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AI | Now + Next

  1. 1. AI | Now + Next
  2. 2. Outline • What is AI? • Current state of AI • Limitations of AI Today • AI Achievements • AI Talent war • Machine Learning • Deep Learning • AI Trends
  3. 3. What is AI? Artificial Intelligence is the science and engineering of making intelligent machine, especially intelligent computer programs - John Mccarthy, Father of AI
  4. 4. Current State of AI Strong AI • Computers thinking at a level that meets or surpasses people • Computers engaging in abstract reasoning & thinking Weak AI • Computers solve problems by detecting useful patterns • Pattern-based AI is an Extremely powerful tool • Has been used to automate many processes today - Driving, language translation
  5. 5. Limitations on AI Today • Many things still beyond the realm of AI • No thinking computers • No Abstract Reasoning • Often AI systems Have Accuracy Limits • Many things difficult to capture in data • Sometimes Hard to interpret Systems
  6. 6. AI Achievements
  7. 7. Is AI == ML today? ML is the dominant mode of AI today
  8. 8. AI Talent War There are around 300,000 qualified AI researchers. But demand is in millions Demand for data scientists will surpass demand for engineers. According to IBM, demand for data scientists will increase to 2.7 million by 2020.
  9. 9. Machine Learning
  10. 10. Supervised Learning Demo
  11. 11. Unsupervised Learning
  12. 12. Reinforcement Learning
  13. 13. Video: https://www.youtube.com/watch?v=qy_mIEnnlF4&t=89s
  14. 14. Deep Learning
  15. 15. What have we achieved with this?
  16. 16. What have we achieved with this?
  17. 17. Yann LeCun in 1993 at Bell Lab Video: https://www.youtube.com/watch?v=FwFduRA_L6Q
  18. 18. Deep Learning Timeline
  19. 19. GPU vs CPU Ref: http://www.nvidia.com/object/what-is-gpu-computing.html Compared to CPUs 20x speedups are typical GPUs also excel at floating-point vector operations because neurons are nothing more than vector multiplication and addition. All of these characteristics make neural networks on GPUs what's
  20. 20. Single Layer Perceptron (Model Iteration 0) Problems: • The model outputs a real number whose value correlates with the concept of likelihood (higher values imply a greater probability the image represents stairs) but there’s no basis to interpret the values as probabilities, especially since they can be outside the range [0, 1]. • The model can’t capture the non-linear relationship between the variables and the target. To see this, consider the following hypothetical scenarios:
  21. 21. Single Layer Perceptron with Sigmoid activation function (Model Iteration 1) A B
  22. 22. Multi-Layer Perceptron with Sigmoid activation function (Model Iteration 2) Deep Learning
  23. 23. Back Propagation Algorithm
  24. 24. Deep Learning Architectures Ref: https://www.ibm.com/developerworks/library/cc-machine-learning-deep-learning-architectures/index.html Ref: http://www.asimovinstitute.org/neural-network-zoo/
  25. 25. Computer Vision Applications
  26. 26. YOLO (You Only Look Once) YOLO Video: https://www.youtube.com/watch?v=MPU2HistivI
  27. 27. YOLO YOLO uses a single CNN network for both classification and localising the object using bounding boxes.
  28. 28. Real-Time Autonomous Checkout
  29. 29. AI Trends Taking AI to the edge Decentralization and Democratization AI-as-a-Service Meta-learning solutions Quantum Neural Blockchain AI Capsule Networks Amazon, Google, Microsoft dominate enterprise AI AI is coming to clinical diagnostics DIY AI Automation Blue & White colored jobs No UI is the New UI Chatbots, Voice enabled systems
  30. 30. AI at the Edge
  31. 31. AI-as-a-Service Amazon SageMaker
  32. 32. Meta-learning for Deep Learning Models
  33. 33. Capsule Networks CapsNet will require less training data CapsNet may be less prone to hacking attacks
  34. 34. NO UI IS THE NEW UI Google Soli EMOTIV TEXT BRAIN VOICE NO UI IS THE NEW UINO UI IS THE NEW UI
  35. 35. The AI 100 2018
  36. 36. DISRUPTION EVERYWHERE
  37. 37. ROAD TO DATA SCIENTIST NOT ENOUGH
  38. 38. THANK YOU!

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