3. {
Introduction to A.I.
What is A.I.
Goals of A.I.
Approaches of A.I.
Types of A.I.
Machine Learning
K-Mean Algorithms
Applications of A.I.
Advantages & Disadvantages
Limitation of A.I.
Conclusions on A.I.
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.
OR
The study in development of intelligent machines and software.
5. o 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.
o It is similar to the similar task of using computers to understand
human intelligence.
o A.I. deals with symbolic, Algorithmic-Methods of problem solving.
o A.I. works with pattern matching methods which attempts to
describe objects, events or processes in terms of there qualitative
features and logical and computational Relationship.
6. The Definition of A.I. gives four possible goals to pursue:
Systems that think like humans.
Systems that think rationally.
Systems that act like humans.
Systems that act rationally.
Human-Like Rationally
Think (1) Cognitive science Approach (2) Laws of thought Approach
Act (3) Turing test Approach (4) Rational agent Approach
7. 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.
8. Develop concepts, theory and practice of building intelligent
machines.
Emphasis is on system building.
9. Develop concepts, mechanisms and vocabulary to understand
biological.
Intelligent behavior.
Emphasis is on understanding intelligent behaior.
11. Knowledge representation and Commonsense knowledge.
Automated planning and scheduling.
Machine learning.
Natural language processing.
Machine perception and Computer vision and Speech recognition.
Affective computing.
Computational creativity.
Artificial general intelligence and Al-complete.
12. Machine:
A machine is a tool containing 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:
Ability of a machine to improve its own performance through the
use of software that implies artificial intelligence techniques to mimic the ways
by which humans seem to learn, such as repetition and experience.
13. 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.
4. Compute the distance from x each centroid.
5. Assign x to the closest centroid //a centroid represent a cluster.
6. End for.
7. Re-compute the centroid using the current cluster membership.
8. Until the stopping the criterion is met.
18. It cannot understand natural language robustly (e.g. read and
understand the articles in a newspaper).
Surf the web.
Intercept 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.
19. In its short existence, A.I. has increased understanding of
the nature of intelligence and provided an impressive array
of an 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.
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