Supervised learning models often make systematic errors on rare subsets of the data. However, such systematic errors can be difficult to identify, as model performance can only be broken down across sensitive groups when these groups are known and explicitly labelled. This paper introduces a method for discovering systematic errors, which we call the spotlight. The key idea is that similar inputs tend to have similar representations in the final hidden layer of a neural network. We leverage this structure by "shining a spotlight" on this representation space to find contiguous regions where the model performs poorly. We show that the spotlight surfaces semantically meaningful areas of weakness in a wide variety of model architectures, including image classifiers, language models, and recommender systems.

Originally to be helmed by Stanley Kubrick before the baton was passed over to Steven Spielberg, A.I. Artificial Intelligence is emblazoned with visual motifs indicative of both filmmakers' catalogs. Though Kubrick died two years before the film's release, the distinct essence of both filmmakers is palpable due to Spielberg's script closely following the original treatment from Kubrick's fledgling work on the project in the '70s. Though many critics have unduly attributed certain aspects of A.I.'s contrasting tone of surreal, uncanny darkness and whimsical adventure to the wrong directors, the exploration of these two realms and the moral dilemmas they pose on a futuristic, dystopian level are never more tangible than when delving into the construction of gender. Against public misconception, Spielberg remains faithfully fixated on the sinister ethical conundrums presented in A.I., unsettling audiences with the implications of this far-off 2141 society outsourcing human emotions to machines. During the opening sequence of the film, an otherwise supplementary character simply credited as "female colleague" (April Grace) raises an uncomfortable philosophical question.

Researchers in Germany have developed a drone that can locate humans by their screams. Rather than a terrifying'Terminator'-like scenario, the autonomous devices would be used to assist first responders in rescuing hard-to-find survivors after a natural disaster. The engineers recorded themselves making sounds someone in jeopardy might create, like screams, bangs and claps. Then they trained the drone's AI algorithm to recognize those noises, while filtering out the hum of its rotors and other background noise. A prototype drone has been programmed to recognize'impulsive' noises humans in crisis might make--like screams, claps and kicks--and locate survivors of human disasters.

Boston Dynamics has released two incredible videos of its famous robotic dog, Spot, pulling off some very impressive dance moves. The first clip shows seven Spot robots performing a highly choreographed dance in union to the music of South Korean K-pop sensation BTS. In a second bit of footage released by the Boston-based firm Spot is seen meeting and showing the boy band its competent dance moves. Boston Dynamics said the videos are'in celebration' of its full acquisition by South Korean motor company Hyundai, which was announced last week. Funky: Seven units of the robotic dog Spot are seen performing a variety of impressive moves to K-pop band BTS's music in a new video released by Boston Dynamics In time with the music, the seven Spot's arms shoot out into a fluid series of elaborate patterns In the first video, the seven Spots are dancing to the band's 2020 song'IONIQ: I'm On It'.

Chinese tech giant Huawei is another powerful addition to this pool of technological resources. One of GAC MOTOR's core brand values is technology innovation, that is, finding ways to make cars more intelligent, more efficient, and more enjoyable to drive. A world-class smartphone provider, Huawei regularly produces cutting-edge technology in fields such as voice and facial recognition, IoT connectivity, "smart" appliances, cameras and charging technology, to name just a few. Having cooperative access to Huawei's research and technology gives GAC a strong edge in the production of world-class in-car systems. One exciting project that GAC is working towards in cooperation with Huawei and Didi is "Level 4" autonomous vehicles, which can operate almost entirely without input from humans (current driver-assist mechanisms are classed as Level 2 autonomy).

"I thought this would be hard for you to understand. You were created to be so young." This heartbreaking line arrives toward the end of AI: Artificial Intelligence, many centuries after David, an uncommonly sophisticated mechanical child (or "Mecha"), has embarked on a quest to become "a real boy", like Pinocchio, and reunite with the human mother he's been programmed to love. The years have not aged him, of course. He is eternally young, incapable of acquiring the wisdom and perspective that come with age.

In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users' opportunities for discovery. We propose an evaluation procedure based on stochastic reachability to quantify the maximum probability of recommending a target piece of content to an user for a set of allowable strategic modifications. This framework allows us to compute an upper bound on the likelihood of recommendation with minimal assumptions about user behavior. Stochastic reachability can be used to detect biases in the availability of content and diagnose limitations in the opportunities for discovery granted to users. We show that this metric can be computed efficiently as a convex program for a variety of practical settings, and further argue that reachability is not inherently at odds with accuracy. We demonstrate evaluations of recommendation algorithms trained on large datasets of explicit and implicit ratings. Our results illustrate how preference models, selection rules, and user interventions impact reachability and how these effects can be distributed unevenly.

Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges -- the affective gap, perception subjectivity, and label noise and absence. We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison of label noise and dataset bias. We then summarize and compare the representative approaches on (1) emotion feature extraction, including both handcrafted and deep features, (2) learning methods on dominant emotion recognition, personalized emotion prediction, emotion distribution learning, and learning from noisy data or few labels, and (3) AICA based applications. Finally, we discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.

I'll admit, I wasn't impressed when Amazon added a rotating base to the new Echo Show 10. Sure, the swiveling screen is useful for following you around the room during video calls, but it also felt gimmicky and unnecessary. Plus, it needs a lot of room to move around so you're losing a significant amount of counter space. That's why I'm glad the Echo Show 8 and 5 haven't repeated that design. In fact, Amazon has changed very little between this edition and the last, but trust me when I say that's a good thing. It's the Echo Show 8 that has seen the most changes, but most of those are under the hood: It now has a faster octa-core processor plus a much-improved 13-megapixel wide-angle camera (the previous model only had a 1-megapixel sensor).

New guidance from the World Health Organization[CB1] (WHO) has found that AI has great potential for improving international healthcare, but ethics …