Cybersecurity is a top priority for federal agencies. Since taking office, President Donald Trump has proposed a cyber funding increase of 4 percent across the government, including significant hikes for the Department of Homeland Security and the Pentagon. There are even larger cyber funding spikes at key agencies, with a 23 percent jump at the Energy Department, a 33 percent jump at the Nuclear Regulatory Commission and a 16 percent hike at the Department of Veterans Affairs. IT analyst firm Gartner, too, has reported government CIOs will increase spending on cybersecurity. Its survey showed that CIOs at defense and intelligence agencies see artificial intelligence as a crucial technology investment -- more so than their counterparts in other industries.

The rare rebuke from the U.S. agency, which investigates significant accidents involving various transportation modes, comes amid mounting scrutiny of partial and fully automated driving technologies. The NTSB is also probing the March 18 death of a pedestrian fatally struck by an Uber Technologies Inc. self-driving vehicle with a safety operator behind the wheel in Tempe, Ariz. Not long after the NTSB expressed its disapproval Sunday, Tesla Chief Executive Elon Musk joked on Twitter that the company was going bankrupt. "Despite intense efforts to raise money, including a last-ditch mass sale of Easter eggs, we are sad to report that Tesla has gone completely and totally bankrupt," he wrote. "So bankrupt, you can't believe it." Mr. Musk's tweet, which doesn't appear to be in response to the NTSB, was timed for April Fools' Day and Easter.

NASA is no stranger to backing unusual technology if it'll help with exploring the cosmos, and its latest move is proof of that adventurous mindset. The administration has invested in 25 "early-stage" tech proposals that could improve both human and robotic exploration, and some of them are particularly inventive. The Shapeshifter concept you see above, for instance, envisions a horde of robots that combine into different forms to explore virtually every surface on Saturn's moon Titan. It could form an aircraft for high-altitude missions, a ball on the ground or a torpedo for under-liquid expeditions. Another proposal, Biobot, would offload some of an astronauts' life support and consumables to a companion robot tethered by an umbilical system.

In the public imagination, the Amish are famous for renouncing modern technology. In truth, many Amish farms hum with machines: milk vats, mechanical agitators, diesel engines, and pneumatic belt sanders are all found in their barns and workshops. The Amish don't actually oppose technology. Rather, the community must vote on whether to adopt a given item. To do so, they must agree almost unanimously, says Jameson Wetmore, a social science researcher at Arizona State University. Whereas the outside world may see innovation as good until proven otherwise, the Amish first decide whether a new technology might erode the community values they're trying to preserve.

Purpose: To determine if deep learning networks could be trained to forecast a future 24-2 Humphrey Visual Field (HVF). Participants: All patients who obtained a HVF 24-2 at the University of Washington. Methods: All datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington database. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction. Results: More than 1.7 million perimetry points were extracted to the hundredth decibel from 32,443 24-2 HVFs. The best performing model with 20 million trainable parameters, CascadeNet-5, was selected. The overall MAE for the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB). The 100 fully trained models were able to successfully predict progressive field loss in glaucomatous eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of predicted and actual future HVF (p < 2.2 x 10 -16 ) and an average difference of 0.41 dB. Conclusions: Using unfiltered real-world datasets, deep learning networks show an impressive ability to not only learn spatio-temporal HVF changes but also to generate predictions for future HVFs up to 5.5 years, given only a single HVF.

Legislators, the Federal Trade Commission and other agencies now are considering rules to protect the privacy of users of social networks like Facebook. While those efforts remain in the early stages, even tech companies say privately they expect some regulation to happen down the road. Yet some law-enforcement agencies, including the Federal Bureau of Investigation, and national-security advocates point to a tradeoff, noting that too much privacy can be as bad as too little. Bad actors take advantage of both extremes, abusing access to individuals on networks that are too open or freely conspiring on systems that are too closed. Law-enforcement agencies rely on access to user data as an important tool for tracking criminals or preventing terrorist attacks.

The robots are coming for our jobs--and we should welcome them. Why? Automation may help the U.S. economy break out of its productivity malaise. From 2007 through 2016, according to the Bureau of Labor Statistics, U.S. nonfarm business productivity grew just 1.2% annually, or well below past performance. In the 1990s, for example, nonfarm productivity grew 2.2% per year. A recent report by the McKinsey Global Institute argues that technologies such as A.I. and robotics offer a solution.

In the Central American country of Honduras, a political story has been unfolding which deserves more coverage than it has been getting. Close to 40 people have been killed and more than 2,000 arrested, following the contested re-election of President Juan Orlando Hernandez to a second term in office. With 54 percent of the votes counted, the trend was a clear win for left-wing opposition candidate, Salvador Nasralla. But then the computer system mysteriously broke down. When it finally came back online a full day later, the vote count had been turned upside down: the right-wing incumbent, Juan Orlando Hernandez, was suddenly ahead.

When a person screws up we call it human nature. So what does it mean when a machine that's trying to imitate our intelligence makes a mistake? According to the doomsayers, it means robots could attack us because of faulty reasoning – and that's scary. But, it's hard to fear a machine that can be defeated with tropical fruit. That's why we've gathered some of the best robot fails we could find to remind everyone we're still in charge. For starters, who could forget the Boston Dynamics' Atlas robot demonstration?

Since computers first became useful tools, there has been malware to infect them. In the nearly 50 years since the first virus emerged, cyber-criminals have become smarter and sneakier, developing more powerful and less detectable methods of entering devices and stealing information. Fortunately, cyber-defenses have also improved in that time. Now, every day brings an improvement to one side or the other, and both sides' weaponry has become so efficient that any slight edge seems significant. Unfortunately, a problem is developing -- a problem that will undoubtedly impact all aspects of modern civilization, but cyber security first: artificial intelligence. Machines that can gather data, analyze it for trends, and make accurate predictions are undeniably formidable tools, especially in the fast-paced cybersecurity war.