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AI and ML

Introduction to Artificial Intelligence and Machine learning.

paramvirsingh88
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AI and ML

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  1. AI/ML and Mobile

  2. Hello! I am Paramvir Singh I am Android developer and consultant. You can find me at: @paramvirsingh88 2

  3. Agenda ◎ Understand AI and ML ◎ Different tools and frameworks ◎ Using ML in mobile apps - ML Kit 3

  4. 1. Artificial Intelligence and Machine Learning 4

  5. “ Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. —Larry Page 5

  6. AI vs ML AI A branch of computer science the objective of which is to make machine work as smart as human. Cognitive ability of machines. ML A computer program is fed a lot of data and learns from experiences. A subset of AI. 6

  7. 7

  8. A new Tech? Large datasets, advancements in data science, powerful hardware 8

  9. Types of Learning Supervised Output is fed into the system. Labeled data. Unsupervised Success and failure is not known to the system. No labeling. Pattern recognition. Reinforcement Once result is obtained, it is categorized as success or failure. Similar to unsupervised. 9

  10. Understanding Machine Learning ◎ Convolution matrix ◎ Deep neural network, tensors ◎ Dynamic computation, computational graphs ◎ Automatic differentiation ◎ Relation Extraction, Coreference Resolution 10

  11. Understanding Machine Learning ◎ Convolution matrix ◎ Deep neural network, tensors ◎ Dynamic computation, computational graphs ◎ Automatic differentiation ◎ Relation Extraction, Coreference Resolution 11

  12. Instead we will go the easier route ◎ Data ◎ Model ◎ Training ◎ Learning ◎ Experience ◎ Decision 12

  13. How ML models work Process Data Train Model Apply model 13

  14. How it’s all spun together ● Huge datasets required ● Create and train models ● Choose best model ● Apply the model 14

  15. ML models ● 15

  16. ML models ● 16

  17. Applications ● Image processing ● NLP and Speech recognition ● Predictions ○ Diagnostic ○ Business and customers ○ Natural events ● Robotics/real time decisions 17

  18. Some recent examples ■ First AI art ■ Cambridge Analytica case ■ Cancer detection ■ Self driving cars ■ Google Translate and email smart reply 18

  19. ML frameworks ■ Cloud Vision ■ ML kit ■ TensorFlow ■ Amazon Rekognition ■ Facebook Pytorch ■ OpenCV ■ Azure cloud ML 19

  20. ML Kit Easy Machine Learning 20

  21. ML Kit Mobile focused API Based Younger sibling of Google Cloud Vision. No mode creation or model training required. Uses models provided and hosted by Google. Focussed on Vision but will expand in future. 21

  22. What problem does it solve? ■ Doing ML from scratch is doable but hard ■ Huge data required ■ Knowledge of math and ML science required ■ Domain knowledge required 22

  23. What ML Kit provides On device ML processing Model hosting in cloud Firebase API’s 23

  24. ML Kit features Image Labeling Text Detection Face detection Barcode scanning Landmark detection Smart Reply* *coming soon 24

  25. Integration in Android Integrate Firebase and you are good to go. 25

  26. Thanks! Any questions? You can find me at: ◎ @paramvirsingh88 ◎ paramvir.singh88@gmail.com 26

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