The 1556 earthquake that killed an estimated 830,000 people in the Shaanxi Province is but the deadliest example of China's long history with the natural disaster. The 1920 Haiyuan quake killed 273,000; the 1976 Tangshan earthquake claimed about 232,000 lives. Whether or not they hold to the historic view that earthquakes indicate heaven's displeasure, the modern Chinese aren't sitting idle as the ground trembles. Starting in the mid-1960s, the country established a system to improve prediction capabilities, response training, and public communications to reduce the impact of calamities. They enlisted satellites to shape post-disaster responses, but since quakes have a habit of knocking out the ground-based systems that deliver their images to those who need them, a view from space isn't always much help.

When people hear about artificial intelligence, they have one of two responses: they are terrified of a Skynet dystopia, or they are excited for the new possibilities afforded by machine learning and robotics. While 2017 will not be the year that humanoid, Westworld-esque robots work alongside us or take over all of our jobs, we will definitely be seeing an even smarter circulation of "alternative facts". We will see greater capabilities from AI in facilitating business processes such as services, software delivery and IT infrastructure changes. Google has built a hub for chatbots to fetch information from the net, Freshdesk acquired Chatimity to strengthen its customer service chatbot capabilities, and Microsoft has had another bash at its AI chatbot with Zo. In process flow scenarios, ChatOps bots will be more fluid in enabling processes using simple commands. You could write something like "I need help with ticket 6876 from network, database, and payment processing," and all necessary information would be pulled for you, from across all relevant systems.

Thanks to science fiction, we can't think about artificial intelligence without summoning the ghost of 2001: A Space Odyssey's killer AI HAL 9000; we have to make the obligatory Terminator reference in any story about cutting edge robots; and the picture that immediately springs to mind when we mention robotic exoskeletons are bulky pieces of kit straight out of Iron Man or the underrated Tom Cruise flick Edge of Tomorrow. However, when it comes to that last research topic, investigators at the Harvard Biodesign Lab are doing everything they can to change our perceptions and convince us that robotic exoskeletons don't have to be rigid cages surrounding a person's limbs, applying torque directly to their joints to aid them with a specific motion. While this approach can be hugely promising in helping individuals with paraplegia, it's less necessary in other scenarios. For example, in situations where you have an individual with gait impairments, but who still has function in their lower limbs (such as a post-stroke patients or individuals with multiple sclerosis), applying the same approach is a bit like using a sledgehammer to crack a nut. The considerable weight of rigid exoskeletons and the energy expenditure they therefore prompt in the person wearing them isn't something to be sniffed at.

Last spring the startup Flow Health began a five-year contract with the Department of Veteran Affairs to examine all historic and ongoing medical records. The startup will use information obtained from those records to train artificial intelligence to, among other things, fight illness and predict disease for the more than eight million people cared for by the Department of Veteran Affairs. Advice and predictions from Flow Health will be presented to health care professionals through Vista, the DoD's open source system for electronic medical records. Doctors can then choose to apply or ignore the advice drawn from the VA's vast storage of medical records. "When a veteran comes in and presents certain clinical symptoms, we can better understand and make predictions about'What is the likely diagnosis? What is the best diagnostic test? What's the best care pathway?' and so forth," CEO Alex Meshkin told VentureBeat in a Skype interview.

Robert Morley, chief operating officer at Excello Law, examines the use of AI in the legal industry. Alongside Brexit and Donald Trump, artificial Intelligence (AI) kept headline writers working overtime in 2016 – each competing for our attention in a world of increasing uncertainty. Online, a clear winner has emerged from this unlikely trio of scary topics: AI returns nearly two billion results in Google search, whereas'The Donald' scores a more modest 368 million and Brexit a mere 108 million. Among articles and blogs about the future of law firms, AI has similarly dominated attention as the number one theme. Underpinned by dystopian visions of lawyers being replaced by robots and the growing ranks of tech companies dedicated to replacing the human element from much of day-to-day legal practice, the future for the lawyer has seemed bleak.

If you're trying to figure out how best to measure human performance under extreme conditions, the skies are a great place to start. Not much pushes the body harder than space travel or extreme flight. Successful missions depend on knowing precisely how astronauts and pilots respond to huge physical strain, and that requires a level of real-time biometric tracking way beyond your average heart-rate chest strap. That's where wearable innovator LifeBEAM comes in. Founded in 2011, the New York-based business has spent years developing cutting-edge solutions that give NASA and the US Air Force mission-critical insights.

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As a postdoc, plant biologist Christopher Topp was not satisfied with the usual way of studying root development: growing plants on agar dishes and placing them on flatbed scanners to measure root lengths and angles. Five years later, the idea of using detailed imaging to study plant form and function has caught on. The use of drones and robots is also on the rise as researchers pursue the'quantified plant'--one in which each trait has been carefully and precisely measured from nearly every angle, from the length of its root hairs to the volatile chemicals it emits under duress. Such traits are known as an organism's phenotype, and researchers are looking for faster and more comprehensive ways of characterizing it. From February 10 to 14, scientists will gather in Tucson, Arizona, to compare their methods.

Reading Data Science – Machine Learning vs Rules Based Systems by Karthik Guruswamy Data Scientist at Teradata got me thinking about how machine learning is replacing rule-based systems in how we manage and use enterprise content, knowledge, and ideas. These types of rules are extremely easy to understand and easy to code. But, Karthik points out, things quickly get out of hand: "When a system gets operationalized, one starts with 100 scenarios with 100 rules to handle it. As time goes by we encounter more and more exceptions and start making more rules to keep exceptions under control. Think US Tax Code – things get unwieldy and cumbersome over time."

RICHMOND, VIRGINIA – Robots about the size of a beer cooler could soon be rolling down Virginia sidewalks to deliver sandwiches, groceries or packages. Supporters say some pending legislation would make the state the first in the nation to regulate such devices. State lawmakers partnered with European company Starship Technologies on bills allowing Virginia cities to join two others in the U.S. and many across Europe where the company is testing its largely autonomous earthbound robots. Much like other tech companies' attempts at airborne drone deliveries, Starship aims to revolutionize the way people get their parcels. Representatives from the company visited Richmond on Wednesday for a demonstration.