Artificial intelligence (AI) and machine learning have emerged as a major talking point for the likes of IBM, Google and Microsoft, with all keen to talk up the work they are doing to help enterprises make the most of these technologies. What to move, where and when. Corporate E-mail Address: This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. Corporate E-mail Address: This email address is already registered.

When Bill McDermott, CEO of SAP, the world's largest business software provider, believes the next five to 10 years will be far more disruptive than the five to 10 we just lived through, it's time to sit up and pay attention. "I believe very strongly that intelligent applications will fundamentally change the way you do work in the enterprise and the way you collaborate with your trading partners," said McDermott. Take a closer look at what this technology is capable of doing in the examples below. There's a good reason the below machine learning demo has over three million views on YouTube – it's a brilliant example of how this technology learns and predicts in a classic video game environment.

In-memory data store Redis recently acquired a module architecture to expand functionality. The latest module is a machine learning add-on that accelerates delivery of results from trained data rather than training itself. Redis-ML, or the Redis Module for Machine Learning, comes courtesy of the commercial outfit that drives Redis development, Redis Labs. It speeds the execution of machine learning models while still allowing those models to be trained in familiar ways. Redis works as an in-memory cache backed by disk storage, and its creators claim machine learning models can be executed orders of magnitude more quickly with it.

Humans are on the cusp of augmenting their lives in extraordinary ways with AI. At IBM Research Labs around the globe, we envision and develop next-generation systems that work side-by side with humans, accelerating our ability to create, learn, make decisions and think. We also architect the future of Watson, which has evolved from an IBM Research project to the world's first and most-advanced AI platform. Whether exploring new technical capabilities, collaborating on ethical practices or applying Watson technology to cancer research, financial decision-making, oil exploration or educational toys, IBM Research is shaping the future of AI.

The newest prime number is more than 22 million digits long Watch This Robot Solve a Rubik's Cube in Less Than 2 Seconds Thanks to a $28 million grant, Harvard is researching artificial in... The world has lost one of its greatest minds in science.

The'Netflix AI' set to hunt for aliens: Machine learning algorithm developed for online recommendations will scour the skies for systems that could sustain life Researchers are using machine learning to find stable planetary systems It uses techniques developed for Google's and Netflix recommendations The tool will also reveal the mass and how elliptical an exoplanet's orbit is Will be used to analyse data from NASA planet hunting mission It uses techniques developed for Google's and Netflix recommendations The tool will also reveal the mass and how elliptical an exoplanet's orbit is Machine learning software (pictured) that pull inspiration from Google and Netflix's algorithms could soon discover alien life in outer space. Did HALLUCINOGENS spark the Salem witch trials? Experts say... Iron Man suits, X ray detectors and a fake Facebook and... Hello there! Chimps can recognise friends with a single... How Donald Trump's administration could change the internet:... Did HALLUCINOGENS spark the Salem witch trials? Experts say... Iron Man suits, X ray detectors and a fake Facebook and... Hello there!

HARMAN participated at IBM's World of Watson event in Las Vegas to announce how we are integrating the cognitive technology capabilities of IBM Watson with HARMAN's powerful enterprise IOT solutions. Drawing on our extensive enterprise IOT solutions, HARMAN is providing the'central nervous system' to the IBM Watson'brain' for more intuitive, connected experiences in healthcare, hospitality and corporate settings. The result: voice enabled cognitive rooms by HARMAN. Mohit Parasher, executive vice president and president, Professional Solutions at HARMAN, participated on stage to discuss how HARMAN and IBM successfully tested a prototype voice-activated JBL speaker solution at Thomson Jefferson University Hospital in Philadelphia that allows patients to control their environment directly and answer questions with the goal of improving patient experiences and care. Eighty percent of physicians describe themselves as overextended or at capacity.

I'm sure you've heard by now, that artificial intelligence is coming to Human Resources. Of course, the manifold marketing materials and click baiting content dedicated to this growing, uh, phenomenon are predicated on the assumption that there was actual intelligence in the HR function to begin with. This point can probably be debated, which brings us to the larger question: why are we talking about AI in HR, anyways? The fact of the matter is, the problems most endemic to HR, the biggest challenges facing our profession are inherently the holes in even the most sophisticated AI solutions. Whether in reality or in speculative Science Fiction (or somewhere in between, like your provider's "product roadmap"), true AI is the HR Technology equivalent of tilting at windmills.

I lead the Machine Learning Team at Udacity.

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result.1 Examples abound. Profiling and classification algorithms determine how individuals and groups are shaped and managed (Floridi, 2012). Recommendation systems give users directions about when and how to exercise, what to buy, which route to take, and who to contact (Vries, 2010: 81). Data mining algorithms are said to show promise in helping make sense of emerging streams of behavioural data generated by the'Internet of Things' (Portmess and Tower, 2014: 1). Online service providers continue to mediate how information is accessed with personalisation and filtering algorithms (Newell and Marabelli, 2015; Taddeo and Floridi, 2015). Machine learning algorithms automatically identify misleading, biased or inaccurate knowledge at the point of creation (e.g.