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Analysis of Algorithm
Project Title: ARTIFICIAL INTELLIGENCE
Term Paper
Artificial Intelligence
Intelligence:
Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds
and degrees of intelligence occur in people, many animals and some machines. It is the ability to
think and understand instead of doing things by instinct or automatically. It is the ability to learn
and understand, to solve problems and to make decisions.
Now thinking according to dictionary is
“Thinking is an activity of using your brain to consider a problem or to create an idea.”
Can computers can be intelligent? OR Can machines think?
“Artificial intelligence (AI) as a science makes machines do things that would require
intelligence if done by humans.”
However, the answer is not a simple Yes or No but rather a vague or fuzzy one.
What is Artificial Intelligence?
There is a huge amount of published research and popular literature in the field of AI (Artificial
Intelligence-a & b, n.d.; Minsky 1960; AI Journals & Associations, n.d.). John McCarthy coined
the phrase Artificial Intelligence as the topic of a 1956 conference held at Dartmouth (Buchanan,
n.d.)
Here are three definitions of AI. The first is from Marvin Minsky, a pioneer in the field. The
second is from Allen Newell, a contemporary of Marvin Minsky. The third is a more modern,
1990 definition, and it is quite similar to the earlier definitions.
In the early 1960s Marvin Minsky indicated that “artificial intelligence is the
science of making machines do things that would require intelligence if done by
men.” Feigenbaum and Feldman (1963) contains substantial material written by
Minsky, including “Steps Toward Artificial Intelligence” (pp 406-450) and “A
Selected Descriptor: Indexed Bibliography to the Literature on Artificial
Intelligence” (pp 453-475)
In Unified Theories of Cognition, Allen Newell defines intelligence as: the degree
to which a system approximates a knowledge-level system. Perfect intelligence is
defined as the ability to bring all the knowledge a system has at its disposal to
bear in the solution of a problem (which is synonymous with goal achievement).
This may be distinguished from ignorance, a lack of knowledge about a given
problem space.
Artificial Intelligence, in light of this definition of intelligence, is simply the
application of artificial or non-naturally occurring systems that use the
knowledge-level to achieve goals. (Theories and Hypotheses)
What is artificial intelligence? It is often difficult to construct a definition of a
discipline that is satisfying to all of its practitioners. AI research encompasses a
spectrum of related topics. Broadly, AI is the computer-based exploration of
methods for solving challenging tasks that have traditionally depended on people
for solution. Such tasks include complex logical inference, diagnosis, visual
recognition, comprehension of natural language, game playing, explanation, and
planning (Horvitz, 1990).
Artificial intelligence (AI) is a of the field of computer and information science. It focuses on
developing hardware and software systems that solve problems and accomplish tasks, such as
perception, reasoning and learning and develop systems to perform those tasks. The field of AI
includes studying and developing machines such as robots, automatic pilots for airplanes and
space ships, and “smart” military weapons.
Artificial Intelligence is the study of computer systems that attempt to model and apply the
intelligence of the human mind. It is the science and engineering of making intelligent machines,
especially intelligent computer programs. It is related to the similar task of using computers to
understand human intelligence.
Moreover it is:
1. Ability to interact with the real world, to perceive, understands, and act
E.g. speech recognition and understanding and synthesis
E.g. image understanding
E.g. ability to take actions, have an effect
2. Reasoning and Planning
Modeling the external world, given input
Solving new problems, planning, and making decisions
Ability to deal with unexpected problems, uncertainties
3. Learning and Adaptation
We are continuously learning and adapting
Our internal models are always being “updated”
E.g. learning to categorize.
AI involves Perceiving, recognizing, understanding the real world, Reasoning and planning
about the external world, Also Learning and adaptation. AI researchers responded by developing
new technologies, including streamlined methods for eliciting expert knowledge, automatic
methods for learning and refining knowledge, and common sense knowledge to cover the gaps in
expert information. These technologies have given rise to a new generation of expert systems
that are easier to develop, maintain, and adapt to changing needs.
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
Traditionally, all four goals have been followed and the approaches were:
Most of AI work falls into category (2) and (4).
General AI Goal
 Replicate human intelligence: still a distant goal.
 Solve knowledge intensive tasks.
