BY –mynk

ARTIFICIAL INTELLIGENCE

INTODUCTION OF AI

EVOLUTION OF A.I.

BRANCHES AND APPLICATIONS OF A.I

WHAT WE ACHIEVED IN A.I.

CONCLUSION

CONTENTS -

Artificial- Not naturalIntelligence- Capability to learn and take

decisionsA.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.

What is Artificial Intellignece

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.

Early Developments in AI (1940-65)

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

Language Processing (1965-1975)

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.

Turing Test

ARTIFICIAL NUERAL SYSTEM

COMPUTER VISION

NATURAL LANGUAGE PROCESSING (N.L.P)

MACHINE LEARNING

ROBOTICS

BRANCHES OF A.I.-

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

Artificial Neural Systems

AdvantagesThey can learn without needing to be reprogrammed

DisadvantagesTime consuming and requires a lot of technical expertise to set upCan’t tell the reason behind the decision.

Advantages & Disadvantages of ANS-

COMPUTER VISION-

COMPUTER VISION-

Difficulties with Vision Systems

Shadows on Objects Identifying the Edge

of the ImageGlareObjects hiding other

parts of the ImageViewing from

different angles

Stages1. Input Image using Digital

Camera2. Detect Edges of Object3. Compare to Knowledge

Base – Pattern Matching Uses

Security systems, recognizing faces at airports

Inspection of manufactured goods judging quality of production

Vision systems on automated cars

Interpretation of Satellite photos for military use

TRADITIONAL VISION-

COMPUTER VISION-

LATEST(NEURAL) VISION-

TRDITIONAL VISION-

COMPUTER VISION-

LATEST(NEURAL) VISION-

NLP or Speech Recognition is where an AI system can be controlled and respond to verbal commands

ExamplesSpeech-driven word processorsMilitary weapon controlMobile phones(SIRI)Customer query lines

NATURAL LANGUAGE PROCESSING (N.L.P.) OR SPEECH RECOGNITION-

TRADITIONAL N.L.P.

NATURAL LANGUAGE PROCESSING (N.L.P.) OR SPEECH RECOGNITION-

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''

MACHINE LEARNING:-

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

TECHNIQUES OF MACHINE LEARNING-

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 SystemsThe ability to learn and improve performanceRobot that can walk rather than on wheelsNLP response

ExamplesThe delivery of goods in warehousesThe inspection of pipesBomb DisposalExploration of Ocean floor or space

ROBOTICS-

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.

WHAT WE ACHIVED-(ASIMO)

• 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.

WHAT WE ACHIVED-(STANLEY)

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.

WHAT WE ACHIVED-(STAN. HELICOPTER)

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

WHAT WE ACHIVED-(IBM WATSON-)

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.

WHAT WE ACHIVED-(MYON)

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. 

WHAT WE ACHIVED-(KISMET)

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

CONCLUSION-