LUIS is now generally available in the Australia East, Brazil South, West US 2, South Central US, East US, East Asia, and North Europe regions, in addition to the current availability in the East US 2, West Central US, West US, West Europe, and Southeast Asia regions. General availability (GA) pricing will begin on February 1, 2018. Usage prior to February 1, 2018, will be billed at preview rates.

Continuing our series of articles looking forward to 2018, Dorian Selz, CEO of Squirro take a look at advances in Artificial Intelligence (AI) and what we can expect in the coming year. Given the impressive results that some organisations have seen from their artificial intelligence deployments – and the constant hype around AI in the press and at IT industry events – one would be forgiven for thinking that AI is already a truly mainstream technology. But despite some notable exceptions, this is not the case, with many organisations still very much in the discussion phase when it comes to AI and machine learning. But that could be set to change in 2018. As businesses begin to realise the potential for AI to transform certain elements of their organisation can outweigh any concerns or reservations they might have, so investment in AI will increase. Will 2018 be the year that AI hits the business mainstream?

Venture capitalists lavished $10.8 billion on artificial intelligence and machine learning technology companies in 2017, according to PitchBook Data Inc. They've placed major bets that AI innovation can't go far or fast enough to meet demand. But controversial use cases -- like when algorithms decide the fate of the criminally tried -- and the danger of coded-in bias suggest it's gone too far already without regulatory oversight. "This technology's coming at us so fast, we don't have all the policies figured out," said Beena Ammanath (pictured), global vice president of big data, artificial intelligence and new tech innovation at Hewlett Packard Enterprise Co. While consumer, business and government users embrace AI software that makes their jobs and lives simpler, they are simultaneously tasked with building the guardrails around the tech.

Hyderabad: A smart police robot that can recognise people, take complaints, detect bombs, identify suspects, interact with people and answer their queries will be launched here on Friday. This world's first smart police robot, a beta version, was manufactured by H-Bot Robotics, a city-based startup. H-Bot had announced the manufacture of world's second humanoid robot cop in July and the designs were also finalised. The first robot cop was made in France and deployed in Dubai. During the process of making the humanoid, the makers conceived the idea of a smart police robot, with additional facilities based on artificial intelligence and machine learning and built indigenously.

I must not have got the memo, because as a young lecturer in computer science at the University of Southampton in 1985 I was unaware that "women didn't do computing". Southampton had always recruited a healthy number of women to study computing in our fledgling department, and a quarter of the staff were women, but the student lists for the new academic year showed that quite suddenly, or so it appeared, we'd achieved the unenviable record of having no female students in that year's intake. Many women made important contributions to computing in its early decades, figures such as Karen Spärck Jones in Britain or Grace Hopper in the US, among many others who worked in the vital field of cryptography during the Second World War or, later, on the enormous challenges of the space race. But it had become clear that by the mid-1980s something fundamental had changed. We found that UK university admission figures revealed that the number of girls studying computing had fallen dramatically compared to the number of boys: from 25% percent in 1978 to just 10% in 1985.

Forgetting is an operation on knowledge bases that has been addressed in different areas of Knowledge Representation and with respect to different formalisms, including classical propositional and first-order logic, modal logics, logic programming, and description logics. Definitions of forgetting have been expressed in terms of manipulation of formulas, sets of postulates, isomorphisms between models, bisimulations, second-order quantification, elementary equivalence, and others. In this paper, forgetting is regarded as an abstract belief change operator, independent of the underlying logic. The central thesis is that forgetting amounts to a reduction in the language, specifically the signature, of a logic. The main definition is simple: the result of forgetting a portion of a signature in a theory is given by the set of logical consequences of this theory over the reduced language. This definition offers several advantages. Foremost, it provides a uniform approach to forgetting, with a definition that is applicable to any logic with a well-defined consequence relation. Hence it generalises a disparate set of logic-specific definitions with a general, high-level definition. Results obtained in this approach are thus applicable to all subsumed formal systems, and many results are obtained much more straightforwardly. This view also leads to insights with respect to specific logics: for example, forgetting in first-order logic is somewhat different from the accepted approach. Moreover, the approach clarifies the relation between forgetting and related operations, including belief contraction.

The main goal of search engines is ad hoc retrieval: ranking documents in a corpus by their relevance to the information need expressed by a query. The Probability Ranking Principle (PRP) --- ranking the documents by their relevance probabilities --- is the theoretical foundation of most existing ad hoc document retrieval methods. A key observation that motivates our work is that the PRP does not account for potential post-ranking effects; specifically, changes to documents that result from a given ranking. Yet, in adversarial retrieval settings such as the Web, authors may consistently try to promote their documents in rankings by changing them. We prove that, indeed, the PRP can be sub-optimal in adversarial retrieval settings. We do so by presenting a novel game theoretic analysis of the adversarial setting. The analysis is performed for different types of documents (single-topic and multi-topic) and is based on different assumptions about the writing qualities of documents' authors. We show that in some cases, introducing randomization into the document ranking function yields an overall user utility that transcends that of applying the PRP.

We establish the equivalence of two very general theories: the first is the decision-theoretic formalisation of incomplete preferences based on the mixture independence axiom; the second is the theory of coherent sets of desirable gambles (bounded variables) developed in the context of imprecise probability and extended here to vector-valued gambles. Such an equivalence allows us to analyse the theory of incomplete preferences from the point of view of desirability. Among other things, this leads us to uncover an unexpected and clarifying relation: that the notion of `state independence'---the traditional assumption that we can have separate models for beliefs (probabilities) and values (utilities)---coincides with that of `strong independence' in imprecise probability; this connection leads us also to propose much weaker, and arguably more realistic, notions of state independence. Then we simplify the treatment of complete beliefs and values by putting them on a more equal footing. We study the role of the Archimedean condition---which allows us to actually talk of expected utility---, identify some weaknesses and propose alternatives that solve these. More generally speaking, we show that desirability is a valuable alternative foundation to preferences for decision theory that streamlines and unifies a number of concepts while preserving great generality. In addition, the mentioned equivalence shows for the first time how to extend the theory of desirability to imprecise non-linear utility, thus enabling us to formulate one of the most powerful self-consistent theories of reasoning and decision-making available today.

We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-specific latent variables (dynamic fitnesses) describing the propensity of each node to create links. Assuming a Markov dynamics for both mechanisms, we propose an Expectation-Maximization algorithm for model estimation and inference of the latent variables. The estimated parameters and fitnesses can be used to forecast the presence of a link in the future. We apply our methodology to the e-MID interbank network for which the two linkage mechanisms are associated with two different trading behaviors in the process of network formation, namely preferential trading and trading driven by node-specific characteristics. The empirical results allow to recognise preferential lending in the interbank market and indicate how a method that does not account for time-varying network topologies tends to overestimate preferential linkage.

In this paper, we study Reiter's propositional default logic when the treewidth of a certain graph representation (semi-primal graph) of the input theory is bounded. We establish a dynamic programming algorithm on tree decompositions that decides whether a theory has a consistent stable extension (Ext). Our algorithm can even be used to enumerate all generating defaults (ExtEnum) that lead to stable extensions. We show that our algorithm decides Ext in linear time in the input theory and triple exponential time in the treewidth (so-called fixed-parameter linear algorithm). Further, our algorithm solves ExtEnum with a pre-computation step that is linear in the input theory and triple exponential in the treewidth followed by a linear delay to output solutions.