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Py paris2017 / promises and perils in artificial intelligence, by Andreas Muller

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PyParis 2017
http://pyparis.org

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Py paris2017 / promises and perils in artificial intelligence, by Andreas Muller

  1. 1. Promises and Perils in Artificial Intelligence and why you shouldn’t trust anyone that uses the phrase Artificial Intelligence Andreas Müller Columbia University, scikit-learn
  2. 2. Promises and Perils in Artificial Intelligence and why you shouldn’t trust anyone that uses the phrase Artificial Intelligence Andreas Müller Columbia University, scikit-learn
  3. 3. Promises and Perils in Artificial Intelligence and why you shouldn’t trust anyone that uses the phrase Artificial Intelligence Andreas Müller Columbia University, scikit-learn
  4. 4. Promises and Perils in Artificial Intelligence and why you shouldn’t trust anyone that uses the phrase Artificial Intelligence Andreas Müller Columbia University, scikit-learn
  5. 5. Hope and Hype
  6. 6. What is Data Science?
  7. 7. What is Data Science? “The practice of, and methods for, reporting, inference, and decision making based on data.”
  8. 8. What is Machine Learning?
  9. 9. What is (supervised) Machine Learning? “Extracting information from data to make predictions on new observations.”
  10. 10. Data Science How many people clicked on this ad? Are men more likely to click on this ad then women? Machine learning Will Andy click on this ad given his history of facebook activity, profile information, and social network?
  11. 11. Observe the past – predict for the future Given examples: generalize the pattern Generalization
  12. 12. Hope Hype Deep Learning Big data Machine Learning AI Counting Statistics Data Visualization Data Science Bots
  13. 13. Hope Hype Deep Learning Big data Machine Learning AI Counting Statistics Data Visualization Data Science Bots
  14. 14. Where we are in ML
  15. 15. Ball Snake Carpet Python
  16. 16. Ball Snake Carpet Python x 1000 x 1000
  17. 17. ?
  18. 18. The power of more data
  19. 19. The power of more data
  20. 20. Human learning vs machine learning
  21. 21. Structure is hard
  22. 22. Structure is hard
  23. 23. Knowledge is hard
  24. 24. Knowledge is hard
  25. 25. Classifying with many examples Works in the real world, sometimes superhuman Generalizing to new concepts using few examples “works” in papers Producing text or complex objects “works” in papers Reasoning with world-knowledge no-one knows
  26. 26. Where is ML? Everywhere!
  27. 27. Perils (in ML and counting)
  28. 28. Interpretability / Understandability
  29. 29. Interpretability / Understandability
  30. 30. Interpretability / Understandability
  31. 31. Bias
  32. 32. Bias
  33. 33. Bias fatml.org
  34. 34. some optimism
  35. 35. Where does Python (and scikit-learn fit in)?
  36. 36. Where does Python (and scikit-learn fit in)?
  37. 37. Curation, contrib, ...
  38. 38. Curation, contrib, ...
  39. 39. amueller.github.io @amuellerml @amueller t3kcit@gmail.com https://github.com/amueller/talks_odt/

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