this same powerful facility for understanding their surrounding. Here A.I helps computer to understand what they see through attached cameras.
- 14. ROBOTICS A Robot is a electro-mechanical device that can by programmed to perform manual tasks or a reprogrammable multi functional manipulator designed to move materials, parts, tools, or specialized devices through variable programmed motions for performance of variety of tasks. An ‘intelligent’ robot includes some kind of sensory apparatus that allows it to respond to change in it’s environment.
- 15. ADVANTAGES DISADVANTAGES Increased Efficiency and Productivity Personalization Improved Healthcare Enhanced Communication Better Decision-Making Increased Safety Job Displacement. Privacy Concerns. Bias and Discrimination. Lack of Transparency.