RAGE AI significantly extends the frontier of deep learning and machine intelligence technology from "natural language processing" to "natural language understanding." RAGE AI incorporates deep linguistic parsing and proprietary innovations to understand meaning in context, which makes its solutions completely transparent, auditable and flexible. The platform facilitates unsupervised to supervised learning and contains several innovations to support automated knowledge acquisition including pragmatic knowledge. RAGE AI is not a black box and does not rely on statistical patterns present in training data. Introduced in 2011, RAGE AI is an integral part of the broader RAGE Enterprise platform, a provider of all process orchestration and automation capabilities.

A treasure hunter went missing in the Rocky Mountains, and a computer algorithm found him ... Drive.ai wants to give self-driving cars more brainpower, personality Drive.ai

Apple was already quite successful when Steve Job's gut told him that the iPhone would be a major success. But nobody could have predicted just how much the invention of the iPhone would transform the company and the world. That kind of intuition that Jobs displayed is crucial for business owners/leaders whether it's big or small. Google's AlphaGo computer displayed similar intuition recently when it defeated the world champion of the complex board game "Go." The programmers who created AlphaGo fed it every move of 150,000 "Go" matches to give it a feel for the game and the best strategies for achieving victory, and they were just getting started.

When Deborah DiSanzo, a veteran Massachusetts health care technology executive, went looking for her next career move, she made what might seem like an unusual choice: IBM Corp. But IBM's shift from computer hardware to software and services has taken the New York company deep into the world of doctors, hospitals, and drug companies. DiSanzo signed on as leader of its new health care division, called Watson Health, and got lucky when IBM decided to plant the unit's headquarters in Kendall Square: no relocation necessary. IBM is making a big bet on health care, and it's doing it here in the technology and life sciences hub of Massachusetts. Since IBM Watson Health was launched in 2015, the company has made four acquisitions worth about 4 billion and forged numerous partnerships with major hospitals, drug makers, and other companies.

A.I. is easier than ever to leverage, but not what client-side digital marketing or agency folks are used to. First, it's not much to look at (in fact, it's nothing to look at - it's syntax, vocabulary and diction-based). The technology is different, it's not about pages, apps or ads (NLP Libraries!? Parsers?! And to do it right, it's a very tight combination of technical and creative, medium and message. Moreover, there are practical barriers to just getting started.

Emerging from stealth mode, self-driving car start-up Drive.ai Drive.ai announced today its first commercial foray, a retrofit kit to make fleet vehicles, from delivery trucks to car services, self-driving. The kit includes a sensor array, computer and an LED sign to communicate with pedestrians and other drivers. Self-driving car technology is being developed by most major automakers, automotive equipment suppliers and start-up companies such as Drive.ai. The intent is to eliminate the 95 percent of fatal car accidents every year that are attributable to human error.

Reports this week of Russian intrusions into U.S. election systems have startled many voters, but computer experts are not surprised. They have long warned that Americans vote in a way that's so insecure that hackers could change the outcome of races at the local, state and even national level. Multibillion-dollar investments in better election technology after the troubled 2000 presidential election count prompted widespread abandonment of flawed paper-based systems, such as punch ballots. But the rush to embrace electronic voting technology -- and leave old-fashioned paper tallies behind -- created new sets of vulnerabilities that have taken years to fix. "There are computers used in all points of the election process, and they can all be hacked," said Princeton computer scientist Andrew Appel, an expert in voting technologies.

A revolution that has been quietly brewing in the sphere of machine learning and predictive analytics is taking flight. For customers, the analytics revolution is putting powerful new analytical tools in the hands of financial planners and business managers in every area of the business, enabling more comprehensive, effective, and reliable planning. While the value of machine learning has been known for some time, the advances needed to make the practice accessible to a mainstream audience have been made only recently. As KDnuggets reports,[1] the convergence of three key trends is breaking down the barriers that have impeded the growth and employment of machine learning:[2] The abundance of data enables more features and better machine learning models to be created. Data scientists no longer are needed to manage the machine learning infrastructure or implement custom code.

With the growing focus on renewable energy, there is a need to accurately forecast energy production. In this video, we explore a typical work flow when forecasting wind energy and wrap up the conversation with possible predictive maintenance use cases for the wind turbines. Although the discussion focuses on wind energy domain, this work can be easily reused with minor tweaks for other renewable energy sources.

We built BOTbot as an experiment to learn about self-service bot creation platforms, user/bot interaction, bot promotion and management. Below we run down the process we took to creating a code-free MVP, the results from our initial user interactions and how scripted journeys, rails, NLP and AI all have their place in business bots -- dependent on use case. After looking at various'quick&easy' bot platforms we decided to use Chatfuel because: We continue to be impressed with Chatfuel's simplicity and use it for demos and tests. But we don't use Chatfuel for live production bots because: Having chosen the platform to build on we launched BotBot without buttons, optimistically hoping to use NLP to create an open conversational dialogue about chatbots. Limited domains, where a data set is filtered across a handful of axis (e.g.