Robot law pioneer Ryan Calo (previously) teamed up with U Washington computer science and law-school colleagues to write Is Tricking a Robot Hacking? — a University of Washington School of Law Research Paper.
Calo and co are looking at the intersection of adversarial examples (blind spots in machine learning systems that make itRead More
There are 50 hospitals on 5 continents that use Watson for Oncology, an IBM product that charges doctors to ingest their cancer patients’ records and then make treatment recommendations and suggest journal articles for further reading.
The doctors who use the service assume that it’s a data-driven AI that’s using data fromRead More
Robot law pioneer Ryan Calo (previously) has published a “roadmap” for an “artificial intelligence policy…to help policymakers, investors, technologists, scholars, and students understand the contemporary policy environment around AI at least well enough to initiative their own exploration.”
Calo cites a lot of our favorites, like Cathy O’Neil and Julia Angwin, and neatlyRead More
The Chinese government’s wish-list for AI researchers is pretty ambitious: “Breakthroughs should be made in basic theories of AI, such as big data intelligence, multimedia aware computing, human-machine hybrid intelligence, swarm intelligence and automated decision-making.”
They’ll get right on that, I’m sure.
A common technology system should be developedRead More
The Open AI researchers were intrigued by a claim that self-driving cars would be intrinsically hard to fool (tricking them into sudden braking maneuvers, say), because “they capture images from multiple scales, angles, perspectives, and the like.”
So they created a set of image-presentation techniques that reliably trick image classifiers, showing that theirRead More