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Demystifying AI

  1. AI, Humanity & Human Civilization AI is bringing up philosophical and Ethical Questions
  2. Philosophically Fundamental Questions Can a machine act intelligently? Can it solve any problem that a person would solve by thinking? 01 Are human intelligence and machine intelligence the same? Is the human brain essentially a computer? 02 Can a machine have a mind, mental states, and consciousness in the same way that a human being can? Can it feel how things are? 03
  3. AI impact on Humanity
  4. AI is the next digital frontier In 2016 Companies invested In artificial Intelligence Tech Giants Startups AI Adopters - 20% in multiple technology areas AI Partial Adopters - 40% skeptical about Business Cases and ROI Laggards - 40% contemplators
  5. AI Adoption Story Digitally Mature Large Businesses AI adoption in Core Activities Focus in Growth over Savings C Level Executives support AI EarlyAIAdopters High Tech Companies Automotive & Assembly Financial Services MediumAIAdopters Retail Media & Entertainment LaggardAIAdopters Education Travel & Tourism HealthCare
  6. Four Areas where AI creates significant Value Smarter R&D and Forecasting 1 Optimized Production and Maintenance 2 Targeted Sales and Marketing 3 Enhanced User Experience 4
  7. Machine Learning & Deep Learning Two important approaches in Artificial Intelligence
  8. AI, Machine Learning, Deep Learning… ARTIFICIAL INTELLIGENCE Approaches that help enable machines to mimic humans intelligence MACHINE LEARNING Specific Algorithms that help machines learns patterns from data DEEP LEARNING Algorithms based on artificial neural networks that help machines to learn patterns from data
  9. The AI Landscape Machine Learning (Non Specific) Computer Vision Natural Languages Autonomous Vehicles Virtual Agents Smart Robotics
  10. The AI Algorithms Information Theoretic Algorithms Neural Networks Probabilistic, Evolutionary Modelling Algorithms
  11. What does AI help us to do ? Human Beings can write a lot of algorithms but some algorithms are intrinsically difficult and hard.. • Write an algorithm that can looking at Medical Scans identify diseases • Write an algorithm to drive a Car • Write an Algorithm that helps to identify the speakers of different voice samples • Write an Algorithm that determines the emotions implicit in a piece of text • Write an algorithm that recommends new products to customers depending on what they have been purchasing for the last few years. And so on….these are hard problems to write algorithms for, for even the “smartest Practioners of that Domain”
  12. We want Machines to help us write these difficult programs Enter Machine Learning! Output Input data Programmer writes a program based on rules Result is produced by the program written by programmers Input Human Written Algorithm A model Is discovered that allows For Input  Output Set of training data Inputs and Outputs A machine learning algorithm Output is an automatically learned algorithm which is called as model Prepared Input Machine Learning Algorithm Expected Output Traditional Programming Paradigm Machine Learning Paradigm
  13. Even preparing Inputs is hard … Enter Deep Learning!! Model / Program Is discovered Set of training data Inputs and Outputs A Deep learning algorithm Output is a automatically learned program which is called as model Raw Input Deep Learning Algorithm Expected Output Deep Learning Paradigm Model / Program Is discovered A machine learning algorithm Output is a automatically learned program which is called as model Prepared Input Machine Learning Algorithm Expected Output Machine Learning Paradigm
  14. How does it do that? Mathematical Modelling Probability, Statistics, Linear Algebra And Calculus Computational Programming Computational Libraries Visualization Libraries Powerful Platforms GPU Hardware Parallel/Multi Processing
  15. The Essential Idea Problem Representation Model the problem using Mathematics Transform Representation Transform the representation In mathematical space Iterative Experimentation Use fast hardware to Facilitate this transformation
  16. A simple example Problem Representation Model a picture as a vector of pixel values and outcome as 0 if cat and 1 if dog Transforming Representation Transform the input representation and optimally find the parameters that give you the best representation Iterative Experimentation Try different approaches and Deploy the code/model that satisfies the expectations of the Client
  17. Two important Problem Modelling Problem Representation Transforming Representation Iterative Experimentation Supervised Modelling Un-Supervised Modelling • Predictive Modelling (Classification and Regression) • Discover Groups in Data • Reduce Complexity of Data • Visualization of Data • Anomaly Identification
  18. Two different Approaches Problem Representation Transforming Representation Iterative Experimentation Machine Learning Algorithms Deep Learning Algorithms • Multiple Algorithms • Works with smaller Datasets • Requires Feature Engineering • Multiple Architectures • Works with larger Datasets • Requires no Domain Knowledge
  19. What is happening behind the scenes Input Data/Output Labels ML Algorithm Use the ML Algorithm Represents the Input and Output in An appropriate Mathematical Space Starts making informed guesses Of the Output given the Input Calculate how far is the guess from the actual outcome in that Mathematical space Adjusts its parameters/knobs To minimize the distances Between its guesses and The actual output
  20. What is the AI workflow? Build Model Optimize Model Predict & Deploy Validation Data Training Data Test Data Acquire Data
  21. Images Photographs, Scans, Graphic Art, Hand writing, Paintings Time Series Data Financial Data, Transactional Data, Sensor Data Video Surveillance Videos, Motion Picture Videos, Streaming Video Sound/Speech Recordings Voice Samples, Conversations Natural Language Text Reports, SMS, Tweets, Web Content, Newspaper, Books.. Structured data Databases, CSV Data, JSON Data, Excel Data Different forms of Data –Possible AI use cases exist..
  22. ‘Data-Specific’ Use Cases Train a ML model with Disease Scan Images like X-Ray, MRI, ultrasound and make it diagnose diseases Train a ML model to convert speech to Text Train a ML model to convert evaluate Stock Movement and predict Stock price into the future Train a ML model to monitor security surveillance cameras and alarm when there is a security breach Train a ML model to understand the User preferences when they come shopping. Train a ML model to understand the Research Reports and send you a summary of the important ones.
  23. Some more ideas in AI Anomaly Detection/Fraud Detection Items, events or observations which do not conform to an expected pattern or other items in the dataset Cluster Analysis Find Natural groups in the Dataset based on certain attributes Dimensionality Reduction Reduce the complexity without compromising on information loss of the data Visualization Take Complex Data and make it humanly visual
  24. The Sciences behind AI Linear Algebra and Matrix Mechanics 1 Statistical and Probability Based Modeling 2 Machine Learning Algorithms 3 Neural Net Architectures 4 The Scientific Method of iterating - Hypothesis, Experimentation and Conclusion Building 5
  25. The important technologies behind AI Python 1 Tensor flow, Theano, pytorch 2 CUDA and GPUs 3