Morlock Elloi on Fri, 29 Dec 2017 08:05:48 +0100 (CET)


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<nettime> Deep Fool


There was one remarkable presentation at 34C3 - "Deep Learning Blindspots" by Katharine Jarmul, available here https://media.ccc.de/v/34c3-8860-deep_learning_blindspots and here https://www.youtube.com/watch?v=BVJT-sE0WWQ .

Tl;Dr: the arms race in machine learning ("AI" for consumers) has started, it's not one-way onslaught any more. It's possible to effectively strike back (and not just by begging the government, although the talk considers that as well.)

Longer version and remarks: current ML systems appear to be linear, so it's possible to synthesize diversions even without knowing how a particular system works. Systems can be fooled to miscatergorize visual images (turtle gets recognized as a rifle in live video, face is not classified as face in still photo, etc.), and autonomous vehicles can be made to misread signals and signs (apparently most research goes there.)

It's easy to envision platforms that create false wake while enabling effective use of the Internet (such platforms will require full ownership of technology that touches humans - iOS and similar are out) and devices to derail autonomous vehicles (a must for every taxi driver and truck stop.) It appears that the whole field was based on the silly premise that everyone will cooperate and be math-challenged.

The other side will likely respond by deploying more complex ML models, but they are likely facing significant difficulties and limits. The initial monopoly age of ML is over - the other side now also has the bomb.

Happy New Year!

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