Finland, in the spirit of Norway and Denmark, introduced ranking system for academic publication channels (referring to scientific journals, conference series, book publishers etc.) called as Jufo (i.e. "Julkaisufoorumi" in Finnish, "Publication Forum" in English) in 2010, together with the renewed university legislation. The ranking of a publication channel, ranging from 0 (non-peer- reviewed) to 3 (most distinguished academic publication forums), is decided by a specially nominated panel of a particular scientific discipline. These panels decide the rankings based on their academic expertise in regular meetings. Because the rankings are directly linked to the allocated funding of the universities, there has been and is a lot of discussion about the fairness and objectivity of the ranks. A versatile analysis of the 2015 Jufo-rankings was done in [10]. There, by using association rule mining, decision trees, and confusion matrices with respect to Norwegian and Danish ranks, it was shown that most of the expert-based rankings could be predicted and explained with machine learning methods. Moreover, it was found out that those publication channels, for which the Finnish expert-based rank is higher than the estimated one, are characterized by higher publication activity or recent upgrade of the rank. Hence, the outcomes of the system, the publication ranks, need to be assessed and evaluated regularly and rigorously. 1

Companies regularly spend millions of dollars producing electronically-stored documents in legal matters. Recently, parties on both sides of the 'legal aisle' are accepting the use of machine learning techniques like text classification to cull massive volumes of data and to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs in legal matters, it also faces a peculiar perception challenge: amongst lawyers, this technology is sometimes looked upon as a "black box", little information provided for attorneys to understand why documents are classified as responsive. In recent years, a group of AI and ML researchers have been actively researching Explainable AI, in which actions or decisions are human understandable. In legal document review scenarios, a document can be identified as responsive, if one or more of its text snippets are deemed responsive. In these scenarios, if text classification can be used to locate these snippets, then attorneys could easily evaluate the model's classification decision. When deployed with defined and explainable results, text classification can drastically enhance overall quality and speed of the review process by reducing the review time. Moreover, explainable predictive coding provides lawyers with greater confidence in the results of that supervised learning task. This paper describes a framework for explainable text classification as a valuable tool in legal services: for enhancing the quality and efficiency of legal document review and for assisting in locating responsive snippets within responsive documents. This framework has been implemented in our legal analytics product, which has been used in hundreds of legal matters. We also report our experimental results using the data from an actual legal matter that used this type of document review.

BRUSSELS (Reuters) - U.S. chipmaker Nvidia (NVDA.O) is set to win unconditional EU antitrust approval for its $6.8 billion acquisition of Mellanox Technologies (MLNX.O), people familiar with the matter said on Wednesday. Nvidia, known for its powerful gaming graphics chips, is looking to boost its data center and artificial intelligence business via the takeover, its biggest deal, helping it to better compete with rival Intel (INTC.O). The European Commission, which is scheduled to decide on the deal by Dec. 19, declined to comment. Nvidia and Mellanox also declined to comment. U.S. authorities have already cleared the deal without conditions while approval is still pending in China where Mellanox has major customers such as Alibaba (BABA.N) and Baidu (BIDU.O) .

Some of the largest employers in the world are increasingly, and in some cases controversially, relying on AI-based technologies to hire new workers. Companies like Tesla, Accenture and LinkedIn are using technology from Pymetrics to better vet qualified candidates and reduce the time and resources required for what has traditionally been a labor-intensive hiring process. The company, which boasts more than 80 global clients, uses a blend of data science and I/O psychology to create its "people recommendation engine." The Pymetrics platform is designed to improve employee retention while also increasing efficiency and diversity throughout the recruiting process. The results are parsed by AI to generate measurements related to candidates' problem-solving skills, ability to multitask and even their levels of altruism.

