We’ve got three events on the horizon. Join us, or contact us with questions if you’d like to learn more but are unable to attend!
Wednesday, November 11 | Mountain View, CA
Hilary Mason will give a keynote at H2O World, where participants discuss how to use machine learning to build intelligent applications. Hilary will explain how Fast Forward Labs helps companies discover and build exciting new products from their existing data assets.
Tuesday, November 17 | Washington, DC
Mike Williams, the latest addition to the Fast Forward Labs research team, will give a talk about how sentiment analysis algorithms have unavoidable tendencies to pay more attention to men, thereby amplifying their privilege. He’s presenting at the Data Science DC MeetUp at the Pew Research Center.
Wednesday, November 18 | Chicago, IL
Hilary Mason will join Kris Hammond, Chief Scientist at Narrative Science, for a fireside chat explaining how some of the latest “intelligent” or “cognitive” technologies actually work and what practical value they add to businesses. 1871 will host this free session. Learn more and register here.
See you there!
More from the Blog
Oct 29 2015
with — Machine learning technologies increasingly shape our sense of reality and the choices we make in our daily lives. They power Amazon’s product recommendations. They classify documents relevant for a lawsuit. They enable computers to play chess like the masters. As machine learning applications expand to influence our civic, professional and private lives, it’s important that we all have a basic...
with Pedro Domingos
Nov 17 2015
Possibly true statement: the Fast Forward Labs dog is the cutest dog in the world. Our General Counsel Ryan picked up the puppy a month ago and we’ve yet to name him. Ryan likes Renfield, which, as Bram Stoker fans know, evokes slightly different thoughts than “super cute,” particularly when played by the ever-guttural Tom Waits. But the fact that we’re in no rush to name him tells us somethi...
Sep 17 2018
Deep learning has provided extraordinary advances in problem spaces that are poorly solved by other approaches. This success is due to several key departures from traditional machine learning that allow it to excel when applied to unstructured data. Today, deep learning models can play games, detect cancer, talk to humans, and drive cars. But the differences that make deep learning power...