Doctor House is going electronic. Medics charged with diagnosing the kind of extremely rare diseases that Hugh Laurie's consultant routinely spots in TV drama House have found that artificial intelligence can do a similar job – but in seconds rather than days or weeks. From December, doctors at the University Hospital of Marburg's Centre for Undiagnosed and Rare Diseases (known as ZusE in German) will start using IBM's Watson to speed up their diagnoses. In 2011, Watson famously won the gameshow Jeopardy! Doctors are now training it on peer-reviewed rare disease literature to help them spot unusual illnesses.

Computers are becoming increasingly self-aware. Technological progress based on complex algorithms, combined with greater computing and processing power, has removed several key constraints. Through machine learning and deep learning techniques, computer scientists are now able to train computers to recognize patterns when presented with new image and audio files. Already, intelligent systems are capable of moderately accurate transcriptions from video feeds, as demonstrated by artists in Amsterdam, and we can expect the accuracy rate to continue to rise. Machine learning is still a nascent technology, but self-learning machines have huge potential to help scientists and researchers by identifying trends in the data from lab experiment instrumentation for materials innovation.

If you are a data scientist, business analyst or a machine learning engineer, you need model management – a system that manages and orchestrates the entire lifecycle of your learning model. Analytical models must be trained, compared and monitored before deploying into production, requiring many steps to take place in order to operationalize a model's lifecycle. In this blog, I will describe how SQL Server can enable you to automate, simplify and accelerate machine learning model management at scale – from build, train, test and deploy all the way to monitor, retrain and redeploy or retire. SQL Server treats models just like data – storing them as serialized varbinary objects. As a result, it is pretty agnostic to the analytics engines that were used to build models, thus making it a pretty good model management tool for not only R models (because R is now built-in into SQL Server 2016) but for other runtimes as well.

Whether we believe it or not, there is a shift in the pattern of tech companies as they adopt newer technologies with open arms. Artificial Intelligence, seems to be on the agenda of most of these companies, be it Microsoft, Google, Facebook or even Apple. To ramp up AI, Apple has made a prominent hire of a AI researcher from Carnegie Mellon University – Russ Salakhutdinov. The announcement came from Salakhutdinov via Twitter. This role will be in addition to his work at CMU.

The vision of the so-called internet of things -- giving all sorts of physical things a digital makeover -- has been years ahead of reality. But that gap is closing fast. Today, the range of things being computerized and connected to networks is stunning, from watches, appliances and clothing to cars, jet engines and factory equipment. Even roadways and farm fields are being upgraded with digital sensors. In the last two years, the number of internet-of-things devices in the world has surged nearly 70 percent to 6.4 billion, according to Gartner, a research firm.

Apple continues to bulk up on artificial intelligence and data crunching smarts. Ruslan Salakhutdinov, an associate professor at Carnegie Mellon University and its computer science school's machine learning department, said Monday via Twitter that he is joining Apple aapl as its director of A.I. research. He will continue to work at Carnegie Mellon while at Apple. Excited about joining Apple as a director of AI research in addition to my work at CMU. Apply to work with my teamhttps://t.co/U2hQl2GdhA Among Salakhutdinov's areas of research is the hot field of deep learning, an A.I. technique in which software systems called neural networks are given vast quantities of data to find patterns within.

Apple is making a visible push in the direction of AI today by hiring Carnegie Mellon University professor Ruslan Salakhutdinov for what appears to be a newly minted position: director of AI research. Salakhutdinov, who announced his new position on Twitter, works in the fields of deep learning and neural networks; he's published dozens of papers in the last couple of years alone. The topics he's worked on run the gamut, but the main thread is one of human-like understanding of various media: recognizing objects in images, actions in videos, and so on. We are looking for exceptional hands-on research scientists with a proven track record in a variety of machine learning methods; from the realms of deep learning, reinforcement learning, unsupervised learning, and computer perception. You will be joining a world-class, multidisciplinary team and will be participating in cutting-edge research in deep learning, machine intelligence, and artificial intelligence.

Apple is getting serious about artificial intelligence. It's just hired its first director of AI. And not just anyone -- it's hired Ruslan Salakhutdinov, an associate professor in machine learning at one of the top institutions for AI, Carnegie Mellon University. Salakhutdinov has been working on some pretty intense AI research. He primarily researches deep learning and neural networks, where computers learn from a large pool of examples.

Idling in rush-hour traffic can be mind numbing. It also carries other costs. Traffic congestion costs the U.S. economy 121 billion a year, mostly due to lost productivity, and produces about 25 billion kilograms of carbon dioxide emissions, Carnegie Mellon University professor of robotics Stephen Smith told the audience at a White House Frontiers Conference last week. In urban areas, drivers spend 40 percent of their time idling in traffic, he added. The big reason is that today's traffic signals are dumb.

In this blog post, we will learn techniques to classify urban sounds into categories using machine learning. Earlier blog posts covered classification problems where data can be easily expressed in vector form. For example, in the textual dataset, each word in the corpus becomes feature and tf-idf score becomes its value. Likewise, in anomaly detection dataset we saw two features "throughput" and "latency" that fed into a classifier. But when it comes to sound, feature extraction is not quite straightforward.