Recommender Systems are nowadays successfully used by all major web sites (from e-commerce to social media) to filter content and make suggestions in a personalized way. Academic research largely focuses on the value of recommenders for consumers, e.g., in terms of reduced information overload. To what extent and in which ways recommender systems create business value is, however, much less clear, and the literature on the topic is scattered. In this research commentary, we review existing publications on field tests of recommender systems and report which business-related performance measures were used in such real-world deployments. We summarize common challenges of measuring the business value in practice and critically discuss the value of algorithmic improvements and offline experiments as commonly done in academic environments. Overall, our review indicates that various open questions remain both regarding the realistic quantification of the business effects of recommenders and the performance assessment of recommendation algorithms in academia.

Science-fiction can sometimes be a good guide to the future. In the film Upgrade (2018) Grey Trace, the main character, is shot in the neck. His wife is shot dead. Trace wakes up to discover that not only has he lost his wife, but he now faces a future as a wheelchair-bound quadriplegic. He is implanted with a computer chip called Stem designed by famous tech innovator Eron Keen – any similarity with Elon Musk must be coincidental – which will let him walk again.

Ever wondered how video-streaming services such as YouTube and Netflix fetch videos that you like? Or how Google and Facebook find stories that are interesting to you? This is because these services are powered by Artificial Intelligence (AI) and Machine Learning (ML) algorithms – These algorithms are coded using a programming language in such a way that they can analyze your behavior at a granular level to find out your interests and preferences. This article focuses on Python programming language and explains why it is the most effective AI and ML language. AI and ML are seeping into nearly every aspect of our lives, helping us in ways that augment our abilities and make us better at what we do.

I see two main points of interest personally. The first is adversarial examples. There have been adversarially robust generative models developed, but it seems to me that there is more to be understood here. Obviously the'adversarial examples are features, not bugs' paper lays out a convincing argument around the theoretical meaning of the problem, but... is there some overarching pattern that can help distinguish useful features from brittle features? The main area I'm personally interested in though (nowhere near knowledgable enough to be caught up with current research, but it's what I'm working towards at the moment) is unsupervised model based reinforcement learning.

--Regulatory compliance is an organization's adherence to laws, regulations, guidelines and specifications relevant to its business. Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements. Keeping up with the changes entail two main tasks: fetching the regulatory announcements that actually contain changes of interest, and incorporating those changes in the business process. In this paper we focus on the first task, and present a Compliance Change Tracking System, that gathers regulatory announcements from government sites, news sites, email subscriptions; classifies their importance i.e Actionability through a hierarchical classifier, and business process applicability through a multi-class classifier . Na ıve Bayes, logistic regression etc.), hierarchical classification method, rule based approach, hybrid approach with various preprocessing and feature selection methods; and show that despite the richness of other models, a simple hierarchical classification with bag-of-words features works the best for Actionability classifier and multi-class logistic regression works the best for Applicability classifier . The system has been deployed in global delivery centers, and has received positive feedback from payroll compliance officers. Organizations are faced with rapidly changing regulatory policies, and ever-growing number of regulations.

Will producers be replaced by artificial intelligence? Dame and Pain also debate the existence of industry plants in the music industry and how conspiracy theories in the music business hurt artists.

Artificial intelligence has promised to revolutionize our lives, taking over the mundane tasks of daily existence, from prewriting "smart" email replies to driving our car through rush hour traffic. In the PR realm, AI has been touted as equal parts something to celebrate (no more manual coverage reports!) and fear (er, so long, means of employment). But the truth, as usual, lies somewhere in between. Some form of intelligent technology is already embedded in the PR industry, from the tools we use to find new audiences and monitor evolving conversations to modern media placement. Bloomberg News uses AI to generate coverage on some 3,500 earnings reports every quarter.

Here's the good news about artificial intelligence: the Terminator vision of the future, where smart machines turn on humanity, is unlikely. But here's the bad news: we could be heading for disaster anyway thanks to this revolutionary technology. That, at least, is the conclusion of the filmmakers behind Machine, who spent the past year researching the state of play in AI in the hope their documentary might provoke some serious thinking on the subject before it's too late. The documentary Machine ponders the ethical questions posed by the rise of artificial intelligence, including the nature of interactions between humans and sexbots.Credit:Finch "There's a lot of decisions we're making right now that will have ripple effects for decades to come," says Justin Krook, the director of the film. "In the whole history of humanity we've never had so much power at our disposal, and we only have one chance to get these decisions right. "People are worried about the robot apocalypse but that's not exactly the biggest threat we're facing here.

We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to predict which statements in the debate should be prioritized for fact-checking. While different fact-checking organizations would naturally make different choices when analyzing the same debate, we show that it pays to learn from multiple sources simultaneously (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post) in a multi-task learning setup, even when a particular source is chosen as a target to imitate. Our evaluation shows state-of-the-art results on a standard dataset for the task of check-worthiness prediction.

Abstract--Mining causality from text is a complex and crucial natural language understanding task. Most of the early attempts at its solution can group into two categories: 1) utilizing co-occurrence frequency and world knowledge for causality detection; 2) extracting cause-effect pairs by using connectives and syntax patterns directly. However, because causality has various linguistic expressions, the noisy data and ignoring implicit expressions problems induced by these methods cannot be avoided. In this paper, we present a neural causality detection model, namely Multilevel Causality Detection Network (MCDN), to address this problem. Specifically, we adopt multi-head self-attention to acquire semantic feature at word level and integrate a novel Relation Network to infer causality at segment level. To the best of our knowledge, in touch with the causality tasks, this is the first time that the Relation Network is applied. The experimental results on the AltLex dataset, demonstrate that: a) MCDN is highly effective for the ambiguous and implicit causality inference; b) comparing with the regular text classification task, causality detection requires stronger inference capability; c) the proposed approach achieved state-of- the-art performance. I. Introduction Automatic text causality mining is a critical but difficult task because causality is thought to play an essential role in human cognition when making decisions [1]. Thus, automatic text causality has been studied extensively in a wide range of areas, such as industry [2], physics [3] and healthcare [4], etc. A tool to automatically scour the plethora of textual content on the web and extract meaningful causal relations could help us construct causal chains to unveil previously unknown relationships between events [5] and accelerates the discovery of the intrinsic logic of the events [6]. Many research efforts have been made to mine causality from text corpus with complex sentence structures in the books or newspapers. In Causal-TimeBank [7] authors introduced "CLINK" and "C-SIGNAL" tag to mark events causal relation and causal signals respectively based on specific templates (e.g., "A happened because of B").