It was one of those amazing "we're living in the future" moments. In an October 2013 press release, IBM declared that MD Anderson, the cancer center that is part of the University of Texas, "is using the IBM Watson cognitive computing system for its mission to eradicate cancer." Well, now that future is past. The partnership between IBM and one of the world's top cancer research institutions is falling apart. The project is on hold, MD Anderson confirms, and has been since late last year.

Many people have fond memories of using the Game Boy Camera, but to call its low-resolution black-and-white shots "photos" would be... generous. Don't tell that to Roland Meertens, though. He recently devised a neural network that turns Game Boy Camera images into more presentable pictures. He trained the AI to clean up, colorize and fill in details for images by feeding it thousands of photos reduced to Game Boy-level image quality. The results aren't exactly good enough to frame for posterity, but they're far easier on the eyes.

The use of artificial intelligence and the "next-generation" of virtual learning environments (VLEs) are two areas of technology that have been forecast to have a major impact on higher education in the future, according to the expert panel of a major new report. The NMC Horizon Report: 2017 Higher Education Edition is produced by the New Media Consortium – a community of hundreds of universities, colleges, museums and research organisations driving innovation across their campuses – and is the flagship publication of the NMC Horizon Project, which analyses emerging technology uptake in education. Artificial intelligence, the report notes, has the "potential to enhance online learning, adaptive learning software, and research processes in ways that more intuitively respond to and engage with students". Samantha Adams Becker, senior director of publications and communications at NMC and the report's editor, said that the higher education world was already seeing the initial benefits of AI, which was "very much driving" the adaptive learning field. "If you think about online courses where there may be hundreds of students, it's currently very difficult for a professor or instructor to maybe get a good grasp on how students not only are performing, but are feeling about the material…as they're lecturing or a video's playing," she said. "Virtual avatars and chatbots…have the ability to assess that on an individual level, and if the student seems stuck then maybe you can replay part of the video.

The most successful artificial intelligence (AI) systems will be those comprising an emotional intelligence almost indistinguishable from human-to-human interaction, according to Bronwyn van der Merwe, group director at Fjord Australia and New Zealand -- Accenture Interactive's design and innovation arm. While the concept of AI is not new, in 2017 van der Merwe expects emotional intelligence to emerge as the driving force behind what she called the next generation in AI, as humans will be drawn to human-like interaction. As businesses continue to experiment with the Internet of Things, interesting use cases are emerging. Here are some of the most common ways IoT is deployed in the enterprise. Speaking with ZDNet, van der Merwe explained that building on the first phase of AI technology, emotional intelligence enhances AI's ability to understand emotional input, and continually adapt to and learn from information to provide human-like responses in real time.

There seems to be no end to what artificial intelligence can do. After thrashing humans at sophisticated games like chess and go, AI's next conquest appears to be the workplace. Many already hold jobs at forward-looking companies across industries. Others serve as lawyers, surgeons, and chefs. Given that astonishing range of capabilities, no one would be astonished if robots eventually run your enterprise back office, where the tasks are more often tedious than prestigious.

AI-powered chatbots are the next big trend in emerging technology, with Venture Beat reporting there are currently 11,000 messenger chatbots live on Facebook. Several industries, from eCommerce to athletics to medicine, are looking for ways to implement artificial intelligence into their operations. While these chatbots are relatively new, they've already attracted a lot of attention in finance. Historically, banking has been among the most resistant industries to embrace digital transformation, but technology-driven customers want to interact with banks on their own terms. Calling in or waiting in teller lines is no longer a reasonable customer expectation, any online and mobile services have taken banks in the right direction.

Actually, this is about two R versions (standard and improved), a Python version, and a Perl version of a new machine learning technique recently published here. We asked for help to translate the original Perl script to Python and R, and finally decided to work with Naveenkumar Ramaraju, who is currently pursuing a master's in Data Science at Indiana University. So the Python and R versions are from him. We believe that this code comparison and translation will be very valuable to anyone learning Python or R with the purpose of applying it to data science and machine learning. The source code is easy to read and has deliberately made longer than needed to provide enough details, avoid complicated iterations, and facilitate maintenance.The main output file is hdt-out2.txt.

Self-driving vehicles could begin tooling down a bustling Atlanta street full of cars, buses, bicyclists and college students, as the city vies with other communities nationwide to test the emerging technology. Atlanta would become one of the largest urban areas for testing self-driving vehicles if plans come together for a demonstration as early as September. Nationwide, 10 sites were designated last month as'proving grounds' for automated vehicles by the U.S. Department of Transportation. North Avenue is being eyed as a real-world proving ground for self-driving vehicles, one of several communities nationwide vying to be test sites for the emerging technology. They include North Carolina turnpikes, the eastern Iowa prairie and a Michigan site where World War II bombing aircraft were produced in a factory built by automobile pioneer Henry Ford.

Since the past 4-5 years, we have seen a change in the shopping behavior of users, both online as well as offline. It has resulted from user's reviews and recommendations about the products ranging from fashion to home to technology, all thanks to social websites like Facebook, Pinterest, Instagram and many other global as well as regional social sites. Social commerce is a term, coined by Yahoo in 2005, as a set of online shopping tools that take into account the user liking patterns, sharing reviews, information and advices on products, as per their usages, thus affecting the sales of those products. There are two types of social commerce strategies -- one is offsite where the e-retailer brings in the social angle from external social platforms, separate from their own websites, thus enhancing the sales and second is onsite social commerce platform where the website/platform uses its own channel to enhance sales based on content, context, and reviews etc. AI and ML Tech comes into play after these reviews and recommendations have been provided by the users and then placing the same in front of potential buyers for better decision making. Artificial and machine learning technologies have been used by giants like Google, Microsoft, Facebook, and Apple for more than a decade to enhance their platforms for better user experience which can now be seen to be mandatory adaptation for most of the internet based businesses, not only as it shows better ROI, but also open countless doors for future digital opportunities.

The Consumer Electronics Show (CES) held in Las Vegas in the first week of Jan each year sets a nice pointer to technologies that are going to remain in focus in the new year. This year artificial intelligence (AI) with the associated technologies such as smart IoT sensors and increasingly powerful and seamless human machine interfaces (HMIs) proved to be the cynosure of all eyes that passed by. Most global automobile companies are working on driverless cars that are based on continuous advances in computer vision and deep learning technologies. The Japanese automobile giant Honda showcased a tiny optical sensor that can fit in the rear view mirror of a car and measure throat movement of the passengers in the car. This helps to improve the clarity of conversations amongst passengers in noisy environments.