As an example, Mediatrix Capital makes complex and profitable decisions based on nine algorithms that have specific hedging and correlated counter positions to track changes in market direction. Multiple strategies maximize returns and are completely unbiased to market direction. Stansberry Research is one of the leading independent financial research firms in the world, delivering unbiased investment intelligence to self-directed investors seeking an edge in a wide variety of sectors. Stansberry Research employs over 12 analysts and researchers, including former hedge fund managers and buy-side analysts who publish proprietary insights to over 500,000 paying subscribers in over 120 countries worldwide.

Apple Inc. is pressing ahead with the development of an Echo-like smart-home device based on the Siri voice assistant, according to people familiar with the matter. Started more than two years ago, the project has exited the research and development lab and is now in prototype testing, said the people, who asked not to be identified discussing unannounced Apple projects. Like Amazon Inc.'s Echo, the device is designed to control appliances, locks, lights and curtains via voice activation, the people said. Apple hasn't finalized plans for the device and could still scrap the project. If a product reaches the market, it would be Apple's most significant piece of new hardware since the company announced the Apple Watch in 2014.

Articles recently posted on DSC. But taking into account that we want data scientists to have a balance of common sense, business skills, creativity, a geeky love of statistics, and data skills management skills, I was curious what sorts of traits would be the opposite? What would add up to make a bad data scientist? Click here to read the article. Code samples is another great tool to start learning R, especially if you already use a different programming language.

Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers. Banks, telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow, EBITDA, etc.) because the cost of retaining an existing customer is far less than acquiring a new one. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients. Churn prediction is one of the most popular Big Data use cases in business. It consists of detecting customers who are likely to cancel a subscription to a service.

A subset of those scientists, engineers and programmers have a different goal: They're trying to use computer science to solve one of the most complex and deadly challenges humans face: Cancer. And, for the most part, they are doing so with algorithms and computers instead of test tubes and beakers. "We are trying to change the way research is done on a daily basis in biology," said Jasmin Fisher, a biologist by training who works in the programming principles and tools group in Microsoft's Cambridge, U.K., lab. One team of researchers is using machine learning and natural language processing to help the world's leading oncologists figure out the most effective, individualized cancer treatment for their patients, by providing an intuitive way to sort through all the research data available. Another is pairing machine learning with computer vision to give radiologists a more detailed understanding of how their patients' tumors are progressing.

Sales professionals make critical decisions every day, deciding which prospects to reach out to, what product and service offerings to highlight, and which communication channels will work best. Many salespeople make these decisions based on intuition or follow an organizational playbook. NAVIK SalesAI offers a better alternative by making the buyer central to the process, and applying artificial intelligence to the data. It provides weekly guidance identifying the most promising contacts, specifying which products or services each contact is likely to purchase next and suggesting the most effective communication channel to use. Product recommendations outline the likely reasons motivating a purchase, giving the sales person talking points that resonate with their prospect.

With a new scientific paper published every 20 seconds, startups and tech veterans alike are racing to apply artificial intelligence to help academics and businesses stay on top the relentless influx of new information. We at Sparrho are testing a new incentive model for researchers to communicate their academic publications to a wider audience. Perhaps a loved one suffers from insomnia and you want to keep up-to-date with the newest research, or you are a blogger or television researcher looking for new themes to cover, or in fact, you just want to know about the latest advances in hydrogen car technology before anybody else. Google your topic of interest, and you'll be greeted by plenty of news articles and popular science blog posts, but original research articles are hard to find. Even if you find them, how will you know which articles are most relevant and, more importantly, trusted by experts?

Even today, a hacker with a command of artificial intelligence may be able to force a self-driving car to miss a stop sign, or a facial recognition system to believe it's seeing a completely different person in a security setting. Researchers have shown that virtual personal assistants like Siri or Google Now can be tricked into visiting potentially malicious websites by audio that sounds like white noise to humans. To thwart such hackers, Elon Musk's OpenAI and Pennsylvania State University released a new tool this week called "cleverhans," that lets artificial intelligence researchers test how vulnerable their AI is to adversarial examples, or purposefully malicious data meant to confuse the algorithms. Once the vulnerability has been found, a defense to the attack can automatically be applied. The tool is meant to be a "collection of attacks and defenses, along with tutorials on how to use them," according to Nicolas Papernot, co-creator and security researcher at Pennsylvania State University, in an email to Quartz.

Speaker: Richard E. Fairley, PhD, Principal Associate of Software and Systems Engineering Associates (S2EA) View bio (PDF, 228 KB) This full-day presentation will cover practical applications of systems engineering processes and methods as documented in SEBoK and 15288. SEBOK is the guide to the systems engineering body of knowledge; 15288 is the ISO/IEC/IEEE standard 15288:2015 for system engineering processes. Examples and case studies will be used to illustrate processes, methods, and techniques for developing purposefully engineered systems. In addition, the problems that arise when systems engineers and software engineers work together will be covered, as will approaches that can be used to mitigate those problems. This presentation is intended for those who are, or will be, involved in development or modification of a multidisciplinary engineered system and others who wish to learn more about systems engineering when software is a system element.

Work quality in crowdsourcing task sessions can change over time due to both internal factors, such as learning and boredom, and external factors like the provision of monetary interventions. Prior studies on crowd work quality have focused on characterizing the temporal behavior pattern as a result of the internal factors. In this paper, we propose to explicitly take the impact of external factors into consideration for modeling crowd work quality. We present a series of seven models from three categories (supervised learning models, autoregressive models and Markov models) and conduct an empirical comparison on how well these models can predict crowd work quality under monetary interventions on three datasets that are collected from Amazon Mechanical Turk. Our results show that all these models outperform the baseline models that don’t consider the impact of monetary interventions. Our empirical comparison further identifies the random forests model as an excellent model to use in practice as it consistently provides accurate predictions with high confidence across different datasets, and it also demonstrates robustness against limited training data and limited access to the ground truth.