Prism Courage, a 134,000-tonne commercial tanker, recently sailed from the Gulf of Mexico to South Korea while controlled mostly by an artificial intelligence system called HiNAS 2.0. Avikus, a subsidiary of South Korean technology giant Hyundai, recently announced that Prism Courage, a tanker designed to transport natural gas, had become the first large ship to make an ocean passage of over 10,000 km (6,210 miles) autonomously. The key to this incredible achievement was HiNAS 2.0, an AI-powered system capable of analyzing different kinds of sensor readings in real-time and responding to them swiftly, efficiently, and, most importantly, in accordance with the rules of maritime laws. Just like airplanes, ships have very advanced auto-pilots capable of keeping them on a steady course, responding to GPS waypoints and currents, and even bringing them into harbor in case the human crew is no longer present on board or capable of doing it. However, sailing autonomously for tens of thousands of kilometers through the Atlantic is a lot more complex than putting a ship on autopilot. Apart from steering the tanker in real0-time, Avikus' HiNAS 2.0 system is capable of picking the optimal routes and best speeds to reach its destination, by analyzing data collected through advanced sensors.

Anyone who has more money than they know what to do with eventually tries to cure aging. Google founder Larry Page has tried it. Jeff Bezos has tried it. Tech billionaires Larry Ellison and Peter Thiel have tried it. Now the oil-rich kingdom of Saudi Arabia, which has around as much money as all of them put together, is going to try it.

Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g. compatible with the CALPHAD method), albeit focused on the reduced set of phases observed at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local (metastable) free energy minimum, which may exhibit desirable properties. Here, we introduce an automated workflow that integrates first-principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases to construct “metastable” phase diagrams for materials far-from-equilibrium. Using carbon as a prototypical system, we demonstrate automated metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to far-from-equilibrium (400 meV/atom). We incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for efficient construction of metastable phase diagrams. We use the metastable phase diagram and identify domains of relative stability and synthesizability of metastable materials. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy (HRTEM) confirm our metastable phase predictions. In particular, we identify the previously ambiguous structure of n-diamond as a cubic-analog of diaphite-like lonsdaelite phase. Exploration of metastable phases of a given elemental composition is a data-intensive task. Here the authors integrate first-principles atomistic simulations with machine learning and high-performance computing to allow a rapid exploration of the metastable phases of carbon.

Autonomous ships just took a small but important step forward. Hyundai's Avikus subsidiary says it has completed the world's first autonomous navigation of a large ship across the ocean. The Prism Courage (pictured) left Freeport in the Gulf of Mexico on May 1st, and used Avikus' AI-powered HiNAS 2.0 system to steer the vessel for half of its roughly 12,427-mile journey to the Boryeong LNG Terminal in South Korea's western Chungcheong Province. The Level 2 self-steering tech was good enough to account for other ships, the weather and differing wave heights. The autonomy spared the crew some work, of course, but it may also have helped the planet. Avikus claims HiNAS' optimal route planning improved the Prism Courage's fuel efficiency by about seven percent, and reduced emissions by five percent.

A robot army is beginning its march across rural America, promising to transform the future of food. Twenty-five intelligent machines were dispatched last month to the Midwest and the Mississippi Delta, where they will advance over newly planted fields at 12 miles an hour, annihilating baby weeds. Produced by John Deere and created by the startup Blue River Technology, these robotic weeders look much like standard industrial sprayers at first glance, but each is rigged with an intricate system of 36 cameras and a mass of tiny hoses. They use computer vision to distinguish between crops and weeds and then deploy with sniper-like precision tiny jets of herbicide onto the weeds -- sparing the crop and ending the common practice of broadcast-spraying chemicals across billions of acres.

AI is revolutionizing the world we live in--from the way we entertain ourselves, to the products and services that we consume, to the way we care for our bodies, and how we go about our daily work. Organizations are leveraging the power of AI to transform our lives by accelerating superior product innovations, increasing organization competitiveness no matter their size or available resources, and immersing us into more amazing, photo-realistic virtual worlds in movies and games. At Microsoft, our mission is to empower every person and every organization on the planet to achieve more. With the power and scalability available through Microsoft Azure, we provide the compute tools and capabilities for all organizations no matter their size or resources to do more, faster. AI is a key tool to help organizations innovate and create new capabilities, discover new insights and deliver superior products and services.

AUSTIN – The Railroad Commission has turned to artificial intelligence to optimize the time the agency's technical analysts spend on seismicity reviews to ensure residents and the environment are protected. Seismicity reviews are conducted by the Underground Injection Control (UIC) Department for injection/disposal well permits in areas susceptible to earthquakes and in certain geologic zones. UIC staff programed a machine learning algorithm to help with the large amount of information that needs to be processed and digested. Along with some other changes, tasks performed by the machine learning algorithm have enabled UIC to wipe out a backlog of seismic reviews to zero today. Like the technical analyst, the AI program weighs many factors related to the number, severity and proximity of earthquakes and uses a decision tree to assign a grade to the review.