On August 21, 2017, there's going to be a total eclipse of the Sun visible on a line across the US. But when exactly will the solar eclipse occur at a given location? Being able to predict astronomical events has historically been one of the great triumphs of exact science. But in 2017, how well can it actually be done? Stephen Wolfram is a computer scientist, physicist, and businessman. Sign up to get Backchannel's weekly newsletter. The answer, I think, is well enough that even though the edge of totality moves at just over 1000 miles per hour it should be possible to predict when it will arrive at a given location to within perhaps a second. And as a demonstration of this, we've created a website to let anyone enter their geo location (or address) and then immediately compute when the eclipse will reach them--as well as generate many pages of other information. These days it's easy to find out when the next solar eclipse will be; indeed built right into the Wolfram Language there's ...

Bill Gates has previously called for a tax on robots to balance the Government's income as jobs are lost to automation. He said the levy could help slow down the pace of change and provide money to hire additional employees in sectors that require people, such as health care. "Right now, the human worker who does, say, $50,000 worth of work in a factory, that income is taxed and you get income tax, social security tax, all those things," said Gates in February. "If a robot comes in to do the same thing, you'd think that we'd tax the robot at a similar level."

Dan Hunter is a prison guard's worst nightmare. But he's not a hardened crim. As dean of Swinburne University's Law School, he's working to have most wardens replaced by a system of advanced artificial intelligence connected to a network of high-tech sensors. Called the Technological Incarceration Project, the idea is to make not so much an internet of things as an internet of incarceration. Professor Hunter's team is researching an advanced form of home detention, using artificial intelligence, machine-learning algorithms and lightweight electronic sensors to monitor convicted offenders on a 24-hour basis.

The Defense Advanced Research Projects Agencies is looking to apply the same kind of machine learning to the radio spectrum as is used by advanced systems for applications ranging from voice recognition to management of internet-of-things devices to autonomous vehicles. DARPA has issued a broad agency announcement for a new Radio Frequency Machine Learning Systems (RFMLS) program that will address the need for enhanced situational awareness regarding the ever-changing composition of RF signals in the IoT and spectrum sharing. Machine learning is widely used to manage data and images, but the similar work in the radio spectrum offers unique challenges, making a more compelling case for developing a native approach. "What I am imagining is the ability of an RF machine learning system to see and understand the composition of the radio frequency spectrum – the kinds of signals occupying it, differentiating those that are'important' from the background, and identifying those that don't follow the rules," said DARPA's Microsystems Technology Office Program Manager Paul Tilghman. An RFMLS would be able to discern subtle differences in the RF signals among identical, mass-manufactured IoT devices and identify signals intended to spoof or hack into these devices.

Spatio-temporal data and processes are prevalent across a wide variety of scientific disciplines. These processes are often characterized by nonlinear time dynamics that include interactions across multiple scales of spatial and temporal variability. The data sets associated with many of these processes are increasing in size due to advances in automated data measurement, management, and numerical simulator output. Non- linear spatio-temporal models have only recently seen interest in statistics, but there are many classes of such models in the engineering and geophysical sciences. Tradi- tionally, these models are more heuristic than those that have been presented in the statistics literature, but are often intuitive and quite efficient computationally. We show here that with fairly simple, but important, enhancements, the echo state net- work (ESN) machine learning approach can be used to generate long-lead forecasts of nonlinear spatio-temporal processes, with reasonable uncertainty quantification, and at only a fraction of the computational expense of a traditional parametric nonlinear spatio-temporal models.

The world was atwitter last week as President Trump crudely took North Korea to task over its nuclear program, a threat that has slowly but surely eclipsed the distractions of our imperialist wars in the Middle East. But according to tech mogul Elon Musk, humans face a far graver threat from something they use in their everyday lives and could be holding in their hands as they read these words: algorithmic artificial intelligence. Specifically, algorithmic AI that evolves into malevolent super-intelligent entities and seeks to end their meatbag parent species -- us. Late last week, Musk tweeted that AI is far more dangerous than North Korea, adding that he believes regulation will be necessary to contain the burgeoning technology. If you're not concerned about AI safety, you should be.

Cybersecurity machine learning is growing in popularity, according to Jon Oltsik, an analyst with Enterprise Strategy Group Inc. in Milford, Mass. Oltsik attended the recent Black Hat conference, where technology vendors were abuzz with talk of cybersecurity machine learning. ESG research asked 412 respondents about their understanding of artificial intelligence (AI) and cybersecurity machine learning, which revealed that only 30% said they were very knowledgeable on the subject. Only 12% of respondents said their organizations had deployed these systems widely. According to Olstik, the cybersecurity industry sees an opportunity, because only 6% of respondents in surveys said their organizations were not considering AI or machine learning deployments.

The combat environment of the future will be defined by adaptive human-machine teaming. Panelists at Defense One's Tech Summit July 13 discussed how advances in artificial intelligence and autonomous systems will yield a new class of warfighter--more capable, aware, and connected. Imagine an Air Force pilot flying an aircraft equipped with highly complex sensor platforms collecting data not only about the jet and the surrounding environment, but the pilot as well. Those sensors could measure the pilot's responsiveness and heart rate to determine his or her capacity to execute the mission--are they conscious and alert? Should the aircraft take corrective measures (such as unloading G's of force) to get the pilot back to maximum performance?

This article originally appeared in the July 3, 2017 issue of SpaceNews magazine. Mars 2020 is an ambitious mission. NASA plans to gather 20 rock cores and soil samples within 1.25 Mars years, or about 28 Earth months -- a task that would be impossible without artificial intelligence because the rover would waste too much time waiting for instructions. It currently takes the Mars Science Laboratory team at NASA's Jet Propulsion Laboratory eight hours to plan daily activities for the Curiosity rover before sending instructions through NASA's over-subscribed Deep Space Network. Program managers tell the rover when to wake up, how long to warm up its instruments and how to steer clear of rocks that damage its already beat-up wheels.

The moon isn't nearly as exciting as it used to be. Once we convinced ourselves that it was mostly just a big dead pile of rock (which didn't take long), interest and the funding that comes with it moved to Mars and beyond. The biggest thing that the moon has going for it is that it's relatively close to us: Spacecraft can get there in just a few days, and it takes only a couple of seconds for a signal to get there from here. The European Space Agency has been trying to encourage a hybrid approach to lunar usefulness, combining science with business (like mining and tourism) to help promote exploration in general. In partnership with the SpaceTech program at the Graz University of Technology in Austria, ESA is encouraging startups to develop ways of making money on the moon.