Iran's recent seizure of unmanned US Navy boats shined a light on a pioneering Pentagon program to develop networks of air, surface, and underwater drones for patrolling large regions, meshing their surveillance with artificial intelligence. The year-old program operates numerous unmanned surface vessels, or USVs, in the waters around the Arabian peninsula, gathering data and images to be beamed back to collection centers in the Gulf. The program operated without incident until Iranian forces tried to grab three seven-meter Saildrone Explorer USVs in two incidents, on August 29-30 and September 1. In the first, a ship of Iran's Islamic Revolutionary Guard Corps hooked a line to a Saildrone in the Gulf and began towing it away, only releasing it when a US Navy Patrol boat and helicopter sped to the scene. In the second, an Iranian destroyer picked up two Saildrones in the Red Sea, hoisting them aboard.

Iran's recent seizure of unmanned US Navy boats shined a light on a pioneering Pentagon program to develop networks of air, surface and underwater drones for patrolling large regions, meshing their surveillance with artificial intelligence. The year-old program operates numerous unmanned surface vessels, or USVs, in the waters around the Arabian peninsula, gathering data and images to be beamed back to collection centers in the Gulf. The program operated without incident until Iranian forces tried to grab three seven-meter Saildrone Explorer USVs in two incidents, on August 29-30 and September 1. In the first, a ship of Iran's Islamic Revolutionary Guard Corps hooked a line to a Saildrone in the Gulf and began towing it away, only releasing it when a US Navy Patrol boat and helicopter sped to the scene. In the second, an Iranian destroyer picked up two Saildrones in the Red Sea, hoisting them aboard.

Iran's recent seizure of unmanned US Navy boats shined a light on a pioneering Pentagon program to develop networks of air, surface and underwater drones for patrolling large regions, meshing their surveillance with artificial intelligence. The year-old program operates numerous unmanned surface vessels, or USVs, in the waters around the Arabian peninsula, gathering data and images to be beamed back to collection centers in the Gulf. The program operated without incident until Iranian forces tried to grab three seven-meter Saildrone Explorer USVs in two incidents, on August 29-30 and September 1. In the first, a ship of Iran's Islamic Revolutionary Guard Corps hooked a line to a Saildrone in the Gulf and began towing it away, only releasing it when a US Navy Patrol boat and helicopter sped to the scene. In the second, an Iranian destroyer picked up two Saildrones in the Red Sea, hoisting them aboard.

Self-supervised methods have shown tremendous success in the field of computer vision, including applications in remote sensing and medical imaging. Most popular contrastive-loss based methods like SimCLR, MoCo, MoCo-v2 use multiple views of the same image by applying contrived augmentations on the image to create positive pairs and contrast them with negative examples. Although these techniques work well, most of these techniques have been tuned on ImageNet (and similar computer vision datasets). While there have been some attempts to capture a richer set of deformations in the positive samples, in this work, we explore a promising alternative to generating positive examples for remote sensing data within the contrastive learning framework. Images captured from different sensors at the same location and nearby timestamps can be thought of as strongly augmented instances of the same scene, thus removing the need to explore and tune a set of hand crafted strong augmentations. In this paper, we propose a simple dual-encoder framework, which is pre-trained on a large unlabeled dataset (~1M) of Sentinel-1 and Sentinel-2 image pairs. We test the embeddings on two remote sensing downstream tasks: flood segmentation and land cover mapping, and empirically show that embeddings learnt from this technique outperform the conventional technique of collecting positive examples via aggressive data augmentations.

Antarctica's Thwaites Glacier is'holding on by its fingernails', experts say, after discovering that it has retreated twice as fast as previously thought over the past 200 years. The West Antarctica glacier – which is about the size of Florida – has been an important consideration for scientists trying to make predictions about global sea level rise. The potential impact of its retreat is huge because a total loss of Thwaites and its surrounding icy basins could raise global sea levels by up to 10 feet. That is why it is widely nicknamed the'Doomsday Glacier.' For the first time, scientists mapped in high-resolution a critical area of the seafloor in front of Thwaites that gives them a window into how fast the glacier has retreated and moved in the past.

Methods of eXplainable Artificial Intelligence (XAI) are used in geoscientific applications to gain insights into the decision-making strategy of Neural Networks (NNs) highlighting which features in the input contribute the most to a NN prediction. Here, we discuss our lesson learned that the task of attributing a prediction to the input does not have a single solution. Instead, the attribution results and their interpretation depend greatly on the considered baseline (sometimes referred to as reference point) that the XAI method utilizes; a fact that has been overlooked so far in the literature. This baseline can be chosen by the user or it is set by construction in the method s algorithm, often without the user being aware of that choice. We highlight that different baselines can lead to different insights for different science questions and, thus, should be chosen accordingly. To illustrate the impact of the baseline, we use a large ensemble of historical and future climate simulations forced with the SSP3-7.0 scenario and train a fully connected NN to predict the ensemble- and global-mean temperature (i.e., the forced global warming signal) given an annual temperature map from an individual ensemble member. We then use various XAI methods and different baselines to attribute the network predictions to the input. We show that attributions differ substantially when considering different baselines, as they correspond to answering different science questions. We conclude by discussing some important implications and considerations about the use of baselines in XAI research.

Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth. Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. For example, agents may negotiate and reach climate agreements, but there is no central authority to enforce adherence to those agreements. Hence, it is critical to design negotiation and agreement frameworks that foster cooperation, allow all agents to meet their individual policy objectives, and incentivize long-term adherence. This is an interdisciplinary challenge that calls for collaboration between researchers in machine learning, economics, climate science, law, policy, ethics, and other fields. In particular, we argue that machine learning is a critical tool to address the complexity of this domain. To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks. We also describe how to use multi-agent reinforcement learning to train rational agents using RICE-N. This framework underpinsAI for Global Climate Cooperation, a working group collaboration and competition on climate negotiation and agreement design. Here, we invite the scientific community to design and evaluate their solutions using RICE-N, machine learning, economic intuition, and other domain knowledge. More information can be found on www.ai4climatecoop.org.

Various existing studies have analyzed what social biases are inherited by NLP models. These biases may directly or indirectly harm people, therefore previous studies have focused only on human attributes. However, until recently no research on social biases in NLP regarding nonhumans existed. In this paper, we analyze biases to nonhuman animals, i.e. speciesist bias, inherent in English Masked Language Models such as BERT. We analyzed speciesist bias against 46 animal names using template-based and corpus-extracted sentences containing speciesist (or non-speciesist) language. We found that pre-trained masked language models tend to associate harmful words with nonhuman animals and have a bias toward using speciesist language for some nonhuman animal names. Our code for reproducing the experiments will be made available on GitHub.

Definition: The terms "robotics in snow and ice" refers to robotic systems being studied, developed, and used in areas where water can be found in its solid state. This specialized branch of field robotics investigates the impact of extreme conditions related to cold environments on autonomous vehicles.

… enabling planetary scale analysis through artificial intelligence and machine learning. The company also supports a diverse set of federal …