Spending money on research and development is a funny thing. It's hard to tell what's going to be a success, and sometimes the results aren't valuable for decades. For example, Uber is investing heavily in driverless cars with the explicit goal of revolutionizing transportation. Even if the results aren't always successful we're convinced R&D investments are a leading indicator of long term economic strength. We found the numbers for our visualization thanks to the UNESCO Institute for Statistics, which collected the data through a series of smaller regional surveys.

Researchers at the Massachusetts Institute of Technology (MIT) have developed what is likely a world first -- a "psychopathic" artificial intelligence (AI). Stay up to date regarding Samsung's upcoming Galaxy Note 9, to be unveiled August 9. The experiment is based on the 1921 Rorschach test, which identifies traits in humans deemed to be psychopathic based on their perception of inkblots, alongside what is known as thought disorders. Norman is an AI experiment born from the test and "extended exposure to the darkest corners of Reddit," according to MIT, in order to explore how datasets and bias can influence the behavior and decision-making capabilities of artificial intelligence. "When people talk about AI algorithms being biased and unfair, the culprit is often not the algorithm itself, but the biased data that was fed to it," the researchers say.

Chinese venture capital firm Gaorong Capital has joined CMB International to co-lead a RMB180 million (US$26 million) in Chinese artificial intelligence start-up Tianrang. Other investors Ziniu Fund and Chinese fintech company Wacai also participated in the round, according to Tianrang's announcement. Based on deep learning and other AI technology, Tianrang provides data analysis and smart solutions for enterprises. In one user case, Tianrang's technology can automatically collect and analyze marketing trends and purchase related information on Alibaba's e-commerce platform 24/7 to help vendors make smarter marketing decisions. "With algorithm and AI, Tianrang lowers the requirement of complex machine decision-making and makes it accessible and scalable for commercial use," said Wang Hongbo, chief investment officer at CMB International Capital.

Samsung is still two days away from officially unveiling its new Galaxy device, but a series of leaks surrounding the Note 9 in recent weeks means there's not much left to unveil. The 9 August "Unpacked" event has been preceded by numerous images and specs sheets revealing key features and details about the premium smartphone, including a possible price. Depending on the variant, the Galaxy Note 9 will cost between £899 (128GB version) and £1,099 (512GB version), according to the latest leak from Roland Quandt. So what can customers expect to get for this price? The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Affective computing [31] is "computing that relates to, arises from, or influences emotions." It is very important in human-machine interaction, as humans cannot have longlasting intimate relationships with machines if they cannot understand our affects and respond appropriately. Both affect classification and regression have been extensively studied in the literature [24], [43], [45], [46], [48]. For affect classification, the most commonly used categories are the six basic emotions (anger, disgust, fear, happiness, sadness, and surprise) proposed by Ekman et al. [5]. For regression, affects are usually represented as numbers in the 2D space of arousal and valence [35], or in the 3D space of arousal, valence, and dominance [25]. Recently, Yannakakis et al. [50] also argued that the nature of emotions is ordinal, and hence preference learning [51] should also play an important role in affective computing. Various input signals could be used in affective computing, e.g., speech [21], [47], facial expressions [8], [29], physiological signals [7], [43], and multimodal combination [26], [53]. Numerous features could be extracted from each modality. For example, 6,373 acoustic features were extracted by OpenSMILE [6] in the InterSpeech 2013 Computational Paralinguistics Challenge.

The multi-armed bandit is a sequential allocation task where an agent must learn a policy that maximizes long term payoff, where only the reward of the played arm is observed at each iteration. In the stochastic setting, the reward for each action is generated from an unknown distribution, which depends on a given 'context', available at each interaction with the world. Thompson sampling is a generative, interpretable multi-armed bandit algorithm that has been shown both to perform well in practice, and to enjoy optimality properties for certain reward functions. Nevertheless, Thompson sampling requires sampling from parameter posteriors and calculation of expected rewards, which are possible for a very limited choice of distributions. We here extend Thompson sampling to more complex scenarios by adopting a very flexible set of reward distributions: nonparametric Gaussian mixture models. The generative process of Bayesian nonparametric mixtures naturally aligns with the Bayesian modeling of multi-armed bandits. This allows for the implementation of an efficient and flexible Thompson sampling algorithm: the nonparametric model autonomously determines its complexity in an online fashion, as it observes new rewards for the played arms. We show how the proposed method sequentially learns the nonparametric mixture model that best approximates the true underlying reward distribution. Our contribution is valuable for practical scenarios, as it avoids stringent model specifications, and yet attains reduced regret.

In this paper, we study the problem of compressed sensing using binary measurement matrices, and $\ell_1$-norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust sparse recovery with binary matrices. We establish sufficient conditions for a column-regular binary matrix to satisfy the robust null space property (RNSP), and show that the sparsity bounds for robust sparse recovery obtained using the RNSP are better by a factor of $(3 \sqrt{3})/2 \approx 2.6$ compared to the restricted isometry property (RIP). Next we derive universal lower bounds on the number of measurements that any binary matrix needs to have in order to satisfy the weaker sufficient condition based on the RNSP, and show that bipartite graphs of girth six are optimal. Then we display two classes of binary matrices, namely parity check matrices of array codes, and Euler squares, that have girth six and are nearly optimal in the sense of almost satisfying the lower bound. In principle randomly generated Gaussian measurement matrices are "order-optimal." So we compare the phase transition behavior of the basis pursuit formulation using binary array code and Gaussian matrices, and show that (i) there is essentially no difference between the phase transition boundaries in the two cases, and (ii) the CPU time of basis pursuit with binary matrices is hundreds of times faster than with Gaussian matrices, and the storage requirements are less. Therefore it is suggested that binary matrices are a viable alternative to Gaussian matrices for compressed sensing using basis pursuit.

This paper studies a class of adaptive gradient based momentum algorithms that update the search directions and learning rates simultaneously using past gradients. This class, which we refer to as the "Adam-type", includes the popular algorithms such as the Adam, AMSGrad and AdaGrad. Despite their popularity in training deep neural networks, the convergence of these algorithms for solving nonconvex problems remains an open question. This paper provides a set of mild sufficient conditions that guarantee the convergence for the Adam-type methods. We prove that under our derived conditions, these methods can achieve the convergence rate of order $O(\log{T}/\sqrt{T})$ for nonconvex stochastic optimization. We show the conditions are essential in the sense that violating them may make the algorithm diverge. Moreover, we propose and analyze a class of (deterministic) incremental adaptive gradient algorithms, which has the same $O(\log{T}/\sqrt{T})$ convergence rate. Our study could also be extended to a broader class of adaptive gradient methods in machine learning and optimization.

Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.

An important application of Lebesgue integral quadrature[1] is developed. Given two random processes, $f(x)$ and $g(x)$, two generalized eigenvalue problems can be formulated and solved. In addition to obtaining two Lebesgue quadratures (for $f$ and $g$) from two eigenproblems, the projections of $f$-- and $g$-- eigenvectors on each other allow to build a joint distribution estimator, the most general form of which is a density--matrix correlation. The examples of the density--matrix correlation can be the value--correlation $V_{f_i;g_j}$, similar to the regular correlation concept, and a new one, the probability--correlation $P_{f_i;g_j}$. The theory is implemented numerically; the software is available under the GPLv3 license.