Foundry’s WTF (29-Apr-2017)

The Foundry @ Cornell Tech

The Foundry’s Weekly Technology Fix (WTF) is a curated list of articles/posts/etc. we found interesting. The Foundry @ Cornell Tech transforms research and ideas into products.

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Department of 🎮 💉 (Digital Addictions)

Whodunit using Fitbit; couple counseling via wearables; and digital optical illusions.

Department of 💡🏙 (Smarter Cities)

Recycled plastic for stronger roads; bike routes to avoid scary streets; and better signaling to preserver biking "flow".

Department of 🤑 🔌 (Shameless Plugs)

Design awards; the new Amazon brick and mortar bookstore; napping classes; and the Smart Cities conference in NYC (where I am giving a workshop on city innovation).

Department of 🌈🎆(Silver Linings)

Great ad about how to help people have a conversation; from coal to code; and another batch of adorable pets+toys pictures.

Department of 🇺🇸 🗳 (US Elections)

Fake accounts to create fake news; and two takes on the White House correspondents' dinner.

Department of 🎬 📖 (worthy quotes)

« A student remarked to Pixar’s Ed Catmull he must be surrounded by geniuses because his team had so many algorithms named after them. His reply was that at the time nothing existed to do the things they wanted, so to do anything they had to invent it. Innovation is easy on the cutting edge. It is no more difficult than creatively hacking legacy software to keep up with the status quo, but much more rewarding. The further back you are from the frontier the harder it is to come up with anything new. » – Phil Holden.

« In the past, we assumed that when machines reached near-human performance in tasks like image recognition, it would be thanks to fundamental breakthroughs into the nature of cognition. We would be able to lift the lid on the human mind and see all the little gears turning. What’s happened instead is odd. We found a way to get terrific results by combining fairly simple math with enormous data sets. But this discovery did not advance our understanding. The mathematical techniques used in machine learning don’t have a complex, intelligible internal structure we can reason about. Like our brains, they are a wild, interconnected tangle. » – Maciej Cegłowski, Build a Better Monster.

« As Zeynep Tufekci has argued, the algorithm is irreducibly alien, a creature of linear algebra. […] This is what I mean by alien failure modes. The mistakes classifiers make have no relationship to how human vision works, and because the image classifier is normally so good, it shocks us to see it fail in this way. » — Maciej Cegłowski, Build a Better Monster.

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