Piracy has always been a problem for content like online video, but Facebook looks to have an unconventional pitch to content owners who've had their videos reuploaded: You'll still be able to make money on them. In a post Thursday, Facebook announced a tweak to how it processes videos that are pirated and reuploaded from one owner to another. When Facebook's rights management system detects a video that has been pirated, the original owner can now choose to receive ad earnings from the duplicated clip. With Rights Manager, rights owners can find matches of their video content on Facebook; these matches are surfaced on a dashboard. Previously, the rights owner would review these matches in the dashboard to take action.

Building a chatbot is really about taking computer-human conversation to a whole new level. Technology experts generally talk about two methods of building chatbots. The first is a rule-based approach, where the developer writes rules for the system, or in other words, employs hard coding in building the chatbot. The second method entails the use of machine learning, where a massive amount of streaming data is used, and the system learns on its own. AIM caught up with Aditya Chavan, Head of Marketing, and Shashank Prasad, Head of Infrastructure, representing Machaao.

Email marketing is in for a complete overhaul. With AI, email marketing isn't limited to rule-based triggers but has evolved into a means of more in-the-moment personalization. AI has filled in the missing gap between traditional shopping and online shopping with 1:1 personalization. Here are four of the ways AI has changed email marketing for better. You have three seconds to seize the shopper's attention.

Formal concepts and closed itemsets proved to be of big importance for knowledge discovery, both as a tool for concise representation of association rules and a tool for clustering and constructing domain taxonomies and ontologies. Exponential explosion makes it difficult to consider the whole concept lattice arising from data, one needs to select most useful and interesting concepts. In this paper interestingness measures of concepts are considered and compared with respect to various aspects, such as efficiency of computation and applicability to noisy data and performing ranking correlation.

TokensRegex is a generic framework included in Stanford CoreNLP for defining patterns over text (sequences of tokens) and mapping it to semantic objects represented as Java objects. TokensRegex emphasizes describing text as a sequence of tokens (words, punctuation marks, etc.), which may have additional attributes, and writing patterns over those tokens, rather than working at the character level, as with standard regular expression packages. TokensRegex was used to develop SUTime, a rule-based temporal tagger for recognizing and normalizing temporal expressions. An included set of slides provides an overview of this package. There is quite detailed Javadoc for several of the key classes: for the matching patterns, see the Javadoc for TokenSequencePattern and for actions, see the Javadoc for Expressions.

Search Beyond is a forum that brings together the latest discussion from some of the UK's most innovative independent digital agencies. A handful of senior practitioners meet on a quarterly basis to debate a topic facing the digital marketing industry, with the insights and output appearing here on Think with Google. In our second Search Beyond session, we wanted to hear how independent agencies are preparing to face down increasing complexities in the digital landscape by adopting machine learning. The participants launched into the discussion by revealing how they're already putting these tools to use in their own work on behalf of clients today. "An element of automation is machine learning, so that is a lot of what we do", said Maria Yiangou, Group Account Director at All Response Media.

We present an algorithm for building probabilistic rule lists that is two orders of magnitude faster than previous work. Rule list algorithms are competitors for decision tree algorithms. They are associative classifiers, in that they are built from pre-mined association rules. They have a logical structure that is a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree. Instead of using greedy splitting and pruning like decision tree algorithms, we fully optimize over rule lists, striking a practical balance between accuracy, interpretability, and computational speed. The algorithm presented here uses a mixture of theoretical bounds (tight enough to have practical implications as a screening or bounding procedure), computational reuse, and highly tuned language libraries to achieve computational efficiency. Currently, for many practical problems, this method achieves better accuracy and sparsity than decision trees; further, in many cases, the computational time is practical and often less than that of decision trees. The result is a probabilistic classifier (which estimates P(y = 1|x) for each x) that optimizes the posterior of a Bayesian hierarchical model over rule lists.

Part of the promise of artificial intelligence is that it will impact how software is developed. Disruptive technologies have become commonplace in the software industry, and lately, artificial intelligence (AI) is on many companies' radars. The month of November 2016 alone saw much activity in the AI space, including Amazon launching an AI platform; General Electric acquiring two AI startups to help it try and compete with IBM's Watson; Google launching an AI group for its cloud, and an AI startup backed by entrepreneur Elon Musk signing a cloud agreement with Microsoft. All of this comes on the heels of a report released earlier last fall by the Obama administration's National Science and Technology Council's Committee on Technology examining potential use cases of AI. The study, "Preparing For the Future of Artificial Intelligence," observes that AI-related technologies already "have opened up new markets and new opportunities for progress in critical areas such as health, education, energy, and the environment."

AI applications have been deployed and used for industrial, government, and consumer purposes for many years. The experiences have been documented in IAAI conference proceedings since 1989. Over the years, the breadth of applications has expanded many times over and AI systems have become more commonplace. Indeed, AI has recently become a focal point in the industrial and consumer consciousness. This article focuses on changes in the world of computing over the last three decades that made building AI applications more feasible. We then examine lessons learned during this time and distill these lessons into succinct advice for future application builders.

Moreover, 2 keynote and 2 tutorial papers were invited. Most regular papers were presented in one of these tracks: Smart Contracts, Blockchain, and Rules, Constraint Handling Rules, Event Driven Architectures and Active Database Systems, Legal Rules and Reasoning, Rule-and Ontology-Based Data Access and Transformation, Rule Induction, and Learning. Following up on previous years, RuleML also hosted the 6th RuleML Doctoral Consortium and the 10th International Rule Challenge, which this year was dedicated to applications of rule-based reasoning, such as Rules in Retail, Rules in Tourism, Rules in Transportation, Rules in Geography, Rules in Location-Based Search, Rules in Insurance Regulation, Rules in Medicine, and Rules in Ecosystem Research. The 10th International Rule Challenge Awards went to Ingmar Dasseville, Laurent Janssens, Gerda Janssens, Jan Vanthienen, and Marc Denecker, for their paper Combining DMN and the Knowledge Base Paradigm for Flexible Decision Enactment, and Jacob Feldman for his paper What-If Analyzer for DMN-based Decision Models. As in previous years, RuleML 2016 was also a place for presentations and face-to-face meetings about rule technology standardizations, which this year Mark Your Calendars!