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Explainable AI : Machine Learning for Humans - AI Paris2018
- 1. Explainable AI: Machine Learning for Humans Yannick Martel Bleckwen Director 12 June, 2018 @bleckwen_ai Leonardo Noleto Bleckwen Senior Data Scientist www.linkedin.com/company/bleckwen
- 2. Artificial Intelligence is Everywhere
- 3. Booking an appointment for you
- 4. Beating the Go Master
- 9. When a task can be automated at a low cost, the task will be automated. Image from Kurzweil’s The Singularity Is Near, published in 2005.
- 11. We believe in a collaboration between Human and Artificial Intelligence. Interpretability is the key enabler.
- 12. Why you should care about Interpretability? Image from Drive.ai, a self-driving car service for public use in Frisco, Texas
- 13. “Interpretability is the degree to which a human can understand the cause of a decision.” Miller, Tim. 2017. “Explanation in Artificial Intelligence: Insights from the Social Sciences.”
- 14. Why do we need interpretability? STRENGTHEN TRUST AND TRANSPARENCY SATISFY REGULATORY REQUIREMENTS EXPLAIN DECISIONS IMPROVE MODELS Get more information in our blog post
- 15. But what about the trade-off Accuracy vs. Interpretability? Image source: https://blog.bigml.com/2018/05/01/prediction-explanation-adding-transparency-to-machine-learning/amp/
- 16. Explaining the taxonomy of Interpretability globallocal
- 17. Explaining the taxonomy of Interpretability Model- agnostic Model-specific
- 18. Explaining the taxonomy of Interpretability globallocal Model- agnostic Model-specific
- 19. Explaining the taxonomy of Interpretability globallocal Model- agnostic Model-specific Added value for industries
- 20. How we are helping the banking industry accelerate the adoption of AI?
- 21. Our clients are fighting against financial fraud with the power of Machine Learning and the Behavioral Profiling while keeping decisions interpretable.
- 22. How is that possible?
- 23. Complex Model Local surrogate model Introducing LIME Local Interpretable Model-Agnostic Explanations
- 24. Introducing LIME Local Interpretable Model-Agnostic Explanations Extracting explanations from a linear model
- 25. We combine best-in-class Machine Learning models with interpretation technologies to get the best from a collective artificial and human intelligence Get more information in our blog post
- 26. Collaboration between Human Being and Artificial Intelligence will secure the world.
- 27. DEEP TRUTH REVEALED Anti-fraud solution for banks Thank you for your attention. www.bleckwen.ai Tous droits réservés, toute reproduction ou diffusion du contenu est interdite sans l'autorisation écrite de Bleckwen
Editor's Notes
- Use case: google assistant that makes a call a human to book a hair cut booking for a client
- Use case: AlphaGo defeated the Maste Go Lee Sedol, 4-1
- Use case: Alibaba's smart warehouse where robots do 70% of the work. They can carry up to 500 kilograms above them around the warehouse floor. They have special sensors to avoid colliding into each other and they can be summoned using wifi.
- Eyes of Watson: This image presents IBM's Eyes of Watson demo for breast cancer detection, which was presented at the 2016 Annual Meeting of the Radiological Society of North America (RSNA). The demo highlights IBM's capabilities in medical imaging with a question-answer format. The Watson-based technology is designed to serve as a cognitive assistant to radiologists in their workflows. Machine learning tools are also used to combine multimodal semantic image descriptions (for mammography, ultrasound and MRI) with clinical data, facilitating estimation of correct differential diagnosis and patient management recommendation.
- Use case: google app for translating images
- Use case: use of transfer style in CNN
- Todo: increase font
- Todo: insert zoom in local zone