In Texas, a company called Alegion is helping disabled veterans take part in the new digital economy. The vets' job: preparing data so that an artificial intelligence (AI) system can learn from it. "There's a whole new industry sprouting on the shoulders of AI," says Alegion CEO Nathaniel Gates in an interview with IEEE Spectrum. When people talk about AI, they're often referring to software that gets very good at a particular task via a technique called deep learning. With this method, AI systems are given vast amounts of labeled data, and as they run through it, they learn to draw conclusions.

IIT Delhi and IBM on November 29 joined hands to partake in a multi-year research collaboration on Artificial Intelligence (AI) in India. IBM researchers will collaborate with students and professors from the Department of Computer Science and Engineering at IIT-D to inculcate in AI systems some key traits like reasoning, comprehension and inferencing. These may benefit sectors such as healthcare and medicine, finance, and customer support which deal with a complex set of questions and require reasoning, the tech giant said in a statement. "While working with AI systems, organizations require explicit reasoning and comprehension to reach a particular conclusion. We believe advancement in AI can tackle such problems," said Michael Karasick, Vice President, Global Labs, IBM Research. As part of the partnership, the varsity will join IBM's "AI Horizons Network" -- an international consortium of leading universities working with the software major to develop technologies needed to help fulfill the promise of AI.

AI-powered robo-painters are getting somewhat better at ripping off masterpieces, judging by the following fresh research. A team of academics at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Princeton University in America, and Chulalongkorn University in Thailand, have crafted a new system dubbed RePrint. It's split into two parts: a 3D printer that outputs layers of resin ink, and a trained neural network. When shown a photo of an oil painting, the system identifies the overall layout of the image, adjusts the lighting to compensate for whatever conditions the input picture was taken, and then predicts which colours are the right ones to mix and how to apply them to recreate the work using the printer. The key thing here is to mechanically produce a painting based on a digital snap, mixing and daubing the inks as necessary, imperfections and all.

A survey by research firm Gartner found that the use of AI in digital commerce companies is usually considered a success, with 70% of the organisations claiming very, or extremely successful, implementation of the technology. A total of 307 digital commerce organisations were surveyed for the study. These companies are currently using or piloting the technology to understand the adoption, value, success and challenges of AI in digital commerce. Organisations participated in this study were from the US, Canada, Brazil, France, Germany, the UK, Australia, New Zealand, India and China. Among the respondents, three-quarters said that they are seeing double-digit improvements in the outcomes they measure.

Instagram has finally added the option to properly control who sees your stories. The new Close Friends feature will allow users to pick who their most trusted or best followers are and only show them the personal information they don't want pushed out into the world. Until now, Instagram's stories have been all or nothing: they are either shared with anyone who sees your profile, even if they don't follow you, or are not shared at all. The company has offered the option to block specific people from viewing them, but that has to be done individually for each person. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona.

Deep learning within the context of point clouds has gained much research interest in recent years mostly due to the promising results that have been achieved on a number of challenging benchmarks, such as 3D shape recognition and scene semantic segmentation. In many realistic settings however, snapshots of the environment are often taken from a single view, which only contains a partial set of the scene due to the field of view restriction of commodity cameras. 3D scene semantic understanding on partial point clouds is considered as a challenging task. In this work, we propose a processing approach for 3D point cloud data based on a multiview representation of the existing 360{\deg} point clouds. By fusing the original 360{\deg} point clouds and their corresponding 3D multiview representations as input data, a neural network is able to recognize partial point sets while improving the general performance on complete point sets, resulting in an overall increase of 31.9% and 4.3% in segmentation accuracy for partial and complete scene semantic understanding, respectively. This method can also be applied in a wider 3D recognition context such as 3D part segmentation.

Hypothesis testing for graphs has been an important tool in applied research fields for more than two decades, and still remains a challenging problem as one often needs to draw inference from few replicates of large graphs. Recent studies in statistics and learning theory have provided some theoretical insights about such high-dimensional graph testing problems, but the practicality of the developed theoretical methods remains an open question. In this paper, we consider the problem of two-sample testing of large graphs. We demonstrate the practical merits and limitations of existing theoretical tests and their bootstrapped variants. We also propose two new tests based on asymptotic distributions. We show that these tests are computationally less expensive and, in some cases, more reliable than the existing methods.

Since with massive data growth, the need for autonomous and generic anomaly detection system is increased. However, developing one stand-alone generic anomaly detection system that is accurate and fast is still a challenge. In this paper, we propose conventional time-series analysis approaches, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Seasonal Trend decomposition using Loess (STL), to detect complex and various anomalies. Usually, SARIMA and STL are used only for stationary and periodic time-series, but by combining, we show they can detect anomalies with high accuracy for data that is even noisy and non-periodic. We compared the algorithm to Long Short Term Memory (LSTM), a deep-learning-based algorithm used for anomaly detection system. We used a total of seven real-world datasets and four artificial datasets with different time-series properties to verify the performance of the proposed algorithm.

This project presents the results of a partnership between the Data Science for Social Good fellowship, Jakarta Smart City and Pulse Lab Jakarta to create a video analysis pipeline for the purpose of improving traffic safety in Jakarta. The pipeline transforms raw traffic video footage into databases that are ready to be used for traffic analysis. By analyzing these patterns, the city of Jakarta will better understand how human behavior and built infrastructure contribute to traffic challenges and safety risks. The results of this work should also be broadly applicable to smart city initiatives around the globe as they improve urban planning and sustainability through data science approaches.

We present a formal measure-theoretical theory of neural networks (NN) built on probability coupling theory. Our main contributions are summarized as follows. * Built on the formalism of probability coupling theory, we derive an algorithm framework, named Hierarchical Measure Group and Approximate System (HMGAS), nicknamed S-System, that is designed to learn the complex hierarchical, statistical dependency in the physical world. * We show that NNs are special cases of S-System when the probability kernels assume certain exponential family distributions. Activation Functions are derived formally. We further endow geometry on NNs through information geometry, show that intermediate feature spaces of NNs are stochastic manifolds, and prove that "distance" between samples is contracted as layers stack up. * S-System shows NNs are inherently stochastic, and under a set of realistic boundedness and diversity conditions, it enables us to prove that for large size nonlinear deep NNs with a class of losses, including the hinge loss, all local minima are global minima with zero loss errors, and regions around the minima are flat basins where all eigenvalues of Hessians are concentrated around zero, using tools and ideas from mean field theory, random matrix theory, and nonlinear operator equations. * S-System, the information-geometry structure and the optimization behaviors combined completes the analog between Renormalization Group (RG) and NNs. It shows that a NN is a complex adaptive system that estimates the statistic dependency of microscopic object, e.g., pixels, in multiple scales. Unlike clear-cut physical quantity produced by RG in physics, e.g., temperature, NNs renormalize/recompose manifolds emerging through learning/optimization that divide the sample space into highly semantically meaningful groups that are dictated by supervised labels (in supervised NNs).