Introduction to Artificial Intelligence
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This presentation talks about what is Artificial Intelligence, what are key Algorithms (CNN, RNN, Reinforcement Learning), their applications. AI use cases such as detecting fish species and Spoting Distracted Driver
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Introduction to Artificial Intelligence
- 1. Introduction to Artificial Intelligence Sanjay Kumar 1 Managing Principal and Chief Data Scientist(AI/ML), Innolitica www.inolitica.com Sanjay.kumar@innolitica.com
- 2. • Background • Introduction to AI • Case Studies • How to get into AI Agenda
- 4. Where we have served
- 5. AI is being used to find new planet Kepler 90i How AI is impacting human life … AI beats Alphago Champion Ke Jei
- 6. … How AI is impacting human life AI is helping to detect cancer cells AI is being used to diagnose heart diseases using retina
- 7. AI is an “Intelligent” system or machine that can “Act” and “Behave” like Human What Is Artificial Intelligence(AI)?
- 8. What are Key Components of AI? Artificial Intelligence Techniques Vision Language Speech • Machine Learning • Deep Learning • Image recognition • Object Detection • Image Similarity • Understanding • Generation • Processing • Speech to Text • Text to Speech
- 10. Key Artificial Intelligence (AI) Techniques Complexity More Less Traditional Advanced LikelihoodtobeusedinAI Core AI Techniques Descriptive Statistics Naïve Bayes Statistical Inference Markov Process Regression Analysis Clustering Linear Classifiers Monto Carlo Method Instance – based learning Random Forest Ensemble learning Dimensionality Reduction Deep learning (feed-forward network, CNN, RNN) Transfer Learning Reinforcement Learning Topic Model Supervised learning Un–Supervised learning Reinforcement learning Support Vector Machine
- 11. Core of AI is Neural Networks … Single Neuron Multi Hidden Layer Neural Network
- 12. Deep Convolutional Neural Network (CNN) Convolution VGG16 Network a
- 13. Applications of Deep CNN Find Similar Clothes Identify People Detect Emotions a
- 14. Recurrent Neural Network (RNN) Hidden Input Output RNN is needed for • Language Modeling • Time Series Analysis • Speech Processing • Much more ….. Bi-Directional RNN Simple RNN RNN : A family of neural networks that • Take sequential input of any length • Apply the same weights on each step • Can optionally produce output on each step b
- 15. Applications of RNN Alexa Chat Bots Speech Generation Source: https://medium.com/@samim/obama-rnn-machine-generated- political-speeches-c8abd18a2ea0 b
- 16. Application of RNN – Language Translation Recurrent Neural Network with Encode-Decoder and Attention b
- 17. Reinforcement Learning RL is a general-purpose framework for sequential decision-making • RL is for an agent with the capacity to act • Each action influences the agent’s future state • Success is measured by a scalar reward signal • Goal: select actions to maximize future reward c
- 18. Applications of Reinforcement Learning • Observations: images from camera, joint angle • Actions: joint torques • Rewards: navigate to target location, serve and protect humans Motion Control • Observations: current inventory levels and sales history • Actions: number of units of each product to purchase • Rewards: future profit Supply Chain Games c
- 19. Case Studies
- 20. Detect and Classify Species of Fish from Fishing Vessels • Fish is one of the main sources of protein. • Cameras are being used to monitor fishing activities. • However, sorting of fish is done manually which is cumbersome and expensive. Develop Algorithms to Automatically Detect and Classify species of fish Fish classes 1
- 21. Sample Data 1
- 22. Challenges … Highly Un-Balanced Data Set Sample Lacks Diversity 1
- 25. Case Study - 2
- 26. Can AI Spot Distracted Drivers? • According to the CDC motor vehicle safety division, one in five car accidents is caused by a distracted driver • Sadly, this translates to 425,000 people injured and 3,000 people killed by distracted driving every year • Whether dashboard cameras can automatically detect drivers engaging in distracted behaviors? Develop an Algorithm to Classify Driver’s Behavior 2
- 27. Sample Data 2
- 29. Training Data Set Distribution 2
- 31. How Kellogg Students Can get-into AI?
- 32. Analytics Roles and Responsibilities Source McKinsey & Co.
- 33. What Kellogg Students should do? • Know your strengths – Technical, Analytical, Business • Get familiar with AI tools and techniques • Develop deeper understanding of your domain • Understand data sources – what is available and what is needed • Internal • External • Challenge conventional thinking • Develop data driven thought process
- 34. Finnov Corporation – Strictly Confidential – For Internal Discussion Use Only Thank You 34