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.

The Anglo-Saxon vessel found in the Sutton Hoo burial mound in Suffolk 80 years ago will sail again as experts look to rebuild the ship from digital 3D models. Dated back to the early 7th century, the 90 foot (27 metre) -long vessel is oft dubbed a'ghost ship' thanks to its manner of preservation. In the mound -- thought the resting place of King Rædwald -- only the impression of the ship and its iron rivets remained, the timber having long rotted away. Nevertheless, a team of archaeologists and shipbuilders have succeeded in creating a three-dimensional digital mock-up of the vessel to allow it to be reconstructed. Expert hope that recreating a full-size, fully-operational version of the ship will help shine light on how the Anglo-Saxons began England's tradition of seafaring.

Artificial intelligence is coming for America's high-paid professions as it creates winners and losers across the labor market like never before. White-collar jobs and better-educated occupations along with production workers are among the most susceptible to AI's spread into the economy, according to a Brookings Institution report Wednesday that draws on a new analysis of patent data by a Stanford University economist. "Just as the impacts of robotics and software tend to be sizable and negative on exposed middle- and low-skill occupations, so AI's inroads are projected to negatively impact higher-skill occupations," researchers Mark Muro, Jacob Whiton and Robert Maxim wrote. Workers with graduate or professional degrees will be almost four times as exposed to AI as workers with just a high school degree, the report showed. The researchers also concluded that AI appears most likely to affect men, prime-age and white and Asian American workers.

Prototype of the Force 12 Xplorer being tested near Victoria, British Columbia. It uses a rigid ... [ ] wingsail for propulsion. It's been a great year for Open Ocean Robotics, a British Columbia-based startup that makes solar-powered drones that can gather environmental data in real time and help address a multitude of issues. During 2019, Open Ocean Robotics won a most-promising startup award from the National Community for Angels, Incubators, and Accelerators; $100,000 in a Spring Impact Investor Challenge; and was a finalist in a New Ventures BC Competition, to name a few. So how do you follow that up for 2020?

Neural networks have become increasingly prevalent within the geosciences for applications ranging from numerical model parameterizations to the prediction of extreme weather. A common limitation of neural networks has been the lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have typically been used within the geosciences to accurately identify a desired output given a set of inputs, with the interpretation of what the network learns being used - if used at all - as a secondary metric to ensure the network is making the right decision for the right reason. Network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of a neural network can enable the discovery of scientifically meaningful connections within geoscientific data. By training neural networks to use one or more components of the earth system to identify another, interpretation methods can be used to gain scientific insights into how and why the two components are related. In particular, we use two methods for neural network interpretation. These methods project the decision pathways of a network back onto the original input dimensions, and are called "optimal input" and layerwise relevance propagation (LRP). We then show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network-related geoscience research.

Recently, scientist Gagandeep Kang had to forcefully remind a room full of senior colleagues -- all men -- that she was the chair and that they should speak only when their turn comes. This kind of thing happens all the time, and you become so inured to it that you don't realise it," she says. Kang is the first Indian woman to be elected as a fellow of the Royal Society, but even that, evidently, does not protect you from microaggressions from men. It is a reminder of the kind of bias that women in science have to deal with. Prejudice at many levels is one reason why there are far fewer women scientists than men in the higher echelons of science in India. A 2016-17 report, "Status of Women in Science Among Select Institutions in India: Policy Implications", supported by NITI Aayog, found that while women constitute over a third of science graduates and postgraduates, they make up only 15-20% of tenured faculty across research institutions and universities in India. "As a group, it is not easy for women to stay in science. Only 14% of scientists are women," science writers Nandita Jayaraj and Aashima Dogra write in their recent book, 31 Fantastic Adventures in Science: Women Scientists in India. However, there are women who have beaten odds and shattered stereotypes and glass ceilings. This special feature looks at nine such women who are doing critical work in science and technology in India. They work on an array of complex problems -- in fields ranging from quantum computation to paleoecology. Neuroscientist Vidita Vaidya is looking to decode how experiences and the environment affect the circuits in our brain, which might offer a clue to how we develop psychiatric disorders. Aditi Sen De, the first woman to receive the Shanti Swarup Bhatnagar Prize in physical sciences, is working on different aspects of quantum communication, a field that uses the laws of quantum physics to protect data. This is by no means an exhaustive list of exceptional women scientists, but they are representative of the brilliant minds that have striven and made it to the top and become exemplars. As Kang says, "If you see role models, you see areas you can aspire to.

Artificial intelligence is everywhere but it is considered in a wholly ahistorical way. To understand the impact AI will have on our lives, it is vital to appreciate the context in which the field was established. After all, statistics and state control have evolved hand in hand for hundreds of years. Its origins have been traced not only to analytic philosophy, pure mathematics and Alan Turing, but perhaps surprisingly, to the history of public administration. In "The Government Machine: A Revolutionary History of the Computer" from 2003, Jon Agar of University College London charts the development of the British civil service as it ballooned from 16,000 employees in 1797 to 460,000 by 1999.

The assignment of individual fish to its stock of origin is important for reliable stock assessment and fisheries management. Otolith shape is commonly used as the marker of distinct stocks in discrimination studies. Our literature review showed that the application and comparison of alternative statistical classifiers to discriminate fish stocks based on otolith shape is limited. Therefore, we compared the performance of two traditional and four machine learning classifiers based on Fourier analysis of otolith shape using selected stocks of Atlantic cod (Gadus morhua) in the southern Baltic and Atlantic herring (Clupea harengus) in the western Norwegian Sea, Skagerrak and the southern Baltic Sea. Our results showed that the stocks can be successfully discriminated based on their otolith shapes. We observed significant differences in the accuracy obtained by the tested classifiers.

Mainali's expertise is already being shared outside of the Chesapeake Bay watershed, including a project concerning water flow management in Colorado in an area where water is a scarce resource. To help manage the demands of water for agriculture and wildlife, Mainali wrote algorithms that optimize the water flow and release from reservoirs by various agencies. These algorithms will be used for informed decision making about sharing the responsibility of water release from multiple reservoirs on a daily basis.

Artificial intelligence is coming for America's high-paid professions as it creates winners and losers across the labor market like never before. White-collar jobs and better-educated occupations along with production workers are among the most susceptible to AI's spread into the economy, according to a Brookings Institution report Wednesday that draws on a new analysis of patent data by Stanford University graduate student Michael Webb. "Webb's modeling suggests that just as the impacts of robotics and software tend to be sizable and negative on exposed middle- and low-skill occupations, so AI's inroads are projected to negatively impact higher-skill occupations," researchers Mark Muro, Jacob Whiton and Robert Maxim wrote, noting that their analysis shows potential impacts can be both positive and negative. Workers with graduate or professional degrees will be almost four times as exposed to AI as workers with just a high school degree, the report showed. The researchers also concluded that AI appears most likely to affect men, prime-age and white and Asian American workers.