 Make an intelligent connection between perception and action.
 Enhance human-human, human-computer and computer to computer
 Interaction / communication.
Engineering based AI Goal
 Develop concepts, theory and practice of building intelligent machines
 Emphasis is on system building.
Science based AI Goal
 Develop concepts, mechanisms and vocabulary to understand biological
 Intelligent behavior.
 Emphasis is on understanding intelligent behavior.
AI Approaches:
The approaches followed are defined by choosing goals of the computational model, and basis
for evaluating performance of the system.
1. Cognitive science : Think human-like
• An exciting new effort to make computers think; that it is, the machines with
minds, in the full and literal sense.
• Focus is not just on behavior and I/O, but looks at reasoning process.
• Computational model as to how results were obtained.
• Goal is not just to produce human-like behavior but to produce a sequence of
steps of the reasoning process, similar to the steps followed by a human in
solving the same task.
2. Laws of Thought : Think Rationally
• The study of mental faculties through the use of computational models; that it
is, the study of the computations that make it possible to perceive, reason, and
act.
• Focus is on inference mechanisms that are probably correct and guarantee an
optimal solution.
• Develop systems of representation to allow inferences to be like “Socrates is a
man. All men are mortal. Therefore Socrates is mortal.”
• Goal is to formalize the reasoning process as a system of logical rules and
procedures for inference.
• The issue is, not all problems can be solved just by reasoning and inferences.
3. Turing Test : Act Human-like
• The art of creating machines that perform functions requiring intelligence when
performed by people; that it is the study of, how to make computers do things
which at the moment people do better.
• Focus is on action, and not intelligent behavior centered around representation
of the world.
• A Behaviorist approach is not concerned with how to get results but to the
similarity to what human results are.
• Goal is to develop systems that are human-like.
4. Rational Agent : Act Rationally
• Tries to explain and emulate intelligent behavior in terms of computational
processes; that it is concerned with the automation of intelligence.
• Focus is on systems that act sufficiently if not optimally in all situations.
• It is passable to have imperfect reasoning if the job gets done.
• Goal is to develop systems that are rational and sufficient.
Different Types of Artificial Intelligence
1. Knowledge representation and Commonsense knowledge
2. Automated planning and scheduling
3. Machine learning
4. Natural language processing
5. Machine perception, Computer vision and Speech recognition
6. Affective computing
7. Computational creativity
8. Artificial general intelligence and AI-complete
Machine learning
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.
In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the
ability to learn without being explicitly programmed".
What is machine learning?
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.
Machine learning can be considered a subfield of computer science and statistics. It has strong
ties to artificial intelligence and optimization, which deliver methods, theory and application
domains to the field.
Machine learning and statistics:
ML (Machine learning and Statistics) are closely interrelated. From methodological principles to
theoretical tools, ideas of ML have had a lengthy pre-history in Stat. Michael I. Jordan suggested
the Data science as a placeholder to call the overall field.
Learning from Data:
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?
Types of problems:
1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning
Reinforcement learning:
It is learning from interaction with an environment; from the consequences of action, rather than
from explicit teaching. RL is conducted within the mathematical framework of Markov decision
processes (MDPs).
Supervised learning:
Training
data
includes
both
the
input
and
the desired
results.
For
some
examples
the
correct
are
 known
and
are
given
in
input
to
the
model
during

the
learning
process. The construction
of
a
proper
training, 
validation
and
 test
set is

crucial.
These
methods
are
usually
fast
and
accurate
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
results
during
the
training. It can
be

used to
cluster
the
input
data
in
classes
on
the
basis
of
their
statistical
properties
only.

It is further divided into:
1. Clustering
2. Hidden Markov models
3. Blind signal separation
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.
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.
A clustering algorithm has following types:
1. Partitional clustering
• k-Means (and EM)
• k-Medoids
2. Hierarchical clustering
• Agglomerative
• Divisive
• BIRCH
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
K-means
 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
 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:
o For each point, put the point in the cluster to whose centroid it is closest
o Recomputed the cluster centroids
o Repeat loop (until there is no change in clusters between two consecutive
iterations.)
K-means Algorithm:
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
Example with Explanation:
Random Selection of k and cluster assignment
Draw distance from two pints and draw perpendicular bisector
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
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
Now draw distance from two centroids and draw perpendicular bisector
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
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.
