In this Oct. 31, 2018, photo, a screen displays a computer-generated image of a Watrix employee walking during a demonstration of their firm's gait recognition software at their company's offices in Beijing. A Chinese technology startup hopes to begin selling software that recognizes people by their body shape and how they walk, enabling identification when faces are hidden from cameras. Already used by police on the streets of Beijing and Shanghai, "gait recognition" is part of a major push to develop artificial-intelligence and data-driven surveillance across China, raising concern about how far the technology will go. As many businesses prepare for the coming year, one of the key priorities is determining best use case and strategic implementation of artificial intelligence as it applies to the core competencies of the company. This is a fairly challenging area on a variety of levels. But as this work occurs, one of the most important narratives in the arena is also further coming to light.

To illustrate how artificial intelligence (AI) could affect the future battlefield, consider the following scenario based on a future book I am writing entitled The Senkaku Paradox: Risking Great Power War over Limited Stakes. The scenario, imagined to occur sometime between now and 2040, begins with a hypothesized Russian "green men" attack against a small farming village in eastern Estonia or Latvia. Russia's presumed motive would be to sow discord and dissent within NATO, weakening the alliance. Estonia and Latvia are NATO member states, and thus the United States is sworn to defend them. But in the event of such a Russian aggression, a huge, direct NATO response may or may not be wise.

ICICI Bank customers will now to able to put money lying idle in savings bank accounts to better use with the help of an investment advisory platform powered by software robotics and AI (artificial intelligence). The platform, dubbed Money Coach, is the latest in a long line of products and features that India's largest private sector lender has been rolling out over the years as part of its digital banking initiative. "Money Coach will manage the entire investment journey of a customer from building an investible corpus to investing in recommended portfolios," said the bank's chief technology and digital officer Madhivanan Balakrishnan told TechCircle during a media interaction recently. The launch of the platform also marked two decades of digital banking for the lender. It claims that it was the first to launch internet banking in 1998. It introduced its mobile banking application, iMobile, in 2008 and a digital wallet in 2015.

To help news industry use Artificial Intelligence (AI) in more innovative ways, Google has announced a partnership with Polis, the international journalism think-tank at London School of Economics and Political Science, to create "Journalism AI". Part of the Google News Initiative (GNI), the "Journalism AI" project will focus on research and training for newsrooms on the intersection of AI and journalism. "As part of'Journalism AI', next year, we'll publish a global survey about how the media is currently using -- and could further benefit from -- this technology," Google said in a statement on Friday as it organised GNI Innovation Forum here. "We'll also collaborate with newsrooms and academic institutions to create a best practices handbook and produce free online training on how to use AI in the newsroom for journalists worldwide," informed Matt Cooke, Head of Partnerships and Training, Google News Lab. After testing with partners over the last two years, Google also introduced a new tool called Google Earth Studio which is an animation tool for Google Earth's satellite and 3D imagery.

Google has collaborated with Polis, the international journalism think-tank at London School of Economics and Political Science, to create "Journalism AI". This new AI technology wants to help journalists fight fake news. Journalism AI" project will focus on research and training for newsrooms. To help news industry use Artificial Intelligence (AI) in more innovative ways. Part of the Google News Initiative (GNI), the "Journalism AI" project will focus on research and training for newsrooms on the intersection of AI and journalism.

The complex and computationally expensive features of the forward landscape and sedimentary basin evolution models pose a major challenge in the development of efficient inference and optimization methods. Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters. In our previous work, parallel tempering Bayeslands was developed as a framework for parameter estimation and uncertainty quantification for the landscape and basin evolution modelling software Badlands. Parallel tempering Bayeslands features high-performance computing with dozens of processing cores running in parallel to enhance computational efficiency. Although parallel computing is used, the procedure remains computationally challenging since thousands of samples need to be drawn and evaluated. In large-scale landscape and basin evolution problems, a single model evaluation can take from several minutes to hours, and in certain cases, even days. Surrogate-assisted optimization has been with successfully applied to a number of engineering problems. This motivates its use in optimisation and inference methods suited for complex models in geology and geophysics. Surrogates can speed up parallel tempering Bayeslands by developing computationally inexpensive surrogates to mimic expensive models. In this paper, we present an application of surrogate-assisted parallel tempering where that surrogate mimics a landscape evolution model including erosion, sediment transport and deposition, by estimating the likelihood function that is given by the model. We employ a machine learning model as a surrogate that learns from the samples generated by the parallel tempering algorithm. The results show that the methodology is effective in lowering the overall computational cost significantly while retaining the quality of solutions.

In this paper, we propose a new method of Bayesian measurement for spectral deconvolution, which regresses spectral data into the sum of unimodal basis function such as Gaussian or Lorentzian functions. Bayesian measurement is a framework for considering not only the target physical model but also the measurement model as a probabilistic model, and enables us to estimate the parameter of a physical model with its confidence interval through a Bayesian posterior distribution given a measurement data set. The measurement with Poisson noise is one of the most effective system to apply our proposed method. Since the measurement time is strongly related to the signal-to-noise ratio for the Poisson noise model, Bayesian measurement with Poisson noise model enables us to clarify the relationship between the measurement time and the limit of estimation. In this study, we establish the probabilistic model with Poisson noise for spectral deconvolution. Bayesian measurement enables us to perform virtual and computer simulation for a certain measurement through the established probabilistic model. This property is called "Virtual Measurement Analytics(VMA)" in this paper. We also show that the relationship between the measurement time and the limit of estimation can be extracted by using the proposed method in a simulation of synthetic data and real data for XPS measurement of MoS$_2$.

Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio. In this chapter, we present neural network architectures that are able to learn efficient representations of molecules and materials. In particular, the continuous-filter convolutional network SchNet accurately predicts chemical properties across compositional and configurational space on a variety of datasets. Beyond that, we analyze the obtained representations to find evidence that their spatial and chemical properties agree with chemical intuition.

The P300 Brain-Computer Interface (BCI) is a well-established communication channel for severely disabled people. The P300 event-related potential is mostly characterized by its amplitude or its area, which correlate with the spelling accuracy of the P300 speller. Here, we introduce a novel approach for estimating the efficiency of this BCI by considering the P300 signal-to-noise ratio (SNR), a parameter that estimates the spatial and temporal noise levels and has a significantly stronger correlation with spelling accuracy. Furthermore, we suggest a Gaussian noise model, which utilizes the P300 event-related potential SNR to predict spelling accuracy under various conditions for LDA-based classification. We demonstrate the utility of this analysis using real data and discuss its potential applications, such as speeding up the process of electrode selection.

Using the outputs obtained from the online testing, it is not so difficult to collect a large-scale of learning data. We may be able to actively tackle the collected data to find the optimal strategies for better learning methods. It is also important to analyze the data theoretically (see [23]). This paper is aimed at obtaining effective learning strategies for students at risk for failing courses and/or dropping out, using a large-scale of learning data collected from the online testings. In this paper, unlike the conventional methods using the correct answer rate (CAR) to identify the ability of a student (e.g., see [13]), we use the ability obtained from the item response theory (IRT, e.g., see [1], [4], [17]), and we show a new method to identify students at risk as early as possible using the IRT results.