Artificial Intelligence has taken the center stage when it comes to the technology battleground and there is no reason to deny the major role it will play in pivoting the tech industry in general to the next level of innovation. It is one of the very few domains where it will make the computers work on behalf of humans and thus reduce the efforts on the human front. Technology is expected to become more intelligent, more conversational, more contextual and will allow business owners to feel the pulse of their customers and help them solve some of the impending challenges. Microsoft has always been at the forefront when it comes to AI and apart from the apps and features the company is also focussing on leveraging AI and extend it to their enterprise solutions as well. The company is also leading a global conversation around AI's transformative potential.

Kay Firth-Butterfield: One of the things that stick out in my mind is some research that McKinsey did recently, where they describe AI as a contributing factor to the transformation of society. And I just want to quote what they're saying about the transformation of our society: that it's happening ten times faster, and at three hundred times the scale, or roughly three thousand times faster than the impact of the industrial revolution. And you know, a lot of people compare this revolution to the industrial revolution. But, I think it's the speed and the real, core underpinning that AI is contributing to that transformation of our society that makes these discussions so important. David Bray: It's not just about handing over judgment and decisions to a machine that a human would do otherwise.

At present, most of the time the term, 'AI' is used it's nothing but the manipulation of massive amounts of data, which only a computer can do. The major change from 20 years ago is now the speed with which it processes the logic arguments. This'Big Data' processing isn't new & some great success stories can be seen which have generated business and in many cases, added employment. Let's call this Narrow AI (ANI). I still believe we are many years away from the culmination of ANI to become General AI (AGI), with the ability to reason, understand complex ideas, think in the abstract and generally learn from its mistakes.

A personal touch is one of the greatest tactics to gain and keep customers, as most people know. But that personal touch can be automated, surprisingly, and chatbot automation is making personalized customer service easier and faster than ever. Chatbots both understand and anticipate consumers needs, making them happier and cutting down on costs and mundane customer service needs for you. Ankesh Kumar, Founder and CEO of Personic.AI, is one of the pioneers of chatbot messaging. His company has built chatbots for brands around the world, and I got to speak with him regarding the future of chatbots and AI.

Next time you call customer support, the person on the other end of the line may be getting a little help from emotionally intelligent AI software. Some call-center workers are now receiving real-time coaching from software that analyzes their speech and the nature of their dialogue interactions with customers. As they are talking to someone the software might recommend that they talk more slowly or interrupt less often, or warn that the person on the other end of the line seems upset. This gives us a fascinating glimpse of how AI and humans might increasingly work together in the future. Plenty of routine work is becoming automated in call centers and other back office settings, but real human interaction seems likely to resist automation for a long while yet.

Diagnosing medical conditions is among the more classic examples of actually useful, achievable real-world machine learning. Machines have data, lots of it, and they have the capacity to process all of that data in ways that humans can't. Crucially, machines should be able to pick up on-the-edge cases, the rarest diseases that may go undiagnosed for simple lack of experience on the part of even the most exceptional doctors. Here, machines are to augment humans, rather than replace them. To this end, a group of Chinese ophthalmologists and computer scientists has demonstrated a machine learning algorithm for identifying congenital cataracts, a rare eye disease that's nonetheless responsible for some 10 percent of all vision loss in children worldwide. The algorithm was able to catch the disease with accuracies exceeding 90 percent, putting it on par with individual human ophthalmologists.

The information security industry's rush to adopt machine learning will help businesses burn US$96 billion on big data, intelligence, and analytics by 2021, says research house ABI . The report by lead number cruncher Dimitrios Pavlakis claims User and Entity Behavior Analytics (UEBA) and "deep learning algorithm designs" will be widely adopted by security companies as they collectively put big data to work detecting threats. The former machine learning technology, UEBA, is correlation on steroids, capable of detecting anomalies that can indicate if staff logins have been compromised and are being tested across the enterprise network. It can learn the activities and services most typical of a user to generate alerts when something anomalous occurs, like login attempts to odd network shares. Vendors are buying up across the space including Splunk's buy of Caspida, and Arksight selling Securonix.

TensorFlow, Google's open source deep learning framework, has announced a release candidate for a full-blown version 1.0. Version 1.0 not only brings improvements to the framework's gallery of machine learning functions, but also eases TensorFlow development to Python and Java users and improves debugging. A new compiler that optimizes TensorFlow computations opens the door to a new class of machine learning apps that can run on smartphone-grade hardware. Since Python's one of the biggest platforms for building and working with machine learning applications, it's only fitting that TensorFlow 1.0 focuses on improving Python interactions. The TensorFlow Python API has been upgraded so that the syntax and metaphors TensorFlow uses are a better match for Python's own, offering better consistency between the two.

It was almost a flat year for Intel (NASDAQ:INTC) in 2016, as the stock was up roughly 5% over the year. In comparison, most of the Intel's competitors such as Advanced Micro Devices (AMD), Qualcomm (QCOM) and NVIDIA (NVDA) delivered huge returns to stockholders. However, that does not means Intel is out of the game, as it still holds 80% market share in the worldwide microprocessor market mainly due to its leading position in the PC industry. The company has been generating huge profits by selling high-end desktop processing chips to data centers. It is highly likely that CPUs will continue to see reasonable growth in data centers, as the company requires huge computation power to make use of the big data they have gathered. According to marketsandmarkets.com, the data center market is projected to reach $32.30 billion by 2020 from $18.56 billion in 2015, which represents a compound annual growth rate of 11.7%.

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