Now draw distance from two centroids and draw perpendicular bisector
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.
Again draw distance from two pints and draw perpendicular bisector
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
Time Complexity of K-Mean Algorithm:
Complexity is O (n * K * I)
• n = number of points,
• K = number of clusters,
• I = number of iterations,
Applications of AI:
1. Game playing
• 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.
2. Speech Recognition
• A process of converting a speech signal to a sequence of words;
• In 1990s, computer speech recognition reached a practical level for limited
purposes.
• Using computers recognizing speech is quite convenient, but most users find
the keyboard and the mouse still more convenient.
• The typical usages are :
◊ Voice dialing (Call home)
◊ Call routing (collect call)
◊ Data entry (credit card number)
◊ Speaker recognition
3. Understanding Natural Language:
Natural language processing (NLP) does automated generation and understanding
of natural human languages.
• Natural language generation system:
Converts information from computer databases into normal-sounding
human language.
• Natural language understanding system:
Converts samples of human language into more formal representations
that are easier for computer programs to manipulate.
• Some major tasks in NLP:
◊ Text-to-Speech (TTS) system:
Converts normal language text into speech.
◊ Speech recognition (SR) system:
Process of converting a speech signal to a sequence of words.
◊ Machine translation (MT) system:
Translate text or speech from one natural language to another.
◊ Information retrieval (IR) system:
Search for information from databases such as Internet or World
Wide Web or Intranets.
4. Computer Vision
• It is a combination of concepts, techniques and ideas from: Digital Image
Processing, Pattern Recognition, Artificial Intelligence and Computer
Graphics.
• The world is composed of 3-D objects, but the inputs to the human eye and
computers' TV cameras are 2-D.
• Some useful programs can work solely in 2-D, but full computer vision
requires partial 3-D information that is not just a set of 2-D views.
• At present there are only limited ways of representing 3-D information
directly, and they are not as good as what humans evidently use.
• Examples
◊ Face recognition: the programs in use by banks
◊ Autonomous driving: The ALVINN system, autonomously drove a van
from Washington, D.C. to San Diego, averaging 63 mph day and night,
and in all weather conditions.
◊ Other usages: Handwriting recognition, Baggage inspection,
Manufacturing inspection, Photo interpretation, etc.
5. Expert Systems
Systems in which human expertise is held in the form of rules
• It enables the system to diagnose situations without the human expert being
present.
• A Man-machine system with specialized problem-solving expertise. The
"expertise" consists of knowledge about a particular domain, understanding of
problems within that domain, and "skill" at solving some of these problems.
• Knowledge base; A knowledge engineer interviews experts in a certain
domain and tries to embody their knowledge in a computer program for
carrying out some task.
• One of the first expert systems was MYCIN in 1974, which diagnosed
bacterial infections of the blood and suggested treatments.
• Expert systems rely on knowledge of human experts, e.g.
◊ Diagnosis and Troubleshooting: deduces faults and suggest corrective
actions for a malfunctioning device or process
◊ Planning and Scheduling: analyzing a set of goals to determine and
ordering a set of actions taking into account the constraints; e.g. airline
scheduling of flights.
◊ Financial Decision Making: an advisory program assists bankers to
make loans, Insurance companies to assess the risk presented by the
customer, etc.
◊ Process Monitoring and Control: analyzes real-time data, noticing
anomalies, predicting trends, and controlling optimality and do failure
correction.
6. Robotics
A Robot is an electro-mechanical device that can be 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 its environment.
Daily Life Examples:
 Post Office:
Automatic address recognition and sorting of mail
 Banks:
Automatic check readers, signature verification systems
Automated loan application classification
 Customer Service:
Automatic voice recognition
 The Web:
Identifying your age, gender, location, from your Web surfing
Automated fraud detection
 Digital Cameras:
Automated face detection and focusing
 Computer Games:
Intelligent characters/agents
 Speech synthesis, recognition and understanding:
Very useful for limited vocabulary applications
 Robotics
Limitations of AI
 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
 To date, all the traits of human intelligence have not been captured and
applied together to spawn an intelligent artificial creature.
