We’re super excited that our CEO & Founder, Hilary Mason, will deliver the opening keynote tomorrow at the Grace Hopper conference in Houston, TX. You can catch the keynote live at 9:30 EDT / 8:30 CDT on the conference livestream website.
As a brief preview, Hilary’s talk will cover: how and why machine intelligence is revolutionizing computing; what today’s tech ecosystem looks like and how aspiring female technologists have to maneuver to succeed (be that landing the right job or starting a company); and how Fast Forward Labs is an example of a “hack” of the system, rethinking the future of applied research.
We are extremely proud that Hilary serves as a role model for women in technology. Commodore Hopper put it best:
“The most important thing I’ve accomplished, other than building the compiler, is training young people. They come to me, you know, and say, ‘Do you think we can do this?’ I say, “Try it.” And I back ‘em up. They need that. I keep track of them as they get older and I stir ‘em up at intervals so they don’t forget to take chances.”
More from the Blog
Oct 6 2015
Last week, Hilary spoke about opportunities for mid-sized companies to use data at Accelerating America’s Middle Market, hosted by the Wall Street Journal. This morning, I spoke about opportunities for large, established companies to use data at a Corporate Longevity Leadership Briefing, hosted by the Financial Times. Here are two key takeaways from the panels: Memes generate excitement; exci...
Oct 20 2015
Last week, Hilary, Fast Forward Labs founder and CEO, gave the opening keynote at the Grace Hopper Celebration of Women in Computing in Houston, TX. Her talk inspired an audience of over 12,000 women to embrace the unimaginable possibilities that will shape the careers of future technologists. Sure, aspiring engineers and data scientists have to endure the angst that they can no longer chart t...
Aug 15 2017
by — The Tabula Rogeriana, a world map created by Muhammad al-Idrisi through traveler interviews in 1154. The Wikipedia corpus is one of the favorite datasets of the machine learning community. It is often used for experimenting, benchmarking and providing how-to examples. These experiments are generally presented separate from the Wikipedia user interface, however, which has remained true to the...