Louis Columbus in 10 Ways AI Is Going To Improve Fintech In 2020 stated that "Bottom Line: AI & machine learning will improve Fintech in 2020 by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools." Since that time the Covid-19 crisis and tragedy arose. On the one hand Paul Clarke noted that UK fintech investment slumps by 40% amid Covid-19 crisis, whilst on the other Deloitte in Beyond COVID-19: New opportunities for Fintech companies note that "As the COVID-19 pandemic continues to create uncertainty, many fintechs are under stress on a number of fronts. But, as the broader economy shifts from "respond" to "recover", new opportunities may be created for some fintechs. A key question is how fintechs may leverage their unique assets and skills to seize new opportunities in the future. It could be an opportune time to think big and act boldly." Pavrita R considered the impact of Covid-19 and noted in 5 U.S. FinTech startups reimagining the healthcare industry notes that FinTech is undoubtedly shaping the face of the Health Care industry. "FinTech companies leverage powerful innovations blockchain, Artificial Intelligence, and Machine Learning to eliminate the inefficiencies and knowledge gaps endemic to most healthcare payment plans." The likes of Nigel Wilson (@nigewillson) and Brian Ahier (@ahier) have stressed the importance to apply AI to positive use cases such as preventative medicine and improved Health Care outcomes. McKinsey in an article entitled AI-bank of the future: Can banks meet the AI challenge? " The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually (Exhibit 1)." Source for image above: AI-bank of the future: Can banks meet the AI challenge? "While for many financial services firms, the use of AI is episodic and focused on specific use cases, an increasing number of banking leaders are taking a comprehensive approach to deploying advanced AI, and embedding it across the full lifecycle, from the front- to the back-office (Exhibit 2)" Source for image above: AI-bank of the future: Can banks meet the AI challenge? The Covid-19 crisis is a challenge both in terms of human health and also to the Fintech world.

Addis Ababa — The recently inaugurated Artificial Intelligence Center has signed Memorandums of Understanding with five institutions to cooperate …

The speech-to-text technology has made our lives easy. Now that there are a lot of use cases in our daily life for this technology too, we need it even more. It lets us save time and effort, and provides the required information in a matter of minutes. Tech giants like Google and Amazon are exploring and empowering this field of speech recognition technologies with the help of their Google Speech and Amazon Transcribe products. Amazon launched Alexa in 2014 and more than 100m of its Echo and Dot gadgets are available in homes around the world today. Alexa is considered to be the most Intelligent Of All DPAs.

The field of machine translation (MT), the automatic translation of written text from one natural language into another, has experienced a major paradigm shift in recent years. Statistical MT, which mainly relies on various count-based models and which used to dominate MT research for decades, has largely been superseded by neural machine translation (NMT), which tackles translation with a single neural network. In this work we will trace back the origins of modern NMT architectures to word and sentence embeddings and earlier examples of the encoder-decoder network family. We will conclude with a short survey of more recent trends in the field.

Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators. Inspired by the ability of these systems to express a rich set of outputs while keeping (gradients of) state variables bounded, we propose a novel architecture for recurrent neural networks. Our proposed RNN is based on a time-discretization of a system of second-order ordinary differential equations, modeling networks of controlled nonlinear oscillators. We prove precise bounds on the gradients of the hidden states, leading to the mitigation of the exploding and vanishing gradient problem for this RNN. Experiments show that the proposed RNN is comparable in performance to the state of the art on a variety of benchmarks, demonstrating the potential of this architecture to provide stable and accurate RNNs for processing complex sequential data.

A shallow semantical embedding for public announcement logic with relativized common knowledge is presented. This embedding enables the first-time automation of this logic with off-the-shelf theorem provers for classical higher-order logic. It is demonstrated (i) how meta-theoretical studies can be automated this way, and (ii) how non-trivial reasoning in the target logic (public announcement logic), required e.g. to obtain a convincing encoding and automation of the wise men puzzle, can be realized. Key to the presented semantical embedding -- in contrast, e.g., to related work on the semantical embedding of normal modal logics -- is that evaluation domains are modeled explicitly and treated as additional parameter in the encodings of the constituents of the embedded target logic, while they were previously implicitly shared between meta logic and target logic.

The advent of technology has brought many divergent views from all walks of life. Some are of the view that technology will replace human beings, others remain adamant – we will continue to be in charge and never be threatened by technology. Interestingly, there are those that are neutral. They see technology and human beings collaborating. There is no denying the fact that the Fourth Industrial Revolution (4IR) has transformed the workplace.

The purpose of this article is to review the achievements made in the last few years towards the understanding of the reasons behind the success and subtleties of neural network-based machine learning. In the tradition of good old applied mathematics, we will not only give attention to rigorous mathematical results, but also the insight we have gained from careful numerical experiments as well as the analysis of simplified models. Along the way, we also list the open problems which we believe to be the most important topics for further study. This is not a complete overview over this quickly moving field, but we hope to provide a perspective which may be helpful especially to new researchers in the area.

Combinatorial problems which have been proven to be NP-hard are faced in Higher Education Institutions and researches have extensively investigated some of the well-known combinatorial problems such as the timetabling and student project allocation problems. However, NP-hard problems faced in Higher Education Institutions are not only confined to these categories of combinatorial problems. The majority of NP-hard problems faced in institutions involve grouping students and/or resources, albeit with each problem having its own unique set of constraints. Thus, it can be argued that techniques to solve NP-hard problems in Higher Education Institutions can be transferred across the different problem categories. As no method is guaranteed to outperform all others in all problems, it is necessary to investigate heuristic techniques for solving lesser-known problems in order to guide stakeholders or software developers to the most appropriate algorithm for each unique class of NP-hard problems faced in Higher Education Institutions. To this end, this study described an optimization problem faced in a real university that involved grouping students for the presentation of semester results. Ordering based heuristics, genetic algorithm and the ant colony optimization algorithm implemented in Python programming language were used to find feasible solutions to this problem, with the ant colony optimization algorithm performing better or equal in 75% of the test instances and the genetic algorithm producing better or equal results in 38% of the test instances.

This book is devoted to the problem of sequential probability forecasting, that is, predicting the probabilities of the next outcome of a growing sequence of observations given the past. This problem is considered in a very general setting that unifies commonly used probabilistic and non-probabilistic settings, trying to make as few as possible assumptions on the mechanism generating the observations. A common form that arises in various formulations of this problem is that of mixture predictors, which are formed as a combination of a finite or infinite set of other predictors attempting to combine their predictive powers. The main subject of this book are such mixture predictors, and the main results demonstrate the universality of this method in a very general probabilistic setting, but also show some of its limitations. While the problems considered are motivated by practical applications, involving, for example, financial, biological or behavioural data, this motivation is left implicit and all the results exposed are theoretical. The book targets graduate students and researchers interested in the problem of sequential prediction, and, more generally, in theoretical analysis of problems in machine learning and non-parametric statistics, as well as mathematical and philosophical foundations of these fields. The material in this volume is presented in a way that presumes familiarity with basic concepts of probability and statistics, up to and including probability distributions over spaces of infinite sequences. Familiarity with the literature on learning or stochastic processes is not required.