See the video here: http://gracehopper.anitaborg.org/conference-overview/livestream-schedule-2015/wednesday-livestream-2015/ My GHC keynote covers the state of technology in Machine Intelligence, and also what it means to have a career as a computer scientist in a field where the technology and the products are evolving so quickly. (I use a presentation style with lots of images and very little content in the slides, and this presentation was not designed to stand alone.)

1. Machine Intelligence:
A Startup Adventure
Hilary Mason
@hmason

2. Good morning!

5. Technology is changing.

6. (and people stay the same)

7. Machine Intelligence.

9. By Gene Kogan, based on the paper by Gatys, Ecker, Bethge.

10. @pentametron

11. Data products are everywhere.

15. (it’s boring)

18. Rules-
based
Creative
Repetitive

19. Why now?

20. Affordable computation.

21. Algorithms.

22. Data!!

26. And we are just at the beginning!

27. [robot dog?]

28. None of this is entirely new.

30. [slides to come]

32. What it means to be a Computer
Scientist is changing.

34. math &
stats
code
communication
nerds
nerds
nerds
awesome
nerds!
Data science:

35. Predicting the future is hard.

36. Where does innovation come from?

37. Academia

38. Enterprise!

39. Startups.

40. The System is Complex.

41. you

42. you

43. Creating an organization is just deploying a
different kind of infrastructure.

45. We focus on innovation
opportunities through data and
algorithms.

46. Academic Research
Startups
Established Companies

49. The biggest opportunities
might surprise you.

51. What makes a machine intelligence
technology ‘interesting’?

52. 1) a theoretical
breakthrough

53. 2) a change in economics
http://www.mkomo.com/cost-per-gigabyte

54. 3) a capability becomes a commodity

56. WTF??

57. 4) new data
is available

58. 4b) data is made useful

60. We create reports and prototypes
around each emerging capability,
and give advice.

64. papers + prototypes
“nerd friends”
growth

65. Working with a broad set of
companies leads us to generally
appealing applications.

68. Efficient models for learning from
streams of data as they are
generated.

74. pictograph.us

76. [caution]

77. What’s next?

80. You’re building the future.
Please build the one you want to live in.
¯_(ツ)_/¯

81. I’m Hilary. @hmason. hilary@fastforwardlabs.com
Thank you!!

82. Photo & Project Credits
• NASA’s Marshall Space Flight Center, http://luvit.me/1Oown6H
• Pentametron, by Ranjit Bhatnagar, https://twitter.com/pentametron
• StyleNet paper by Gatys, Ecker, Bethge http://arxiv.org/abs/1508.06576
• Hand on smartphone, www.japanexperterna.se/?attachment_id=3068
• Sunflower by Raffaele Camardella, http://luvit.me/1LISN1T
• Magic Rabbit by Jenn and Tony Bot, http://upvot.es/1MnLp6u
• ProPublica Surgeon Scorecard, https://projects.propublica.org/surgeons/
• Google Deep Dream, https://github.com/google/deepdream