Redescription mining is a field of knowledge discovery that aims at finding different descriptions of similar subsets of instances in the data. These descriptions are represented as rules inferred from one or more disjoint sets of attributes, called views. As such, they support knowledge discovery process and help domain experts in formulating new hypotheses or constructing new knowledge bases and decision support systems. In contrast to previous approaches that typically create one smaller set of redescriptions satisfying a pre-defined set of constraints, we introduce a framework that creates large and heterogeneous redescription set from which user/expert can extract compact sets of differing properties, according to its own preferences. Construction of large and heterogeneous redescription set relies on CLUS-RM algorithm and a novel, conjunctive refinement procedure that facilitates generation of larger and more accurate redescription sets. The work also introduces the variability of redescription accuracy when missing values are present in the data, which significantly extends applicability of the method. Crucial part of the framework is the redescription set extraction based on heuristic multi-objective optimization procedure that allows user to define importance levels towards one or more redescription quality criteria. We provide both theoretical and empirical comparison of the novel framework against current state of the art redescription mining algorithms and show that it represents more efficient and versatile approach for mining redescriptions from data.

You have great SaaS business app ideas. You want to turn your idea quickly into a functional and engaging proof of concept. You need to be able to modify it to meet customers' needs, and you need to deliver a complete and secure SaaS application. How could you achieve all the above and yet avoid unforeseen IT requirements that add unnecessary cost and complexity? You also want your app to be responsive in any device at any time.

WASHINGTON – A century-old U.S. trade group on Monday pledged to work with the incoming administration to improve access to overseas markets for American companies, but also to fight against protectionism. Rufus Yerxa, head of the 300-company National Foreign Trade Council, said his organization is prepared to work with President-elect Donald Trump to improve conditions for U.S. exporters. However, he declined to be drawn into discussing some of Tump's more inflammatory statements, like threats to impose huge punitive tariffs on China and renegotiate free trade deals, saying he would wait to see what the actual policies are. "The reason I want to be optimistic is because I've seen this before, prior incoming administrations where there's a lot of dire predictions about what's going to happen, and in the end very positive things have happened," said Yerxa, a former deputy director general of the World Trade Organization. "I don't want to rule out that possibility," he told reporters. "I think we can make progress once it becomes clear to people that protectionism isn't the answer."

Artificial intelligence came alive in the '80s with many startups, governments, and large enterprises deploying new systems that executed tasks typically performed by human experts. These were largely rule-based systems that encoded behaviors in rules instead of using the strict procedural logic of traditional programming languages. Then, as memory became more affordable, systems were able to handle much more computationally intense tasks, such as machine learning, planning and scheduling, and natural language understanding. Now in the age of big data, many believe AI has completely changed the tech landscape, but in some ways, as the Talking Heads song goes, it's the "same as it ever was." What remains the same are the core elements of an intelligent application.

Artificial intelligence came alive in the 80s with many startups, governments, and large enterprises deploying new systems that executed tasks typically performed by human experts. These were largely rule-based systems that encoded behaviors in rules versus the strict procedural logic of traditional programming languages. Then, as memory became more affordable, systems were able to handle much more computationally-intense tasks such as machine learning, planning and scheduling, and natural language understanding. Now in the age of Big Data, many believe AI has completely changed the tech landscape, but in some ways, as the Talking Heads song goes, it's the "same as it ever was". What remains the same are the core elements of an intelligent application.

Job Title: Senior Software Engineer - NLP & Machine Learning Artigen Corporation is seeking a Senior Software Engineer/Team Lead NOTE: This is a Hands on position, Software Development, Design, Framework, Programming etc. NO OPT, NO Sponsorship, No relocation assistance, Local applicants ONLY, Face-to-face interview required. Artigen is a software development company that intends to specialize in enabling Artificial Intelligence software integration with e-commerce site and back end network operations. We are looking for Senior Team Lead/Software Engineer specifically in the development of software encompassing Artificial Intelligence, Machine Learning, Natural Language Processing to develop a virtual agent for Artigen's platform of software and services for Global B2B/B2C clients/customers. The role will develop and mentor a software team dedicated to Ai and cognitive technologies, utilizing existing technologies (such as IBM Watson API, Google's Nuance, Apple's Siri, other open source upcoming platforms such as Viv) and platforms to develop and customize a platform for Artigen's own Ai platform. This role will also require hands-on complex programming in various languages, platforms and must understand and develop machine learning algorithms, data integration and manipulation.

Our SMS search engine, called Text Engine, was originally created in 2013. The idea was to create a utility that would enable users to search the Web without needing to use a web browser and without using data. Text Engine accomplishes this by giving you access to vital, basic web information just by sending and receiving text messages. To keep Text Engine relevant for an ever-changing mobile market, our investors suggested that we think about adding a chatbot experience to Text Engine. So that's what we did.

Uber released a new set of rules for passengers on Thursday, banning vandalism, "vomiting due to excessive alcohol consumption" and flirting. It is the first time Uber has published specific guidelines for passengers. The rules set out specific examples of unacceptable behaviour, and people flouting the rules could be permanently banned from the service. "Most riders show drivers the respect they deserve," the company said in a statement. "But some don't – whether it's leaving trash in the car, throwing up in the back seat after too much alcohol or asking a driver to break the speed limit so they can get to their appointment on time. Some of the guidelines relate to sexual misconduct. There have been a number of cases where Uber drivers have been accused of rape and sexual assault since its inception. While setting out rules for passenger-driver interactions, some of the guidelines appear to be aimed at people using UberPool – the money-saving service where separate passengers are collected and dropped off at different locations in the same car. "Don't touch or flirt with other people in the car," the rules state. Drivers are also banned from flirting. "As a reminder, Uber has a no sex rule.

From Amazon and Facebook to Google and Microsoft, leaders of the world's most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). While there is growing interest in AI, the field is understood mainly by specialists. Our goal for this primer is to make this important field accessible to a broader audience. We'll begin by explaining the meaning of'AI' and key terms including'machine learning'. We'll illustrate how one of the most productive areas of AI, called'deep learning', works.

You may know Watson as IBM's Jeopardy-winning, cookbook-writing, dress-designing, weather-predicting supercomputer-of-all trades. Starting today, 40 organizations will rely upon the clever computers cognitive power to help spot cybercrime. The Watson for Cybersecurity beta program helps IBM too, because Watson's real-world experience will help it hone its skills and work within specific industries. After all, the threats that keep security experts at Sun Life Financial up at night differ from those that spook the cybersleuths at University of New Brunswick. IBM researchers started training Watson in the fundamentals of cybersecurity last spring so the computer could begin to analysize and prevent threats.