From anywhere and with just a mobile phone, anyone can become an air traffic controller, or at least a virtual air traffic controller. One can follow the world traffic flow of airplanes live and find out where an aircraft is coming from and where it is headed. One just has to take advantage of the millions of pieces of data that fly across the Internet. This is the magic power of Big Data. Artificial intelligence then enters the picture to find patterns and give meaning to the massive and heterogeneous information stream.

Digital banking offerings might not be new in countries like the U.S. and U.K., but it's still an emerging offering in some. Recent partnerships and product launches are bringing digital capabilities to financial institutions (FIs) and consumers in countries where access to financial tools was previously limited to brick-and-mortar branches. And, in regions where digital tools have long been available, new innovations are providing more intelligent financial insights than ever before. In the June edition of the Digital Banking Tracker, PYMNTS explores the latest digital developments in the banking world -- and the roadblocks standing in the way of widespread tech adoption. Digital banking capabilities are currently making their big debut in markets that had yet to tap in to the potential of mobile and online finance management solutions.

About Jen Booton Jen is a senior writer at SportTechie covering the many ways technology is disrupting sports. On any given day she may cover a wide variety of stories ranging from the newest virtual reality training tools for the NFL, the rise of eSports leagues and the infiltration of drones in extreme sports. Prior to joining SportTechie, Jen was a technology reporter at MarketWatch, where she covered major Silicon Valley companies, such as Apple, Amazon, Google and Facebook. Jen is a licensed skydiver who jumps out of planes, helicopters and hot air balloons for fun in her spare time. She's a former NCAA cross country athlete and currently lives in Hoboken, New Jersey.

The Pistoia Alliance has released results of a survey that found that 72 per cent of science professionals believe their sector is lagging behind other industries in the development of AI. To accelerate the successful use of AI, The Pistoia Alliance has launched its Centre of Excellence for AI in Life Sciences, aiming to encourage greater collaboration between stakeholders to bridge the gap between technology and science. The aim of the centre is to bring together best practice, adoption strategy, events, and hackathons covering a range of challenges. The survey found adoption of AI is high, with 69 per cent of companies using AI, machine learning, deep learning, and chatbots; an increase from when the same question was asked in September 2017, where 44 per cent of respondents were using or experimenting with AI. This survey shows interest in AI remains strong, but there is still a challenge with moving past the hype to a reality where AI is delivering insights with the power to truly augment researchers' work," commented Dr Steve Arlington, president of The Pistoia Alliance. "It is significant that a majority of people in our own industry believe we are trailing other sectors in the use of AI, and we must address this issue by working closely with each other and with stakeholders in other sectors.

Long-lead forecasting for spatio-temporal problems can often entail complex nonlinear dynamics that are difficult to specify it a priori. Current statistical methodologies for modeling these processes are often overparameterized and thus, struggle from a computational perspective. One potential parsimonious solution to this problem is a method from the dynamical systems and engineering literature referred to as an echo state network (ESN). ESN models use so-called reservoir computing to efficiently estimate a dynamical neural network forecast, model referred to as a recurrent neural network (RNN). Moreover, so-called deep models have recently been shown to be successful at predicting high-dimensional complex nonlinear processes. These same traits can be used to characterize many spatio-temporal processes. Here we introduce a deep ensemble ESN (D-EESN) model. Through the use of an ensemble framework, this model is able to generate forecasts that are accompanied by uncertainty estimates. After introducing the D-EESN, we then develop a hierarchical Bayesian implementation. We use a general hierarchical Bayesian framework that accommodates non-Gaussian data types and multiple levels of uncertainties. The proposed methodology is first applied to a data set simulated from a novel non-Gaussian multiscale Lorenz-96 dynamical system simulation model and then to a long-lead United States (U.S.) soil moisture forecasting application.

