National Oceanic and Atmospheric Administration scientists are preparing to use machine learning (ML) to more easily monitor threatened ice seal populations in Alaska between April and May. Ice flows are critical to seal life cycles but are melting due to climate change -- which has hit the Arctic and sub-Arctic regions hardest. So scientists are trying to track species' population distributions. But surveying millions of aerial photographs of sea ice a year for ice seals takes months. And the data is outdated by the time statisticians analyze it and share it with the NOAA assistant regional administrator for protected resources in Juneau, according to a Microsoft blog post.

Moreland's project combines AI technology with improved cameras on a NOAA turboprop airplane that will fly over the Beaufort Sea north of Alaska this April and May, scanning and classifying the imagery to produce a population count of ice seals and polar bears that will be ready in hours instead of months. Her colleague Manuel Castellote, a NOAA affiliate scientist, will apply a similar algorithm to the recordings he'll pick up from equipment scattered across the bottom of Alaska's Cook Inlet, helping him quickly decipher how the shrinking population of endangered belugas spent its winter.

Meet Spot, the first robot to get its own employee number at Norwegian oil producer Aker BP. Developed by Boston Dynamics, the robot is set to start patrolling Aker BP's oil and gas production vessel at the Skarv field in the Norwegian Sea this year, testing its ability to run inspections, detect hydrocarbon leaks, gather data and generate reports. The upshot for Aker BP, which is seeking to be a front-runner in the digitalization of the oil industry, is to make offshore operations safer and more efficient, the company said as it presented the robot at its capital markets day in Oslo on Tuesday. Aker BP will run the tests with Cognite, the software venture controlled by the oil company's main owner, Aker ASA. "These things never get tired, they have a larger ability to adapt and to gather data," Kjetel Digre, Aker BP's senior vice president for operations, said in an interview.

The Norwegian oil company Aker BP ASA has announced it will bring aboard the infamous robotic watchdog Spot on the company's ships in the Skarv region of the Norwegian Sea. According to Aker, Spot will be charged with sniffing out hydrocarbon leaks, inspecting ship equipment, taking mechanical readings, generating reports, and completing inspections in areas that might be too dangerous for human workers. Spot was developed by the Massachusetts-based robotics company Boston Dynamics, which specializes in developing autonomous and humanoid machines. The Norwegian oil company Aker BP ASA announced it will begin using Boston Dynamics' robotic watchdog on Spot (pictured above) to help monitor equipment on its ships in the Norwegian Sea'These things never get tired, they have a larger ability to adapt and to gather data,' Aker BP ASA's Kjetel Digre told Bloomberg. The announcement is part of the Aker's new emphasis on'digitalization,' which it hopes will make its ships safer and more productive.

Seaborne robots have made a startling discovery beneath a 20-mile glacier in Alaska. The technology found the massive rivers of ice may be melting under the LeConte Glacier much faster than previously thought. Scientists programmed autonomous kayaks to swim near the icy cliffs of the glacier to measure the'ambient meltwater intrusions', which shows how much fresh water is flowing into the ocean from underneath the glacier. The study found ambient melting was 100 times higher than models had estimated. This is the first time experts have been able to analyze plumes of meltwater - the water released when snow or ice melts, where glaciers meet the ocean- because the feat is far too dangerous for ships due to falling ice of slabs from the glacier.

Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology. Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields. The first steps towards developing a deep learning model that can detect and classify microscopic foraminifera are proposed. The proposed model is based on a VGG16 model that has been pretrained on the ImageNet dataset, and adapted to the foraminifera task using transfer learning. Additionally, a novel image dataset consisting of microscopic foraminifera and sediments from the Barents Sea region is introduced.

The potential for advances in information-age technologies to undermine nuclear deterrence and influence the potential for nuclear escalation represents a critical question for international politics. One challenge is that uncertainty about the trajectory of technologies such as autonomous systems and artificial intelligence (AI) makes assessments difficult. This paper evaluates the relative impact of autonomous systems and artificial intelligence in three areas: nuclear command and control, nuclear delivery platforms and vehicles, and conventional applications of autonomous systems with consequences for nuclear stability. We argue that countries may be more likely to use risky forms of autonomy when they fear that their second-strike capabilities will be undermined. Additionally, the potential deployment of uninhabited, autonomous nuclear delivery platforms and vehicles could raise the prospect for accidents and miscalculation. Conventional military applications of autonomous systems could simultaneously influence nuclear force postures and first-strike stability in previously unanticipated ways. In particular, the need to fight at machine speed and the cognitive risk introduced by automation bias could increase the risk of unintended escalation. Finally, used properly, there should be many applications of more autonomous systems in nuclear operations that can increase reliability, reduce the risk of accidents, and buy more time for decision-makers in a crisis.

Science needs to understand and predict how climate change--and the growing onslaught of hurricanes, fires, and floods it's bringing--affects tropical forests. Will the forests respond to the assault with shorter trees? Will they store less carbon, or support less tree and plant diversity and fewer wildlife species? To better understand the effects a changing climate will have on tropical forests, Maria Uriarte, Columbia University professor of ecology, evolution, and environmental biology, needs to analyze images of forests. These bird's-eye view images are the size of a postage stamp.

Reliable maps of sea-ice conditions and forecasts are of vital importance for maritime safety, safe navigation and planning. The continued retreating and thinning of Arctic sea ice calls for a more effective way of producing detailed and timely ice information--which is where artificial intelligence comes in. Manual ice-charting from multi-sensor satellite data has been used for years, but it is time-consuming because of the vast area of the Arctic Ocean. In order to provide relevant ice data, there is a need for automated ice observations from satellite data, to integrate into ice forecast models. In response to this, the Danish Meteorological Institute (DMI) and Technical University of Denmark have initiated the project Automated Sea Ice Products (ASIP) – funded by the Innovation Fund Denmark.

The race to incorporate artificial intelligence in modern weapons threatens to outstrip the technology's capabilities -- and the world's ability to control them. 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 their environment and opponents and make "decisions" about altitude, speed, course -- and targets. "Work in this area is under way," 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. 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.