As the thousands of high-flying dunks, alley oops and fadeaway jumper clips come out of NBA All-Star weekend, chances are the highlights were created by artificial intelligence. The league says they are using machine learning to create more highlights than ever before this All-Star weekend. Since 2014, the NBA has been employing and experimenting with technology from an Israeli company called WSC Sports to analyze key moments of each game and spit out some of the best highlights. As social media has emerged as an important destination to reach fans, the need for more and customized highlights has grown. This All-Star weekend, the software will automatically create multiple clips and content for every single player.

Wellington, Feb 12: New Zealand Police in a step to modernise its services unveiled its first Artificial intelligence (AI) officer named "Ella"on Wednesday. "Ella is a digital person that is powered by AI and uses real-time animation to emulate face-to face interactions," said Bush. Ella, which stands for'Electronic Lifelike Assistant', is part of two new digital kiosks the New Zealand Police has designed to help reduce queues in stations and to provide a modern way to connect with the public, NZ Herald reported. Ella will be stationed in the lobby of New Zealand Police's National Headquarters from next week assisting the concierge team and talking to visitors about Police topics such as the 105 nonemergency number and police vetting. Touch-screen electronic service points called Police Connect was also revealed on Wednesday, which will provide basic non-emergency services to the public 24 hours a day.

The police have unveiled their first AI officer, with hopes she'll soon be smiling and blinking out of screens in stations all around New Zealand. Ella, the artificial intelligence cop at the centre of the police's new digital services, was revealed at the police national headquarters in Wellington this morning. Ella, which stands for Electronic Lifelike Assistant, is part of two new digital kiosks police have designed to help reduce queues in stations and to provide a modern way to connect with the public. Designed as a mix of 26 different people, Ella is the brainchild of project manager Erin Greally, and will primarily be available only at the headquarters building in Molesworth St, where users can ask for information or be connected to whoever they're visiting. If the three-month pilot goes well, police hope to have Ella's friendly, CGI face spread across kiosks throughout the country.

Epoch gradient descent method (a.k.a. Epoch-GD) proposed by (Hazan and Kale, 2011) was deemed a breakthrough for stochastic strongly convex minimization, which achieves the optimal convergence rate of O(1/T) with T iterative updates for the objective gap. However, its extension to solving stochastic min-max problems with strong convexity and strong concavity still remains open, and it is still unclear whether a fast rate of O(1/T) for the duality gap is achievable for stochastic min-max optimization under strong convexity and strong concavity. Although some recent studies have proposed stochastic algorithms with fast convergence rates for min-max problems, they require additional assumptions about the problem, e.g., smoothness, bi-linear structure, etc. In this paper, we bridge this gap by providing a sharp analysis of epoch-wise stochastic gradient descent ascent method (referred to as Epoch-GDA) for solving strongly convex strongly concave (SCSC) min-max problems, without imposing any additional assumptions about smoothness or its structure. To the best of our knowledge, our result is the first one that shows Epoch-GDA can achieve the fast rate of O(1/T) for the duality gap of general SCSC min-max problems. We emphasize that such generalization of Epoch-GD for strongly convex minimization problems to Epoch-GDA for SCSC min-max problems is non-trivial and requires novel technical analysis. Moreover, we notice that the key lemma can be also used for proving the convergence of Epoch-GDA for weakly-convex strongly-concave min-max problems, leading to the best complexity as well without smoothness or other structural conditions.

We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcement Learning (RL) problems where the goal is to find a policy (using data from several tasks represented by Markov Decision Processes (MDPs)) that can be updated by one step of stochastic policy gradient for the realized MDP. In particular, using stochastic gradients in MAML update step is crucial for RL problems since computation of exact gradients requires access to a large number of possible trajectories. For this formulation, we propose a variant of the MAML method, named Stochastic Gradient Meta-Reinforcement Learning (SG-MRL), and study its convergence properties. We derive the iteration and sample complexity of SG-MRL to find an $\epsilon$-first-order stationary point, which, to the best of our knowledge, provides the first convergence guarantee for model-agnostic meta-reinforcement learning algorithms. We further show how our results extend to the case where more than one step of stochastic policy gradient method is used in the update during the test time.

