Before delving into the nitty gritty of exactly how sentiment analysis works, let's break the concept down into something a little more tangible, shall we. Have you ever wondered what the South African public thought about, let's say, Iceland's football team defeating England in the Euro 2016? Well, that right there my friends, is why sentiment analysis software exists – to make vast quantities of data easily understandable at a glance. Think of it like a snapshot of the emotional response to a given topic. You might be asking yourself, but what about online surveys and polls, isn't that their purpose?

Regulators are beginning to teach robots who's the boss. After spending billions of dollars on cutting-edge artificial intelligence technologies, Europe's banks and insurers face tougher scrutiny of the tools they use to help root out fraud, check borrowers' creditworthiness and automate claims decisions. European Union General Data Protection Regulation (GDPR) rules starting this week will stress human oversight and consumer protection, which may hamper companies trying to build the tools of the future. "Companies developing AI technologies will have to consider and embed the data protection issues into the design process," said David Martin, senior legal officer at Brussels-based consumer advocate Beuc. "It's not something where they can just tick a box at the end."

Can you imagine a computer recommending to oncologists what type of treatment would be best for their patients? Similar technology is already used by IBM Watson, a computing system providing them the assistance they need to make more informed and personalised treatment decisions. The system was trained by a specialised team from the Memorial Sloan Kettering in the US, and then spread throughout the world. The Watson for Oncology helps doctors quickly identify key information in a patient's medical record, relevant surface evidence and explore treatment options. You can find it also in Slovakia: it has been used by 10 hospitals united in the Svet Zdravia network since last March.

San Diego's Mitek Systems, best known for powering mobile check deposit apps for major U.S. banks, has acquired French artificial intelligence firm A2iA for $50 million in cash and stock. The deal boosts Mitek's check processing and document verification capabilities, as well as expands its footprint into overseas markets. A2iA's check scanning technology, which can recognize cursive handwriting, is used by numerous U.S. financial institutions. It is also deployed in all United Kingdom banks and 90 percent of French and Brazilian banks. In all, A2iA operates in 42 countries and processes documents in 11 languages.

Artificial intelligence (AI) may be among the latest buzzwords in finance, but applying it to investment decision making will disrupt the industry and benefit those investors who harness its power. If used correctly, AI can add high alpha potential within a more stable modeling framework. AI is the basis for a different quantitative investment paradigm. It is a nonlinear, high-dimensional learning approach that typically seeks to replicate human reasoning. One interpretation of this new paradigm can be thought of as learning (and learning to apply) "Graham and Dodd"–style systematic rules.

The biggest danger AI poses today isn't the potential of killer robots or Roko's Basilisk--it's the potential to scale bias and racism to the size of the internet. The latest example of this is an "ethnicity detection" algorithm marketed by Moscow-based NtechLab as an "upcoming feature" to the facial recognition technology it sells. The new algorithm which promises to accurately look at images of people and determine their ethnic background; an image that was on the site, but has since been removed due to public backlash, showed classifications like "European," "African," and "Arabic." While the image has been removed, ethnicity recognition is still listed as an upcoming product on the NtechLab site. Privacy advocates like the American Civil Liberties Union already decry the use of facial-recognition AI in most cases, making the case that widespread adoption of the technology would mean we would live under constant surveillance by police or large tech companies.

The Church of England is offering worshippers the chance to use voice-activated virtual assistants to pray. People can ask Amazon's Alexa device to read a prayer of the day, the Ten Commandments or the Lord's Prayer or to recite grace before a meal. But the smart speakers will also have a "church near you" function to encourage people to visit their local church. The move is part of an online campaign by the Church after figures showed fewer people were attending services. The Church of England also hopes to also offer the service through Google Play in the future.

Recommender systems (RSs) have been the most important technology for increasing the business in Taobao, the largest online consumer-to-consumer (C2C) platform in China. The billion-scale data in Taobao creates three major challenges to Taobao's RS: scalability, sparsity and cold start. In this paper, we present our technical solutions to address these three challenges. The methods are based on the graph embedding framework. We first construct an item graph from users' behavior history. Each item is then represented as a vector using graph embedding. The item embeddings are employed to compute pairwise similarities between all items, which are then used in the recommendation process. To alleviate the sparsity and cold start problems, side information is incorporated into the embedding framework. We propose two aggregation methods to integrate the embeddings of items and the corresponding side information. Experimental results from offline experiments show that methods incorporating side information are superior to those that do not. Further, we describe the platform upon which the embedding methods are deployed and the workflow to process the billion-scale data in Taobao. Using online A/B test, we show that the online Click-Through-Rate (CTRs) are improved comparing to the previous recommendation methods widely used in Taobao, further demonstrating the effectiveness and feasibility of our proposed methods in Taobao's live production environment.

Dynamic epistemic logic (DEL) is a logical framework for representing and reasoning about knowledge change for multiple agents. An important computational task in this framework is the model checking problem, which has been shown to be PSPACE-hard even for S5 models and two agents. We answer open questions in the literature about the complexity of this problem in more restricted settings. We provide a detailed complexity analysis of the model checking problem for DEL, where we consider various combinations of restrictions, such as the number of agents, whether the models are single-pointed or multi-pointed, and whether postconditions are allowed in the updates. In particular, we show that the problem is already PSPACE-hard in (1) the case of one agent, multi-pointed S5 models, and no postconditions, and (2) the case of two agents, only single-pointed S5 models, and no postconditions. In addition, we study the setting where only semi-private announcements are allowed as updates. We show that for this case the problem is already PSPACE-hard when restricted to two agents and three propositional variables.

A personalized learning system needs a large pool of items for learners to solve. When working with a large pool of items, it is useful to measure the similarity of items. We outline a general approach to measuring the similarity of items and discuss specific measures for items used in introductory programming. Evaluation of quality of similarity measures is difficult. To this end, we propose an evaluation approach utilizing three levels of abstraction. We illustrate our approach to measuring similarity and provide evaluation using items from three diverse programming environments.