Newswire) Investorideas.com, a global news source and investor resource covering actively traded sectors announces it has expanded distribution for the Investor Ideas Newswire with the recent addition of AI Investor Ideas http://www.aiinvestorideas.com, Investorideas.com is currently developing six artificial intelligence websites on the Grid https://thegrid.io/ Investor Ideas was one of the founding members and beta testers for the Grid https://thegrid.io/ This is not another do-it-yourself website builder. The Grid harnesses the power of artificial intelligence to take everything you throw at it - videos, images, text, urls and more - and automatically shape them into a custom website unique to you.

The Falcons are facing third and goal on the Patriots' five-yard line. Matt Ryan takes the snap and hands off to Devonta Freeman, already running hard at the goal line. Then, with a crunch audible to the topmost rows of NRG Stadium, Freeman is brought down by Dont'a Hightower right at the goal line. Touchdown?! Silence falls as all eyes turn… not to the referees on the sidelines (there aren't any) but to giant LCD panels behind the end zones. The screens remain black for several long moments until "TOUCHDOWN" lights up.

The Falcons are facing 3rd and goal on the Patriots' 5 yard line. Matt Ryan takes the snap and hands off to Devonta Freeman, already running hard at the goal line. Then, with a crunch audible to the topmost rows of NRG Stadium, Freeman is brought down by Dont'a Hightower right at the goal line. Silence falls as all eyes turn… not to the referees on the sidelines (there aren't any) but to giant LCD panels behind the end zones. The screens remain black for several long moments until "TOUCHDOWN" lights up.

On the Stanford University campus, you could practically throw a rock and hit 100 graduate students who are building apps that enable people to communicate more effectively. But Terry Winograd is particularly enthusiastic about the app one of his graduate students, Catalin Voss, is working on. Voss, a native of Germany who completed his bachelor's and master's degrees last June at the age of 21, is working on an app that deploys Google Glass, linked to a smartphone, to help autistic children recognize human emotions through facial expressions. Venture capitalists weren't interested, even though Voss had created and sold a startup that used eye-tracking technology to monitor attentiveness to a Toyota subsidiary while still a freshman. But Terry Winograd was interested. "It runs, it has AI [artificial intelligence]," says Winograd, who 20-odd years ago advised another graduate student on the then nascent field of searching the World Wide Web. "It's at a stage where we've actually put 30 devices into homes. Our goal is to have 100 in the trial." Voss says his objective is to build a medical product that insurers will be willing to pay for. "We want to prove the investors wrong, who didn't believe in it, and build an aid for people with autism, and other mental disorders as well," he says. "We believe we've built a fairly holistic system for mental health." Winograd was Voss's first choice for an advisor even though the 70-year-old professor retired from teaching three years ago.

The ability to predict the stock market is, as any Wall Street quantitative trader (or quant) will tell you, a license to print money. So it should be of no small interest to anyone who likes money that a new system that works in a radically different way than previous automated trading schemes appears to be able to beat Wall Street's best quantitative mutual funds at their own game. It's called the Arizona Financial Text system, or AZFinText, and it works by ingesting large quantities of financial news stories (in initial tests, from Yahoo Finance) along with minute-by-minute stock price data, and then using the former to figure out how to predict the latter. Then it buys, or shorts, every stock it believes will move more than 1% of its current price in the next 20 minutes - and it never holds a stock for longer. The system was developed by Robert P. Schumaker of Iona College in New Rochelle and and Hsinchun Chen of the University of Arizona, and was first described in a paper published early this year.

This paper maps the large-scale variation of the Spanish language by employing a corpus based on geographically tagged Twitter messages. Lexical dialects are extracted from an analysis of variants of tens of concepts. The resulting maps show linguistic variation on an unprecedented scale across the globe. We discuss the properties of the main dialects within a machine learning approach and find that varieties spoken in urban areas have an international character in contrast to country areas where dialects show a more regional uniformity.

In this special guest feature, Cecilia Pizzurro, Senior Director, Strategic Data Projects at LOGICnow, discusses the convergence of data/machine learning and cybersecurity, and the idea that these two are playing off of each other in a more meaningful way than ever before. Cecilia leads a team of data scientists and software engineers in Cambridge (US) and Newcastle (UK). These teams use machine learning and big data analytics to find business value in the vast amount of customer data gathered from LOGICnow's products. She was also the co-founder and CTO of the The Dolomite Group, a South American mining consortium, pioneering machine learning and big data analyses to improve mining efficiency and reduce environmental impact in Peru. This company is currently finalizing its acquisition by a Chilean mining company.

Using analogical model formulation with sketches to solve Bennett Mechanical Comprehension Test problems.

The Falcons are facing 3rd and goal on the Patriots' 5 yard line. Matt Ryan takes the snap and hands off to Devonta Freeman, already running hard at the goal line. Then, with a crunch audible to the topmost rows of NRG Stadium, Freeman is brought down by Dont'a Hightower right at the goal line. Touchdown?! Silence falls as all eyes turn… not to the referees on the sidelines (there aren't any) but to giant LCD panels behind the end zones. The screens remain black for several long moments until "TOUCHDOWN" lights up.

This paper presents an approach to automatically extracting and representing narrative information from stories written in natural language. Specifically, we present our results in extracting story graphs, a formalism that captures the entities (e.g., characters, props, locations) and their interactions in a story. The long-term goal of this research is to automatically extract this narrative information in order to use it in computational narrative systems such as story generators or interactive fiction systems. Our approach combines narrative domain knowledge and off-the-shelf natural language processing (NLP) tools into a machine learning framework to build story graphs by automatically identifying entities, actions, and narrative roles. We report the performance of our fully automated system in a corpus of 21 stories and provide examples of the extracted story graphs and their uses in computational narrative systems.