Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence
Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
3. INTRODUCTION TO A.I.
WHAT IS A.I
GOALS OF A.I
APPROCHES OF AI
TYPES OF A.I.
MACHINE LEARING
K-MEAN ALGORITHEM
APPLICATION OF A.I
ADVANATAGES & DISADVATGES
LIMITATION OF A.I
CONCLUSIONS ON A.I
CONTENTS
4. Artificial = a human creation that did not occur naturally
Intelligence = the ability to learn and apply knowledge
Artificial Intelligence = man-made device/system that is able
to act with and/or mimic human intelligence
The study in development of intelligent machines and
software
INTRODUCTION
5. ARTICIACAL INTELLEGENCE
WHAT IS A.I. ?
Artificial Intelligence (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
It is related to the similar task of using computers to
understand human intelligence.
6. * A.I deals with symbolic,
Algorithmic-Methods of problem
solving.
* AI works with pattern matching methods which attempt to
describe objects, events or processes in terms of their qualitative
features and logical and computational Relationship.
ARTICIACAL INTELLEGENCE
7. GOALS OF AI
The definition of AI gives four possible goals to
pursue:
1. Systems that think like humans.
2. Systems that think rationally.
3. Systems that act like humans
4. Systems that act rationally
Most of AI work falls into category (2) and (4).
8. Replicate human intelligence: still a distant goal.
Solve knowledge intensive tasks.
Make an intelligent connection between
perception and action.
Enhance human-computer and computer to
computer Interaction / communication.
GENERAL GOALS OF AI
9. Develop concepts, theory and practice of building
intelligent machines
Emphasis is on system building.
ENGINEERING BASED GOALS
10. Develop concepts, mechanisms and vocabulary to
understand biological
Intelligent behavior.
Emphasis is on understanding intelligent behavior.
SCIENCE BASES GOALS OF AI
12. Knowledge representation and Commonsense
knowledge
Automated planning and scheduling
Machine learning
Natural language processing
Machine perception, Computer vision and Speech
recognition
Affective computing
Computational creativity
Artificial general intelligence and AI-complete
TYPES OF AI
13. Machine:
A machine is a tool containing one or more parts
that uses energy to perform an intended action.
Learning:
Learning is the act of acquiring new, or modifying
and reinforcing, existing knowledge, behaviors,
skills, values, or preferences and may involve
synthesizing different types of information.
MACHINE LEARNING
14. Ability of a machine to improve its own
performance through the use of software that
employs artificial intelligence techniques to
mimic the ways by which humans seem to learn,
such as repetition and experience.
WHAT IS MACHINE
LEARING?
15. Data is recorded from some real-world phenomenon.
What might we want to do with that data?
Prediction:
- What can we predict about this phenomenon?
Description:
- How can we describe/understand this
phenomenon in a new way?
LEARNING FROM DATA
17. Reinforcement learning:
It is learning from interaction with an environment; from the
consequences of action, rather than from explicit teaching.
Supervised learning:
Training data includes both the input and desired results. For
example the correct are known and are given in input to the
model during the learning process. The construction of a proper,
validation and attest set crucial.
MACHINE LEARNING
18. Unsupervised learning:
The data have no target attribute. We want to explore
the data to find some intrinsic structures in them. The
model is not provided with the correct result during
the training . It can be used to cluster the input data
in classes on the basis of their statistical properties. It
is further divided into:
1.Clustering
2.Hidden Markov models
3.Blind signal separation
MACHINE LEARNING
19. Clustering:
Clustering of data is a method by which large sets
of data are grouped into clusters of smaller sets of
similar data.
The example below demonstrates the clustering of
balls of same colors. There are a total of 9 balls
which are of three different colors. We are
interested in clustering of balls of the three
different colors into three different groups.
SUPERVISED LEARNING
20. The balls of same color are clustered into a group as
shown below
Thus, we see clustering means grouping of data or
dividing a large data set into smaller data sets of
some similarity.
CLUSTERING
21. A clustering algorithm has following types:
Partitional clustering
1. k-Means (and EM)
2. k-Medoids
Hierarchical clustering
1. Agglomerative
2. Divisive
3. BIRCH
CLUSTERING
22. Examples of Clustering Applications:
Marketing: Help marketers discover distinct groups in
their customer bases, and then use this knowledge to
develop targeted marketing programs
Land use: Identification of areas of similar land use in
an earth observation database.
Insurance: Identifying groups of motor insurance
policy holders with a high average claim cost.
Urban planning: Identifying groups of houses
according to their house type, value, and geographical
location.
