We investigate the vulnerability of computer-vision-based signal classifiers to adversarial perturbations of their inputs, where the signals and perturbations are subject to physical constraints. We consider a scenario in which a source and interferer emit signals that propagate as waves to a detector, which attempts to classify the source by analyzing the spectrogram of the signal it receives using a pre-trained neural network. By solving PDE-constrained optimization problems, we construct interfering signals that cause the detector to misclassify the source even though the perturbations to the spectrogram of the received signal are nearly imperceptible. Though such problems can have millions of decision variables, we introduce methods to solve them efficiently. Our experiments demonstrate that one can compute effective and physically realizable adversarial perturbations for a variety of machine learning models under various physical conditions.

Deterministic chaos permits a precise notion of a "perfect measurement" as one that, when obtained repeatedly, captures all of the information created by the system's evolution with minimal redundancy. Finding an optimal measurement is challenging, and has generally required intimate knowledge of the dynamics in the few cases where it has been done. We establish an equivalence between a perfect measurement and a variant of the information bottleneck. As a consequence, we can employ machine learning to optimize measurement processes that efficiently extract information from trajectory data. We obtain approximately optimal measurements for multiple chaotic maps and lay the necessary groundwork for efficient information extraction from general time series.

When Sunshine Henle's mother, Linda, died unexpectedly at the age of 72, Henle, a 42-year-old Floridian, was left with what she describes as a "gaping hole of silence" in her life. Even though Linda had lived in New York, where she worked as a Sunday school teacher, the pair had kept in constant contact through phone calls and texting. "I always knew she was there, no matter what – if I was upset, or if I just needed to talk. She would always respond," says Henle. In November, Linda collapsed in her home and was unable to move. Henle's brother Sam and her sister-in-law Julie took her to urgent care.

Throughout life, we might seek a calling, companions, skills, entertainment, truth, self-knowledge, beauty, and edification. The practice of curiosity can be viewed as an extended and open-ended search for valuable information with hidden identity and location in a complex space of interconnected information. Despite its importance, curiosity has been challenging to computationally model because the practice of curiosity often flourishes without specific goals, external reward, or immediate feedback. Here, we show how network science, statistical physics, and philosophy can be integrated into an approach that coheres with and expands the psychological taxonomies of specific-diversive and perceptual-epistemic curiosity. Using this interdisciplinary approach, we distill functional modes of curious information seeking as searching movements in information space. The kinesthetic model of curiosity offers a vibrant counterpart to the deliberative predictions of model-based reinforcement learning. In doing so, this model unearths new computational opportunities for identifying what makes curiosity curious.

Statistical uncertainties are rarely incorporated in machine learning algorithms, especially for anomaly detection. Here we present the Bayesian Anomaly Detection And Classification (BADAC) formalism, which provides a unified statistical approach to classification and anomaly detection within a hierarchical Bayesian framework. BADAC deals with uncertainties by marginalising over the unknown, true, value of the data. Using simulated data with Gaussian noise, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties, though with significantly increased computational cost. Additionally, BADAC provides well-calibrated classification probabilities, valuable for use in scientific pipelines. We show that BADAC can work in online mode and is fairly robust to model errors, which can be diagnosed through model-selection methods. In addition it can perform unsupervised new class detection and can naturally be extended to search for anomalous subsets of data. BADAC is therefore ideal where computational cost is not a limiting factor and statistical rigour is important. We discuss approximations to speed up BADAC, such as the use of Gaussian processes, and finally introduce a new metric, the Rank-Weighted Score (RWS), that is particularly suited to evaluating the ability of algorithms to detect anomalies.

Richard Lifton, filmmaker Laura Mulvey, artificial intelligence pioneer Judea Pearl, Jazz musician Willie Ruff, and Rowan Williams, the 104th Archbishop of Canterbury, joined Tyson and Bassett in receiving honorary degrees from the school this year.

The US military will have more robot soldiers on the battlefield than real ones by 2025, a top British intelligence expert has claimed. John Bassett, a security consultant with a 20-year career at GCHQ, believes deadly combat robots are rapidly becoming "a reality" of modern day warfare. These state-of-the art military units will consist of human soldiers and robots and are aimed at maximizing performance on future battlefields. Combat robots will rapidly become an inherent part of US fighting forces within the next 10-15 years, defense experts say. Washington is apparently seeking to gain military edge over China, Russia and other rivals investing in research and development of robotic systems.

Virginia-based private company Six3 Advanced Systems was awarded an $11-million fixed-fee contract "to design, develop, and validate system prototypes for a combined-arms squad, which combines humans and unmanned assets, ubiquitous communications and information," according to a Pentagon press release. Six3, a subsidiary to the major American defense contractor CACI International, provides sensor development and signal processing technologies for the US Intelligence Community and the military, according to Bloomberg. The statement added the next-generation system should bring "advanced capabilities in all domains to maximize squad performance in increasingly complex operational environments." The news follows scientific predictions that the future warfare will see extensive use of robotic platforms powered by artificial intelligence (AI) and equipped with next-generation weapons systems. Combat robots and military-use AI solutions will become an inherent part of the US military within the next 10-15 years, defense experts say.