This document provides an overview of artificial intelligence, including its definition, history, types (narrow AI and general AI), machine learning techniques (supervised, unsupervised, semi-supervised, and reinforced learning), an example of an AI robot named Sophia, current applications of AI in mobile phones, games, GPS, and robotics, the future potential of AI such as in self-driving cars and medical diagnosis, as well as both the advantages and disadvantages of AI technology.

1. ARTIFICIAL INTELLIGENCE

2. CONTENTS:
DEFINITION
HISTORY
TYPES OF AI
TYPES OF LEARNING IN AI
SOPHIA AI ROBOT
 ADVANTAGES & DISADVANTAGES OF AI
CURRENT STATUS OF AI
FUTURE OF AI
CONCLUSION

3. DEFINITION:
 Generally, Artificial intelligence is the intelligence
demonstrated by the machines, which differs from the
natural intelligence displayed by the humans and other
animals.
 In computer science, AI is defined as the study of
“intelligent agents” any device that perceives its
environment and takes actions that has maximum chance of
successive rate to achieve Goals.
Artificial Intelligence  Specific intelligence
Human Brain  Generalised intelligence

4. HISTORY:
 1941 : First Electronic Computer ( technology finally available)
 1956 :Term Artificial Intelligence Introduced
 1960 : Checkers – playing program that allows to play with
opponents
 1980 : Quality control systems
 2000 : First Sophisticated Walking Robot

5. TYPES OF AI:
1.NARROWAI:
NarrowAI is designed to perform one task at a time and
to continue improving its execution.The goal is to find an automated
solution to a problem or inconvenience or to simply improve something
that already works, but can work better.
Currently, most ofArtificial Intelligence is Narrow AI.
NarrowAI tends to be software that is automating an activity typically
performed by humans, and in the majority of the cases it exceeds or aims
to exceed, human ability in efficiency and endurance
Examples of narrow AI:
 Self-driving cars : Google and Uber cars
 Recognizing your face at your nearby bank office
 Weather report by smartphones

6. 2.GENERALAI:
The goal is the machine’s ability to think generally, to be able
to make decisions based on learning rather than previous training. It would
have the ability to take training into consideration but then make a judgement
on whether there is another, more appropriate course of action to be taken.
Independent learning from experience, which is the way humans learn and
reason, is the goal.
This is also called as ‘TheTrue AI’ because it is the next
step towards more comprehensive machine intelligence. Rather than focusing
on a single task, the goal is to teach the machine to comprehend and reason on
a wide level just like a human would.
We are talking about creating an intelligence that is
equivalent to that of a human being.That is a lofty task and one that we are
still so far from accomplishing.

7. MACHINE LEARNING:
Machine learning is simply a way of achieving AI. Machine
learning is an application of artificial intelligence (AI) that
enables systems to learn and advance based on experience
without being clearly programmed. Machine learning focuses
on the development of computer programs that can access
data and use it for their own learning.
There are 4 types of machine learning
 Supervised learning
 Unsupervised learning
 Semi-supervised learning
 Reinforced learning

8. 1. SUPERVISED LEARNING:
Supervised machine learning can take what it has learned in the
past and apply that to new data using labelled examples to predict
future patterns and events. It learns by explicit example.
Supervised learning requires that the algorithm’s possible outputs
are already known and that the data used to train the algorithm is
already labelled with correct answers.
2. UNSUPERVISED LEARNING:
Unsupervised learning is used against data without any historical
labels.The system is not given a pre-determined set of outputs or
correlations between inputs and outputs or a "correct answer."
The algorithm must figure out what it is seeing by itself, it has no
storage of reference points.The goal is to explore the data and
find some sort of patterns of structure.

9. 3. SEMI-SUPERVISED LEARNING (SSL):
Unlike supervised learning which uses labelled data and
unsupervised which is given no labelled data at all, SSL uses both.
More often than not the scales tip in favour of unlabelled data since
it is cheaper and easier to acquire, leaving the volume of available
labelled data in the minority.The AI learns from the labelled data to
then make a judgement on the unlabelled data and find patterns,
relationships and structures.
4. REINFORCEMENT LEARNING:
Reinforcement learning is a type of dynamic programming that
trains algorithms using a system of reward and punishment.
It receives rewards by performing correctly and penalties for doing
so incorrectly.Therefore, it learns without having to be directly
taught by a human – it learns by seeking the greatest reward and
minimising penalty.

10. SOPHIA AI ROBOT:
Sophia is a social humanoid robot developed by Hong Kong-based
company Hanson Robotics. Sophia was activated onApril 19, 2015
and made her first public appearance at South by Southwest Festival
(SXSW) in mid-March 2016 in Austin,Texas, United States. She is able
to display more than 50 facial expressions.

11.  Precision & Accuracy
 Space Exploration
 Used for mining
 Fraud detection & records
 Lacking the emotional side
 Can do repetitive and time
taking tasks
 Diagnosis & treatment
 Cost for the maintenance &
Repair
 Lack the human touch
 Lack a creative mind
 Unemployment
 Abilities of humans may diminish
 Robots suspending humans
 Wrong hands causes destruction

12. CURRENT STATUS OF AI:
 In Mobile phones (SIRI / CORTANA)
 Video Game characters
 GPS /Voice recognition
 Robotics
 Life on other planets like satellites and drones on planets

13. FUTURE OF AI:
 Self driving cars
 Picture search
 Super Human Doctor
 Smart Investor
 Self thinking Robots

14. CONCLUSION:
In its short existence, AI has increased the understanding of nature
of intelligence and provided an impressive array of application in
wide range of areas. It has sharpened understanding of human
reasoning, and nature of intelligence in general. It also provides
the complexity of human reasoning and rich challenges for future.

15. ANY QUESTIONS

16. THANK
YOU