Michael Kagan: The ever-growing demand for higher performance drives technology innovations for HPC, which then spreads to other markets. We have witnessed several technology transitions over the years, such as the transition from SMP to clusters, or from single core to multi-core. We are now going through another technology transition, which some call Co-Design. There are many technology efforts to re-architect the data center from a CPU-centric architecture to a data-centric architecture in order to overcome the new performance bottlenecks. The new data centers will need to allow data operations and analysis everywhere in order to get insights in real time.

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This morning, millions of people woke up and impulsively checked Facebook. They were greeted immediately by content curated by Facebook's newsfeed algorithms. To some degree, this news might have influenced their perceptions of the day's news, the economy's outlook, and the state of the election. Every year, millions of people apply for jobs. Increasingly, their success might lie, in part, in the hands of computer programs tasked with matching applications to job openings.

Too often, machine learning requires a massive investment of time and terabytes of data before it can deliver meaningful insights. But that doesn't have to be the case. A well-configured machine learning analytics tool can rapidly provide initial results -- a key advantage for developers who can then start using those results to create value.

Technology is advancing so rapidly that we will experience radical changes in society not only in our lifetimes but in the coming years. We have already begun to see ways in which computing, sensors, artificial intelligence and genomics are reshaping entire industries and our daily lives. As we undergo this rapid change, many of the old assumptions that we have relied on will no longer apply. Technology is creating a new set of rules that will change our very existence. Digitization began with words and numbers. Then we moved into games and later into rich media, such as movies, images and music.

The head of Google's cloud business says she does not expect machine intelligence to exceed that of humans during her lifetime, despite recent rapid progress that has surprised many. Diane Greene, who turns 61 this year, said that while researchers are making strides in programming intelligence into computers, there is still a long way to go. Incapability "There is a lot that machine learning doesn't do that humans can do really, really well," she said on Tuesday at the Code Enterprise conference in San Francisco. Her remarks came hours after Google said Greene's division had hired two leading machine learning and artificial intelligence experts: Fei-Fei Li, who was director of AI at Stanford University, and Jia Li, who headed up research at Snap, the operator of SnapChat. "Nobody expected some of the advances we are seeing as quickly as we're seeing them," she said, "but, the singularity I don't see it in my sentient lifetime."

Facebook has snapped up a facial recognition startup to help it win the war it waged against Snapchat. The social network has acquired FacioMetrics, a Carnegie Mellon University spinoff that developed a few face detection apps, including one that can recognize seven different emotions in human faces. Those applications are no longer available in any app store. A Facebook spokesperson told TechCrunch that the company plans to use the startup's technology to enhance its Snapchat-like AR filters for Facebook videos and Live broadcasts. It could lead to new AR masks, new special effects and even new ways to trigger their animations.

General Electric builds jet engines and wind turbines and medical gear. But the 124-year-old industrial giant is also transforming itself for the digital age. It's fashioning software that pulls data from all this hardware, hoping to gain an insight into industrial operations that was never possible in the past. The problem is that analyzing all this data is difficult, and the talent needed to make it happen is scarce. So GE is going shopping.

The utopia of driverless cars may still be out of reach, but General Motors wants to ensure that today's vehicles provide a unique or individual experience. The carmaker has partnered with IBM to develop what GM calls a "cognitive mobility platform" that will deliver personalized content while on the road. The partnership brings together GM's connected vehicle system OnStar with IBM's learning supercomputer Watson in the form of a platform called OnStar Go. According to a press release, OnStar Go is the auto industry's first such cognitive computing platform and will give drivers the opportunity to connect or interact with their favored brands while behind the wheel. Or to put it another way, the platform will make sure the driver makes the most of her or his time in the car. "Combining OnStar's industry leading vehicle connectivity and data capabilities with IBM Watson APIs will create experiences that allow drivers and passengers to achieve greater levels of efficiency and safety," said General Motors.