Seismology: Observed earth quake epicenters should
be clustered along continent faults
CLUSTERING
23. K-means is a partitional clustering algorithm
Let the set of data points (or instances) D be
{x1, x2, …, xn},
where xi = (xi1, xi2, …, xir) is a vector in a real-
valued space X Rr, and r is the number of
attributes (dimensions) in the data.
The k-means algorithm partitions the given data
into k clusters.
• Each cluster has a cluster center, called
centroid.
• k is specified by the user
K-MEANS
24. Works when we know k, the number of clusters
we want to find
Randomly pick k points as the “centroids” of the k
clusters
Loop:
For each point, put the point in the cluster to whose
centroid it is closest
Recomputed the cluster centroids
Repeat loop (until there is no change in clusters
between two consecutive iterations.)
K-MEANS
25. Algorithm k-mean (k,D)
1. Choose k data point as the initial centroids (cluster
centers)
2. Repeat
3. For each data point x ∈ to D do
4. Compute the distance from x each centroid.
5. Assign x to the closest centroid //a
centroid represent a cluster
6. endfor
7. re-compute the centroid using the current
cluster membership
8. Until the stopping criterion is met
K-MEANS ALGORITHEM
27. Draw distance from two pints & draw
perpendicular bisector
K-MEANS EXPLANATION
28. The clustered will be colored According to
centroids base on perpendicular bisector
left side of cluster line give the red colors
and right side are colored yellow
K-MEANS EXPLANATION
29. Now will take the average of the each cluster,
the average will be new position of the centroid
And the centroid move to new position, this is
first iterations
K-MEANS EXPLANATION
30. Now draw distance from two centroids and
draw perpendicular bisector
K-MEANS EXPLANATION
31. Now the clustered will be colored
according to centroids base on
perpendicular bisector
left side of cluster line give the red colors
and right side are colored yellow
K-MEANS EXPLANATION
32. Now will take the average of the each
cluster, the average will be new position of
the centroid
And the centroid move to new position, this
is second iterations
K-MEANS EXPLANATION
33. Now draw distance from two centroids and
draw perpendicular bisector
K-MEANS EXPLANATION
34. Now the clustered will be colored
according to centroids base on
perpendicular bisector and will take the
average of the each cluster, the average
will be new position of the centroid
And the centroid move to new position, this
is third iterations.
K-MEANS EXPLANATION
35. Again draw distance from two pints and
draw perpendicular bisector
K-MEANS EXPLANATION
36. Again it will take the average of each
cluster and at this time centroids average
does not change/move. So this it stop. And
it is our fourth iterations
K-MEANS EXPLANATION
37. Time Complexity of K-Mean Algorithm:
Complexity is O (n * K * I )
n = number of points,
K = number of clusters,
I = number of iterations,
TIME COMPLEXITY
38. Expert systems
Natural Language Processing (NLP)
Speech recognition
Computer vision
Robotics
Automatic Programming
APPLICATION OF AI
39. This prospered greatly with the Digital Revolution, and
helped introduce people, especially children, to a life of
dealing with various types of Artificial Intelligence
Games are Interactive computer program, an emerging
area in which the goals of human-level AI are pursued.
Games are made by creating human level artificially
intelligent entities, e.g. enemies, partners, and support
characters that act just like humans.
GAME PLAYING
40. 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.
NATURAL LEARNING PROCESS
41. 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.
SPEECH RECOGNIZATION
42. People generally use vision as their primary
means of sensing their environment, we generally see
more than we hear, feel 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.
COUMPUTER VISION
43. A ``knowledge engineer'' interviews experts in a certain
domain and tries to embody their knowledge in a
computer program for carrying out some task.
Expert systems currently are designed to assist experts,
not to replace them, They have been used in medical
diagnosis, chemical analysis, geological explorations etc.
Domain of E.S.
Knowledge base
Facts Heuristics
Phases in Expert System
EXPERT SYSTEM
44. 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.
ROBOTICS
48. It cannot understand natural language robustly
(e.g., read and understand articles in a newspaper)
Surf the web
Interpret an arbitrary visual scene
Learn a natural language
Construct plans in dynamic real-time domains
Exhibit true autonomy and intelligence
Still need greater software flexibility
LIMITATION OF AI
49. CONCLUSION
In its short existence, AI has increased understanding of the nature
of intelligence and provided an impressive array of application in
a wide range of areas. It has sharpened understanding of human
reasoning and of the nature of intelligence in general. At the same
time, it has revealed the complexity of modeling human reasoning
providing new areas and rich challenges for the future.