Grace Hopper Conference Opening Keynote
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Hilary Mason discusses the transformative potential of machine intelligence and its implications for innovation in technology and data science. She emphasizes the importance of theoretical breakthroughs, economic changes, and new data availability in identifying valuable machine intelligence opportunities. The focus is on utilizing data and algorithms to create reports and prototypes while encouraging industry stakeholders to build a desirable future.
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![[slides to come]](https://image.slidesharecdn.com/ghcoct2015-151016154414-lva1-app6891/85/Grace-Hopper-Conference-Opening-Keynote-30-320.jpg)
![[caution]](https://image.slidesharecdn.com/ghcoct2015-151016154414-lva1-app6891/85/Grace-Hopper-Conference-Opening-Keynote-76-320.jpg)
Grace Hopper Conference Opening Keynote
- 1. Machine Intelligence: A Startup Adventure Hilary Mason @hmason
- 6. (and people stay the same)
- 9. By Gene Kogan, based on the paper by Gatys, Ecker, Bethge.
- 10. @pentametron
- 11. Data products are everywhere.
- 15. (it’s boring)
- 19. Why now?
- 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.
- 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.
- 49. The biggest opportunities might surprise you.
- 51. What makes a machine intelligence technology ‘interesting’?
- 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. [email protected] 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