Transcript artificial intelligency

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
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
1947. 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
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 1955.
-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 1978.
-He get A.M.Turing award in 1971 & was
elected President of
“American Association for Artificial Intelligence“
for 1983-1984.
-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:Locally 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.
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:Delve: Data and software for evaluating learning algorithms
Xerion: Unix software for neural network simulation
Specific Applications:Video Processing: using Bayesian Networks to learn the structure of
video sequences
Phase Unwrapping: using variational inference for 2D signal processing
Elastic models : Using deformable models to recognize hand-written
digits
Glove-Talk: A neural network that converts gestures into real-time
speech
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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.”