On March 25, 1909, Wilbur Wright (of the Wright brothers) told a reporter at the Cairo, Illinois bulletin that “no airship will ever fly from New York to Paris.” As with most quotes inherited from the past, people often misinterpret Wright’s quote as reactionary because they read it out of context. He continues: “What limits the flight is the motor. No known motor can run at the requisite speed for four days without stopping, and you can’t be sure of finding the proper winds for soaring…But the history of civilization has usually shown that every new invention has brought in its train new needs it can satisfy, and so what the airship will eventually be used for is probably what we can least predict at the present.”
On July 2, 2015, our founder Hilary Mason boarded one of those airships (actually, if we follow Flyopia, Wright was referring to a Zeppelin or a blimp rather than an airplane) in New York to deliver a talk on innovation and data science in Paris. Her talk explained why new data science methods are on the rise using arguments akin to Wright’s.
First, computing power is faster, cheaper and more readily accessible than it was just a few years ago. Just as motors needed to run at faster speeds for longer periods of time to enable cross continent travel, so too did we require hardware like graphic processing units to truly unpack the power of the neural networks enabling deep learning and image-object recognition. (Our Deep Learning report is coming soon!)
Second, we’re all aware that there’s now tons of data at our fingertips, but can’t always predict what it will be used for in the future. Hilary referenced an example where data engineers at Jawbone repurposed data on users’ sleep patterns to identify the epicenter of the South Napa Earthquake in 2014, basing conclusions on data about when the quake woke sleepers up. Applications for enterprises are infinite. In our next newsletter, we’ll explore how fashion companies are repurposing data collected from security sensors to build out unique customer profiles and improve inventory management. Sign up to receive our updates!
And fortunately, Hilary’s airship returned home safely to our own native shores, where we welcomed here back at the Fast Forward headquarters in New York.
More from the Blog
Jul 16 2015
Last night was the New York Times’ Open Source Science Fair. It kicked off with a keynote speech by our own Hilary Mason. Photo by Chrys Wu: http://t.co/m7G7G3g1ih After a dinner meet-and-greet session, the attendees and exhibitors got down to business, showing off their open source software projects, science fair style. Fast Forward Labs was an exhibitor. Micha Gorelick impressed the ...
Aug 7 2015
Today’s post is inspired by a slow-motion recording we captured of a Stirling engine that Ryan, Fast Forward’s General Counsel, just so happened to have lying around our New York City offices. For the non-mechanics among us, a Stirling engine is a heat engine that operates by cyclic compression and expansion of air and other gas at different temperatures; the temperature differential translates...
Jun 26 2018
by — Today’s machines can identify objects in photographs, predict loan repayments or defaults, write short summaries of long articles, or recommend movies you may like. Up until now, machines have achieved mastery through laser-like focus; most machine learning algorithms today train models to master one task, and one task only. We are excited to introduce multi-task learning in our upcoming webina...