The instability phenomenon in deep learning for image reconstruction

Our paper On instabilities of deep learning in image reconstruction and the potential costs of AI was just published in PNAS:

In it, we show that current deep learning approaches for image reconstruction are unstable: namely, small perturbations in the measurements lead to a myriad of artefacts in the recovered images. This has potentially serious consequences for the safe and secure deployment of machine learning techniques in imaging applications.

Here is some press coverage: Cambridge University News, EurekAlert, The Register, Health Care Business, Radiology Business, Science Daily,   Psychology Today, Government Computing, Diagnostic Imaging, News Medical, Press Release Point, Tech Xplore, Aunt Minnie, My Science, Digit, The Talking Machines