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.

Google announced its Google Cloud Machine Learning Group to be led by two machine-learning experts: Fei-Fei Li and Jia Li. The group will focus on delivering cloud-based machine learning software to businesses. The new group evolves from Google's Cloud Machine Learning alpha application it launched in March. In conjunction with announcing the new group, Google also introduced the new Google Cloud Jobs API to help people advance their careers. "Over the past year, Google has developed a new machine-learning model that has the potential to greatly improve the recruitment efforts of any company," writes Rob Craft, group lead for Google Cloud Machine Learning, in a corporate blog posting.

Google's Cloud Machine Learning service launched earlier this year and, already, the company is calling it one of its "fastest growing product areas." Today, the company is announcing a number of new features for Cloud Machine Learning users and developers who want to run their own machine learning workloads in Google's cloud. Unlike its competitors, like AWS and Azure, Google never offered developers access to virtual machines with high-end graphics processing units (GPUs). Machine learning (as well as a number of other specialized workloads, mostly in the sciences) heavily depends on GPUs to power the core algorithms that have made this technique so successful. Sadly, you'll have to wait a bit before you can get started with running your own machine-learning workloads on the Google Cloud Platform.

This week, our 5 bots to try list features three services that our Bots Channel readers may be familiar with. Two of them, Icon8bot and Azkarbot, were featured two weeks ago. And Octane AI, which makes its debut, became embroiled in a controversy shortly after launching, as reported by VentureBeat. But it's not enough to just read about them. Please give them a try and let us know what you think.

Oren Etzioni, a well-known AI researcher, complains about news coverage of potential long-term risks arising from future success in AI research (see "No, Experts Don't Think Superintelligent AI is a Threat to Humanity"). After pointing the finger squarely at Oxford philosopher Nick Bostrom and his recent book, Superintelligence, Etzioni complains that Bostrom's "main source of data on the advent of human-level intelligence" consists of surveys on the opinions of AI researchers. He then surveys the opinions of AI researchers, arguing that his results refute Bostrom's. It's important to understand that Etzioni is not even addressing the reason Superintelligence has had the impact he decries: its clear explanation of why superintelligent AI may have arbitrarily negative consequences and why it's important to begin addressing the issue well in advance. Bostrom does not base his case on predictions that superhuman AI systems are imminent.

A leading expert on the development of artificial intelligence spoke about where she thinks the future of AI is heading. The shift toward more autonomy will have a major impact on the job market, low skilled jobs will be more scarce and demand for highly skilled roboticists will skyrocket.