The retail banking services are one of the pillars of the modern economic growth. However, the evolution of the client's habits in modern societies and the recent European regulations promoting more competition mean the retail banks will encounter serious challenges for the next few years, endangering their activities. They now face an impossible compromise: maximizing the satisfaction of their hyper-connected clients while avoiding any risk of default and being regulatory compliant. Therefore, advanced and novel research concepts are a serious game-changer to gain a competitive advantage. In this context, we investigate in this thesis different concepts bridging the gap between persistent homology, neural networks, recommender engines and reinforcement learning with the aim of improving the quality of the retail banking services. Our contribution is threefold. First, we highlight how to overcome insufficient financial data by generating artificial data using generative models and persistent homology. Then, we present how to perform accurate financial recommendations in multi-dimensions. Finally, we underline a reinforcement learning model-free approach to determine the optimal policy of money management based on the aggregated financial transactions of the clients. Our experimental data sets, extracted from well-known institutions where the privacy and the confidentiality of the clients were not put at risk, support our contributions. In this work, we provide the motivations of our retail banking research project, describe the theory employed to improve the financial services quality and evaluate quantitatively and qualitatively our methodologies for each of the proposed research scenarios.

Behavior initiation is a form of leadership and is an important aspect of social organization that affects the processes of group formation, dynamics, and decision-making in human societies and other social animal species. In this work, we formalize the "Coordination Initiator Inference Problem" and propose a simple yet powerful framework for extracting periods of coordinated activity and determining individuals who initiated this coordination, based solely on the activity of individuals within a group during those periods. The proposed approach, given arbitrary individual time series, automatically (1) identifies times of coordinated group activity, (2) determines the identities of initiators of those activities, and (3) classifies the likely mechanism by which the group coordination occurred, all of which are novel computational tasks. We demonstrate our framework on both simulated and real-world data: trajectories tracking of animals as well as stock market data. Our method is competitive with existing global leadership inference methods but provides the first approaches for local leadership and coordination mechanism classification. Our results are consistent with ground-truthed biological data and the framework finds many known events in financial data which are not otherwise reflected in the aggregate NASDAQ index. Our method is easily generalizable to any coordinated time-series data from interacting entities.

"It is now too late to stop a future collapse of our societies because of climate change." These are not the words of a tinfoil hat-donning survivalist. This is from a paper delivered by a senior sustainability academic at a leading business school to the European Commission in Brussels, earlier this year. Before that, he delivered a similar message to a UN conference: "Climate change is now a planetary emergency posing an existential threat to humanity." In the age of climate chaos, the collapse of civilization has moved from being a fringe, taboo issue to a more mainstream concern. As the world reels under each new outbreak of crisis--record heatwaves across the Western hemisphere, devastating fires across the Amazon rainforest, the slow-moving Hurricane Dorian, severe ice melting at the poles--the question of how bad things might get, and how soon, has become increasingly urgent. The fear of collapse is evident in the framing of movements such as'Extinction Rebellion' and in resounding warnings that business-as-usual means heading toward an uninhabitable planet. But a growing number of experts not only point at the looming possibility that human civilization itself is at risk; some believe that the science shows it is already too late to prevent collapse. The outcome of the debate on this is obviously critical: it throws light on whether and how societies should adjust to this uncertain landscape. Yet this is not just a scientific debate. It also raises difficult moral questions about what kind of action is warranted to prepare for, or attempt to avoid, the worst. Scientists may disagree about the timeline of collapse, but many argue that this is entirely beside the point. While scientists and politicians quibble over timelines and half measures, or how bad it'll all be, we are losing precious time.

Try Hyperloop, rocket travel, and robotic avatars. Hyperloop is currently working towards 670 mph (1080 kph) passenger pods, capable of zipping us from Los Angeles to downtown Las Vegas in under 30 minutes. Rocket Travel (think SpaceX's Starship) promises to deliver you almost anywhere on the planet in under an hour. Think New York to Shanghai in 39 minutes. As 5G connectivity, hyper-realistic virtual reality, and next-gen robotics continue their exponential progress, the emergence of "robotic avatars" will all but nullify the concept of distance, replacing human travel with immediate remote telepresence.