 Currently, Artificial Intelligence rather seems to focus on lucrative domain
specific applications, which do not necessarily require the full extent of AI
capabilities.
 There is little doubt among the community that artificial machines will be
capable of intelligent thought in the near future.
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.
We conclude that if the machine could successfully pretend to be human to a knowledgeable
observer then you certainly should consider it intelligent. AI systems are now in routine use in
various field such as economics, medicine, engineering and the military, as well as being built
into many common home computer software applications, traditional strategy games etc.
References:
1. "Artificial Intelligence", by Elaine Rich and Kevin Knight, (2006), McGraw Hill companies
Inc.
2. "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig, (2002),
Prentice Hall.3. "Computational Intelligence: A Logical Approach", by David Poole, Alan
Mackworth, and Randy Goebel, (1998), Oxford University Press
4. "Artificial Intelligence: Structures and Strategies for Complex Problem Solving", by George
F. Luger, (2002), Addison-Wesley.
5. "AI: A New Synthesis", by Nils J. Nilsson, (1998), Morgan Kaufmann Inc.
6. "Artificial Intelligence: Theory and Practice", by Thomas Dean, (1994).
7. Related documents from open source, mainly internet:
a. http://en.wikipedia.org/wiki/Artificial_intelligence
b. https://www.youtube.com/watch?v=4shfFAArxSc
c. https://www.youtube.com/watch?v=_aWzGGNrcic
d. https://www.youtube.com/watch?v=0MQEt10e4NM
e. https://www.youtube.com/watch?v=aiJ8II94qck
f. https://www.youtube.com/watch?v=l77Au76TOok
g. https://www.youtube.com/watch?v=-07-iszyjM0
h. https://www.youtube.com/results?search_query=unsupervised+learning+tutorial
i. http://www.cs.gsu.edu/~cscyqz/courses/ai/aiLectures.html
j. http://www.eecs.qmul.ac.uk/~mmh/AINotes/
k. http://bookboon.com/en/artificial-intelligence-ebooks
l. http://ubiquity.acm.org/article.cfm?id=1041064
m. http://allquestionanswers.blogspot.com/2012/04/disadvantages-of-artificial.html
n. http://papers.nips.cc/paper/2601-the-correlated-correspondence-algorithm-for-
unsupervised-registration-of-nonrigid-surfaces.pdf
o. http://www.heppenstall.ca/academics/doc/370/CIS370.doc
p. http://pages.uoregon.edu/moursund/Books/AIBook/AI.doc

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ARTIFICIAL INTELLIGENCETterm Paper

  • 1. Analysis of Algorithm Project Title: ARTIFICIAL INTELLIGENCE Term Paper
  • 2. Artificial Intelligence Intelligence: Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines. It is the ability to think and understand instead of doing things by instinct or automatically. It is the ability to learn and understand, to solve problems and to make decisions. Now thinking according to dictionary is “Thinking is an activity of using your brain to consider a problem or to create an idea.” Can computers can be intelligent? OR Can machines think? “Artificial intelligence (AI) as a science makes machines do things that would require intelligence if done by humans.” However, the answer is not a simple Yes or No but rather a vague or fuzzy one. What is Artificial Intelligence? There is a huge amount of published research and popular literature in the field of AI (Artificial Intelligence-a & b, n.d.; Minsky 1960; AI Journals & Associations, n.d.). John McCarthy coined the phrase Artificial Intelligence as the topic of a 1956 conference held at Dartmouth (Buchanan, n.d.) Here are three definitions of AI. The first is from Marvin Minsky, a pioneer in the field. The second is from Allen Newell, a contemporary of Marvin Minsky. The third is a more modern, 1990 definition, and it is quite similar to the earlier definitions. In the early 1960s Marvin Minsky indicated that “artificial intelligence is the science of making machines do things that would require intelligence if done by men.” Feigenbaum and Feldman (1963) contains substantial material written by Minsky, including “Steps Toward Artificial Intelligence” (pp 406-450) and “A Selected Descriptor: Indexed Bibliography to the Literature on Artificial Intelligence” (pp 453-475) In Unified Theories of Cognition, Allen Newell defines intelligence as: the degree to which a system approximates a knowledge-level system. Perfect intelligence is defined as the ability to bring all the knowledge a system has at its disposal to bear in the solution of a problem (which is synonymous with goal achievement). This may be distinguished from ignorance, a lack of knowledge about a given problem space. Artificial Intelligence, in light of this definition of intelligence, is simply the application of artificial or non-naturally occurring systems that use the knowledge-level to achieve goals. (Theories and Hypotheses)
