Antarctica's Thwaites glacier has warm water from three directions well under it threatening to destroy the ice sheet and raise global sea levels by up to two feet. A team of scientists from Oregon State University made the most of ice free waters in West Antarctica to look under the glacier - which is about the size of Great Britain. Warm water from the deep ocean is welling up under the glacier from three different directions and mixing under the ice, the researchers discovered. If it collapses it could take other parts of the ice shelf with it and lead to the single largest driver of sea-level rise this century, lead researcher Erin Pettit told Nature. The £39million study involving UK and US scientists was launched after concerns the increasingly unstable glacier may have already started to collapse.

In images taken from a satellite floating 400 kilometers above the Earth, Europe's humanitarian crisis shows up as white pixels against the blue-green vastness of the Mediterranean. Captured by the sensors in space, small overcrowded boats with migrants leaving Africa headed north look like tiny white comets bursting through the ocean, leaving a tail where they stir waves. "It's not that with every image I look at, I think about how someone could be dying right now," said Elisabeth Wittmann as she clicked through satellite footage on her laptop showing the coast west of the Libyan port of Sabratha. "That's also to protect myself," she added. The 26-year-old computer scientist from southern Germany is one of a dozen researchers who have teamed up with a new NGO called Space-Eye to develop artificial intelligence technology that allows computers to detect migrant boats in satellite images.

Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science. Large-scale spatial data often contains complex higher-order correlations across features and locations. While tensor latent factor models can describe higher-order correlations, they are inherently computationally expensive to train. Furthermore, for spatial analysis, these models should not only be predictive but also be spatially coherent. However, latent factor models are sensitive to initialization and can yield inexplicable results. We develop a novel Multi-resolution Tensor Learning (MRTL) algorithm for efficiently learning interpretable spatial patterns. MRTL initializes the latent factors from an approximate full-rank tensor model for improved interpretability and progressively learns from a coarse resolution to the fine resolution for an enormous computation speedup. We also prove the theoretical convergence and computational complexity of MRTL. When applied to two real-world datasets, MRTL demonstrates 4 ~ 5 times speedup compared to a fixed resolution while yielding accurate and interpretable models.

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 maritime and scientific communities have set themselves the ambitious target of 2030 to map Earth's entire ocean floor. You can argue about the numbers but it's in the region of 80% of the global seafloor that's either completely unknown or has had no modern measurement applied to it. The international GEBCO 2030 project was set up to close the data gap and has announced a number of initiatives to get it done. What's clear, however, is that much of this work will have to leverage new technologies or at the very least max the existing ones. Which makes the news from Ocean Infinity - that it's creating a fleet of ocean-going robots - all the more interesting. US-based OI is a relatively new exploration and survey company.

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.

A fleet of 11 uncrewed vessels will traverse the world's oceans over the next ten years in a bid to map the sea floor. The bottom of the world's oceans remains a mystery, with around 80 per cent either poorly imaged or not visualised at all. Ocean Infinity launched in 2016 and has pledged its support to an international collaboration to try and map every inch of the ocean floor within the next decade. It has also attempted to use its technology to try and locate the missing Malaysian Airlines MH370 flight that tragically went missing with 239 people on board nearly six years ago. It has announced it has bought a fleet of 11 uncrewed vessels will traverse the world's oceans over the next ten years in a bid to map the sea floor Uncrewed Surface Vessels (USV) are the latest technology which open up the possibility for long-term marine missions. They have no humans on board and are controlled by computers via a satellite link and a central computer base.

Recent powerful pre-trained language models have achieved remarkable performance on most of the popular datasets for reading comprehension. It is time to introduce more challenging datasets to push the development of this field towards more comprehensive reasoning of text. In this paper, we introduce a new Reading Comprehension dataset requiring logical reasoning (ReClor) extracted from standardized graduate admission examinations. As earlier studies suggest, human-annotated datasets usually contain biases, which are often exploited by models to achieve high accuracy without truly understanding the text. In order to comprehensively evaluate the logical reasoning ability of models on ReClor, we propose to identify biased data points and separate them into EASY set while the rest as HARD set. Empirical results show that state-of-the-art models have an outstanding ability to capture biases contained in the dataset with high accuracy on EASY set. However, they struggle on HARD set with poor performance near that of random guess, indicating more research is needed to essentially enhance the logical reasoning ability of current models. 1

Suppose that in a given planar circular region, there are some smart mobile evaders and we would like to find them using sweeping agents. We assume that the sweeping agents are in a line formation whose total length is 2r. We propose procedures for designing a sweeping process that ensures the successful completion of the task, thereby deriving conditions on the sweeping velocity of the linear formation and its path. Successful completion of the task means that evaders with a given limit on their velocity cannot escape the sweeping agents. A simpler task for the sweeping formation is the confinement of the evaders to their initial domain. The feasibility of completing these tasks depends on geometric and dynamic constraints that impose a lower bound on the velocity that the sweeper line formation must have. This critical velocity is derived to ensure the satisfaction of the confinement task. Increasing the velocity above the lower bound enables the agents to complete the search task as well. We present results on the total search time as a function of the sweeping velocity of the formation given the initial conditions on the size of the search region and the maximal velocity of the evaders.

Modern AI image classifiers have made impressive advances in recent years, but their performance often appears strange or violates expectations of users. This suggests humans engage in cognitive anthropomorphism: expecting AI to have the same nature as human intelligence. This mismatch presents an obstacle to appropriate human-AI interaction. To delineate this mismatch, I examine known properties of human classification, in comparison to image classifier systems. Based on this examination, I offer three strategies for system design that can address the mismatch between human and AI classification: explainable AI, novel methods for training users, and new algorithms that match human cognition.