This document discusses building usable AI tools and challenges related to machine learning applications. It notes that uncertainty, explainability, and lack of training data need to be addressed as user experience problems. Ensuring predictions come with explanations to build trust is important. Techniques like LIME are being developed to explain neural networks. Getting humans involved in providing data through techniques like transfer learning and human-in-the-loop can help address the training data problem. The document distinguishes between tools for doing AI work and intelligent conversational agents.

1. Let’s build usable A.I. tools!
Forget about A.G.I.

2. do as i do, not as i
say!
Ray Kurzweil

3. Forget about A.G.I!

4. within a quarter century, nonbiological
intelligence will match the range and subtlety of
human intelligence. it will then soar past it because
of the continuing acceleration of
information-based technologies, as well as the
ability of machines to instantly share their
knowledge.
Artificial intelligence could lead to
“an immortal dictator from which we
could never escape.”

5. our behaviour co-evolves with the rest of our
bodies, and in response to our changing
environment. Understanding natural
intelligence requires understanding all of these
influences on behaviour, and their interactions.
the world kicks you in the face!
roombas are less intelligent
than e. coli

6. winter is coming!
Gary Marcus

11. Let’s build usable A.I. tools!

12. A.I. Tools
Tools for doing A.I.
Tools for solving business problems, that happen to use A.I.

13. A.I. Tools
Tools for doing A.I.
Tools for solving business problems, that happen to use A.I.

14. Tools for doing A.I.
Microsoft’s ML Studio

15. Tools for doing A.I.
Lobe.ai

16. A.I. Tools
Tools for doing A.I.
Tools for solving business problems, that happen to use A.I.

17. Unique challenges in ML-based applications
● Uncertainty
● Explainability
● The need for training data
These can be treated as UX challenges!

19. Uncertainty is a UX problem.

20. data
Crazy Math
Answers
Deep Learning

21. data
Crazy Math
Answers
Guesses
Deep Learning

22. Uncertainty

23. Terrorist: 70%
Not terrorist: 30%

24. Malignant: 70%
Benign: 30%

25. Explainability is a UX problem.

26. Neural networks: Too cunning to be understood

27. Explaining the unexplainable
A prediction that comes with an explanation is easier to trust
“Why did you recommend that movie to me?”
What constitutes an explanation?
Many different approaches being worked on, e.g. LIME.

28. Wolf
WolfHusky

29. Wolf
Husky Wolf

30. Terrorist

31. Building a UI to get more data.

32. Human-in-the-Loop

33. Human-in-the-Loop
1. Transfer learning
2. Few-shot learning
3. Get a human to do the few shots!

34. Human-in-the-Loop

35. Tools vs. Intelligent Agents

36. Conversational A.I.

37. Conversational A.I.
We are not building these systems in order to pass
the Turing test“ ”-- Bill Mark of SRI International
(research company behind Apple’s Siri)

38. Google Duplex

39. Google Duplex
Google Assistant Human

40. Smart Reply

41. Smart Reply

42. Thanks!
Questions / comments (/debates )?
katherine.bailey@acquia.com
@katherinebailey