To fix ideas, consider ECG recordings from healthy patients during rest.

The accuracy of deep neural networks is significantly influenced by the effectiveness of mini-batch construction during training. In single-label scenarios, such as binary and multi-class classification tasks, it has been demonstrated that batch selection algorithms preferring samples with higher uncertainty achieve better performance than difficulty-based methods. Although there are two batch selection methods tailored for multi-label data, none of them leverage important uncertainty information. Adapting the concept of uncertainty to multi-label data is not a trivial task, since there are two issues that should be tackled. First, traditional variance or entropy-based uncertainty measures ignore fluctuations of predictions within sliding windows and the importance of the current model state. Second, existing multi-label methods do not explicitly exploit the label correlations, particularly the uncertainty-based label correlations that evolve during the training process. In this paper, we propose an uncertainty-based multi-label batch selection algorithm. It assesses uncertainty for each label by considering differences between successive predictions and the confidence of current outputs, and further leverages dynamic uncertainty-based label correlations to emphasize instances whose uncertainty is synergistically expressed across multiple labels. Empirical studies demonstrate the effectiveness of our method in improving the performance and accelerating the convergence of various multi-label deep learning models.

Anti-surveillance makeup, used by people who do not want to be identified to fool facial recognition systems, is bold and striking, not exactly the stuff of cloak and daggers. While experts' opinions vary on the makeup's effectiveness to avoid detection, they agree that its use is not yet widespread. Anti-surveillance makeup relies heavily on machine learning and deep learning models to "break up the symmetry of a typical human face" with highly contrasted markings, says John Magee, an associate computer science professor at Clark University in Worcester, MA, who specializes in computer vision research. However, Magee adds that "If you go out [wearing] that makeup, you're going to draw attention to yourself." The effectiveness of anti-surveillance makeup has been debated because of racial justice protesters who do not want to be tracked, Magee notes.

Erik Learned-Miller is one reason we talk about facial recognition at all. In 2007, years before the current A.I. boom made "deep learning" and "neural networks" common phrases in Silicon Valley, Learned-Miller and three colleagues at the University of Massachusetts Amherst released a dataset of faces titled Labelled Faces in the Wild. To you or me, Labelled Faces in the Wild just looks like folders of unremarkable images. You can download them and look for yourself. There's boxer Joe Gatti, gloves raised mid-fight.

Software engineer Henry Gan got a surprise last summer when he tested his team's new facial recognition system on coworkers at startup Gfycat. The machine-learning software successfully identified most of his colleagues, but the system stumbled with one group. "It got some of our Asian employees mixed up," says Gan, who is Asian. "Which was strange because it got everyone else correctly." Gan could take solace from the fact that similar problems have tripped up much larger companies.