Minesweeper is a popular spatial-based decision-making game that works with incomplete information. As an exemplary NP-complete problem, it is a major area of research employing various artificial intelligence paradigms. The present work models this game as Constraint Satisfaction Problem (CSP) and Markov Decision Process (MDP). We propose a new method named as dependents from the independent set using deterministic solution search (DSScsp) for the faster enumeration of all solutions of a CSP based Minesweeper game and improve the results by introducing heuristics. Using MDP, we implement machine learning methods on these heuristics. We train the classification model on sparse data with results from CSP formulation. We also propose a new rewarding method for applying a modified deep Q-learning for better accuracy and versatile learning in the Minesweeper game. The overall results have been analyzed for different kinds of Minesweeper games and their accuracies have been recorded. Results from these experiments show that the proposed method of MDP based classification model and deep Q-learning overall is the best methods in terms of accuracy for games with given mine densities.

"Trust is a must," she said. "The EU is spearheading the development of new global norms to make sure AI can be trusted. By setting the standards, we can pave the way to ethical technology worldwide." Any fast-moving technology is likely to create mistrust, but Vestager and her colleagues decreed that those in power should do more to tame AI, partly by using such systems more responsibly and being clearer about how these work. The landmark legislation – designed to "guarantee the safety and fundamental rights of people and businesses, while strengthening AI uptake, investment and innovation" – encourages firms to embrace so-called explainable AI.

The Navy is very much on board for integrating artificial intelligence (AI) and machine learning into its networks, but human decision makers must always be part of the decision process in warfighting, an admiral said. "From a warfighting perspective, artificial intelligence subsets would be enablers or augments to the human in the loop," said Rear Adm. Paul Spedero Jr., director, Fleet Integrated Readiness and Analysis, U.S. Fleet Forces Command, speaking April 8 during a Navy League webinar sponsored by Deloitte. "That has always been our approach. I don't see that changing. There are some things that can't be replaced; the experience of a seasoned warfighter in the field being able to assess things that a machine -- no matter how much we teach it -- may never be able to pick up on. There's always going to be a necessity for [experience-based decision making]. That necessity for war fighting will never go away -- to have a human in the loop. "AI will be our wingmen," he said. "It will not be the lead in a fight." Spedero said in the world of data analysis, his current focus, there "certainly is a place for AI, particularly machine learning, as we try to get to that predictive and prescriptive level of data analytics.

AI may benefit or represent a threat to humanity in many ways in numerous fields such as education, environment, health, defense, transportation, space exploration, and so on. To avoid potential drifts of AI and benefit as much as possible from its advantages, AI must be controlled by normative frameworks. Yet, setting legal norms is a difficult and time-consuming process. Therefore, ethics is seen as a convenient and acceptable alternative to laws, since conversely to laws, it is flexible, easily and quickly adjustable, and less constraining than formal rules. The number of Ethics codes that have been issued around the world demonstrates the need to regulate AI while avoiding formal legal constraints.

The role of digital identities is absolutely pervasive and underpins many of the aspects of enterprise digital transformation. In IDC's 2019 Chief Information Officer (CIO) survey, 83% of CIOs said digital trust would be a top priority over the next five years. In addition, 64% of CIOs in Europe indicated that they have already begun implementing a trust platform. Most of the Gulf Cooperation Council (GCC) countries and their neighbors have active digital identity projects within the public sector, which lay the foundation for End to End (E2E) digital identity platforms. Advanced projects like the Emirates Identity Authority (EIDA) and UAE PASS initiatives in the United Arab Emirates -- with the latter serving as the national digital identity and digital signature solution for the country -- along with the National Profile project in Saudi Arabia, are the starting point for identity- and citizen-centric government digital transformation.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Israel shared three cell phone numbers used by Qasem Soleimani with U.S. intelligence in the hours before American drones unleashed Hellfire missiles on the Iranian general last year, Yahoo News reported Saturday. The revelation sheds new light on the role that Israel played in the killing of Soleimani, who the State Department says was responsible for hundreds of U.S. troop deaths as the head of the Revolutionary Guard's elite Quds Force. The drone strike occurred shortly after midnight on Jan. 2, 2020, as Soleimani and his entourage were leaving Baghdad's international airport.

A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound). We propose a Bayesian kernelised correlation test of (in)dependence using a Dirichlet process model. The new measure of (in)dependence allows us to answer some fundamental questions: Based on data, are (mixed-type) variables independent? How likely is dependence/independence to hold? How high is the probability that two mixed-type variables are more than just weakly dependent? We theoretically show the properties of the approach, as well as algorithms for fast computation with it. We empirically demonstrate the effectiveness of the proposed method by analysing its performance and by comparing it with other frequentist and Bayesian approaches on a range of datasets and tasks with mixed-type variables.

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.

AI is defined as software that is developed with one or more specified techniques and approaches (including machine learning and deep learning) that can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations or decisions influencing the environments they interact with.

Work as a data scientist follows a cycle: log in, clean data, define features, test and build a model, and make sure the model is running smoothly. Sounds straightforward enough, except not all parts of the cycle are created equal: data preparation takes 80% of any given data scientist's time. No matter what project you're working on, most days you're cleaning data and converting raw data into features that machine learning models can understand. The monotonous hole of data prep blends hours together and makes each day of work feel identical to the one before it. Why can't you do this tedious process more effectively?