We define a new class of Bayesian point estimators, which we refer to as risk-averse estimators. We then use this definition to formulate several axioms that we claim to be natural requirements for good inference procedures, and show that for two classes of estimation problems the axioms uniquely characterise an estimator. Namely, for estimation problems with a discrete hypothesis space, we show that the axioms lead to the MAP estimate, whereas for well-behaved, purely continuous estimation problems the axioms lead to the Wallace-Freeman estimate. Interestingly, this combined use of MAP and Wallace-Freeman estimation reflects the common practice in the Minimum Message Length (MML) community, but there these two estimators are used as approximations for the information-theoretic Strict MML estimator, whereas we derive them exactly, not as approximations, and do so with no use of encoding or information theory. Keywords: Bayes estimation, risk-averse, inference, axiomatic approach, MML, Wallace-Freeman, invariance 1. Introduction One of the fundamental statistical problems is point estimation. In a Bayesian setting, this can be described as follows. Let (x,θ) X Θ be a pair of random variables with a known joint distribution that assigns positive probability / probability density to any (x,θ) X Θ.

We introduce a new family of margin-based regret guarantees for adversarial contextual bandit learning. Our results are based on multiclass surrogate losses. Using the ramp loss, we derive a universal margin-based regret bound in terms of the sequential metric entropy for a benchmark class of real-valued regression functions. The new margin bound serves as a complete contextual bandit analogue of the classical margin bound from statistical learning. The result applies to large nonparametric classes, improving on the best known results for Lipschitz contextual bandits (Cesa-Bianchi et al., 2017) and, as a special case, generalizes the dimension-independent Banditron regret bound (Kakade et al., 2008) to arbitrary linear classes with smooth norms. On the algorithmic side, we use the hinge loss to derive an efficient algorithm with a $\sqrt{dT}$-type mistake bound against benchmark policies induced by $d$-dimensional regression functions. This provides the first hinge loss-based solution to the open problem of Abernethy and Rakhlin (2009). With an additional i.i.d. assumption we give a simple oracle-efficient algorithm whose regret matches our generic metric entropy-based bound for sufficiently complex nonparametric classes. Under realizability assumptions our results also yield classical regret bounds.

Studying the invertibility of deep neural networks (DNNs) provides a principled approach to better understand the behavior of these powerful models. Despite being a promising diagnostic tool, a consistent theory on their invertibility is still lacking. We derive a theoretically motivated approach to explore the preimages of ReLU-layers and mechanisms affecting the stability of the inverse. Using the developed theory, we numerically show how this approach uncovers characteristic properties of the network.

We study a stylized dynamic assortment planning problem during a selling season of finite length $T$, by considering a nested multinomial logit model with $M$ nests and $N$ items per nest. Our policy simultaneously learns customers' choice behavior and makes dynamic decisions on assortments based on the current knowledge. It achieves the regret at the order of $\tilde{O}(\sqrt{MNT}+MN^2)$, where $M$ is the number of nests and $N$ is the number of products in each nest. We further provide a lower bound result of $\Omega(\sqrt{MT})$, which shows the optimality of the upper bound when $T>M$ and $N$ is small. However, the $N^2$ term in the upper bound is not ideal for applications where $N$ is large as compared to $T$. To address this issue, we further generalize our first policy by introducing a discretization technique, which leads to a regret of $\tilde{O}(\sqrt{M}T^{2/3}+MNT^{1/3})$ with a specific choice of discretization granularity. It improves the previous regret bound whenever $N>T^{1/3}$. We provide numerical results to demonstrate the empirical performance of both proposed policies.

Isotonic regression is a standard problem in shape-constrained estimation where the goal is to estimate an unknown nondecreasing regression function $f$ from independent pairs $(x_i, y_i)$ where $\mathbb{E}[y_i]=f(x_i), i=1, \ldots n$. While this problem is well understood both statistically and computationally, much less is known about its uncoupled counterpart where one is given only the unordered sets $\{x_1, \ldots, x_n\}$ and $\{y_1, \ldots, y_n\}$. In this work, we leverage tools from optimal transport theory to derive minimax rates under weak moments conditions on $y_i$ and to give an efficient algorithm achieving optimal rates. Both upper and lower bounds employ moment-matching arguments that are also pertinent to learning mixtures of distributions and deconvolution.