The document discusses artificial intelligence (AI), including definitions of AI, goals of AI like creating expert systems and implementing human intelligence in machines. It provides examples of AI like Siri, Alexa, Tesla. It also discusses current and future status of AI, advantages like less errors and faster decisions, disadvantages like high costs and unemployment. It summarizes AI tools like Amazon Web Services, AI-One, Deep Learning4J, Apache Mahout, Open Neural Networks Library. Finally, it discusses types of computing environments like personal, time sharing, client server, distributed, grid and cluster computing.

1. ARTIFICIAL
INTELLIGENCE (A.I.)

2. GROUP MEMBERS
• Amisha Pokharna
• Aviraj Nahar
• Ayush Harkawat
• Himanshu Mirani
• Ragini Vyas
• Ridhi Jain

3. WHAT IS ARTIFICIAL
INTELLIGENCE?
• Artificial Intelligence is a way of making a
computer, a computer-controlled robot, or a
software think intelligently, in the similar
manner the intelligent humans think.
• AI is accomplished by studying how human
brain thinks, and how humans learn, decide,
and work while trying to solve a problem, and
then using the outcomes of this study as a basis
of developing intelligent software and systems.

4. Philosophy of AI
• While exploiting the power of the computer
systems, the curiosity of human, lead him to
wonder, “Can a machine think and behave like
humans do?”
• Thus, the development of AI started with the
intention of creating similar intelligence in
machines that we find and regard high in
humans.

5. Goals of AI
To Create Expert Systems
• The systems which exhibit intelligent
behavior, learn, demonstrate, explain, and
advice its users.
To Implement Human Intelligence in
Machines
• Creating systems that understand, think, learn,
and behave like humans.

6. EXAMPLES OF A.I.
• Siri
• Alexa
• Tesla
• Cogito
• Netflix
• Amazon.com
• Nest
• Pandora

7. ARTIFICIAL
INTELLIGENCE TOOLS
1. AMAZON WEB SERVICES
• Amazon Web Services (AWS) comes with several AI
toolkits for developers. For example, AWS
Rekognition utilizes AI to build image interpretation
and facial recognition into apps with common
biometric security features.
• Furthermore, AWS Lex is the open source tool behind
Amazon’s personal assistant Alexa. This technology
enables developers to integrate chatboxs into mobile
and web applications. AWS Polly, on the other hand,
utilizes AI to automate voice to written text in 24
languages and 47 voices.

8. 2. AI-ONE
This is a tool that enables developers to build
intelligent assistants within almost all software
applications. Often referred to as biologically
inspired intelligence, ai-one’s Analyst
Toolbox is equipped with the following:
•APIs
•building agents
•document library
The primary benefit of this tool is the ability to
turn data into generalized sets of rules that
enable in-depth ML and AI structures.

9. 3.DEEP LEARNING4J
Deep learning4j or Deep Learning for Java is a leading open
source deep learning (DL) library written for Java and Java
Virtual Machine (JVM). It’s specifically designed to run on
enterprise applications such as Apache Spark and Hadoop.
It also includes the following:
•Boltzmann machine
•Deep autoencoder
•Deep belief net
•Doc2vec
•Recursive neural tensor network
•Stacked denoising autoencoder
•Word2vec

10. 4.APACHE MAHOUT
This is a library of scalable ML algorithms that can be
implemented on top of Apache Hadoop by utilizing
the MapReduce paradigm. As a result, once all the big
data is stored on Hadoop Distributed File System
(HDFS), you can use the data science tools provided
by Apache Mahout identify valuable patterns in those
big data sets.
The primary advantage of the Apache Mahout project is
that it makes it much easier and faster to derive real
value from big data.

11. 5. OPEN NEURAL NETWORKS
LIBRARY (OPENNN)
•This is another open-source tool that’s essentially a class
library written in the programming language C++ for SL
that is utilized to stimulate neural networks.
•With this OpenNN tool, you can implement neural
networks that are characterized by high performance and
deep architecture.

12. CURRENT STATUS OF
A.I.
 AIhavetaken manyshapesandforms over recent years
o Mobile Phones( Siri/Cortana)
o Video GamesCharacters
o GPS/Voice Recognition
o Robotics
 Googlehasbeen amajor play onAItranscendence and
Deep Learning.
o Deeplearning is amachine learning basedon
algorithms

13. FUTURE OF A.I.
1. Automated Transportation
2. Cyborg Technology
3. Taking over dangerous jobs
4. Solving climate change
5. Robot as friends
6. Improved elder care

14. ADVANTAGES OF ARTIFICIAL
INTELLIGENCE
• Less Errors
Errors are reduced and the chance of reaching accuracy
with a greater degree of precision is a possibility.
• Faster Decisions:
Using Artificial intelligence, decisions can be taken very
fast.
• Daily Applications:
A.I. is used in many applications just like Apple’s Siri,
Google’s OK Google. Using these type of applications
we can communicate with our device using our voice.
Which makes our work easy.

15. •No Breaks:
Unlike humans, machines can work 24*7 without any
break.
•Taking risks on behalf of humans:
Robots can be used instead of Humans to avoid the
risks.
•Public Utilities:
Facial recognition can be used for security in self
driving cars.

16. DISADVANTAGES OF ARTIFICIAL
INTELLIGENCE
• High Costs:
The hardware and software need to get updated with
time to meet the latest requirements.
• Unemployment:
As machines are replacing human resources, the rate
of people losing their jobs will increase.
• Cannot think out of box:
Robots can only do the work that they are
programmed to do. They are not able to act any
different.

17. •Cannot feel Compassion and Sympathy:
There is no doubt that machines are much better when it
comes to working efficiently but they cannot replace the
human connection that makes the team.
•High dependence on machines:
In future with the heavy use of application of artificial
intelligence, human may become fully dependent on
machines, losing their mental capacities.

18. INDIAN COMPUTING
ENVIRONMENT
Computing Environment is a collection of computers
which are used to process and exchange the information
to solve various types of computing problems.
Types of Computing Environments-
•Personal Computing Environment
•Time Sharing Computing Environment
•Client Server Computing Environment
•Distributed Computing Environment
•Grid Computing Environment
•Cluster Computing Environment

19. 1. PERSONAL COMPUTING
ENVIRONMENT
In personal computing environment, the complete
program resides on stand alone machine and executed
from the same machine. Laptops, mobile devices,
printers, scanners and the computer systems we use at
home, office are the examples for personal computing
environment.

20. 2. TIME SHARING
COMPUTING ENVIRONMENT
In Time sharing computing environment single
user can perform multiple operations at a
time by using multitasking operating system.
Here the processor time is divided among
different tasks and this is called “Time
sharing”.

21. 3. Client Server
Computing
EnvironmentThe client server environment contains two machines (Client
machine and Server machine). In this environment, client
requests for data and server provides data to the client. In
the client server environment, the communication between
client and server is performed using HTTP (Hyper Text
Transfer Protocol).

22. 4. Distributed Computing
Environment
In the distributed computing environment, the
complete functionality of a software is not on single
computer but is distributed among multiple computers.
These computers communicate with each other over a
network to perform the complete task. In distributed
computing environment, the data is distributed among
different systems and that data is logically related to
each other.

23. 5.Grid Computing
Environment
Grid computing is a collection of computers from different
locations. All these computers work for a common
problem. A grid can be described as distributed collection
of large number of computers working for a single
application.

24. 6.Cluster Computing
Environment
Cluster computing is a collection of inter
connected computers. These computers work
together to solve a single problem. In cluster
computing environment, a collection of systems
work together as a single system.