  • 3. What is artificial intelligence? It is often difficult to construct a definition of a discipline that is satisfying to all of its practitioners. AI research encompasses a spectrum of related topics. Broadly, AI is the computer-based exploration of methods for solving challenging tasks that have traditionally depended on people for solution. Such tasks include complex logical inference, diagnosis, visual recognition, comprehension of natural language, game playing, explanation, and planning (Horvitz, 1990). Artificial intelligence (AI) is a of the field of computer and information science. It focuses on developing hardware and software systems that solve problems and accomplish tasks, such as perception, reasoning and learning and develop systems to perform those tasks. The field of AI includes studying and developing machines such as robots, automatic pilots for airplanes and space ships, and “smart” military weapons. Artificial Intelligence is the study of computer systems that attempt to model and apply the intelligence of the human mind. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence. Moreover it is: 1. Ability to interact with the real world, to perceive, understands, and act E.g. speech recognition and understanding and synthesis E.g. image understanding E.g. ability to take actions, have an effect 2. Reasoning and Planning Modeling the external world, given input Solving new problems, planning, and making decisions Ability to deal with unexpected problems, uncertainties 3. Learning and Adaptation We are continuously learning and adapting Our internal models are always being “updated” E.g. learning to categorize. AI involves Perceiving, recognizing, understanding the real world, Reasoning and planning about the external world, Also Learning and adaptation. AI researchers responded by developing new technologies, including streamlined methods for eliciting expert knowledge, automatic methods for learning and refining knowledge, and common sense knowledge to cover the gaps in expert information. These technologies have given rise to a new generation of expert systems that are easier to develop, maintain, and adapt to changing needs.
  • 4. 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 Traditionally, all four goals have been followed and the approaches were: Most of AI work falls into category (2) and (4). General AI Goal  Replicate human intelligence: still a distant goal.  Solve knowledge intensive tasks.  Make an intelligent connection between perception and action.  Enhance human-human, human-computer and computer to computer  Interaction / communication. Engineering based AI Goal  Develop concepts, theory and practice of building intelligent machines  Emphasis is on system building. Science based AI Goal  Develop concepts, mechanisms and vocabulary to understand biological  Intelligent behavior.  Emphasis is on understanding intelligent behavior.
  • 5. AI Approaches: The approaches followed are defined by choosing goals of the computational model, and basis for evaluating performance of the system. 1. Cognitive science : Think human-like • An exciting new effort to make computers think; that it is, the machines with minds, in the full and literal sense. • Focus is not just on behavior and I/O, but looks at reasoning process. • Computational model as to how results were obtained. • Goal is not just to produce human-like behavior but to produce a sequence of steps of the reasoning process, similar to the steps followed by a human in solving the same task. 2. Laws of Thought : Think Rationally • The study of mental faculties through the use of computational models; that it is, the study of the computations that make it possible to perceive, reason, and act. • Focus is on inference mechanisms that are probably correct and guarantee an optimal solution. • Develop systems of representation to allow inferences to be like “Socrates is a man. All men are mortal. Therefore Socrates is mortal.” • Goal is to formalize the reasoning process as a system of logical rules and procedures for inference. • The issue is, not all problems can be solved just by reasoning and inferences. 3. Turing Test : Act Human-like • The art of creating machines that perform functions requiring intelligence when performed by people; that it is the study of, how to make computers do things which at the moment people do better. • Focus is on action, and not intelligent behavior centered around representation of the world. • A Behaviorist approach is not concerned with how to get results but to the similarity to what human results are. • Goal is to develop systems that are human-like. 4. Rational Agent : Act Rationally • Tries to explain and emulate intelligent behavior in terms of computational processes; that it is concerned with the automation of intelligence. • Focus is on systems that act sufficiently if not optimally in all situations. • It is passable to have imperfect reasoning if the job gets done. • Goal is to develop systems that are rational and sufficient.