This paper proposes an active learning system for sound event detection (SED). It aims at maximizing the accuracy of a learned SED model with limited annotation effort. The proposed system analyzes an initially unlabeled audio dataset, from which it selects sound segments for manual annotation. The candidate segments are generated based on a proposed change point detection approach, and the selection is based on the principle of mismatch-first farthest-traversal. During the training of SED models, recordings are used as training inputs, preserving the long-term context for annotated segments. The proposed system clearly outperforms reference methods in the two datasets used for evaluation (TUT Rare Sound 2017 and TAU Spatial Sound 2019). Training with recordings as context outperforms training with only annotated segments. Mismatch-first farthest-traversal outperforms reference sample selection methods based on random sampling and uncertainty sampling. Remarkably, the required annotation effort can be greatly reduced on the dataset where target sound events are rare: by annotating only 2% of the training data, the achieved SED performance is similar to annotating all the training data.

Recently, federated learning (FL) has drawn significant attention due to its capability of training a model over the network without knowing the client's private raw data. In this paper, we study the unsupervised clustering problem under the FL setting. By adopting a generalized matrix factorization model for clustering, we propose two novel (first-order) federated clustering (FedC) algorithms based on principles of model averaging and gradient sharing, respectively, and present their theoretical convergence conditions. We show that both algorithms have a O(1/T) convergence rate, where T is the total number of gradient evaluations per client, and the communication cost can be effectively reduced by controlling the local epoch length and allowing partial client participation within each communication round. Numerical experiments show that the FedC algorithm based on gradient sharing outperforms that based on model averaging, especially in scenarios with non-i.i.d. I. INTRODUCTION As one of the most fundamental data mining tasks, unsupervised clustering has a vast range of applications [1]. In view of the increasing volume of real-life data, distributed clustering methods that can process large-scale datasets in parallel computing environments have gained significant interests in the last decade [2], [3], [4]. However, recent emphasis on user privacy has called for new distributed schemes that can perform clustering without directly accessing the users' raw data.

The increasing deployment of machine learning as well as legal regulations such as EU's GDPR cause a need for user-friendly explanations of decisions proposed by machine learning models. Counterfactual explanations are considered as one of the most popular techniques to explain a specific decision of a model. While the computation of "arbitrary" counterfactual explanations is well studied, it is still an open research problem how to efficiently compute plausible and feasible counterfactual explanations. We build upon recent work and propose and study a formal definition of plausible counterfactual explanations. In particular, we investigate how to use density estimators for enforcing plausibility and feasibility of counterfactual explanations. For the purpose of efficient computations, we propose convex density constraints that ensure that the resulting counterfactual is located in a region of the data space of high density.

Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity. In contrast, studies of reinforcement learning in mixed-motive games have primarily leveraged homogeneous approaches. Given the defining characteristic of mixed-motive games--the imperfect correlation of incentives between group members--we study the effect of population heterogeneity on mixed-motive reinforcement learning. We draw on interdependence theory from social psychology and imbue reinforcement learning agents with Social Value Orientation (SVO), a flexible formalization of preferences over group outcome distributions. We subsequently explore the effects of diversity in SVO on populations of reinforcement learning agents in two mixed-motive Markov games. We demonstrate that heterogeneity in SVO generates meaningful and complex behavioral variation among agents similar to that suggested by interdependence theory. Empirical results in these mixed-motive dilemmas suggest agents trained in heterogeneous populations develop particularly generalized, high-performing policies relative to those trained in homogeneous populations.

According to a new scientific study, a nuclear attack of 100 bombs could harm the entire planet including the aggressor nation. Since the creation of the atom bomb, the threat of nuclear war has loomed. Endless films and books have dealt with the nuclear apocalypse and its aftermath, but what would a nuclear apocalypse really look like? Rutgers University Professor Alan Robock spoke with Fox News about the Armageddon and his team's new study regarding a nuclear war's effects on ocean life. If you live in a major city when a nuke hits, needless to say, you're in big trouble.