The use of artificial intelligence (AI) technology at banks will be the difference between success and failure for them in the coming years, according to around three-quarters of bankers. According to a survey by the Economist Intelligence Unit (EIU), for Temenos, only cyber security will be a bigger primary focus for technology investment than AI in the next few years. A total of 35% of executives said cyber security is their primary technology investment focus, compared with 33% prioritising AI platforms. Banks recognise the importance of investing in technology to improve customer services, with AI's potential to personalise customer experience seen as an attractive prospect. Some 77% of respondents said AI will separate the winners and the losers.

Synechron Inc., a leading digital enterprise transformation consulting and technology services provider in global financial services, has announced a new partnership with public cloud provider, Google Cloud. Synechron will work with Google Cloud to further build its established global Centre of Excellence (COE) and to expand its existing cloud offering to Synechron's leading financial services clients, in an effort to move towards enterprise-scale architecture modernisation and cloud migration. In their new partnership, Synechron will provide cloud-based solution architecture and operating models to support both the migration of legacy processes, as well as establishing the cloud for new business activities for its clients. Synechron's Google Cloud Centre of Excellence comprises a global group of Google Cloud accredited architects, data engineers and developers who have designed and developed enterprise-grade, cloud-hosted solutions. Mutual clients will be able to create an application landing zone with confidence for their ecosystems with Synechron's cloud migration expertise and managed services framework, increasing operational efficiency and delivering cost leverage.

According to McKinsey's study "Visualizing the uses and potential impact of AI and other analytics", 2018, the estimated impact of artificial intelligence and other analytics on all industries regarding anomaly detection is between $1.0T and $1.4T. Anomaly detection is the critical success factor in predictive maintenance, which tries to anticipate when maintenance is required. This differs from the classical preventive approach, in which activities are planned on a regularly scheduled basis, or condition-based maintenance activities, in which assets are monitored through IoT sensors. Applying anomaly detection algorithms based on machine learning, it's possible to perform prognostics to estimate the condition of a system or a component and its remaining useful life (RUL), in order to predict an incoming failure. One of the most famous algorithms is the MSET-SPRT, well-described with a use case in this blog post: "Machine Learning Use Case: Real-Time Support for Engineered Systems."

Numerical methods such as finite element have been flourishing in the past decades for modeling solid mechanics problems via solving governing partial differential equations (PDEs). A salient aspect that distinguishes these numerical methods is how they approximate the physical fields of interest. Physics-informed deep learning is a novel approach recently developed for modeling PDE solutions and shows promise to solve computational mechanics problems without using any labeled data. The philosophy behind it is to approximate the quantity of interest (e.g., PDE solution variables) by a deep neural network (DNN) and embed the physical law to regularize the network. To this end, training the network is equivalent to minimization of a well-designed loss function that contains the PDE residuals and initial/boundary conditions (I/BCs). In this paper, we present a physics-informed neural network (PINN) with mixed-variable output to model elastodynamics problems without resort to labeled data, in which the I/BCs are hardly imposed. In particular, both the displacement and stress components are taken as the DNN output, inspired by the hybrid finite element analysis, which largely improves the accuracy and trainability of the network. Since the conventional PINN framework augments all the residual loss components in a "soft" manner with Lagrange multipliers, the weakly imposed I/BCs cannot not be well satisfied especially when complex I/BCs are present. To overcome this issue, a composite scheme of DNNs is established based on multiple single DNNs such that the I/BCs can be satisfied forcibly in a "hard" manner. The propose PINN framework is demonstrated on several numerical elasticity examples with different I/BCs, including both static and dynamic problems as well as wave propagation in truncated domains. Results show the promise of PINN in the context of computational mechanics applications.

We create a computationally tractable algorithm for contextual bandits with continuous actions having unknown structure. Our reduction-style algorithm composes with most supervised learning representations. We prove that it works in a general sense and verify the new functionality with large-scale experiments.

We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule learning algorithm which takes the form of a short and simple list of rules. State-of-the-art learning algorithms are often referred to as ''black boxes'' because of the high number of operations involved in their prediction process. Despite their powerful predictivity, this lack of interpretability may be highly restrictive for applications with critical decisions at stake. On the other hand, algorithms with a simple structure-typically decision trees, rule algorithms, or sparse linear models-are well known for their instability. This undesirable feature makes the conclusions of the data analysis unreliable and turns out to be a strong operational limitation. This motivates the design of SIRUS, which combines a simple structure with a remarkable stable behavior when data is perturbed. The algorithm is based on random forests, the predictive accuracy of which is preserved. We demonstrate the efficiency of the method both empirically (through experiments) and theoretically (with the proof of its asymptotic stability). Our R/C++ software implementation sirus is available from CRAN.

Distributed optimization is vital in solving large-scale machine learning problems. A widely-shared feature of distributed optimization techniques is the requirement that all nodes complete their assigned tasks in each computational epoch before the system can proceed to the next epoch. In such settings, slow nodes, called stragglers, can greatly slow progress. To mitigate the impact of stragglers, we propose an online distributed optimization method called Anytime Minibatch. In this approach, all nodes are given a fixed time to compute the gradients of as many data samples as possible. The result is a variable per-node minibatch size. Workers then get a fixed communication time to average their minibatch gradients via several rounds of consensus, which are then used to update primal variables via dual averaging. Anytime Minibatch prevents stragglers from holding up the system without wasting the work that stragglers can complete. We present a convergence analysis and analyze the wall time performance. Our numerical results show that our approach is up to 1.5 times faster in Amazon EC2 and it is up to five times faster when there is greater variability in compute node performance.

We present a deterministic method to compute the Gaussian average of neural networks used in regression and classification. Our method is based on an equivalence between training with a particular regularized loss, and the expected values of Gaussian averages. We use this equivalence to certify models which perform well on clean data but are not robust to adversarial perturbations. In terms of certified accuracy and adversarial robustness, our method is comparable to known stochastic methods such as randomized smoothing, but requires only a single model evaluation during inference.

Evaluating treatment effect heterogeneity widely informs treatment decision making. At the moment, much emphasis is placed on the estimation of the conditional average treatment effect via flexible machine learning algorithms. While these methods enjoy some theoretical appeal in terms of consistency and convergence rates, they generally perform poorly in terms of uncertainty quantification. This is troubling since assessing risk is crucial for reliable decision-making in sensitive and uncertain environments. In this work, we propose a conformal inference-based approach that can produce reliable interval estimates for counterfactuals and individual treatment effects under the potential outcome framework. For completely randomized or stratified randomized experiments with perfect compliance, the intervals have guaranteed average coverage in finite samples regardless of the unknown data generating mechanism. For randomized experiments with ignorable compliance and general observational studies obeying the strong ignorability assumption, the intervals satisfy a doubly robust property which states the following: the average coverage is approximately controlled if either the propensity score or the conditional quantiles of potential outcomes can be estimated accurately. Numerical studies on both synthetic and real datasets empirically demonstrate that existing methods suffer from a significant coverage deficit even in simple models. In contrast, our methods achieve the desired coverage with reasonably short intervals.

We derive the closed-form expression of the maximum mutual information - the maximum value of $I(X;Z)$ obtainable via training - for a broad family of neural network architectures. The quantity is essential to several branches of machine learning theory and practice. Quantitatively, we show that the maximum mutual information for these families all stem from generalizations of a single catch-all formula. Qualitatively, we show that the maximum mutual information of an architecture is most strongly influenced by the width of the smallest layer of the network - the "information bottleneck" in a different sense of the phrase, and by any statistical invariances captured by the architecture.