You take a picture of a person, upload it and get to see public photos of that person along with links to where those photos appeared. By Kashmir Hill Until recently, Hoan Ton-That's greatest hit was an app that let people put Donald Trump's distinctive yellow hair on their own photos. Then Ton-That did something momentous: He invented a tool that could end your ability to walk down the street anonymously and provided it to hundreds of law enforcement agencies. His tiny company, Clearview AI, devised a groundbreaking facial recognition app. You take a picture of a person, upload it and get to see public photos of that person along with links to where those photos appeared.

Researchers in Japan and The Netherlands have, for the first time, used machine learning techniques, in particular artificial neural networks (ANNs), to predict the Indian Ocean Dipole (IOD), a positive phase of which has affected weather and climate in India and Australia in a spectacular fashion so far in 2019-20. The IOD has both positive and negative phases, and signals large socio-economic impacts on many countries and hence predicting the IOD well in advance will benefit the affected societies, note authors JV Ratnam and Swadhin K Behera (Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama) and HA Dijkstra (Institute for Marine and Atmospheric Research Utrecht, Utrecht University in The Netherlands) in a paper published by Nature. The IOD is a mode of climate variability observed in the Indian Ocean sea surface temperature anomalies with one pole in Sumatra (Indonesia) and the other near East Africa. Therefore, the IOD is represented by an index derived from the gradient between the western equatorial Indian Ocean and the south-eastern equatorial Indian Ocean. It starts sometime in May-June, peaks in September-October and ends in November (2019's rather strong positive phase of the IOD lasted into early January of 2020).

Christmas is just around the corner, which means it's time to start planning your presents. Finding the perfect gift for your loved ones can be tricky, but don't worry, TechRadar is here to help you plan ahead. There's nothing like watching the people you care about erupt into smiles as they tear off your wrapping, and are greeted with a gift they actually love. So if you want to leave a lasting impression, the latest tech gadget can do just that. Technology is evolving so quickly that if you decided on a gizmo last year, there's always something new to choose from this year.

Short text classi cation is a method for classifying short sentence with prede ned labels. However, short text is limited in shortness in text length that leads to a challenging problem of sparse features. Most of existing methods treat each short sentences as independently and identically distributed (IID), local context only in the sentence itself is focused and the relational information between sentences are lost. To overcome these limitations, we propose a PathWalk model that combine the strength of graph networks and short sentences to solve the sparseness of short text. Experimental results on four different available datasets show that our PathWalk method achieves the state-of-the-art results, demonstrating the efficiency and robustness of graph networks for short text classification.

The problem of finding the sparsest vector (direction) in a low dimensional subspace can be considered as a homogeneous variant of the sparse recovery problem, which finds applications in robust subspace recovery, dictionary learning, sparse blind deconvolution, and many other problems in signal processing and machine learning. However, in contrast to the classical sparse recovery problem, the most natural formulation for finding the sparsest vector in a subspace is usually nonconvex. In this paper, we overview recent advances on global nonconvex optimization theory for solving this problem, ranging from geometric analysis of its optimization landscapes, to efficient optimization algorithms for solving the associated nonconvex optimization problem, to applications in machine intelligence, representation learning, and imaging sciences. Finally, we conclude this review by pointing out several interesting open problems for future research.

Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. However, there is few comprehensive study explaining the connections among different GANs variants, and how they have evolved. In this paper, we attempt to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications. Firstly, the motivations, mathematical representations, and structure of most GANs algorithms are introduced in details. Furthermore, GANs have been combined with other machine learning algorithms for specific applications, such as semi-supervised learning, transfer learning, and reinforcement learning. This paper compares the commonalities and differences of these GANs methods. Secondly, theoretical issues related to GANs are investigated. Thirdly, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are illustrated. Finally, the future open research problems for GANs are pointed out.

Bayesian quadrature optimization (BQO) maximizes the expectation of an expensive black-box integrand taken over a known probability distribution. In this work, we study BQO under distributional uncertainty in which the underlying probability distribution is unknown except for a limited set of its i.i.d. samples. A standard BQO approach maximizes the Monte Carlo estimate of the true expected objective given the fixed sample set. Though Monte Carlo estimate is unbiased, it has high variance given a small set of samples; thus can result in a spurious objective function. We adopt the distributionally robust optimization perspective to this problem by maximizing the expected objective under the most adversarial distribution. In particular, we propose a novel posterior sampling based algorithm, namely distributionally robust BQO (DRBQO) for this purpose. We demonstrate the empirical effectiveness of our proposed framework in synthetic and real-world problems, and characterize its theoretical convergence via Bayesian regret.

Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted the attention of information processors/data scientists and information consumers/business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring the credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

Reinforcement learning has been successful in training autonomous agents to accomplish goals in complex environments. Although this has been adapted to multiple settings, including robotics and computer games, human players often find it easier to obtain higher rewards in some environments than reinforcement learning algorithms. This is especially true of high-dimensional state spaces where the reward obtained by the agent is sparse or extremely delayed. In this paper, we seek to effectively integrate feedback signals supplied by a human operator with deep reinforcement learning algorithms in high-dimensional state spaces. We call this FRESH (Feedback-based REward SHaping). During training, a human operator is presented with trajectories from a replay buffer and then provides feedback on states and actions in the trajectory. In order to generalize feedback signals provided by the human operator to previously unseen states and actions at test-time, we use a feedback neural network. We use an ensemble of neural networks with a shared network architecture to represent model uncertainty and the confidence of the neural network in its output. The output of the feedback neural network is converted to a shaping reward that is augmented to the reward provided by the environment. We evaluate our approach on the Bowling and Skiing Atari games in the arcade learning environment. Although human experts have been able to achieve high scores in these environments, state-of-the-art deep learning algorithms perform poorly. We observe that FRESH is able to achieve much higher scores than state-of-the-art deep learning algorithms in both environments. FRESH also achieves a 21.4% higher score than a human expert in Bowling and does as well as a human expert in Skiing.

While this will yield increased profits for companies who can effectively leverage these technologies into new business models, what makes these developments truly revolutionary is their ability to tackle some of the world's most pressing challenges, ranging from education to health. Experts and fellows from the World Economic Forum's Centre for the Fourth Industrial Revolution weigh in with their predictions for the most exciting ways in which new technologies will improve the state of the world in the coming year. When I was born in 1992, I arrived four months premature with every joint in my body bent together as tightly as possible -- from my head being pressed down on my right shoulder all the way down to my toes being pressed against the bottom of my feet and my ankles collapsed against the back of shins like a broken golf club. My twin sister had shared the same environment with me and was 100% healthy. There was only one culprit: a genetic mutation.