Apr 9, 2021
Andy and Dave discuss the latest in AI news, including a report that systematically examined 62 studies on COVID-19 ML methods (from a pool o 2200+ studies), and found that none of the models were of potential clinical use due to methodological flaws or underlying biases. MIT and Amazon identify pervasive label errors in popular ML datasets (such as MNIST, CIFAR, Imagenet) and demonstrate that models may learn systematic patterns of label error in order to improve their accuracy. DARPA’s Air Combat Evolution program upgrades its virtual program to include new weapons systems and multiple aircraft, with live Phase 2 tests on schedule for later in 2021. Researchers at the University of Waterloo and Northeastern University publish research working toward self-walking robotic exoskeletons. British researchers add a buccinators (cheek) muscle to robotic busts to better synchronize speech and mouth movements. Russian Promobot is developing hyper-realistic skin for humanoid robots. And Anderson Cooper takes a tour of Boston Dynamics. In research, Leverhulme, Cambridge, Imperial College London, and DeepMind UK publish research on the direct human-AI comparison in the animal-AI environment, using human children ages 6-10 and animal-AI agents across 10 levels of task groupings. Josh Bongard and Michael Levin publish Living Things Are Not (20th Century) Machines, a thought piece on updating how we think of machines and what they *could* be. Professors Jason Jones and Steven Skiena are publishing a running AI Dashboard on Public Opinion of AI. The Australian Department of Defence publishes A Method for Ethical AI in Defence. Raghavendra Gadagkar publishes Experiments in Animal Behavior. And Peter Singer and August Cole publish An Eye for a Storm, envisioning a future of professional military education for the Australian Defence Force.
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