Siyuan He, Pengcheng Xi, Ashkan Ebadi, Stephane Tremblay, Alexander Wong

Deep learning can help discover mathematical relations that evade human scientists, a recent paper by researchers at DeepMind shows. Like many things coming from the Alphabet-owned artificial intelligence lab, the paper, which is titled “Advancing mathematics by guiding human intuition with AI,” has received much attention from science and tech media.

By ELIZABETH BAKER I passengerterminaltoday

Madeline Shannon, Merced Sun-Star I govtech

 Vineeth Joel Patel I futurecar

Imaging photoplethysmography (iPPG) could greatly improve driver safety systems by enabling capabilities ranging from identifying driver fatigue to unobtrusive early heart failure detection. Unfortunately, the driving context poses unique challenges to iPPG, including illumination and motion. First, drastic illumination variations present during driving can overwhelm the small intensity-based iPPG signals. Second, significant driver head motionduring driving, as well as camera motion (e.g., vibration) make it challenging to recover iPPG signals. To address these two challenges, we present two innovations. First, we demonstrate that we can reduce most outside light variations using narrow-band near-infrared (NIR) video recordings and obtain reliable heart rate estimates. Second, we present a novel optimizationalgorithm, which we call AutoSparsePPG, that leverages the quasi-periodicity of iPPG signals and achieves better performance than the stateof-the art methods. In addition, we release the first publicly available driving dataset that contains both NIR and RGB video recordings of a passenger’s face with simultaneous ground truth pulse oximeter recordings. IEEE Transactions on Intelligent Transportation Systems

Sean O'Kane/The Verge

Mike Rutherford/Auto Exprss

Andy Palmer/FT