The Commander-in-Chief of Russia's air force Viktor Bondarev has told a gathering at the MAKS-2017 international airshow his aircraft would soon be getting cruise missiles with artificial intelligence capable of analysing its environment and opponents and make "decisions" about altitude, speed, course -- and targets. "Work in this area is underway," Russian news agency TASS reports Tactical Missiles Corporation CEO Boris Obnosov as adding. "As of today, certain successes are available, but we'll still have to work for several years to achieve specific results." While neither indicated which missiles were slated to get such enhanced artificial intelligence, there are two apparent contenders among the "super weapons" President Vladimir Putin bragged about last year: the "Avangard" hypersonic glide vehicle and the "Burevestnik" nuclear-powered cruise missile. RELATED: Why the world's most holy place sends people crazy RELATED: Earth's magnetic pole is on the move and we don't know why Much modern weaponry is already capable of making choices -- such as the automated Gatling guns designed to react and shoot-down incoming missiles in the blink of an eye.

Boaty McBoatface may be better known for its name than for its oceangoing prowess. But the autonomous underwater vehicle and darling of the internet is headed to greater things: embarking on the longest journey of an AUV by far, with an uninterrupted, roughly 2,000-mile crossing of the Arctic Ocean. The submersible robot got its moniker when it became the consolation prize in a 2016 publicity stunt. The United Kingdom's Natural Environmental Research Council had created an online poll to name the country's new polar research ship. The public picked "Boaty McBoatface" (suggested by a BBC radio announcer), but the British government nixed the idea and named the ship after naturalist David Attenborough.

Scientists have long struggled with how to measure the effects of climate change on wildlife. This is especially true for birds flying in and out of coastal areas bordering the Arctic Ocean. In the past, researchers depended mainly on information gathered by satellite to follow the movement of birds and animals. But this method can be costly and result in huge amounts of information, which can be difficult to process. Now scientists are turning to another kind of technology to help them follow birds and other wildlife.

The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly. The trained neural network then replaces the traditional sub-grid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multi-year simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth System Model development could play a key role in reducing climate prediction uncertainty in the coming decade.

After midnight in a sweltering room in Pasadena in July 1976, Viking Mars team members sat hunched around a bulky monotone computer monitor, tensely awaiting the first data from the world's first successful Mars probe lander, the only Mars lander ever specifically designed to detect life. Over the next weeks each of Viking's first life-detection experiments came back with a striking signature. As the data trickled back into the Space Operations Facility, it became clear that carbon dioxide was released when organic compounds were added to Martian soil, though not when the mixture was superheated. This was a life signature, and exactly what had happened with the experiment on Earth. When water was added to the soil, oxygen was released, just as on Earth. The remote probe, panning for life, had found its signature in its first two experiments.

The GLOBSEC NATO Adaptation Initiative, led by retired General John R. Allen, presented on Monday (27 November) its final report on the future of the Alliance. General John R. Allen is a retired United States Marine Corps four-star general, and past Deputy Commander of US Central Command, prior to serving as Commander of the International Security Assistance Force and US Forces Afghanistan (USFOR-A). Alexander Russell "Sandy" Vershbow is an American diplomat and former Deputy Secretary General of the North Atlantic Treaty Organisation. From October 2005 to October 2008, he was the United States Ambassador to South Korea. What is GLOBSEC and what is the purpose of the report? Gen. Allen: GLOBSEC is a think tank, a public policy research institute in Slovakia, headquartered in Bratislava.

Temperatures in the Arctic have been rising so fast in recent decades they have confused a computer designed to measure them. Scientists monitoring a site in Alaska have found that an algorithm at the weather station, which has been recording temperatures for nearly 100 years, deleted all of its data from 2017, and even some from 2016. In what the experts are now calling an'ironic exclamation point' to rapid climate change, the algorithm flagged the abnormal temperatures observed at the station, as it assumed they were too high to be accurate. When scientists set out at the beginning of December to review the previous month's climate data, they noticed something'odd': everything from Utqiaġvik, Alaska was missing. The data from 2017 and some of 2016 had been flagged as artificial.

Arctic coastal morphology is governed by multiple factors, many of which are affected by climatological changes. As the season length for shorefast ice decreases and temperatures warm permafrost soils, coastlines are more susceptible to erosion from storm waves. Such coastal erosion is a concern, since the majority of the population centers and infrastructure in the Arctic are located near the coasts. Stakeholders and decision makers increasingly need models capable of scenario-based predictions to assess and mitigate the effects of coastal morphology on infrastructure and land use. Our research uses Gaussian process models to forecast Arctic coastal erosion along the Beaufort Sea near Drew Point, AK. Gaussian process regression is a data-driven modeling methodology capable of extracting patterns and trends from data-sparse environments such as remote Arctic coastlines. To train our model, we use annual coastline positions and near-shore summer temperature averages from existing datasets and extend these data by extracting additional coastlines from satellite imagery. We combine our calibrated models with future climate models to generate a range of plausible future erosion scenarios. Our results show that the Gaussian process methodology substantially improves yearly predictions compared to linear and nonlinear least squares methods, and is capable of generating detailed forecasts suitable for use by decision makers.

Future missions to Mars, like the one planned by Nasa for 2020, have a'high chance' of finding microbial life, according to Professor Brian Cox. The physicist, best known for presenting Stargazing Live and Wonders of the Universe, says that these organisms may be more common than we might think in our solar system. Civilisations are another matter, he believes, and it may still be the case that we are alone in the universe as its only advanced intelligence. Future missions to Mars, like the one planned by Nasa for 2020, have a'high chance' of finding microbial life, according to Professor Brian Cox. Finding evidence of life on Mars will almost certainly involve a rover, and scientists admit it will be tough.

Speckling the surface of one of Mars' oldest impact basins, NASA's Mars Reconnaissance Orbiter has spotted a sprawling expanse of'honeycomb' landforms, with individual cells of up to 6 miles wide. The origin of these textured features has long remained a mystery, as scientists debate which type of natural process could be responsible, from glacial events to wind erosion. It's possible that multiple processes are at play, according to NASA, with evidence suggesting the honeycombs and the surrounding landscape in Mars northwestern Hellas Planitia may still be undergoing activity today. Speckling the surface of one of Mars' oldest impact basins, NASA's Mars Reconnaissance Orbiter has spotted a sprawling expanse of'honeycomb' landforms, with individual cells of up to 6 miles wide. According to NASA, the area has features of different natural processes, suggesting activity may still be reshaping the land today.