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Artificial intelligence

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The rise of Artificial Intelligence applications is accelerating the pace and magnitude of the political, securitarian, and ethical challenges we are now struggling to manage in cyberspace and beyond. So far, the relationship between Artificial Intelligence and cyberspace has been investigated mostly in terms of the effects that AI could have on the digital domain, and thus on our societies. What has been explored less is the opposite relationship, namely, how the cyberspace geopolitics can affect AI. Yet, AI applications have so far suffered from growing unrest, disorder, and lack of normative solutions in cyberspace. As such, from algorithm biases, to surveillance and offensive applications, AI could accelerate multiple growing threats and challenges in and through cyberspace. This report by ISPI and The Brookings Institution is an effort to shed light on this less studied, but extremely relevant, relationship.

Artificial intelligence.

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The second volume of the "Göttingen Colloquia on the Digitalisation of Civil Procedure" contains the written presentations of the speakers of the three Friday Colloquia held in winter term 2021-22. The first colloquium was dedicated to the prospects and limits of the use of artificial intelligence in civil procedure under the heading "Robo Judge." The conference proceedings contain the respective contribution written by Prof. Dr. Thomas Riehm. The second colloquium dealt with "Digitalisation in Mass Proceedings", with the contributions of Prof. Dr. Caroline Meller-Hannich, Dr. Annekathrin Schmoll, Anna Katharina Zitt and Dr. Christopher Unseld. The third colloquium dealt with the hitherto hardly discussed topic of "Digital Hearings of Guardians and Guardians-to-be." This conference volume contains the contributions by Prof. Dr. Anika Gomille, Prof. Dr. Christian Gomille and Bernhard Klasen. In addition, the conference proceedings trace the sometimes controversial and lively discussions in small conference reports.

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Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.

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"The International Journal of Machine Intelligence aims to publish all the latest and outstanding research articles, reviews and letters in all areas of machine intelligence. Current emphasis is on functional mechanism, architecture and evolution of computer networks, studied by experimental perturbation for the integration of applied computing with computer and information science."

Artificial intelligence