When it comes to teaching basketball players how to execute a winning drive to the hoop, a tactic board can be a coach's best friend. But this top-down view of the court has a major limitation: It doesn't reveal how the opposing team will respond. A new program powered by artificial intelligence (AI) could change that. A coach sketches plays on a virtual tactic board on their computer, representing their own players as red dots and the defending team as blue dots. Once they drag their virtual players around to indicate movements and passes, an AI program trained with player movement data from the National Basketball Association converts these simplified sketches into a realistic simulation of how both offensive and defensive players would move during the play.

We are amidst the 4th Industrial Revolution, and technology is evolving faster than ever. Companies and individuals that don't keep up with some of the major tech trends run the risk of being left behind. Understanding the key trends will allow people and businesses to prepare and grasp the opportunities. As a business and technology futurist, it is my job to look ahead and identify the most important trends. In this article, I share with you the seven most imminent trends everyone should get ready for in 2020.

Yesterday, we talked about how Facebook has decided to monitor political speech. Today, we want to tell you about an area where Facebook is boldly going forward - dating. Facebook recently launched this new feature in the U.S. after testing it overseas. We wanted to know how people should feel about trusting Facebook in this particularly sensitive area since the company has long been under scrutiny for the way it handles users' data, so we've called Lisa Bonos. She writes about dating and relationships for the Washington Post.

You are, and welcome to the present and the future of automated machine learning programs that have the ability to significantly increase the speed of analysis of specialized MRI scans. Don't worry though--it's not ready for prime time just yet! Now, new research sheds light on just far we have come in terms of development of such machine learning programs. According to a new study published in the journal, Circulation: Cardiovascular Imaging, analysis of cardiac MRI scans using automated machine learning can be performed significantly faster and with comparable accuracy to human interpretation by trained cardiologists. It generally takes about 13 minutes for a trained physician (cardiologist) to interpret a cardiac MRI.

Artificial intelligence (AI) and facial expression technology is being used for the first time in job interviews in the UK to identify the best candidates. Unilever, the consumer goods giant, is among companies using AI technology to analyse the language, tone and facial expressions of candidates when they are asked a set of identical job questions which they film on their mobile phone or laptop. The algorithms select the best applicants by assessing their performances in the videos against about 25,000 pieces of facial and linguistic information compiled from previous interviews of those who have gone on to prove to be good at the job. Hirevue, the US company which has developed the interview technology, claims it enables hiring firms to interview more candidates in the initial stage rather than simply relying on CVs and that it provides a more reliable and objective indicator of future performance free of human bias. However, academics and campaigners warned that any AI or facial recognition technology would inevitably have in-built biases in its databases that could discriminate against some candidates and exclude talented applicants who might not conform to the norm.

If you've had a driver's licence photo or passport photo taken in Australia in the past few years, it's likely your face will end up in a massive new national network the federal government is trying to create. Victoria and Tasmania have already begun to upload driver's licence details to state databases that will eventually be linked to a future national one. Legislation before federal parliament will allow government agencies and private businesses to access facial IDs held by state and territory traffic authorities, and passport photos held by the foreign affairs department. The justification for what would be the most significant compulsory collection of personal data since My Health Record is cracking down on identity fraud. The home affairs department estimates that the annual cost of ID fraud is $2.2bn, and says introducing a facial component to the government's document verification service would help prevent it.

My son has grown up using a MacBook Air for Minecraft. He swears by the keyboard layout, and having witnessed the blazing speed with which he does things in the game, I understand his reluctance to use his Alienware laptop. Both machines are almost six years old and due a refresh, so I'm looking for a Windows laptop that is powerful enough to run games like Civilization 6 (with mods) but with a keyboard layout that is sufficiently similar to a MacBook that he can continue to use the muscle memory he has built up over the years. Does such a beast exist? One alternative is to buy a MacBook Pro and use it to run Windows (either Bootcamp or Parallels), but the Windows performance strikes me as a compromise (for more money).

The paper presents new metrics to quantify and test for (i) the equality of distributions and (ii) the independence between two high-dimensional random vectors. We show that the energy distance based on the usual Euclidean distance cannot completely characterize the homogeneity of two high-dimensional distributions in the sense that it only detects the equality of means and the traces of covariance matrices in the high-dimensional setup. We propose a new class of metrics which inherits the desirable properties of the energy distance and maximum mean discrepancy/(generalized) distance covariance and the Hilbert-Schmidt Independence Criterion in the low-dimensional setting and is capable of detecting the homogeneity of/completely characterizing independence between the low-dimensional marginal distributions in the high dimensional setup. We further propose t-tests based on the new metrics to perform high-dimensional two-sample testing/independence testing and study their asymptotic behavior under both high dimension low sample size (HDLSS) and high dimension medium sample size (HDMSS) setups. The computational complexity of the t-tests only grows linearly with the dimension and thus is scalable to very high dimensional data. We demonstrate the superior power behavior of the proposed tests for homogeneity of distributions and independence via both simulated and real datasets.

We present a numerical scheme for the computation of Artificial Neural Networks' weights, without a laborious iterative procedure. The proposed algorithm adheres to the underlying theory, is highly fast, and results in remarkably low errors when applied for regression and classification of complex data-sets, such as the Griewank function of multiple variables $\mathbf{x} \in \mathbb{R}^{100}$ with random noise addition, and MNIST database for handwritten digits recognition, with $7\times10^4$ images. Interestingly, the same mathematical formulation found capable of approximating highly nonlinear functions in multiple dimensions, with low errors (e.g. $10^{-10}$) for the test set of the unknown functions, their higher-order partial derivatives, as well as numerically solving Partial Differential Equations. The method is based on the calculation of the weights of each neuron, in small neighborhoods of data, such that the corresponding local approximation matrix is invertible. Accordingly, the hyperparameters optimization is not necessary, as the neurons' number stems directly from the dimensions of the data, further improving the algorithmic speed. The overfitting is inherently eliminated, and the results are interpretable and reproducible. The complexity of the proposed algorithm is of class P with $\mathcal{O}(mn^3)$ computing time, that is linear for the observations and cubic for the features, in contrast with the NP-Complete class of standard algorithms for training ANNs. The performance of the method is high, for small as well as big datasets, and the test-set errors are similar or smaller than the train errors indicating the generalization efficiency. The supplementary computer code in Julia Language, may reproduce the validation examples, and run for other data-sets.

Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on how combining symbolic reasoning with machine learning-based information extraction systems can improve performance. It gives a clear overview of the used methodologies for temporal reasoning, and explains how temporal reasoning can be, and has been successfully integrated into temporal information extraction systems. Based on the distillation of existing work, this survey also suggests currently unexplored research areas. We argue that the level of temporal reasoning that current systems use is still incomplete for the full task of temporal information extraction, and that a deeper understanding of how the various types of temporal information can be integrated into temporal reasoning is required to drive future research in this area.