In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as its relationship to current developments in neural-symbolic computing.

How have publishers evolved and what do they see ahead? Amid rising fears that artificial intelligence (AI) will threaten journalists' jobs and take over the newsroom, the Journalism AI report – a project by Polis in collaboration with Google News Initiative – sought to find out how exactly AI technologies are being applied to journalism. However, AI is a'significant part of journalism already but it is unevenly distributed' and news organizations are already applying aspects of intelligent technology in their operations, to help them work more efficiently and improve monetization. "One of the key aspects of AI and journalism is that it allows the whole journalism model to become more holistic, with a feedback loop between the different parts of the production and dissemination process" Artificial intelligence systems can be useful in helping newsrooms to categorize content or information at scale for different news gathering purposes. For example, since 2015 The Associated Press have been using a management tool, SAM, which algorithmically sifts through social media platforms to alert the newsroom on likely breaking news events.

Global Artificial Intelligence and Machine Learning Market 2020, presents a professional and in-depth study on the current state of the industry globally, providing basic overview of Artificial Intelligence and Machine Learning market including definitions, classifications, applications and industry chain structure. The report compares this data with the current state of the Artificial Intelligence and Machine Learning market and thus discuss upon the upcoming trends that have brought the Artificial Intelligence and Machine Learning market transformation. Industry predictions along with the statistical implication presented in the report delivers an accurate scenario of the Artificial Intelligence and Machine Learning market. The market forces determining the shaping of the worldwide Artificial Intelligence and Machine Learning market have been evaluated in detail. In addition to this, the supervisory outlook of the Artificial Intelligence and Machine Learning market has been covered in the report from both the Global and local perspective.

MARSEILLE, France--(BUSINESS WIRE)--Traxens, a company that provides high-value data and services for the supply chain industry, announces today that it is now part of the new European DataPorts project, aimed at creating a data platform for cognitive ports of the future. With a total budget of €6.7M ($7.3M), the three-year project will receive €5.7M ($6.2M) from the European Union. It is coordinated by the Technological Institute of Informatics (ITI) in Spain. Today, only three per cent of container terminals are automated. However, the future of the industry points towards smart ports as the best way to overcome the challenges and demands that arise in the sector.

Artificial intelligence (AI) is creating the fourth industrial revolution by shaping every single aspect in the world. AI is taking away jobs, and revolutionizing businesses only to create new human-driven creative opportunities. The new technology has influenced the way we drive cars, grow vegetables, detect cancer. It can even speak, and recognize emotions in speech. AI technology is disrupting right now all industries including digital marketing.

In this article, we study a Radio Resource Allocation (RRA) that was formulated as a non-convex optimization problem whose main aim is to maximize the spectral efficiency subject to satisfaction guarantees in multiservice wireless systems. This problem has already been previously investigated in the literature and efficient heuristics have been proposed. However, in order to assess the performance of Machine Learning (ML) algorithms when solving optimization problems in the context of RRA, we revisit that problem and propose a solution based on a Reinforcement Learning (RL) framework. Specifically, a distributed optimization method based on multi-agent deep RL is developed, where each agent makes its decisions to find a policy by interacting with the local environment, until reaching convergence. Thus, this article focuses on an application of RL and our main proposal consists in a new deep RL based approach to jointly deal with RRA, satisfaction guarantees and Quality of Service (QoS) constraints in multiservice celular networks. Lastly, through computational simulations we compare the state-of-art solutions of the literature with our proposal and we show a near optimal performance of the latter in terms of throughput and outage rate.

As Artificial Intelligence (AI) technology gets more intertwined with every system, people are using AI to make decisions on their everyday activities. In simple contexts, such as Netflix recommendations, or in more complex context like in judicial scenarios, AI is part of people's decisions. People make decisions and usually, they need to explain their decision to others or in some matter. It is particularly critical in contexts where human expertise is central to decision-making. In order to explain their decisions with AI support, people need to understand how AI is part of that decision. When considering the aspect of fairness, the role that AI has on a decision-making process becomes even more sensitive since it affects the fairness and the responsibility of those people making the ultimate decision. We have been exploring an evidence-based explanation design approach to 'tell the story of a decision'. In this position paper, we discuss our approach for AI systems using fairness sensitive cases in the literature.

The golden age for artificial intelligence may have just dawned, but the course is not without its challenges. A plethora of technology glitches seems to indicate that it is not quite there yet. Perhaps machines cannot be not perfect either. Although AI is meant to solve problems, as it turns out, it can create new ones as well. These accounts may alarm or amuse consumers but are very embarrassing for the companies involved.

This paper raises three questions regarding the attribution of beliefs, desires, and intentions to robots. The first one is whether humans in fact engage in robot mindreading. If they do, this raises a second question: does robot mindreading foster trust towards robots? Both of these questions are empirical, and I show that the available evidence is insufficient to answer them. Now, if we assume that the answer to both questions is affirmative, a third and more important question arises: should developers and engineers promote robot mindreading in view of their stated goal of enhancing transparency? My worry here is that by attempting to make robots more mind-readable, they are abandoning the project of understanding automatic decision processes. Features that enhance mind-readability are prone to make the factors that determine automatic decisions even more opaque than they already are. And current strategies to eliminate opacity do not enhance mind-readability. The last part of the paper discusses different ways to analyze this apparent trade-off and suggests that a possible solution must adopt tolerable degrees of opacity that depend on pragmatic factors connected to the level of trust required for the intended uses of the robot.