  • 6. Different Types of Artificial Intelligence 1. Knowledge representation and Commonsense knowledge 2. Automated planning and scheduling 3. Machine learning 4. Natural language processing 5. Machine perception, Computer vision and Speech recognition 6. Affective computing 7. Computational creativity 8. Artificial general intelligence and AI-complete Machine learning 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. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". What is machine learning? 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. Machine learning can be considered a subfield of computer science and statistics. It has strong ties to artificial intelligence and optimization, which deliver methods, theory and application domains to the field. Machine learning and statistics: ML (Machine learning and Statistics) are closely interrelated. From methodological principles to theoretical tools, ideas of ML have had a lengthy pre-history in Stat. Michael I. Jordan suggested the Data science as a placeholder to call the overall field. Learning from Data: Data is recorded from some real-world phenomenon. What might we want to do with that data?
  • 7.  Prediction: - What can we predict about this phenomenon?  Description: - How can we describe/understand this phenomenon in a new way? Types of problems: 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning Reinforcement learning: It is learning from interaction with an environment; from the consequences of action, rather than from explicit teaching. RL is conducted within the mathematical framework of Markov decision processes (MDPs). Supervised learning: Training
data
includes
both
the
input
and
the desired
results. For
some
examples
the
correct
are
 known
and
are
given
in
input
to
the
model
during
 the
learning
process. The construction
of
a
proper
training, 
validation
and
 test
set is
 crucial.
These
methods
are
usually
fast
and
accurate 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
results
during
the
training. It can
be
 used to
cluster
the
input
data
in
classes
on
the
basis
of
their
statistical
properties
only.
 It is further divided into: 1. Clustering 2. Hidden Markov models 3. Blind signal separation 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.
  • 8. 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. A clustering algorithm has following types: 1. Partitional clustering • k-Means (and EM) • k-Medoids 2. Hierarchical clustering • Agglomerative • Divisive • BIRCH 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 K-means  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
  • 9.  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: o For each point, put the point in the cluster to whose centroid it is closest o Recomputed the cluster centroids o Repeat loop (until there is no change in clusters between two consecutive iterations.) K-means Algorithm: 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 Example with Explanation: Random Selection of k and cluster assignment
  • 10. Draw distance from two pints and draw perpendicular bisector 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 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
  • 11. Now draw distance from two centroids and draw perpendicular bisector 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 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.
  • 12. Now draw distance from two centroids and draw perpendicular bisector 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. Again draw distance from two pints and draw perpendicular bisector
  • 13. 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 Time Complexity of K-Mean Algorithm: Complexity is O (n * K * I) • n = number of points, • K = number of clusters, • I = number of iterations, Applications of AI: 1. Game playing • 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. 2. Speech Recognition • A process of converting a speech signal to a sequence of words; • In 1990s, computer speech recognition reached a practical level for limited purposes. • Using computers recognizing speech is quite convenient, but most users find the keyboard and the mouse still more convenient. • The typical usages are : ◊ Voice dialing (Call home) ◊ Call routing (collect call) ◊ Data entry (credit card number) ◊ Speaker recognition
  • 14. 3. Understanding Natural Language: Natural language processing (NLP) does automated generation and understanding of natural human languages. • Natural language generation system: Converts information from computer databases into normal-sounding human language. • Natural language understanding system: Converts samples of human language into more formal representations that are easier for computer programs to manipulate. • Some major tasks in NLP: ◊ Text-to-Speech (TTS) system: Converts normal language text into speech. ◊ Speech recognition (SR) system: Process of converting a speech signal to a sequence of words. ◊ Machine translation (MT) system: Translate text or speech from one natural language to another. ◊ Information retrieval (IR) system: Search for information from databases such as Internet or World Wide Web or Intranets. 4. Computer Vision • It is a combination of concepts, techniques and ideas from: Digital Image Processing, Pattern Recognition, Artificial Intelligence and Computer Graphics. • The world is composed of 3-D objects, but the inputs to the human eye and computers' TV cameras are 2-D. • Some useful programs can work solely in 2-D, but full computer vision requires partial 3-D information that is not just a set of 2-D views. • At present there are only limited ways of representing 3-D information directly, and they are not as good as what humans evidently use. • Examples ◊ Face recognition: the programs in use by banks ◊ Autonomous driving: The ALVINN system, autonomously drove a van from Washington, D.C. to San Diego, averaging 63 mph day and night, and in all weather conditions. ◊ Other usages: Handwriting recognition, Baggage inspection, Manufacturing inspection, Photo interpretation, etc.
