Alan Turing is famous for several reasons, one of which is that he cracked the Nazis' seemingly unbreakable Enigma machine code during World War II. Later in life, Turing also devised what would become known as the Turing test for determining whether a computer was "intelligent" -- what we would now call artificial intelligence (AI). Turing believed that if a person couldn't tell the difference between a computer and a human in a conversation, then that computer was displaying AI. AI and information security have been intertwined practically since the birth of the modern computer in the mid-20th century. For today's enterprises, the relationship can generally be broken down into three categories: incident detection, incident response, and situational awareness -- i.e., helping a business understand its vulnerabilities before an incident occurs.

NASA's bid to crowdsource an arm for its Astrobee cube robot is starting to bear fruit. The agency and Freelancer.com have chosen early winners for the Astrobee Challenges Series, each of which has designed a key component for the robotic appendage. South African grad student Nino Wunderlin produced an attachment mechanism, while Filipino conceptual engineer Myrdal Manzano crafted a "smart" attachment system. Indian software engineer Amit Biswas, in turn, developed a simple deployment mechanism. There's still a ways to go when nine of the contests in the challenge have yet to be unlocked.

In the late 1990s, two graduate students named in Stanford's computer science department set out to organize the world's information. Shortly thereafter, a visiting scholar named Mario Schlosser arrived on campus, set on figuring out how trust could be built into peer-to-peer networks. The original server used by the graduate students, who were now running a little outfit named Google, had formerly been crammed under a desk in the office Schlosser now used. Now, twenty years later, the graduate students have done OK, and they no longer need to borrow server space. But they've continually been stifled by one kind of information that's very hard to organize: health care data. The industry in America is a mess: yoked together with confusing regulations, perverse incentives, and computers running Windows XP. Meanwhile, Schlosser, has moved on from academia and created a company, called Oscar, with Joshua Kushner (brother of Jared) to try to solve those problems. The goal of Oscar is to do to health care what Uber did to the taxi industry: use smart digital technology to make everything faster and easier for customers, and then use the data gathered to build radically new services, which can collect more data that leads to new services. Google invested early in Oscar through its venture capital fund Capital G and its health services spinoff, Verily. Neither company will give exact figures, but it seems that Alphabet will now own roughly ten percent of the Oscar. One of Google earliest employees, Salar Kamangar, the former CEO of YouTube, will also join Oscar's board. I spoke with Schlosser for an hour on Monday about the deal, privacy, data, and whether, one day, we'll actually treat our gastroenteritis through an app.

You may have seen the words'artificial intelligence' and'machine learning' widely used in the technology industry at the moment, and their appearances are no less prominent in cybersecurity. ABI Research predicts that machine learning in cybersecurity will help boost intelligence, analytics, and big data spending to US$96 billion by 2021. "We are in the midst of an artificial intelligence (AI) security revolution," says ABI Research analyst Dimitrios Pavlakis. "This will drive machine learning solutions to soon emerge as the new norm beyond security information and event management (SIEM) and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years." Beyond the numbers and the terminology, there is a simple question: What does machine learning do for cybersecurity, anyway?

Climate change has been bleaching coral reefs, decimating the local marine species that call them home, since at least the first major observations were recorded in the Caribbean in 1980. Thankfully, new A.I. cataloguing designed to identify the geographic regions where coral is still thriving hopes to reverse the trend, saving some of the world's most dense and varied aquatic ecosystems from all-but-certain extinction. There are numerous reasons why we need to care about saving coral reefs, from the ethical to the economic. In addition to housing about a quarter of marine species, these reefs provide $375 billion USD in revenue to the world economy, according to the Guardian, and food security to half a billion people. Without them, researchers say countless species and the entire ocean fishing industry that depends on them would simply evaporate.

The AI Times is a weekly newsletter covering the biggest AI, machine learning, big data, and automation news from around the globe. If you want to read A I before anyone else, make sure to subscribe using the form at the bottom of this page. The five-year research partnership with the University of Toronto will allow LG to build on its Open Platform-Open Partnership-Open Connectivity strategy to expand the AI ecosystem. Forecasts show that AI will underpin many of the future advances -- and leaps -- in business productivity. Delve into how algorithms, data and new workflows will ensure your future AI success.

Smart home enthusiasts take note: You can tie Ambient Weather's WS-2902 Osprey weather station into your smart home system, so your indoor lights won't wait until sunset to turn on when it's gray and dreary outside; your smart sprinkler will stop watering the lawn if it starts to rain; and you can ask Alexa or Google Assistant for a weather report from where you actually live, not from the closest National Weather Service (NWS) monitoring station. You'd think most home weather stations do these things, but they don't. Netatmo comes close, with hooks into Samsung SmartThings and IFTTT, but you'll spend more than $300 to build out a complete Netatmo system that can measure temperature, rainfall, and wind speed. The WS-2902 Osprey is built with connectivity top of mind. It's Wi-Fi capable and it works with IFTTT, so you can program weather events and conditions to trigger actions in your smart home.

This is an extract from my near-future science-fiction novel, Timing. It has been prepared using Amazon's text-to-speech engine called Polly to generate the various voices. It is near the start of the book and relates a conversation between four of the main characters - Mitnash Thakur, his AI companion Slate, and two friends called Parvati and Chandrika... Some pictures are public domain from NASA/JPL

Scientists at the Army Research Laboratory and Carnegie Mellon University's Robotics Institute are teaching robots how to be better mission partners to soldiers -- starting with how to find their way with minimal human intervention. Given that autonomous vehicles have been navigating streets in many U.S. cities for over a year, that may seem like not that big a deal. But according to ARL researcher Maggie Wigness, the challenges facing military robots are much greater. Specifically, unlike the self-driving cars being developed by Google, Uber and others, military robots will be operating in complex environments that don't have the benefit of standardized markings like lanes, street signs, curbs and traffic lights. "Environments that we operate in are highly unstructured compared to [those for] self-driving cars," Wigness said.

The first wave of AI hospital tools, including voice, patient experience and remote monitoring, are relatively easy to implement and deliver care to people with limited access. More highly specialized AI solutions will require higher expenses (until scale and reimbursement broaden access) and new capabilities. In mature markets like the US and EU, hospital viability will depend on their ability to adapt to the AI Era. The 5,534 US hospitals face an existential crisis. Median operating margins plummeted from 3.4 percent in 2015 to 2.7 percent in 2016 to under 2 percent in 2017.