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Aug 5, 2015 · post

A Flying Machine from New York to Paris

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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.

-Kathryn 

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Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
Notebooks

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com
Notebook

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.
https://colab.research.google.com/drive/1pjPzsw_uZew-Zcz646JTkRDhF2GkPk0N

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Cloudera Fast Forward is an applied machine learning reseach group.
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