Twenty-six experts on the security implications of emerging technologies have jointly authored a ground-breaking report--sounding the alarm about the potential malicious use of artificial intelligence (AI) by rogue states, criminals, and terrorists. Forecasting rapid growth in cyber-crime and the misuse of drones during the next decade--as well as an unprecedented rise in the use of'bots' to manipulate everything from elections to the news agenda and social media--the report is a clarion call for governments and corporations worldwide to address the clear and present danger inherent in the myriad applications of AI. However, the report--"The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation"--also recommends interventions to mitigate the threats posed by the malicious use of AI: The co-authors come from a wide range of organizations and disciplines, including Oxford University's Future of Humanity Institute; Cambridge University's Center for the Study of Existential Risk; OpenAI, a leading non-profit AI research company; the Electronic Frontier Foundation, an international non-profit digital rights group; the Center for a New American Security, a U.S.-based bipartisan national security think-tank; and other organizations. The 100-page report identifies three security domains (digital, physical, and political security) as particularly relevant to the malicious use of AI. It suggests that AI will disrupt the trade-off between scale and efficiency and allow large-scale, finely-targeted, and highly-efficient attacks.

Artificial intelligence (AI) that can tell pre-cancerous growths from harmless moles is great. AI that can clone human voices to counterfeit utterly convincing recordings, seeding doubt and misinformation is not so great. As a technology, AI is unprecedented, powerful and deeply pervasive: from voice recognition to self-driving cars to medical diagnosis, it is swiftly weaving its way into our lives at work, home, and everywhere in between. Yet most of us know very little about it. It's easy to say that fear of the unknown is fruitless, but how can we understand and leverage AI to create the best possible society without succumbing to fear, doomsaying, or prophecies of a fate worse than war?

We are at the precipice of an artificial intelligence revolution, in which the technologies we create may be used for the good, the bad and the ugly, according to a new report. The 100-page report is called "The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation." Released on Tuesday, it combines the thoughts of 26 leading experts on AI from various institutions including Oxford and Cambridge universities, OpenAI and the Center for a New American Security. The report begins by acknowledging that AI is making progress in certain fields, but it also says that "less attention has historically been paid to the ways in which artificial intelligence can be used maliciously." That is, if you haven't been watching the dystopian thriller series "Black Mirror."

According to Thomas Kuhn's old, but still useful, epistemological model, every change of the scientific paradigm – rather than the emergence of new material discoveries – radically changes the visions of the world and hence strategic equilibria. Hence, first of all, what is Artificial Intelligence? It consists of a series of mathematical tools, but also of psychology, electronic technology, information technology and computer science tools, through which a machine is taught to think as if it were a human being, but with the speed and security of a computer. The automatic machine must representman's knowledge, namely show it, thus enabling an external operator to change the process and understand its results within the natural language. In practice, AI machines imitate the perceptual vision, the recognition and the reprocessing of language -and even of decision-making – but only when all the data necessary to perform it are available.

As was also clearly stated by Vladimir Putin on September 4, 2017: "whichever country leads the way in Artificial Intelligence research will be the ruler of the world". According to Thomas Kuhn's old, but still useful, epistemological model, every change of the scientific paradigm – rather than the emergence of new material discoveries – radically changes the visions of the world and hence strategic equilibria. Hence, first of all, what is Artificial Intelligence? It consists of a series of mathematical tools, but also of psychology, electronic technology, information technology and computer science tools, through which a machine is taught to think as if it were a human being, but with the speed and security of a computer. The automatic machine must representman's knowledge, namely show it, thus enabling an external operator to change the process and understand its results within the natural language. In practice, AI machines imitate the perceptual vision, the recognition and the reprocessing of language -and even of decision-making – but only when all the data necessary to perform it are available.

Artificial intelligence, upskilling, and an older workforce are a few of the workplace trends we're likely to see in 2018. With unemployment at a 42-year low of 4.3pc, the jobs market remains competitive: the Office for National Statistics recently revealed that UK businesses are struggling to find workers as the pool of potential staff dries up. As a result, employers are having to evolve and develop new strategies to attract top talent. Set against this, the broader outlook is relatively uncertain, with sluggish wages, rising inflation and concerns about Britain leaving the EU dominating the debate.

The assignment of weights to attacks in a classical Argumentation Framework allows to compute semantics by taking into account the different importance of each argument. We represent a Weighted Argumentation Framework by a non-binary matrix, and we characterize the basic extensions (such as w-admissible, w- stable, w-complete) by analysing sub-blocks of this matrix. Also, we show how to reduce the matrix into another one of smaller size, that is equivalent to the original one for the determination of extensions. Furthermore, we provide two algorithms that allow to build incrementally w-grounded and w-preferred extensions starting from a w-admissible extension.

Ordered weighted averaging (OWA) operators have been widely used in decision making these past few years. An important issue facing the OWA operators' users is the determination of the OWA weights. This paper introduces an OWA determination method based on truncated distributions that enables intuitive generation of OWA weights according to a certain level of risk and trade-off. These two dimensions are represented by the two first moments of the truncated distribution. We illustrate our approach with the well-know normal distribution and the definition of a continuous parabolic decision-strategy space. We finally study the impact of the number of criteria on the results.

We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree---"Extremely Fast Decision Tree", a minor modification to the MOA implementation of Hoeffding Tree---obtains significantly superior prequential accuracy on most of the largest classification datasets from the UCI repository. Hoeffding Anytime Tree produces the asymptotic batch tree in the limit, is naturally resilient to concept drift, and can be used as a higher accuracy replacement for Hoeffding Tree in most scenarios, at a small additional computational cost.

We propose new positive definite kernels for permutations. First we introduce a weighted version of the Kendall kernel, which allows to weight unequally the contributions of different item pairs in the permutations depending on their ranks. Like the Kendall kernel, we show that the weighted version is invariant to relabeling of items and can be computed efficiently in $O(n \ln(n))$ operations, where $n$ is the number of items in the permutation. Second, we propose a supervised approach to learn the weights by jointly optimizing them with the function estimated by a kernel machine. Third, while the Kendall kernel considers pairwise comparison between items, we extend it by considering higher-order comparisons among tuples of items and show that the supervised approach of learning the weights can be systematically generalized to higher-order permutation kernels.