Researchers think that adversarial examples could help us maintain privacy from machine learning systems

Machine learning systems are pretty good at finding hidden correlations in data and using them to infer potentially compromising information about the people who generate that data: for example, researchers fed an ML system a bunch of Google Play reviews by reviewers whose locations were explicitly given in their Google Plus reviews; based on this, the model was able to predict the locations of other Google Play reviewers with about 44% accuracy.

Towards a general theory of "adversarial examples," the bizarre, hallucinatory motes in machine learning's all-seeing eye

For several years, I've been covering the bizarre phenomenon of "adversarial examples (AKA "adversarial preturbations"), these being often tiny changes to data than can cause machine-learning classifiers to totally misfire: imperceptible squeaks that make speech-to-text systems hallucinate phantom voices; or tiny shifts to a 3D image of a helicopter that makes image-classifiers hallucinate a rifle

"Intellectual Debt": It's bad enough when AI gets its predictions wrong, but it's potentially WORSE when AI gets it right

Jonathan Zittrain (previously) is consistently a source of interesting insights that often arrive years ahead of their wider acceptance in tech, law, ethics and culture (2008's The Future of the Internet (and how to stop it) is surprisingly relevant 11 years later); in a new long essay on Medium (shorter version in the New Yorker), Zittrain examines the perils of the "intellectual debt" that we incur when we allow machine learning systems that make predictions whose rationale we don't understand, because without an underlying theory of those predictions, we can't know their limitations.

"I Shouldn't Have to Publish This in The New York Times": my op-ed from the future

I was honored to be invited to contribute to the New York Times's excellent "Op-Eds From the Future" series (previously), with an op-ed called "I Shouldn't Have to Publish This in The New York Times," set in the near-future, in which we have decided to solve the problems of Big Tech by making them liable for what their users say and do, thus ushering in an era in which all our speech is vetted by algorithms that delete anything that looks like misinformation, harassment, copyright infringement, incitement to terrorism, etc — with the result that the only place where you can discuss anything of import is newspapers themselves.

There's a literal elephant in machine learning's room

Machine learning image classifiers use context clues to help understand the contents of a room, for example, if they manage to identify a dining-room table with a high degree of confidence, that can help resolve ambiguity about other objects nearby, identifying them as chairs.