The document discusses both the promises and perils of artificial intelligence. It notes that machine learning has made significant progress in areas like image recognition and natural language processing by learning from large datasets, but still struggles with tasks requiring complex reasoning or knowledge. Key challenges mentioned include issues of interpretability, understandability, and bias in machine learning models. The author advocates for optimism in the field's potential while also acknowledging its current limitations.

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. 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. 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. 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. Hope and Hype

6. What is Data Science?

7. What is Data Science?
“The practice of, and methods for, reporting,
inference, and decision making based on data.”

8. What is Machine Learning?

9. What is (supervised) Machine Learning?
“Extracting information from data to make
predictions on new observations.”

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. Observe the past – predict for the future
Given examples: generalize the pattern
Generalization

17. Hope Hype
Deep Learning
Big data
Machine Learning
AI
Counting
Statistics
Data Visualization
Data Science
Bots

19. Hope Hype
Deep Learning
Big data
Machine Learning
AI
Counting
Statistics
Data Visualization
Data Science
Bots

20. Where we are in ML

21. Ball Snake Carpet Python

22. Ball Snake Carpet Python
x 1000 x 1000

24. ?

25. The power of more data

26. The power of more data

27. Human learning vs machine learning

28. Structure is hard

29. Structure is hard

30. Knowledge is hard

31. Knowledge is hard

33. 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

34. Where is ML? Everywhere!

35. Perils
(in ML and counting)

36. Interpretability / Understandability

37. Interpretability / Understandability

38. Interpretability / Understandability

39. Bias

40. Bias

41. Bias
fatml.org

43. some optimism

44. Where does Python
(and scikit-learn fit in)?

45. Where does Python
(and scikit-learn fit in)?

46. Curation, contrib, ...

47. Curation, contrib, ...

48. amueller.github.io
@amuellerml
@amueller
t3kcit@gmail.com
https://github.com/amueller/talks_odt/