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

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

Business of AI

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

Industrial Revolutions

Industry 4.02000 - Present

Industry 1.01760 - 1870

Industry 3.01960 – 2000

Industry 2.01870 - 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

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 sensorsData 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

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.

Web & Mobile Banking (Industrial Revolution

3.0)

Intelligent Robotic Assistant (IRA) of HDFC

(Industrial Revolution 4.0)

Meet CONNIE – Hilton concierge robot using IBM Watson

https://www.youtube.com/watch?v=jC0I08qt5VU

E.g. Hospitality, Robotic Process Automation (RPA), Transport, Security, Military, Banking, Household etc.

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

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

Basics of AI and ML

“AI is the new electricity” – Andrew Ng, Chief Data Scientist, Baidu

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

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.

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

Traditional Analytics

Problem at hand

Production

Review errors

Code the Rules Evaluate

Pass

Fail

Machine Learning approach

Problem at hand

Production

Review errors

Train ML algorithm Evaluate

Pass

Fail

Training Data(x1, y1), (x2, y2)…

How does a regular program work?

Input Data Code (Processing Steps)+ = Output

Machine Learning works a bit differently

Input Data Output+ = LearnedParameters

Training Step

Input Data EvaluationCode+ = Output

Evaluation Step

Training Code+

LearnedParameters +

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.

• 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

A simple intelligence formula

Given input x, predict y : y = f(x)

Why is machine learning hard ?

Real life data is messy

View Point Difference

Real life data is messy

Illumination

Real life data is messy

Deformation

Real life data is messy

Occlusion

Real life data is messy

Interclass Variation

Data is ambiguous

The movie has great location, wonderful songs, intelligence dialogues. Though I care less

Data is ambiguous

Scientists study whales from space

Data is ambiguous

Boy paralyzed after tumor fights back to gain black belt

Turing test

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

Thank you

Vivek SinghalCo-Founder and AI Data Scientist, CellStrat

9742800566vivek@cellstrat.com

Call: +91-9999658436 | t: @CellStrat | #disrupt4.0