ARTIFICIAL INTELLIGENCY Prepared By : Harsh dhruv sreejit

. WHAT IS ARTIFICIAL INTELLIGENCY ? 1)The thing which is manmade known as “ARTIFICIAL”. 2)Ability to archive goals in the world is known as “INTELLIGENCY”. 3)AI is the system which is find OR make the intelligent. 4)AI is the science and engineering of making intelligent machines specially computer programs. 5)AI is the similar task of understanding HUMAN intelligence using computer

HISTORY OF HISTORY OF ARTIFICIAL INTELLIGENGY :- -After WWII, a number of people independently started to work on intelligent machines. The English mathematician Alan Turing may have been the first. He gave a lecture on it in He also may have been the first to decide that AI was best researched by programming computers rather than by building machines. By the late 1950s, there were many researchers on AI, and most of them were basing their work on programming computers. -John McCarthy is the developer of AI.

THE PATH TO HUMAN-LEVEL THE PATH TO HUMAN-LEVEL Artificial Intelligence:- Artificial Intelligence:- -To solve any problem divide it into Epistemological & Heuristic first of all. -Now, solve it by using arithmetic & logical techniques. -John McCarthy who is the professor of Computer Science at Stanford University study this from 1948 & give the heading of AI to it in His main research is commonsense knowledge in this field.

-In 1958 the language LISP was invited by him. -He invite non monotonic circumscription methods in He get A.M.Turing award in 1971 & was elected President of “American Association for Artificial Intelligence“ for He got so many awards like in 1985, Nov.1988, 1990.

“COMPUTER” is best machine for making AI in place of any other man made machines.

ABOUT AI :- #There are two types of AI, 1)BIOLOGYCAL AI :-It is the AI in which the machine study the HUMAN physiology and than imitate that & show its intelligent 2)DIGITAL AI :- It is AI which is use to solve the problems of the world & also to archive the goals and solve it by mathematical & logical techniques. #The relation Between AI & Physiology is that both of them are study mind & common sense

BRANCHESE OF AI :- 1)Logical AI :- To complete own goals by mathematical logical language 2)Search :- By discovering make its program more efficient 3)Pattern Recognition :- To get idea by observation from the scenes which it sees already somewhere 4)Representation :- By mathematical logic language

5)Inference:- i) Monotonic :- Inference with drawing ii) Non-Monotonic :- Inference by using mathematical logical discursion 6)Common Sense Knowledge & Reasoning :- It is the area for progress of AI by new idea 7)Learning From Experience :- Learning of laws of logic by experience 8)Planning :- To generate strategy of archiving goals

9)Epistemology :- To study the kind of knowledge to solve the problems of the world 10)Ontology :- To study the kinds & their basic property which are exists 11)Heuristics:- It is the way of trying to discover something or an idea imbedded in a program 12)Genetic Programming :- Techniques to get programs for solve task

APPLICATIONS OF AI :- 1)Game playing :- Game like “CHESS” have some AI in them 2)Speech Recognition :- To use speech (voice) in place of mouse or key board 3)Understanding Natural Language :- To provides the text of domain

4)Computer Vision :- Computer vision requires three dimensional information 5)Expert Systems :- It possible to make machine which works without any help & give positive result 6)Heuristic Classification :- To put several information in fixed categorized thing

Projects :- Algorithms and Architectures:- Projects :- Algorithms and Architectures:- Locally Linear EmbeddingLocally Linear Embedding: unsupervised learning of nonlinear data manifolds Products of Experts: modeling distributions using renormalized products of simpler learned distributions Helmholtz machines: Unsupervised learning using bottom-up recognition models Learning in Bayesian Networks: Graphical models relating random variables Expectation Conjugate-Gradient: Improving the Speed of EM for Learning Latent Variable Models Multiple-Cause Vector Quantization: Learning parts-based models of data. Combining Discriminative Features To Infer Complex Trajectories: a conditional model for time-series regression. Products of Experts Helmholtz machines Expectation Conjugate-Gradient Multiple-Cause Vector Quantization Combining Discriminative Features To Infer Complex Trajectories Locally Linear Embedding Products of Experts Helmholtz machines Expectation Conjugate-Gradient Multiple-Cause Vector Quantization Combining Discriminative Features To Infer Complex Trajectories

Ensemble Learning and Monte Carlo Methods:- Ensemble learning: Fitting weight distributions without Monte Carlo Bayesian inference: Making predictions using all likely networks, not just one Monte Carlo methods: Solving hard Bayesian inference problems stochastically Older, Unsupported Software Packages:- DelveDelve: Data and software for evaluating learning algorithms Xerion: Unix software for neural network simulation Xerion Delve Xerion

Specific Applications:- Video ProcessingVideo Processing: using Bayesian Networks to learn the structure of video sequences Video Processing Phase UnwrappingPhase Unwrapping: using variational inference for 2D signal processing Phase Unwrapping Elastic modelsElastic models : Using deformable models to recognize hand-written Elastic modelsdigits Glove-TalkGlove-Talk: A neural network that converts gestures into real-time speech Glove-Talk

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BENEFITS & REQURMENTS OF STUDYING AI:- The studying AI is important in getting good jobs. The basic requirement for study AI is “you have to learn at least programming language C,Lisp,Prolog already.”