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Codemotion Meetup 2018 - From narrow to hype: the challenges of AI applications
- 1. From narrow to hype The challenges of AI applications
- 2. Thiago de Faria DataOps & AI Lead Instructor & Speaker Conference Organiser
- 3. AI is da bomb! Can you help us build a _____________? Chatbot Recommendation System Personal Assistant AI stuff Something the marketing team wants
- 4. Is the pendulum stuck? From near-death to hype
- 7. Narrow
- 8. General
- 10. Whole brain emulation Genetic algorithms Recursive algorithms
- 11. Super
- 12. Narrow == Augmentation == Today!
- 13. We have enough papers. Stop publishing, and start transforming people’s lives with technology! ” “ Andrew Ng Co-Founder of Coursera Former head of Baidu AI Group Former head of Google Brain
- 14. Going to the physical world Different types of applications
- 17. Loops, TDD, function debugging, … Traditional software engineering
- 20. Hyperparameters and data pipeline AI engineering
- 25. Change something, may change it all Modularity Issue
- 26. Garbage in - garbage out Data is Master, ML is the slave
- 27. Rebuild VS more data Drifting from goals
- 28. Trump or that architect Model fragility
- 29. Value your data pipeline Built to sustain loads
- 30. 1st world problem of ML Never fail, can’t learn
- 31. Think as an engineer Operations
- 32. Deployment tools Deployment environment Scale & Latency Monitoring Mission-critical tasks Security & Privacy
- 33. Fairness, Transparency & Explainability
- 34. They are the ML bugs Bias
- 35. Solve YOUR Bias Explain the inner bits ML to monitor ML Next Challenges