"Artificial intelligence" (AI) has become one of the buzzwords of the decade, as a potentially important part of the answer to humanity's biggest challenges in everything from addressing climate change to fighting cancer and even halting the ageing process. It is widely seen as the most important technological development since the mass use of electricity, one that will usher in the next phase of human evolution. At the same time, some warnings that AI could lead to widespread unemployment, rising inequality, the development of surveillance dystopias, or even the end of humanity are worryingly convincing. States would, therefore, be well advised to actively guide AI's development and adoption into their societies. For Europe, 2019 was the year of AI strategy development, as a growing number of EU member states put together expert groups, organised public debates, and published strategies designed to grapple with the possible implications of AI. European countries have developed training programmes, allocated investment, and made plans for cooperation in the area. Next year is likely to be an important one for AI in Europe, as member states and the European Union will need to show that they can fulfil their promises by translating ideas into effective policies. But, while Europeans are doing a lot of work on the economic and societal consequences of the growing use of AI in various areas of life, they generally pay too little attention to one aspect of the issue: the use of AI in the military realm. Strikingly, the military implications of AI are absent from many European AI strategies, as governments and officials appear uncomfortable discussing the subject (with the exception of the debate on limiting "killer robots"). Similarly, the academic and expert discourse on AI in the military also tends to overlook Europe, predominantly focusing on developments in the US, China, and, to some extent, Russia. This is likely because most researchers consider Europe to be an unimportant player in the area.

As many of you may know, Deep Neural Networks are highly expressive machine learning networks that have been around for many decades. In 2012, with gains in computing power and improved tooling, a family of these machine learning models called ConvNets started achieving state of the art performance on visual recognition tasks. Up to this point, machine learning algorithms simply didn't work well enough for anyone to be surprised when it failed to do the right thing. In 2014, a group of researchers at Google and NYU found that it was far too easy to fool ConvNets with an imperceivable, but carefully constructed nudge in the input. Let's look at an example.

The Russian space agency Roscosmos is planning to install a nuclear-powered observatory on its future moon base to held spot deadly Earth-threatening asteroids. Establishing a permanent presence near the lunar south pole has been a priority for Roscosmos ever since NASA announced plans to return to the moon earlier this year. The base's telescopes will work in tandem with spacecraft placed in orbit around the Earth to help provide humanity with a space-rock early warning system. In addition, the lunar facility's permanent crew will be made up of robots -- with cosmonauts only visiting to handle more complicated tasks. The plans to establish an observatory on the future moon base were announced by Alexander Bloshenko, Roscosmos' Executive Director for Science and Long-Term Programs, Russian news outlets RT and TASS reported.

AI Machine Learning is being debated due to the "update problem" of adaptiveness. Humans typically learn new things on-the-fly. Let's use jigsaw puzzles to explore the learning process. Imagine that you are asked to solve a jigsaw puzzle and you've not previously had the time nor inclination to solve jigsaw puzzles (yes, there are some people that swear they will never do a jigsaw puzzle, as though it is beneath them or otherwise a useless use of their mind). Upon dumping out onto the table all the pieces from the box, you likely turn all the pieces right side up and do a quick visual scan of the pieces and the picture shown on the box of what you are trying to solve for.

This research was sponsored by the DoD Joint Artificial Intelligence Center (JAIC) and was conducted within the Acquisition and Technology Policy Center of the RAND National Defense Research Institute, a federally funded research and development center sponsored by the Office of the Secretary of Defense, the Joint Staff, the Unified Combatant Commands, the Navy, the Marine Corps, the defense agencies, and the defense Intelligence Community. This report is part of the RAND Corporation research report series. RAND reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND reports undergo rigorous peer review to ensure high standards for research quality and objectivity. Permission is given to duplicate this electronic document for personal use only, as long as it is unaltered and complete.

The cybersecurity provider landscape is cluttered with impossible claims, misrepresentations, and a confusing mix of inconsistent terminology. Worse, every minute you delay making a decision is another minute hackers have to gain access and knowledge about your network. With so much on the line, choosing what kind of platform and which company to trust with your company's data privacy can become a stressful decision. Leaning toward an AI-enabled platform is a step in the right direction, but which platforms actually do what they say they do? Luckily, you don't have to become an expert in AI cybersecurity to learn how to evaluate the efficacy of AI-enabled cybersecurity platforms.