AI news: Neural network learns when it should not be trusted - '99% won't cut it'

Mr Amini said: "It was very calibrated to the errors that the network makes, which we believe was one of the most important things in judging the quality of a new uncertainty estimator." The test revealed the network's ability to flag when users should not place full trust in its decisions. In such examples, "if this is a health care application, maybe we don't trust the diagnosis that the model is giving, and instead seek a second opinion," Amini added. Dr Raia Hadsell, a DeepMind artificial intelligence researcher not involved with the workDeep evidential describes regression as "a simple and elegant approach that advances the field of uncertainty estimation, which is important for robotics and other real-world control systems. She added: "This is done in a novel way that avoids some of the messy aspects of other approaches -- [for example] sampling or ensembles -- which makes it not only elegant but also computationally more efficient -- a winning combination."