Machine learning is gaining popularity across scientific and technical fields, but it's often not clear to researchers, especially young scientists, how they can apply these methods in their work. In many ways, ESS present ideal use cases for ML applications because the problems being addressed--like climate change, weather forecasting, and natural hazards assessment--are globally important; the data are often freely available, voluminous, and of high quality; and computational resources required to develop ML models are steadily becoming more affordable. Free computational languages and ML code libraries are also now available (e.g., scikit-learn, PyTorch, and TensorFlow), contributing to making entry barriers lower than ever. Nevertheless, our experience has been that many young scientists and students interested in applying ML techniques to ESS data do not have a clear sense of how to do so. An ML algorithm can be thought of broadly as a mathematical function containing many free parameters (thousands or even millions) that takes inputs (features) and maps those features into one or more outputs (targets).

Japan's catastrophic Fukushima disaster in 2011 has resulted in a unique species of boar-pig, a new study reveals. Researchers investigating the effects of the nuclear disaster on animals in the area report that radiation has had no adverse effects on their genetics. However, wild boars (Sus scrofa leucomystax) have proliferated in the area, after being left to roam freely from the lack of humans. The boars have bred with domestic pigs (Sus scrofa domesticus) that escaped from nearby properties after farmers had to flee, creating a new hybrid species. Rare spotted wild boar observed inside the evacuated area of Fukushima, Japan, indicative of the'introgression' - the transfer of genetic information from one species to another - with domestic pigs Images from remotely-operated cameras indicate wildlife is flourishing in Fukushima's exclusion zone. Wildlife ecologist James Beasley of the University of Georgia and colleagues used a network of 106 remote cameras to capture images of the wildlife in the area over a four-month period.

The Navy, through its Office of Naval Research, is pursuing artificial intelligence applications across a broad spectrum of the service's responsibilities to man, train and equip, as well as warfighting, sustainment and readiness. Such a wide range of applications and algorithms come with specific data requirements and data management. Curtis Pelzer, chief information officer at the Office of Naval Research, said ONR's data resides in in several places on their network, and it's the job of the data and analytics team to make sure information is provided and kept in the right sets. AI can help reduce toil across the Navy, give autonomy with unmanned systems, and software codes can increase the speed and quality of human decision-making, according to Brett Vaughan, the Navy's chief artificial intelligence officer and Office of Naval Research portfolio manager. Vaughan said any data could potentially fuel AI, but it depends on what problem one aims to solve.

It took Michelle Anderson a month to create her E3 2019 outfit. It took her another hour to put it on. She wore a wig with red Afro puffs, an army-green tactical vest and fake bloodstained bandage. She completed the look with medical gloves and a mask looped around her neck, then took one last look in the mirror before she headed out the door. She was dressed as Lifeline, a playable combat medic from the video game "Apex Legends."

A total of 34% of AI research and development projects fails or are abandoned, according to a recent survey by Rackspace Technology of 1,870 companies. We propose a new strategic framework, aiSTROM, that empowers managers to create a successful AI strategy based on a thorough literature review. This provides a unique and integrated approach that guides managers and lead developers through the various challenges in the implementation process. In the aiSTROM framework, we start by identifying the top n potential projects (typically 3-5). For each of those, seven areas of focus are thoroughly analysed. These areas include creating a data strategy that takes into account unique cross-departmental machine learning data requirements, security, and legal requirements. aiSTROM then guides managers to think about how to put together an interdisciplinary artificial intelligence (AI) implementation team given the scarcity of AI talent. Once an AI team strategy has been established, it needs to be positioned within the organization, either cross-departmental or as a separate division. Other considerations include AI as a service (AIaas), or outsourcing development. Looking at new technologies, we have to consider challenges such as bias, legality of black-box-models, and keeping humans in the loop. Next, like any project, we need value-based key performance indicators (KPIs) to track and validate the progress. Depending on the company's risk-strategy, a SWOT analysis (strengths, weaknesses, opportunities, and threats) can help further classify the shortlisted projects. Finally, we should make sure that our strategy includes continuous education of employees to enable a culture of adoption. This unique and comprehensive framework offers a valuable, literature supported, tool for managers and lead developers.

Military applications of artificial intelligence, we are told, are poised to transform military power. They might make the oceans transparent to sensor systems, threatening at-sea nuclear deterrent systems like the UK's Trident. They might enable autonomous aircraft that could outfight human crewed planes. They could transform intelligence processing in war, enable all sorts of complex weapons that would make things like tanks and aircraft carriers yesterday's news. The sky, it appears, is the limit. In this light, big states are making large investments in military AI. One aspect of the UK's recent Integrated Review (ahem, "Global Britain in a Competitive Age") and Command Paper (ahem, "Defence in a competitive age") is a bet that investment in military applications of artifical intelligence will offset cuts to things like tanks and troop numbers.

Operational forecasting centers are investing in decadal (1-10 year) forecast systems to support long-term decision making for a more climate-resilient society. One method that has previously been employed is the Dynamic Mode Decomposition (DMD) algorithm - also known as the Linear Inverse Model - which fits linear dynamical models to data. While the DMD usually approximates non-linear terms in the true dynamics as a linear system with random noise, we investigate an extension to the DMD that explicitly represents the non-linear terms as a neural network. Our weight initialization allows the network to produce sensible results before training and then improve the prediction after training as data becomes available. In this short paper, we evaluate the proposed architecture for simulating global sea surface temperatures and compare the results with the standard DMD and seasonal forecasts produced by the state-of-the-art dynamical model, CFSv2.

Time series is a special type of sequence data, a set of observations collected at even intervals of time and ordered chronologically. Existing deep learning techniques use generic sequence models (e.g., recurrent neural network, Transformer model, or temporal convolutional network) for time series analysis, which ignore some of its unique properties. For example, the downsampling of time series data often preserves most of the information in the data, while this is not true for general sequence data such as text sequence and DNA sequence. Motivated by the above, in this paper, we propose a novel neural network architecture and apply it for the time series forecasting problem, wherein we conduct sample convolution and interaction at multiple resolutions for temporal modeling. The proposed architecture, namelySCINet, facilitates extracting features with enhanced predictability. Experimental results show that SCINet achieves significant prediction accuracy improvement over existing solutions across various real-world time series forecasting datasets. In particular, it can achieve high fore-casting accuracy for those temporal-spatial datasets without using sophisticated spatial modeling techniques. Our codes and data are presented in the supplemental material.

Orcas have complex social structures that include close friendships, a study that used drones to film the animals suggests. The marine mammals – also known as killer whales – live in groups of related individuals called pods, which have their own distinct cultures. The new findings show each orca spends more time interacting with certain individuals in their pod, and they tend to favour those of the same sex and similar age. But as they get older, whales appear to grow apart, according to research led by the University of Exeter, UK, and the Center for Whale Research, Washington. "Until now, research on killer whale social networks has relied on seeing the whales when they surface, and recording which whales are together," said Michael Weiss at the University of Exeter, the study's lead author.

Most prior work on task-oriented dialogue systems are restricted to limited coverage of domain APIs. However, users oftentimes have requests that are out of the scope of these APIs. This work focuses on responding to these beyond-API-coverage user turns by incorporating external, unstructured knowledge sources. Our approach works in a pipelined manner with knowledge-seeking turn detection, knowledge selection, and response generation in sequence. We introduce novel data augmentation methods for the first two steps and demonstrate that the use of information extracted from dialogue context improves the knowledge selection and end-to-end performances. Through experiments, we achieve state-of-the-art performance for both automatic and human evaluation metrics on the DSTC9 Track 1 benchmark dataset, validating the effectiveness of our contributions.