FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges. Peter Kairouz and H. Brendan McMahan conceived, coordinated, and edited this work.

Blockchain is gaining extensive attention due to its provision of secure and decentralized resource sharing manner. However, the incumbent blockchain systems also suffer from a number of challenges in operational maintenance, quality assurance of smart contracts and malicious behaviour detection of blockchain data. The recent advances in artificial intelligence bring the opportunities in overcoming the above challenges. The integration of blockchain with artificial intelligence can be beneficial to enhance current blockchain systems. This article presents an introduction of the convergence of blockchain and artificial intelligence (namely blockchain intelligence). This article also gives a case study to further demonstrate the feasibility of blockchain intelligence and point out the future directions.

The "Artificial Intelligence Platform Market" report contains data that has been carefully analyzed in the various models and factors that influence the industrial expansion of the Artificial Intelligence Platform market. An assessment of the impact of current market trends and conditions is also included to provide information on the future market expansion. The report contains comprehensive information on the global dynamics of Artificial Intelligence Platform, which provides a better prediction of the progress of the market and its main competitors [Microsoft, Google, IBM, Intel, Infosys, Wipro, Ayasdi, Salesforce, Qualcomm, Amazon Web Services, Absolutdata, SAP, HPE]. The report provides detailed information on the future impact of the various schemes adopted by governments in different sectors of the world market. The Artificial Intelligence Platform market report is crafted with figures, charts, tables, and facts to clarify, revealing the position of the specific sector at the regional and global level.

Serial cybersecurity entrepreneur Shlomo Kramer said in a 2005 interview that cybersecurity is "a bit like Alice in Wonderland" where you run as fast as you can only to stay in place. In 2020, to paraphrase the second part of the Red Queen's observation (actually from Through the Looking Glass), if you wish to stay ahead of cyber criminals, you must run twice--or ten times--as fast as that. The 141 predictions listed here reveal the state-of-mind of key participants in the cybersecurity defense industry and highlight all that's hot today. The future is murky, but we know for sure that on January 1, 2020, the California Consumer Privacy Act (CCPA) will go into effect; that the U.S. presidential election will take place on November 3, 2020; and that on October 1, 2020, if you "wish to fly on commercial aircrafts or access federal facilities" in the U.S., you must have a REAL ID compliant card. Other than these known events, the crystal balls of the participants in this survey warn us ...

--In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human decision maker to ever more automated systems however increases the need to properly handle the failure modes of deep learning modules. In this contribution, we review a set of techniques for the self-monitoring of machine-learning algorithms based on uncertainty quantification. In particular, we apply this to the task of semantic segmentation, where the machine learning algorithm decomposes an image according to semantic categories. We discuss false positive and false negative error modes at instance-level and review techniques for the detection of such errors that have been recently proposed by the authors. We also give an outlook on future research directions. The stunning success of deep learning technology, convolu-tional neural networks (CNN) in particular [1]-[3], has led to a rush towards technology development for new applications that ten years ago would have been considered unrealistic.

Here we present a holistic approach for data exploration on dense knowledge graphs as a novel approach with a proof-of-concept in biomedical research. Knowledge graphs are increasingly becoming a vital factor in knowledge mining and discovery as they connect data using technologies from the semantic web. In this paper we extend a basic knowledge graph extracted from biomedical literature by context data like named entities and relations obtained by text mining and other linked data sources like ontologies and databases. We will present an overview about this novel network. The aim of this work was to extend this current knowledge with approaches from graph theory. This method will build the foundation for quality control, validation of hypothesis, detection of missing data and time series analysis of biomedical knowledge in general. In this context we tried to apply multiple-valued decision diagrams to these questions. In addition this knowledge representation of linked data can be used as FAIR approach to answer semantic questions. This paper sheds new lights on dense and very large knowledge graphs and the importance of a graph-theoretic understanding of these networks.

Multi-agent reinforcement learning (MARL) has long been a significant and everlasting research topic in both machine learning and control. With the recent development of (single-agent) deep RL, there is a resurgence of interests in developing new MARL algorithms, especially those that are backed by theoretical analysis. In this paper, we review some recent advances a sub-area of this topic: decentralized MARL with networked agents. Specifically, multiple agents perform sequential decision-making in a common environment, without the coordination of any central controller. Instead, the agents are allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and smart grid. This review is built upon several our research endeavors in this direction, together with some progresses made by other researchers along the line. We hope this review to inspire the devotion of more research efforts to this exciting yet challenging area.

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted.

Our technical report provides an overview of the relevant parts of an ML lifecycle--selecting the right problem, the right data, and the right math and summarizing the model output for consumption--as well as questions that relate to those areas of focus. As the federally funded research and development center (FFRDC) known for AI engineering, and with its long experience in cybersecurity, the SEI has the expertise to advise you--the decision makers adopting these tools--on evaluating the adequacy of ML tools applied to cybersecurity. To that end, we structured the report around the questions you should ask about ML tools. We chose this framing, rather than proposing a detailed guide of how to build an ML system in cybersecurity, because we want to enable you to learn what a good tool looks like. When decision makers have difficulty identifying a good tool, the market will usually stop providing them.

The Department of Trade and Industry (DTI) sets the wheels in motion for the crafting of the country's artificial-intelligence sector road map as testament to the Philippine potential as an AI powerhouse in the Asean region. Trade Undersecretary Rafaelita M. Aldaba of the Competitiveness and Innovation Group led the formal signing of an agreement with distinguished data scientists, Dr. Christopher P. Monterola and Dr. Erika Fille T. Legara, for the formulation of an AI road map early this month. With the advent of the Fourth Industrial Revolution (4IR) where technology becomes more enmeshed with everyday life, AI advancement is seen as one of the key factors to help keep our country competitive. AI's importance is underscored as it is emerging to be a potential bright spot for our country with wide opportunities for growth for our competent work force. "The formulation of the AI road map is very important and timely. This effort provides the impetus that will move the country forward to keep up with the rapidly changing times," Aldaba emphasized.