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Google's DeepMind algorithm just got one step closer to behaving like a human. Human smart DeepMind has gotten scarily smart in recent months – and we're not just talking "computer smart," but more like "human smart."

According to Gartner, there will be more than 20 billion connected devices worldwide by 2020. Today's enterprises are already benefitting greatly from a strong, connected workforce, but as Internet of Things (IoT) enabled devices move forward, saturating the market, is it possible for them to outpace their own benefits? After all, while the continuing surge of IoT devices is creating an onslaught of data requiring storage and retention, advancements in the IoT world are still bound by how quickly and efficiently data can be computed, and value extracted. Interestingly, the current resurgence of artificial intelligence (AI) technology may provide an antidote to the flood of data today's digital world is facing. With such rapid innovations in both spaces taking place, what can we expect from their converging paths?

Tieto established its new data-driven businesses unit in July 2016 to help Nordic organisations to create innovative data-driven services and new business models. In support of this goal, Tieto is also investigating the opportunities AI can present to the new unit's leadership team. Alicia T. will become a full-fledged member of the management team and also possess the capacity to cast votes. "I am proud that we are the first Nordic company to invite AI to become a member of the leadership team of the new data-driven businesses unit. Our team is on a mission to make the future more intelligent. Our developers at the new data-driven unit want to test how AI – a technology which is increasingly popular in home electronics – could assist organisations in data-driven decision-making. I am confident that Alicia T. will help us in finding information and making data-based decisions that humans don't necessarily think of – and thus perhaps create something yet unforeseen," says Ari Järvelä, Head of Data-Driven Businesses.

SAN FRANCISCO -- New research from Google shows that black students are less likely to have computer science classes in school and are less likely to use computers at home even though they are 1.5 times more interested in studying computer science than their white peers. The findings are part a report released Tuesday by Google in partnership with Gallup that puts the spotlight on the racial and gender gap in K-12 computer science education. Google says its aim with the research, which surveyed thousands of students, parents, teachers, principals and superintendents, is to increase the numbers of women, blacks and Latinos in computer science. Computer science classes are popping up in K-12 schools around the country. The growing effort is coming from many quarters -- the National Science Foundation, the College Board, Freada Kapor's SMASH Academy, Black Girls Code, Girls Who Code, Code.org and major tech companies such as Google -- all searching for the best way to put computers and computer know-how in the hands of kids from all racial, ethnic and socioeconomic backgrounds.

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.