The value of building data-driven businesses with AI at their core is well known today, and business executives are rushing to implement the technology into their operations and gain a competitive advantage, but it's not as simple as creating a data lake and crafting AI models. A large number of AI companies attempting to implement more AI models or build AI-first businesses have experienced challenges. A December 2018 PwC survey found that only 4% of businesses have successfully implemented AI. That's why today the World Economic Forum released the AI toolkit for Boards of Directors. The AI toolkit for Boards of Directors is being released ahead of the annual WEF meeting in Davos, Switzerland where the toolkit will be formally debuted next week.

Will the proliferation of AI and machine learning reinforce the worldwide digital divide? It's one of the questions the Global Talent Competitiveness Index (GTCI) and Global Cities Talent Competitiveness Index (GCTCI) seek to answer by benchmarking the ability of countries and cities to compete for talent. An answer has historically proven elusive, but the 7th annual reports published by Insead, Adecco Group, and Google suggest it might instead provide "significant" opportunities despite the fact that AI skills are "scarce" and "unequally distributed" across nations. "AI is changing many facets of business and society and, if properly used and governed, has potential to foster sustainable development," said Katell Le Goulven, executive director of the Insead Hoffmann Global Institute for Business and Society. "The GTCI report argues that with multi-stakeholder cooperation the technology could help achieve some of the SDGs [the United Nations' Sustainable Development Goals] such as those related to health (via personalized remote diagnosis and big data analysis to track and reduce endemic disease). But it also points to the imperative of closing the global digital skills gap to harness the potential of AI for good."

Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the financial sector to measure the market risk and manage the extreme market movement. The recent link between the quantile score function and the Asymmetric Laplace density has led to a flexible likelihood-based framework for joint modelling of VaR and ES. It is of high interest in financial applications to be able to capture the underlying joint dynamics of these two quantities. We address this problem by developing a hybrid model that is based on the Asymmetric Laplace quasi-likelihood and employs the Long Short-Term Memory (LSTM) time series modelling technique from Machine Learning to capture efficiently the underlying dynamics of VaR and ES. We refer to this model as LSTM-AL. We adopt the adaptive Markov chain Monte Carlo (MCMC) algorithm for Bayesian inference in the LSTM-AL model. Empirical results show that the proposed LSTM-AL model can improve the VaR and ES forecasting accuracy over a range of well-established competing models.

A common problem machine learning developers are faced with is overfitting, that is, fitting a pipeline too closely to the training data that the performance degrades for unseen data. Automated machine learning aims to free (or at least ease) the developer from the burden of pipeline creation, but this overfitting problem can persist. In fact, this can become more of a problem as we look to iteratively optimise the performance of an internal cross-validation (most often \textit{k}-fold). While this internal cross-validation hopes to reduce this overfitting, we show we can still risk overfitting to the particular folds used. In this work, we aim to remedy this problem by introducing dynamic fitness evaluations which approximate repeated \textit{k}-fold cross-validation, at little extra cost over single \textit{k}-fold, and far lower cost than typical repeated \textit{k}-fold. The results show that when time equated, the proposed fitness function results in significant improvement over the current state-of-the-art baseline method which uses an internal single \textit{k}-fold. Furthermore, the proposed extension is very simple to implement on top of existing evolutionary computation methods, and can provide essentially a free boost in generalisation/testing performance.

In this work, we formalize the problem of causal inference over graph-based relational time-series data where each node in the graph has one or more time-series associated to it. We propose causal inference models for this problem that leverage both the graph topology and time-series to accurately estimate local causal effects of nodes. Furthermore, the relational time-series causal inference models are able to estimate local effects for individual nodes by exploiting local node-centric temporal dependencies and topological/structural dependencies. We show that simpler causal models that do not consider the graph topology are recovered as special cases of the proposed relational time-series causal inference model. We describe the conditions under which the resulting estimate can be used to estimate a causal effect, and describe how the Durbin-Wu-Hausman test of specification can be used to test for the consistency of the proposed estimator from data. Empirically, we demonstrate the effectiveness of the causal inference models on both synthetic data with known ground-truth and a large-scale observational relational time-series data set collected from Wikipedia.

How to assess the potential of liking a city or a neighborhood before ever having been there. The concept of urban quality has until now pertained to global city ranking, where cities are evaluated under a grid of given parameters, or either to empirical and sociological approaches, often constrained by the amount of available information. Using state of the art machine learning techniques and thousands of geotagged satellite and perspective images from diverse urban cultures, this research characterizes personal preference in urban spaces and predicts a spectrum of unknown likeable places for a specific observer. Unlike most urban perception studies, our intention is not by any means to provide an objective measure of urban quality, but rather to portray personal views of the city or Cities of Indexes.

Bugs are prevalent in software development. To improve software quality, bugs are filed using a bug tracking system. Properties of a reported bug would consist of a headline, description, project, product, component that is affected by the bug and the severity of the bug. Duplicate bugs rate (% of duplicate bugs) are in the range from single digit (1 to 9%) to double digits (40%) based on the product maturity , size of the code and number of engineers working on the project. Duplicate bugs range are between 9% to 39% in some of the open source projects like Eclipse, Firefox etc. Detection of duplicity deals with identifying whether any two bugs convey the same meaning. This detection of duplicates helps in de-duplication. Detecting duplicate bugs help reduce triaging efforts and saves time for developers in fixing the issues. Traditional natural language processing techniques are less accurate in identifying similarity between sentences. Using the bug data present in a bug tracking system, various approaches were explored including several machine learning algorithms, to obtain a viable approach that can identify duplicate bugs, given a pair of sentences(i.e. the respective bug descriptions). This approach considers multiple sets of features viz. basic text statistical features, semantic features and contextual features. These features are extracted from the headline, description and component and are subsequently used to train a classification algorithm.

Towards a deep understanding of these languages in model theory, we establish model-theoretic characterizations for a number of existential rule languages such as (disjunctive) embedded dependencies, tuple-generating dependencies (TGDs), (frontier-)guarded TGDs and linear TGDs. All these characterizations hold for arbitrary structures, and most of them also work on the class of finite structures. As a natural application of these characterizations, complexity bounds for the rewritability of above languages are also identified. 1 Introduction Existential rule languages, a family of languages that extend Datalog by allowing existential quantifiers in the rule head, had been initially introduced in databases in 1970s to specify the semantics of data stored in a database [ Abiteboul et al., 1995] . Since then, existential rule languages such as tuple-generating dependencies (TGDs), embedded dependencies and equality-generating dependencies have been extensively studied. These language have been recently rediscovered as languages for data exchange [ Fagin et al., 2005 ], data integration [ Lenzerini, 2002 ] and ontology-mediated query answering [ Cal ı et al., 2010 ] .

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I am the CEO and Co-founder of Pricefx and I started the company because I saw a huge opportunity in the Pricing software industry. I had been in the software business for over 20 years already with 5 years in Pricing software. I knew that the industry had to change to meet the evolving needs of businesses. I wasn't only focused on what was important at that time, I knew I had to build something that would grow and change with the future. Existing pricing solutions at the time were expensive and only offered on-premise installations that did not meet the needs of a business needing to act fast – in fact, some of those first-generation solutions took years to implement.