Machine learning is not new. SAS has been doing it for over 20 years and some early machine learning papers date back to the 50's. So why is it one of the hottest topics at the Strata Hadoop World conference later this week? Clearly, Hadoop is playing a major role in the increased focus on machine learning. Patrick Hall is a Senior Machine Learning Scientist at SAS.

What are the differences between data science, data mining, machine learning, statistics, operations research, and so on? Here I compare several analytic disciplines that overlap, to explain the differences and common denominators. Sometimes differences exist for nothing else other than historical reasons. Sometimes the differences are real and subtle. I also provide typical job titles, types of analyses, and industries traditionally attached to each discipline.

New Delhi: Accenture Operations, the 7-billion business segment of technology firm Accenture Plc, which deals with process outsourcing, infrastructure consulting and outsourcing, security and cloud services, is betting on automation platforms backed by artificial intelligence (AI) and machine learning for its next phase of growth, said Manish Sharma, senior managing director, Accenture Operations in an interview earlier this week. "Analytics and automation are critical and are high-focus areas for us. We are using automation technologies at scale for our client base," he said. "We are investing a lot of money and time into the AI piece. In automation, there is simple automation like Mini-Bots and then there is high-end automation solutions like Virtual Assistance and artificial or cognitive solutions," he explained.

DARPA today announced the newest of its Grand Challenges, one designed to ensure that the exponentially growing number of military and civilian wireless devices will have full access to the increasingly crowded electromagnetic spectrum. The agency's Spectrum Collaboration Challenge (SC2) will reward teams for developing smart systems that collaboratively, rather than competitively, adapt in real time to today's fast-changing, congested spectrum environment--redefining the conventional spectrum management roles of humans and machines in order to maximize the flow of radio frequency (RF) signals. DARPA officials unveiled the new Challenge before some 8000 engineers and communications professionals gathered in Las Vegas at the International Wireless Communications Expo (IWCE). The primary goal of SC2 is to imbue radios with advanced machine-learning capabilities so they can collectively develop strategies that optimize use of the wireless spectrum in ways not possible with today's intrinsically inefficient approach of pre-allocating exclusive access to designated frequencies. The challenge is expected to both take advantage of recent significant progress in the fields of artificial intelligence and machine learning and also spur new developments in those research domains, with potential applications in other fields where collaborative decision-making is critical.

We have the expertise to help enterprises of all sizes take on their greatest challenges. Let us show you how our products and services can help you achieve your business goals.

THAT a computer program can repeatedly beat the world champion at Go, a complex board game, is a coup for the fast-moving field of artificial intelligence (AI). Another high-stakes game, however, is taking place behind the scenes, as firms compete to hire the smartest AI experts. Technology giants, including Google, Facebook, Microsoft and Baidu, are racing to expand their AI activities. Last year they spent some 8.5 billion on research, deals and hiring, says Quid, a data firm. That was four times more than in 2010.

Technology is often described as an enabler, but technology trends and concepts can also shape how we view the world. Depending on your point of view, you might see the workplace as a complicated human-powered machine that needs to be maintained for peak performance. Or you might think of it as an organism that needs to be shaped as it grows and changes so it can thrive. When social software emerged and reached the mainstream both inside and outside the workplace, the workplace was viewed as a hyper-connected network of people to be influenced. These perspectives influence how we manage and, in the context of technology, determine what we see as the role of a particular solution.

Microsoft pitched developers to join its ecosystem as it showed off its very latest technologies and platforms at the Microsoft "Build 2016" event in San Francisco.

Recently, another chapter of man vs. machine played out. Google's Deep Mind project team tried out their state of the art algorithm on the game of Go. The Korean pro, Lee Sedol, a world champion several times over and arguably the best player of the game right now, was its opponent. To put it simply, this was the equivalent of Deep Blue v. Gary Kasparov, and as with the IBM Chess playing machine before it, AlphaGo took home the prize, four wins to one loss. Go has been thought to be the one game that computers could not beat a human at because a computer could not brute force the move trees.

At the Build 2016 conference, which kicked off on March 30, developers got a sneak peak at Microsoft's (NASDAQ:MSFT) vision of all pervasive artificial intelligence (AI). CEO Satya Nadella calls this "conversations as a platform," in which human language is the preferred user interface. If Microsoft achieves its vision, it will be a breakthrough that will change the way we interact with our computing devices. One of the staples of science fiction has been artificial intelligence smart enough to have a natural conversation with. Such an advanced capability has been elusive, but in recent years, cloud based speech recognition services from Microsoft, Google (NASDAQ:GOOG) (NASDAQ:GOOGL), and Apple (NASDAQ:AAPL) have gotten closer to the fictional dream.