When we go grocery shopping, we often have a standard list of things to buy. Each shopper has a distinctive list, depending on one's needs and preferences. A housewife might buy healthy ingredients for a family dinner, while a bachelor might buy beer and chips. Understanding these buying patterns can help to increase sales in several ways. While we may know that certain items are frequently bought together, the question is, how do we uncover these associations? Besides increasing sales profits, association rules can also be used in other fields.

SUTime is a library for recognizing and normalizing time expressions. That is, it will convert next wednesday at 3pm to something like 2016-02-17T15:00 (depending on the assumed current reference time). SUTime is available as part of the Stanford CoreNLP pipeline and can be used to annotate documents with temporal information. It is a deterministic rule-based system designed for extensibility. SUTime was developed using TokensRegex, a generic framework for definining patterns over text and mapping to semantic objects.

The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages and standards for rule technologies. RuleML is a leading conference to build bridges between academe and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rule engines/business rule management systems; semantic web rule languages and rule standards; rule-based event processing languages (EPLs) and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules. The 9th International Web Rule Symposium (RuleML 2015) was held in Berlin, Germany, August 2-5.

The dinner speech at the Fischerhuette was given by Jörg Siekmann (University of Saarbrücken). The poster session, consisting of 18 posters and demos, was jointly organized as a get-together with the Berlin Semantic Web Meetup. At the session, wine, beer, and finger food were provided in the greenhouses of the Computer Science Department at The Thirty-First AAAI Conference on Artificial Intelligence the Freie Universität Berlin. The organizers also used (AAAI-17) and the Twenty-Ninth Conference on Innovative this unique opportunity to hold a joint public Applications of Artificial Intelligence (IAAI-17), will be RuleML and RR business meeting as well as an invited held in New Orleans, Louisiana, USA, during the mid-January dinner with all chairs, and invited keynote speakers to mid-February timeframe. AAAI-17 August 1, a boat sightseeing tour from lake Wannsee will arrive in New Orleans just prior to Mardi Gras and festivities to the Reichstag on Sunday, August 2, the CADE exhibitions will already be underway.

The general public shows great potential for utilizing scientific research. For example, a singer discovered her ectopic pregnancy by looking up clinical case reports. However, an exorbitant paywall impedes the public’s access to scientific literature. Our case study on a social network demonstrates a growing need for non-open access publications, especially for biomedical literature. The challenge is that non-open access papers are protected by copyright licenses that bar free distribution. In this paper, we propose a technical framework that leverages the doctrine of "idea-expression dichotomy" to bring ideas across paywalls. Idea-expression dichotomy prevents copyright holders from monopolizing ideas, theories, facts, and concepts. Therefore facts may pass through paywalls unencumbered by copyright license restrictions. Existing fact extraction methods (such as information extraction) require either large training sets or domain knowledge, which is intractable for the diverse biomedical scope spanning from clinical findings to genomics. We therefore develop a rule-based system to represent and extract facts. Social networkers and academics validated the effectiveness of our approach. 7 out of 9 users rated the paper’s information from the facts to be above average (≥6/10). Only 7% of the extracted facts were rated misleading.

Treasury Secretary Jacob Lew said Monday that Americans have reaped significant benefits from the international architecture put in place after World War II and the United States would be making a serious mistake to retreat from its global leadership role. In a speech before the Council on Foreign Relations, Lew sought to counter arguments being advanced by Donald Trump and other Republican presidential candidates that Americans are losing badly in competition with China and other countries in the global economy. Lew said that the United States needs to embrace new players on the global economic stage and make sure they meet the standards of the trading system that the country helped create. "The worst possible outcome would be to step away from our leadership role and let others fill in behind us," he said. Lew's comments came in advance of global finance meetings later this week.

From undergrads in music and social work, to PhDs in philosophy, to those who never graduated high school -- I've had quite a variety of co-workers during the ten years I've been in tech. Of course, in every software company you'll find your traditional computer science and engineering graduates as well. However, there's a significant and growing population of developers who took a different path to learn to code and are building a profession out of it. I hold a degree in linguistics, which in most universities is not a computational program, but an anthropological one. Required coursework includes topics such as historical and cultural language studies.

In this tutorial, you'll be briefly introduced to machine learning with Python (2.x) and Weka, a data processing and machine learning tool. The activity is to build a simple spam filter for emails and learn machine learning concepts. This article is written by the Codementor team and is based on a Codementor Office Hour by Codementor Benjamin Cohen, a Data Scientist with a focus in Natural Language Processing. In a nutshell, machine learning is basically learning from data. Way back when before access to data was plentiful and access to computing power was plentiful, people tried to hand-write rules to solve a lot of problems. E.g., if you see {{some word}}, it's probably spam. That worked all right, but as problems get more and more complicated, the combinations of rules start to grow out of hand, both in terms of writing them and in terms of taking them up and processing them. The number of techniques to do this all fall under the umbrella of machine learning.

The risk of cyberattacks is one of the most dangerous threats facing businesses today. And while new versions of attacks are constantly being born, teams of analysts are rushing to keep up with the latest risks. While many detection systems rely primarily on machine learning for catching attackers, a new AI system at PatternEx depends on human analysts as a vital part of their system of supervised machine learning. In 2015, GE inaugurated a new, Multi-Modal manufacturing facility in Chakan, India. If the company's ambitions for the space are realized, it could drive a massive change in global manufacturing.

While there may be millions of developers, there simply aren't enough data scientists to go around, and most of them are committed to working for large companies with big budgets and humongous data sets. Companies like Montreal-based Fuzzy.io are filling in the talent gap by offering an API to a set of artificial intelligence (AI) services that allows web and mobile developers to easily incorporate AI-based decision-making into their projects -- ranging from recommendations, to dynamic pricing decisions, and matching users in marketplaces. "Most of the existing ML development services are built to be used by data scientists or developers who have expertise in building AI/ML systems," said Fuzzy.io co-founder Matt Fogel. "Additionally, most of these tools require the developer to bring a great deal of data in order to train custom models. The company was founded by Fogel, who was the former produce vice president at Agendize, along with serial entrepreneur and developer Evan Prodromou. The company also recently added Kevin Fox, who, when he was at Google, helped create the user interfaces for Gmail and Google Calendar. These virtual intelligent machines use an adaptive rule base to translate pre-set, intuitive and vague "business rules" into a framework that can generate precise results. It could be as vague as "new", "old", "warm" and "good," as the company explains on its blog: "A fuzzy agent accepts some input variables and maps them onto fuzzy sets -- intuitive terms from the problem domain.