PRNewswire has recently reported on ABI Research regarding Mobile Broadband Operators. They are ramping up spending for big data and machine learning as they transform into digital service providers. With a long history of handling huge datasets, and with their path now blazed by the IT ecosystem, mobile operators will devote more than 50 billion to big data and machine learning analytics through 2021, forecasts ABI Research. Machine learning technologies will lead operators to profoundly change how they manage the telecom business. "Machine learning-based predictive analytics are applicable to all aspects of the telecom business," says Joe Hoffman, Managing Director and Vice President at ABI Research.

Machine learning is at the core of many recent advances in science and technology. With computers beating professionals in games like Go, many people have started asking if machines would also make for better drivers or even better doctors. In many applications of machine learning, users are asked to trust a model to help them make decisions. A doctor will certainly not operate on a patient simply because "the model said so." Even in lower-stakes situations, such as when choosing a movie to watch from Netflix, a certain measure of trust is required before we surrender hours of our time based on a model.

Up until very recently, computers needed a complicated and extremely precise set of instructions in order to accomplish even the simplest of tasks. Who among us remembers programming via punch cards? Computer programming languages have evolved over the years, but the biggest step has been moving towards the elimination of complicated programming. In other words, teaching computers to learn for themselves, dubbed machine learning. Because machine learning is such a promising leap forward in technological ability, it has the very real potential to affect every person in every field of business in the near future.

With all the hustle and bustle going on in the world of chatbots at the moment, one crucial event that is very relevant to the future of A.I. has gone relatively unnoticed. If you are interested in chatbots and A.I., you may have heard about the Loebner Prize, which was created by Hugh Loebner and is a form of the Turing Test that Alan Turing first set up in the 1950s. The Loebner Prize is an annual event in which A.I. specialists from around the world come together to play their bots off against a panel of judges in a battle for the most humanlike A.I. The goal is to trick the judges into thinking they are talking to a real person. The event has been hosted all over the world, but since 2014, it has been held in Bletchley Park in the U.K., due to the connection between Alan Turing and Bletchley during the second World War.

Robots are hailed for their intelligence and work efficiency, but excessive heating from prolonged hours of work often affects their performance. To address the heating problem faced by humanoid robots, Japanese researchers have devised an out-of-the-box solution. Using the analogy of sweating that happens in the human body as a result of continuous activity that cools the heated muscles, researchers at the University of Tokyo's JSK Lab presented a novel method at the IEEE/RSJ International Conference on Intelligent Robots and Systems held in South Korea. Their cooling solution addresses the heating problem of a musculoskeletal humanoid robot called Kengoro, which stands at 1.7 meters (5.6 feet) tall and weighs 56 kilograms (123.5 pounds). The Japanese researchers' cooling solution involves tinkering to make the robot "sweat" water straight out of its frame.

Every now and then I read a paper that makes a really strong connection with me, one where I can't stop thinking about the implications and I can't wait to share it with all of you. For me, this is one such paper. In the great see-saw of popularity for artificial intelligence techniques, symbolic reasoning and neural networks have taken turns, each having their dominant decade(s). The popular wisdom is that data-driven learning techniques (machine learning) won. Symbolic reasoning systems were just too hard and fragile to be successful at scale.

Sieving through resumes for a job seemed to be a universal problem for small and big companies alike. Gifted with technology, we now have access to many online job portals across the world claiming to find the right job for the right person. But how accurate is this service? It is from here that an idea originated in the brains of Arjun Pratap, founder and CEO of EdGE Networks. He believed that most people are in pursuit of passion, money and status and this, they find hard to capture as a package.

Earlier this week, the Obama administration discussed a new strategic plan aimed at fostering the development of AI-centered technologies in the United States. What's striking about it is, the Washington Post notes, although the United States was an early leader in deep-learning research (a subset of the overall branch of AI known as machine learning), China has effectively eclipsed it in terms of the number of papers published annually on the subject (Editor's note: the link could be paywalled; alternate source). From the report: The rate of increase is remarkably steep, reflecting how quickly China's research priorities have shifted. The quality of China's research is also striking. The chart narrows the research to include only those papers that were cited at least once by other researchers, an indication that the papers were influential in the field.

ACCORDING to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Elon Musk and Nick Bostrom about "the singularity" -- when machines become smarter than humans -- have attracted millions of dollars and spawned a multitude of conferences. But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorized and advertised to. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologized; it was unintentional.

I assume that you have a mixed dataset which has both numeric and non-numeric data types. In such cases, clustering based on a Euclidean distance measures will not be relevant. You could try conceptual clustering techniques which are based on concept hierarchy. The technique, called conceptual clustering, subdivides the data incrementally into subgroups based on a probabilistic measure known as "COHESION". A partition score is computed based on a category utility measure at each branch in concept hierarchy.