This ppt gives a insight of AI and Machine learning there working there application risk and benefits and some future scope The Various Content and images has been gathered from various sites on the internet some of them are https://www.wikipedia.org http://scikit-learn.org/stable/

2. Intro To Artificial Intelligence
And
Machine Learning
What we will cover
• Machine learning concepts
• Its use in artificial intelligence development
• Some prominent research's going in artificial intelligence
• Demo of a simple artificial intelligence based on learning curve
• Benefits and risk associated with artificial intelligence
• Real world applications of artificial intelligence
• Future Scope in artificial intelligence

3. INTRO
ARTIFICIAL INTELLIGENCE :-It is the broader concept of machines being able to carry out
tasks in a way that we would consider “smart”
MACHINE LEARNING:- Machine Learning is a application of AI where we give machines access
to the data and let them learn themselves.

4. Machine learning concepts
Machine learning is the subfield of computer science that, according to Arthur
Samuel, gives "computers the ability to learn without being explicitly
programmed." Samuel, an American pioneer in the field of computer gaming and
artificial intelligence, coined the term "machine learning" in 1959 while at IBM.
Machine learning is the concept where a machine uses large amount of data to
make decisions.
Arthur Samuel
4

5. Machine learning tasks are typically classified into three broad categories, depe
a learning system.

6. Supervised learning: The computer is presented with example
inputs and their desired outputs, given by a "teacher", and the goal
is to learn a general rule that maps inputs to outputs.

7. Unsupervised learning: No labels are given to the learning
algorithm, leaving it on its own to find structure in its input.
Unsupervised learning can be a goal in itself (discovering hidden
patterns in data)

8. Reinforcement learning: A computer program interacts with a
dynamic environment in which it must perform a certain goal (such
as driving a vehicle or playing a game against an opponent). The
program is provided feedback in terms of rewards and punishments
as it navigates its problem space.

9. Machine Learning Algorithm

10. Decision tree learning:-Decision tree learning uses a decision tree as a predictive model, which
maps observations about an item to conclusions about the item's target value.
 Decision tree
Association rule learning:-Association rule learning is a method for discovering interesting
relations between variables in large databases.

11. Artificial neural
networks:-
An artificial neural network (ANN) learning algorithm,
usually called "neural network" (NN),
It is a learning algorithm that is inspired by the structure
and functional aspects of biological neural networks.
Computations are structured in terms of an
interconnected group of artificial neurons, processing
information using a connectionist approach
to computation.

12. Deep learning:- It consists of multiple hidden layers in an artificial neural
network. This approach tries to model the way the human brain processes light
and sound into vision and hearing.
Some successful applications of deep learning are computer vision and speech
recognition.
DEEP LEARNING

13. Deep Learning 

14. Clustering:-Cluster analysis is the assignment of a set of observations into subsets
(called clusters) so that observations within the same cluster are similar according to some
predesignated criterion or criteria, while observations drawn from different clusters are dissimilar.
Different clustering techniques make different assumptions on the structure of the data, often
defined by some similarity metric and evaluated for example by internal compactness (similarity
between members of the same cluster) and separation between different clusters.
Other methods are based on estimated density and graph connectivity. Clustering is a method
of unsupervised learning, and a common technique for statistical data analysis.
 Clustering
CLUSTERING ALGORITHM

15. Bayesian networks:-A Bayesian network, belief network or directed acyclic
graphical model is a probabilistic graphical model that represents a set
of random variables and their conditional independencies via a directed
acyclic graph (DAG).
A Bayesian network could represent the probabilistic relationships. The
network can be used to compute the probabilities of the presence of various
diseases. Efficient algorithms exist that perform inference and learning.
Decision curve 

16. Application in artificial intelligence development
Game Playing:-You can buy machines that can play master level chess for a few
hundred dollars. There is some AI in them, but they play well against people mainly
through brute force computation--looking at hundreds of thousands of positions. To
beat a world champion by brute force and known reliable heuristics requires being
able to look at 200 million positions per second.
Speech Recognition:-In the 1990s, computer speech recognition reached a
practical level for limited purposes. Thus United Airlines has replaced its keyboard
tree for flight information by a system using speech recognition of flight numbers
and city names. It is quite convenient. On the other hand, while it is possible to
instruct some computers using speech, most users have gone back to the keyboard
and the mouse as still more convenient.

17. Understanding Natural Language:-Just getting a sequence of words
into a computer is not enough. Parsing sentences is not enough
either. The computer has to be provided with an understanding of the
domain the text is about, and this is presently possible only for very
limited domains.
Computer Vision:-The world is composed of three-dimensional
objects, but the inputs to the human eye and computers' TV cameras
are two dimensional. Some useful programs can work solely in two
dimensions, but full computer vision requires partial three-dimensional
information that is not just a set of two-dimensional views. At present
there are only limited ways of representing three-dimensional
information directly, and they are not as good as what humans
evidently use.

18. Some prominent research's
Autopilot in Cars, Aircraft , Military equipment's etc.
Playing games like chess , checkers by mimicking human moves
Google's Alpha Go AI defeats human in first game of Go contest
IBMs Deep Blue defeats chess champion Garry Kasparov

19. Research Work
The AI system was able to recreate the complex quantum experiment to create an extremely cold gas trapped
in a laser beam, known as a Bose-Einstein condensate in less than an hour while we took 80+years to do
the same!
his intriguing phenomenon—sometimes called the fifth state of matter next to solid, liquid, gas and plasma—was
predicted by Satyendra Nath Bose and Albert Einstein in the 1920s. But it took a long time to develop the
necessary experimental techniques and find suitable materials to actually create it, which finally happened in
1995. Several years later in 2001 the work was recognised with the Nobel Prize in Physics.
Bose-Einstein condensation Experiment

20. Demo of a simple artificial intelligence
Demo of the AI Based on Bayes Theorem and Decision Curve.

21. Risk
The AI is programmed to do something devastating
The AI is programmed to do something beneficial, but it develops a destructive method
for achieving its goal
Elon Musk: regulate AI to combat 'existential threat' before it's too late

23. Real World Applications
Speech recognition
Home automation
Virtual Personal Assistants
Video Games
Purchase Prediction
Security Surveillance
News Generation
Smart Home Devices
Cleaning and housekeeping
Labour intensive work
Used as interactive Toy
Military Application

24. Future Scope in artificial intelligence
• Everything is now becoming inter connected
• Computing is becoming cheaper
• Data is becoming the new oil
• Machine learning is becoming the new
combustion engine
• AI Becomes Open Source (Googles Tensor
Flow Libraries)

25. SPECIAL THANKS TO TEAM I.R.I.S
FOR MAKING THIS POSSIBLE