The travel market is currently undergoing a radical transformation. We've seen the growth of online travel agents whetting the appetite for bespoke holidays, with more options than you could ever explore. On the accommodation front, we can choose to stay in a hotel, a hostel, or experience life in someone's else's shoes via apps like Airbnb or CouchSurfing. Each booking implies an element of personalisation, and the explosion of'mix-and-match' services has only amplified the importance of catering for holidaymakers on a one-to-one level. A personalised, considered and tactical strategy for converting each purchase would be the natural reaction to what we're seeing, if only the market could deliver it.

The area of formal ethics is experiencing a shift from a unique or standard approach to normative reasoning, as exemplified by so-called standard deontic logic, to a variety of application-specific theories. However, the adequate handling of normative concepts such as obligation, permission, prohibition, and moral commitment is challenging, as illustrated by the notorious paradoxes of deontic logic. In this article we introduce an approach to design and evaluate theories of normative reasoning. In particular, we present a formal framework based on higher-order logic, a design methodology, and we discuss tool support. Moreover, we illustrate the approach using an example of an implementation, we demonstrate different ways of using it, and we discuss how the design of normative theories is now made accessible to non-specialist users and developers.

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show that iteratively learning embeddings and rules benefit each other during learning and prediction.

In this paper we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.

AI is almost everywhere in the news today, and the drive to create and implement AI solutions is creating an enormous talent gap. An estimated 80% of companies are already investing in AI and most are facing challenges hiring the capabilities they need to implement a useful AI application or product. It's clear that there is an intensively competitive market for artificial intelligence and machine learning specialists. Many companies first attempt to hire Ph.D.-level data scientists with expertise in AI algorithms and "feature engineering." Some analysts have even equated "AI talent" with such researchers.

What exactly is artificial intelligence? In much the same way that you'd be a bit stumped if someone demanded that you provide a hard and fast definition of say, philosophy, there isn't a satisfactorily rigorous answer to this question. As the Stanford Encyclopaedia explains, AI's definition falls under the category of "remarkably difficult, maybe even eternally unanswerable, questions, especially if the target is a consensus definition". But that hasn't stopped startups from jumping on the buzzword bandwagon. To get around this problem, most AI definitions settle for a muddled approach.

His demand for a ban triggered a legal and moral quagmire, as the Pentagon faced the prospect of throwing out service members who had willingly come forward as transgender after being promised they would be protected and allowed to serve. And as legal battles blocked the ban from taking effect, the Obama-era policy continued and transgender individuals were allowed to begin enlisting in the military a little more than a year ago.

WASHINGTON – The Defense Department has approved a new policy that will largely bar most transgender troops and military recruits from transitioning to another sex, and require most individuals to serve in their birth gender. The new policy comes after a lengthy and complicated legal battle, and it falls short of the all-out transgender ban that was initially ordered by President Donald Trump. But it will likely force the military to eventually discharge transgender individuals who need hormone treatments or surgery and can't or won't serve in their birth gender. The order says the military services must implement the new policy in 30 days, giving some individuals a short window of time to qualify for gender transition if needed. And it allows service secretaries to waive the policy on a case-by-case basis.

For further information on the Summer School on AI & Law, you can write to: aiandlaw@eui.eu.

In addition to being fascinating, Artificial Intelligence (AI), is a game changer. It will have a great impact that go far beyond corporate profits or the economy. It will open vast new opportunities, but also reopen old technical, economical and even philosophical debates. And how should we evaluate it? There is a lot of confusion about what AI is. The term is now so overused it is approaching the status of marketing jargon. Scanning market research reports, it is clear that too many software companies now describe their products as "AI".