The Israelian historian Yuval Noah Harari has achieved international fame for having written a history of Homo Sapiens (humankind), a prophetic prediction of its end, and the beginning of new species called Homo Deus: an immortal cyborg with divine powers. The book that started it all is called: Sapiens – A Brief History of Humankind. In her article, Yuval Noah Harari: The age of the cyborg has begun – and the consequences cannot be known, Carole Cadwalladr asks Harari: In some ways, I say, it struck me that Sapiens isn't actually a history book – it's a philosophy book that asks the big, philosophical questions and attempts to answer them through history. I think that I see history as a philosophy laboratory. Philosophers come up with all these very interesting questions about the human condition, but the way that most of them – though not all – go about answering them is through thought experiments. When I discovered Harari, I came to think about Stephen Hawking s book: A Brief History of Time. In the book Hawking seems to want to surpass Nietzsche s declaration: God is Dead! In the introduction he presents a variety of philosophical questions, whereafter he says: Traditionally these are questions for philosophy; but philosophy is dead. Philosophy has not kept up with modern development in science, particular physics…[See my article: A Critique of Stephen Hawking].

The governments of most countries around the world are willing, if not necessarily eager, to aid in the development of advanced-level assisted driving. Yet, India is not'most countries'. In mid-2017, the country's transportation minister Nitin Gadkari said bluntly that his government "will not allow driverless cars in India." Why? "We are not going to promote any technology or policy that will render people jobless." This categorically states the government's worry.

The conflicts in Somalia have reached alarming levels, year after year many people are victimized by disputes of territory and dominance of spaces. The problem has reached intolerable levels in the international community. This report aims to inform intervention actions through insights that are placed as strategic tools for facing the presented problems. The work is part of Omdena's AI challenge in partnership with the UNHCR -- The UN Refugee Agency. The data is derived from a wide variety of local, regional and national sources and the information is collected by trained data experts around the world.

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

We live in a data society. Journalists are becoming data analysts and data curators, and computation is an essential tool for reporting. Data and computation reshape the way a reporter sees the world and composes a story. They also control the operation of the information ecosystem she sends her journalism into, influencing where it finds audiences and generates discussion. So every reporting beat is now a data beat, and computation is an essential tool for investigation. But digitization is affected by inequities, leaving gaps that often reflect the very disparities reporters seek to illustrate. Computation is creating new systems of power and inequality in the world. We rely on journalists, the "explainers of last resort"[1], to hold these new constellations of power to account. We report on computation, not just with computation. While a term with considerable history and mystery, artificial intelligence (AI) represents the most recent bundling of data and computation to optimize business decisions, automate tasks, and, from the point of view of a reporter, learn about the world. The relationship between a journalist and AI is not unlike the process of developing sources or cultivating fixers. As with human sources, artificial intelligences may be knowledgeable, but they are not free of subjectivetivity in their design -- they also need to be contextualized and qualified. Ethical questions of introducing AI in journalism abound. But since AI has once again captured the public imagination, it is hard to have a clear-eyed discussion about the issues involved with journalism's call to both report on and with these new computational tools. And so our article will alternate a discussion of issues facing the profession today with a "slant narrative" -- indicated because these sections are in italics. The slant narrative starts with the 1964 World's Fair and a partnership between IBM and The New York Times, winds through commentary by Joseph Weizenbaum, a famed figure in AI research in the 1960s, and ends in 1983 with the shuttering of one of the most ambitious information delivery systems of the time. The simplicity of the role of computation in the slant narrative will help us better understand our contemporary situation with AI. But we begin our article with context for the use of data and computation in journalism -- a short, and certainly incomplete, history before we settle into the rhythm of alternating narratives. Reporters depend on data, and through computation they make sense of that data. This reliance is not new. Joseph Pulitzer listed a series of topics that should be taught to aspiring journalists in his 1904 article "The College of Journalism."

The Global Artificial Intelligence in Marketing Market Outlook Report is a comprehensive study of the Artificial Intelligence in Marketing industry and its future prospects.. A comprehensive research report created through extensive primary research (inputs from industry experts, companies, stakeholders) and secondary research, the report aims to present the analysis of Artificial Intelligence in Marketing Market. Artificial Intelligence in Marketing market size will grow from USD 4.99 Billion in 2017 to USD 23.41 Billion by 2023, at an estimated CAGR of 29.38%. The base year considered for the study is 2017, and the market size is projected from 2018 to 2023. Growth in the adoption of customer-centric marketing strategies, increase in demand for virtual assistants, and increased use of social media for advertising are the major factors driving the demand for AI-based marketing and sales solutions.