“AI is the new electricity” – Andrew Ng, former Chief Data Scientist, Baidu Artificial Intelligence is the new frontier for human evolution. It will upend industries, cause fundamental shifts in processes and jobs, and create unprecedented innovation.The question one wishes to answer is: how and why it impacts industry, and how can it be leveraged by businesses. This session will introduce AI and machine learning: the process of creating AI, and go on to discuss the key applications of these emerging technologies. We will also dive into a preliminary review of ML algorithms and how they work. Key Takeaways: - Define AI and ML, and the philosophy behind these new technologies - The impact of AI on jobs, communities, business, and industry - The use cases of AI in different industries like hi-tech, manufacturing, healthcare, publishing and media, education, transportation etc. -Introduction to machine learning algorithms like classification, regression, neural networks etc. Check our webinars series and sign up for future webinar notifications at: www.srijan.net/webinar/past-webinars

2. CellStrat is India’s leading Artificial Intelligence startup specializing in development
and research in emerging areas of AI and Deep Learning.
• Working on multiple world-class Machine Learning innovations for selected
industry verticals.
• Our research and content in AI and ML is unmatched in Indian context.
• Thought leader in AI communities and among Deep-tech researchers.
Intro to CellStrat

3. Focus areas
• Artificial Intelligence Solutions – AI Applications in areas like ePublishing,
marketing automation, text recognition, computer vision etc
• Deep Learning algorithmic design – Probability models, Regression,
Supervised/Un-supervised learning, Neural Networks
• Solution development – Python / Google TensorFlow, Amazon AI and Alexa
Skills Set
• AI Content – India’s leading research and content program in AI space
(www.cellstrat.com/research-blog)
• Community Events – Disrupt 4.0 talk series on Business of AI, Basics and
Advanced Machine Learning Algorithms

4. Business of AI
“AI is the new electricity” – Andrew Ng, ex-Chief Data Scientist, Baidu

5. Industrial Revolutions
Industry 4.0
2000 - Present
Industry 1.0
1760 - 1870
Industry 3.0
1960 – 2000
Industry 2.0
1870 - 1960
Shift from hand based
production to use of
steam engines,
electrical
communications &
chemical
manufacturing etc.
Technology
Revolution came in
with telephone and
radio getting
introduced improving
communication
modes
Switch to Electronics
& IT automated
production,
Automation
Connected age
blurring the lines
between the physical,
digital, and biological
spheres,
Personalisation

6. Data economy
Data is the new oil
Battle for ownership of data as well as
deriving benefits from it.
180 zettabytes of data (180 followed by 21
zeros) by 2025, as per IDC
Real-time flows of often unstructured data
from social media, transportation and all
kinds of sensors
Data earlier used by firms like Facebook
and Google for targeted advertising.
Now it is powering n-number of Artificial
Intelligence (AI) or “cognitive”services,
some of which are revenue-generating.
Data-
network
effect
Use Data to
attract
more users
These users
generate
more data
This helps
improve
services
This attracts
even more
users

7. AI: A branch of Computer Science that creates
intelligent machines that work and react like humans.
AI-based machines can use bigdata that businesses are
collecting to identify patterns and insights more
efficiently than humans can.
E.g. Self Driving Cars, Strategic Game Systems like Go and Chess,
understanding human speech etc.

8. Web & Mobile Banking
(Industrial Revolution
3.0)
Intelligent Robotic Assistant
(IRA) of HDFC
(Industrial Revolution 4.0)

10. Meet CONNIE – Hilton concierge robot using IBM Watson
https://www.youtube.com/w
atch?v=jC0I08qt5VU
E.g. Hospitality, Robotic Process Automation (RPA),
Transport, Security, Military, Banking, Household etc.

11. AI in gaming like Chess, Go, Bridge etc.
Eg. IBM supercomputer Deep Blue beat Grand Master
and World Chess Champion Garry Kasparov in 1997
Eg. Google’s DeepMind AlphaGo beat global GO
Champion Ke Jie of China and Korean Champion
Lee Sedol.
GO : a game with near-infinite moves

12. Applications of AI / ML
Image Processing
• Image tagging / Image
Recognition
• OCR or Optical Character
Recognition
• Self-driving cars
Text Analysis
• Spam Filtering
• Sentiment Analysis
• Information Extraction
Data Mining
• Anomaly Detection
• Association Rules
• Grouping
• Predictions
Healthcare
• Medical Diagnosis
• Imaging Diagnosis
• Oncology
• Drug Trials
Video Games
• Reinforcement Learning
Robotics
• Industrial tasks
• Human simulations

