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