This document provides an overview of machine learning and its future applications. It defines machine learning as using statistical techniques to give systems the ability to learn from data without being explicitly programmed. Examples of current machine learning applications include powering Google, YouTube, Netflix, personal assistants like Siri, self-driving cars from Tesla, and brain-machine interfaces. The document discusses the differences between traditional programming and machine learning approaches. It also outlines statistics on machine learning usage, pros and cons, job and market opportunities, and how to get started in the field through online courses.

1. MACHINE
LEARNING
&
the future of Artificial Intelligence
By Ankan Das, UEM-Kolkata

2. CONTENTS:
∫ What Is Machine Learning?
∫ What Have We Achieved With It?
∫ The Big Difference?
∫ Statistics On The Usage Of Machine Learning
∫ The “Pro Et Contra” Of The System
∫ Jobs And Market
∫ How To Get Started?

3. WHAT IS MACHINE LEARNING?
• Machine learning is a field of
computer science that uses statistical
techniques to give computer systems
the ability to "learn" with data,
without being explicitly programmed.
• The name machine learning was
coined in 1959 by Arthur Samuel.
• Machine Learning is basically a subset
of Artificial Intelligence and in turn, it
gives us Deep Neural Networks.

4. WHAT HAVE WE ACHIEVED?
I. Google, Youtube and Netflix are
powered by it.
II. Even the random people you add
on Facebook aren’t so random
after all.
III. Siri, Alexa and Google Assistant –
All of them use Machine Learning.
IV. Tesla, the world’s first commercial
self-driving car is built on deep
learning.
V. Neuralink, the world’s first human
brain-machine interface is being
built upon it.

5. THE BIG DIFFERENCE!
Data + Program Computer Output
Data + Output Computer Program
Traditional Programming :
Machine Learning :

6. Statistics
on usage
As we speak, some new invention
is being made in this field.

7. PROS AND CONS
I. PROS:
• Accurate weather predictions
• Disaster predictions
• Self-driving cars, thus reducing accidents
• User-tailored news, entertainment and information
• Safety measures and self-flight for flying cars (Read:
https://techcrunch.com/2018/02/10/uber-flying-cars/)
• Easier and cheaper cancer detection.
• Fraud detection to prevent money laundering on the
internet
• Finance industry will be getting a huge boost from the
use of machine learning, trades will be faster
• Pattern recognition and image recognition to help the
police
• Cybersecurity and spam protection

8. PROS AND CONS
I. PROS (continued):
• Space research is and will be getting huge benefits from
this because of efficient data processing
• Processing X-Ray and MRI results and medical imaging is
getting unbelievably fast
• Caretaking of elderly people and children will become
decentralized
• Manufacturing will be much faster and efficient, because
of automated robots handling all the tasks

9. PROS AND CONS
I. PROS (continued):
• Robots will be available for doing the household
chores
• Prosthetic body parts will become cheaper and
many variants will be available
• Drones for long distance deliveries will be much
more efficient than delivery people
• Drones for effective surveillance is possible
• Surgery can be much more efficient and almost
risk-proof because there cannot be a slip of hand in
the case of a robot

10. PROS AND CONS
II. CONS:
• Completely human interruption free storefronts will be
possible
(Read: http://fortune.com/2018/01/21/amazon-go-
opening-to-the-public/
Watch:
https://www.youtube.com/watch?v=NrmMk1Myrxc)
• Robots can also make great companions in the future
(Read: https://www.youtube.com/watch?v=EZYBs3jR6x4)

11. JOBS AND MARKET
• Various companies are hiring Machine Learning experts
• Experience is necessary for working in this field
• The more you know about data, the better the algorithms are you going to
write
• A lot of money is being put into this sector right now
• Companies like NVIDIA are hiring for improving graphical fidelity
• Car and truck manufacturing firms like Audi, Volkswagen, Mercedes-Benz,
Paccar, Tesla, Volvo, Tusimple and Rimac are putting huge stakes in this self-
driving market.
• This is revolutionizing every industry, so more and more jobs are opening up in
this sector.

12. HOW TO GET STARTED?
1. https://in.udacity.com/course/deep-
learning-nanodegree--nd101
2. https://in.udacity.com/course/intro-
to-machine-learning--ud120
1.
https://www.udemy.com/deeplearnin
g/
2.
https://www.udemy.com/machinelear

13. CITINGS AND IMAGES:
Wikipedia:
 Machine Learning
(https://en.wikipedia.org/wiki/Machine_learning)
Youtube:
 Machine Learning and Artificial Intelligence by Arshi Jujara
(https://www.youtube.com/watch?v=1eBxt9HUfh8)
Google Images.

14. PRESENTED BY:
1.Ankan Das
2.Arkojit Dey
3.Joydeep Dhar