14. Basics of AI and ML
“AI is the new electricity” – Andrew Ng, Chief Data Scientist, Baidu

15. Artificial Intelligence
Intelligence in machines : simulate human intelligence
Train machines to learn from data : Machine Learning
Robotics, Computer Vision, Image recognition, Chatbots - Natural Language Processing (NLP), Text
Analysis, Data Mining, Self-driving cars, AI in Retail, Gaming, Credit Risk, Fraud Detection, Hospitality,
Call Centre Agent Match
Healthiply SCAN, Uber self-driving cars, Amazon ECHO product (home control chatbot device),
Amazon GO retail store, Baidu AI Medical Assistant, Haptik or niki.ai chatbot, Boxx.ai retail analytics,
Hilton using Connie – concierge robot from IBM Watson

16. Machine Learning
Traditional analytics relied on hard-coded rules. ML relies on learning patterns based on sample data.
AI systems learn by extracting patterns from data. This capability is called Machine Learning.
ML can learn from labelled data (supervised learning) or unlabelled data (unsupervised learning),
though the latter is a more difficult problem to solve.
Computers can take decisions that appear subjective – eg an ML algorithm called Logistic Regression
can determine when to recommend caesarean delivery. Another algo Naïve Bayes can separate valid
emails from spam.

17. AI and ML
• A Venn diagram showing how deep
learning is a kind of representation
learning, which in turn is a kind of machine
learning. Machine Learning is part of the
AI landscape.
• ML is used for many but not all
approaches for AI.
Image Credit : “Deep Learning” book by Ian Goodfellow

18. Traditional Analytics
Problem at
hand
Production
Review errors
Code the Rules Evaluate
Pass
Fail

19. Machine Learning approach
Problem at
hand
Production
Review errors
Train ML
algorithm
Evaluate
Pass
Fail
Training Data
(x1, y1), (x2, y2)…

20. How does a regular program work?
Input Data Code (Processing Steps)
+ = Output

21. Machine Learning works a bit differently
Input Data Output
+ =
Learned
Parameters
Training Step
Input Data
Evaluation
Code+ = Output
Evaluation Step
Training Code+
Learned
Parameters +

22. Machine Learning and its uses
Classify or categorize (A, B or C)
Trend analysis (how much / many)
Anomaly Detection (odd men out)
How data is organized
Decide on future action
• Data science is the use of statistical
methods to find patterns in data.
• Statistical machine learning uses the same
math as data science, but integrates it into
algorithms that get better on their own.
• Machine Learning is said to facilitate
Artificial Intelligence as it makes machines
learn patterns from data. In that sense
Machine Learning is what connects AI with
Data Science.

23. • Function mapping from a set of pixels to an
object identity is very complicated.
• Deep Learning solves this by breaking the
desired mapping into a series of nested
simple mappings, represented by layers of
the model.
• The hidden layers extract increasingly
abstract features from the image.
• Given the pixels, first layer identifies edges
by comparing the brightness of
neighbouring pixels. Given the first layer, the
second hidden layer identifies contours and
corners, by detecting collection of edges.
Given the second layer, the third layer can
detect parts of specific objects by detecting
collections of contours and corners. Given
the third layer, the entire object can be
detected by checking collection of object
parts.
Feature extraction with Deep Learning
Image Credit : “Deep Learning” book by Ian Goodfellow

24. A simple intelligence formula
Given input x, predict y : y = f(x)

25. Why is machine learning hard ?

26. Real life data is messy
View Point Difference

27. Real life data is messy
Illumination

28. Real life data is messy
Deformation

29. Real life data is messy
Occlusion

30. Real life data is messy
Interclass Variation

31. Data is ambiguous
The movie has great location, wonderful songs, intelligence dialogues.
Though I care less

32. Data is ambiguous
Scientists study whales from space

33. Data is ambiguous
Boy paralyzed after tumor fights back to gain black belt

34. Turing test
https://www.youtube.com/watch?v=XYGzRB4Pnq8

35. Thank you
Vivek Singhal
Co-Founder and AI Data Scientist, CellStrat
9742800566
vivek@cellstrat.com
Call: +91-9999658436 | t: @CellStrat | #disrupt4.0