Governments worldwide requested Facebook users' data nearly 60,000 times in the first half of 2016, a 27 percent increase over requests made in the second half of 2015, according to a Facebook bi-annual report published this week. In addition to government requests for user data, the report details which content Facebook restricts for violating local laws. The company says it studies each request carefully to determine whether or not it has merit, especially in emergency cases where imminent risk of serious injury or harm is involved. It ultimately handed over data in 80 percent of cases. The 27 percent jump for the latest reporting period compares to a 13 percent increase between the first and second halves of 2015, and 18 percent growth between the second half of 2014 and the first half of 2015.

Humanity has a major food problem. The world's population is expected to increase significantly over the next three decades, but our capacity for food production will struggle to keep pace. Although global fertility rates are actually falling, a general increase in life expectancy will mean a steady increase in headcount during our lifetimes. One 2015 UN DESA report claims the world's population will hit 9.7 billion by 2050 – an increase of some 2.3 billion over today. Of course, a general rise in life expectancy reflects a higher standard of living for more of the world, which is cause for celebration.

A visitor uses his mobile phone as he walks past the Microsoft booth with a logo for cloud computing software application at the CeBit computer fair in Hanover, March, 6, 2012. A women holds her laptop as she walks in front of a cloud computing logo at the booth of IBM during preparations for the CeBIT trade fair in Hanover, March 9, 2014. NEW YORK President elect Donald Trump is pro-business and anti-red tape. But what if your business is red tape? Companies whose technology helps banks and investors cope with the welter of post financial crisis regulations and avoid increasingly hefty fines - a sector known as "regtech" - are sanguine about Trump's pledge to dismantle some of those reforms.

The listing carries no penalties but will likely be an embarrassment for Alibaba, which has been trying to burnish its image in international markets. The move by the USTR comes even as the company claims to have used "big data" technologies to zero in, for example, on 13 factories and shops that were selling knockoff RAM modules under Kingston and Samsung brands, according to Alibaba's news hub Alizila. Its counterfeit goods monitoring and identification algorithm, for example, monitors about 100 dimensional characteristics, ranging from price to the online shops decorations, transaction records, product-release pattern and consumer complaints. Merchants and goods are rated on a 0 to 100 scale, with 80 usually treated as a red flag. The company also uses optical character recognition and the scanning and analysis of images and logos for suspicious listings.

Real-time machine learning has access to a continuous flow of transactional data, but what it really needs in order to be effective is a continuous flow of labeled transactional data, and accurate labeling introduces latency. During this year, I heard and read a lot about real-time machine learning. People usually provide this appealing business scenario when discussing credit card fraud detection systems. They say that they can continuously update credit card fraud detection model in real-time (See "What is Apache Spark?", "…real-time use cases…" and "Real time machine learning"). It looks fantastic but not realistic to me.

Since cybersecurity threats have become a topic of nightly newscasts, no longer is anyone shocked by their scope and veracity. What is shocking is the financial damage the attacks are predicted to cause as they reverberate throughout the economy. Cybersecurity Ventures predicts global annual cybercrime costs will grow from $3 trillion in 2015 to $6 trillion annually by 2021, which includes damage and destruction of data, stolen money, lost productivity and theft of intellectual property, personal and financial data, embezzlement and fraud. That doesn't even include post-attack disruption to the normal course of business, forensic investigation, restoration and deletion of hacked data, systems and reputational harm. While traditional security filters like firewalls and reputation lists are good practice, they are no longer enough.

An autonomous Uber malfunctioned while in "self-driving mode" and caused a near collision in San Francisco, according to a business owner whose account raises new safety concerns about the unregulated technology launch. The self-driving car – which Uber introduced without permits, as part of a testing program that California has deemed illegal – accelerated into an intersection while the light was still red and while the automation technology was clearly controlling the car, said Christopher Koff, owner of local cafe AK Subs. Self-driving cars: Uber's open defiance of California shines light on brazen tactics "It looked like the car ran the red light on its own," Koff, 49, said of the self-driving Uber Volvo, which has a driver in the front seat who can take control when needed. Another car that had the green light had to "slam the brakes" to avoid a crash, he said. Koff's story, which advocacy group Consumer Watchdog shared with state officials on Tuesday, directly contradicts Uber's public claims that red-light violations have been the result of "human error" and that the drivers, not the technology, have failed to follow traffic laws.

There was, in hindsight, a clear element of risk to Tesla Motors Inc.'s decision to install Autopilot hardware in every car coming off the production line since October 2014. It paid a price, with federal regulators probing the deadly crash of a Model S while in driver-assist mode and critics slamming Tesla for rolling the technology out too soon. But there was also a reward. The company has collected more than 1.3 billion miles of data from Autopilot-equipped vehicles operating under diverse road and weather conditions around the world. And in the frantic race to roll out the first fully functional autonomous vehicle, that kind of mass, real-world intelligence can be invaluable.

Sparks of electricity emanating from a Tesla coil at the Mendeleyevskaya metro station in Moscow, Russia, January 24, 2016. Editor's Note: This is an excerpt from John Wasik's new book, "Lightning Strikes: Timeless Lessons in Creativity from the Life and Work of Nikola Tesla" (Sterling, 2016), slightly edited for this column. World-changing inventions made Nikola Tesla a celebrity in his own time, but something otherworldly makes him transcend his era and remain a perpetual beacon for our civilization 70 years after his death. He's now an immortal rock star, an icon for billionaires, cyberpunks, artists and "maker" inventors who are still fiddling with everyday machines in their basements and garages. Search engine designers, energy czars, musicians, artists and creators everywhere feel his influence.

This hunger for data, or "statistical inefficiency" is perhaps the most significant practical limitation of current deep learning technology. Many of our clients at Scyfer have problems that could be solved by deep learning, but don't have large annotated datasets. Scyfer Active Learning Platform: once integrated, our system will passively observe the work of a domain expert (whether that's a medical doctor diagnosing patients or a factory worker identifying defective products). As the system is starting to learn how to imitate the expert, it will identify its own weaknesses and ask for guidance from the expert, thereby greatly accelerating its learning without requiring so many examples. Data-efficient deep networks: by building in prior knowledge, like "a rotated teddy bear is still a teddy bear", we can drastically reduce the number of examples required to learn a new concept.