  • 15. 5. Expert Systems Systems in which human expertise is held in the form of rules • It enables the system to diagnose situations without the human expert being present. • A Man-machine system with specialized problem-solving expertise. The "expertise" consists of knowledge about a particular domain, understanding of problems within that domain, and "skill" at solving some of these problems. • Knowledge base; A knowledge engineer interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. • One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. • Expert systems rely on knowledge of human experts, e.g. ◊ Diagnosis and Troubleshooting: deduces faults and suggest corrective actions for a malfunctioning device or process ◊ Planning and Scheduling: analyzing a set of goals to determine and ordering a set of actions taking into account the constraints; e.g. airline scheduling of flights. ◊ Financial Decision Making: an advisory program assists bankers to make loans, Insurance companies to assess the risk presented by the customer, etc. ◊ Process Monitoring and Control: analyzes real-time data, noticing anomalies, predicting trends, and controlling optimality and do failure correction. 6. Robotics A Robot is an electro-mechanical device that can be 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 its environment.
  • 16. Daily Life Examples:  Post Office: Automatic address recognition and sorting of mail  Banks: Automatic check readers, signature verification systems Automated loan application classification  Customer Service: Automatic voice recognition  The Web: Identifying your age, gender, location, from your Web surfing Automated fraud detection  Digital Cameras: Automated face detection and focusing  Computer Games: Intelligent characters/agents  Speech synthesis, recognition and understanding: Very useful for limited vocabulary applications  Robotics Limitations of AI  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  To date, all the traits of human intelligence have not been captured and applied together to spawn an intelligent artificial creature.  Currently, Artificial Intelligence rather seems to focus on lucrative domain specific applications, which do not necessarily require the full extent of AI capabilities.  There is little doubt among the community that artificial machines will be capable of intelligent thought in the near future.
  • 17. 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. We conclude that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. AI systems are now in routine use in various field such as economics, medicine, engineering and the military, as well as being built into many common home computer software applications, traditional strategy games etc.
  • 18. References: 1. "Artificial Intelligence", by Elaine Rich and Kevin Knight, (2006), McGraw Hill companies Inc. 2. "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig, (2002), Prentice Hall.3. "Computational Intelligence: A Logical Approach", by David Poole, Alan Mackworth, and Randy Goebel, (1998), Oxford University Press 4. "Artificial Intelligence: Structures and Strategies for Complex Problem Solving", by George F. Luger, (2002), Addison-Wesley. 5. "AI: A New Synthesis", by Nils J. Nilsson, (1998), Morgan Kaufmann Inc. 6. "Artificial Intelligence: Theory and Practice", by Thomas Dean, (1994). 7. Related documents from open source, mainly internet: a. http://en.wikipedia.org/wiki/Artificial_intelligence b. https://www.youtube.com/watch?v=4shfFAArxSc c. https://www.youtube.com/watch?v=_aWzGGNrcic d. https://www.youtube.com/watch?v=0MQEt10e4NM e. https://www.youtube.com/watch?v=aiJ8II94qck f. https://www.youtube.com/watch?v=l77Au76TOok g. https://www.youtube.com/watch?v=-07-iszyjM0 h. https://www.youtube.com/results?search_query=unsupervised+learning+tutorial i. http://www.cs.gsu.edu/~cscyqz/courses/ai/aiLectures.html j. http://www.eecs.qmul.ac.uk/~mmh/AINotes/ k. http://bookboon.com/en/artificial-intelligence-ebooks l. http://ubiquity.acm.org/article.cfm?id=1041064 m. http://allquestionanswers.blogspot.com/2012/04/disadvantages-of-artificial.html n. http://papers.nips.cc/paper/2601-the-correlated-correspondence-algorithm-for- unsupervised-registration-of-nonrigid-surfaces.pdf o. http://www.heppenstall.ca/academics/doc/370/CIS370.doc p. http://pages.uoregon.edu/moursund/Books/AIBook/AI.doc