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.

Wind speed retrieval at sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologies, especially satellite sensors, provide complementary means to monitor wind speed. As sea surface winds produce sounds that propagate underwater, underwater acoustics recordings can also deliver fine-grained wind-related information. Whereas model-driven schemes, especially data assimilation approaches, are the state-of-the-art schemes to address inverse problems in geoscience, machine learning techniques become more and more appealing to fully exploit the potential of observation datasets. Here, we introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics possibly complemented by other data sources such as weather model reanalyses. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency. Numerical experiments on real data demonstrate that we outperform the state-of-the-art data-driven methods with a relative gain up to 16% in terms of RMSE. Interestingly, these results support the relevance of the time dynamics of underwater acoustic data to better inform the time evolution of wind speed. They also show that multimodal data, here underwater acoustics data combined with ECMWF reanalysis data, may further improve the reconstruction performance, including the robustness with respect to missing underwater acoustics data.

MOUNTAIN VIEW, Calif., Aug 16 (Reuters) - Alphabet Inc's (GOOGL.O) Google is combining the eyes and arms of physical robots with the knowledge and conversation skills of virtual chatbots to help its employees fetch soda and chips from breakrooms with ease. The mechanical waiters, shown in action to reporters last week, embody an artificial intelligence breakthrough that paves the way for multipurpose robots as easy to control as ones that perform single, structured tasks such as vacuuming or standing guard. Google robots are not ready for sale. They perform only a few dozen simple actions, and the company has not yet embedded them with the "OK, Google" summoning feature familiar to consumers. While Google says it is pursuing development responsibly, adoption could ultimately stall over concerns such as robots becoming surveillance machines, or being equipped with chat technology that can give offensive responses, as Meta Platforms Inc (META.O) and others have experienced in recent years.

Lawrence Berkeley National Lab is hiring for Full Time 2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences - San Francisco, CA - a Mid-level AI/ML/Data Science role offering benefits such as Career development, Competitive pay, Equity, Relocation support

The objective of this paper is to employ machine learning (ML) and deep learning (DL) techniques to obtain from input data (storm features) available in or derived from the HURDAT2 database models capable of simulating important hurricane properties such as landfall location and wind speed that are consistent with historical records. In pursuit of this objective, a trajectory model providing the storm center in terms of longitude and latitude, and intensity models providing the central pressure and maximum 1-$min$ wind speed at 10 $m$ elevation were created. The trajectory and intensity models are coupled and must be advanced together, six hours at a time, as the features that serve as inputs to the models at any given step depend on predictions at the previous time steps. Once a synthetic storm database is generated, properties of interest, such as the frequencies of large wind speeds may be extracted from any part of the simulation domain. The coupling of the trajectory and intensity models obviates the need for an intensity decay inland of the coastline. Prediction results are compared to historical data, and the efficacy of the storm simulation models is demonstrated for three examples: New Orleans, Miami and Cape Hatteras.

A year ago researchers from the University of Zurich showcased their autonomous drones that were able to beat the fastest human pilots. However, that race wasn't "fair" in the sense that the AI algorithm commanding the drones had extra information that human pilots didn't have. In particular, the algorithm had access to near-perfect location and velocity estimation of the drones using motion capture systems, high-quality maps of the race course beforehand, and stereo cameras that can give depth information. This year, the team's autonomous drones raced on even playing fields without these handicaps, and its AI was able to beat the best human-controlled time by 0.5s in a three-lap race, a significant lead in the world of drone racing. Our take: This development is representative of AI progress ins many fields, where the researchers first make a working system with additional assumptions and then slowly chip away at these assumptions for a more robust and adaptable AI system.

Role requiring'No experience data provided' months of experience in San Francisco Job DescriptionPosition: Oracle SQL DeveloperLocation: South san Francisco CARequired Skills: Database, Database, Java, Oracle Application Server.Skill Description:•Develop and provide support to all system interfaces and coordinate with all project managers to provide specifications for all core modules.•Coordinate

Role requiring'No experience data provided' months of experience in None We're looking for a Build and Release Engineer to join our team as a master of packages and containers who always delivers the goods. A key member of the CI/CD pipeline from planning all the way through deployment, you'll collaborate with other skilled engineers to identify technical needs, develop solutions, and deploy them using the latest tools available. Sure, you'll use your head to build and maintain the tools, infrastructure, and processes that directly impact our development teams and customers. But you're a developer at heart, and this is a role in which you'll craft code aplenty and keep your finger on the pulse of modern software build engineering practices. Inductive Automation is an innovation company. We are champions for industrial automation software, and we believe in building sensible solutions that provide value for our customers. Our workforce shares a passion for technology.

The ideal candidate will be responsible for architecting automation solutions for QA services, along with providing support for implementation. Candidate will be expected to effectively lead, monitor and improve automation service creation and business growth.

Over the weekend, debris from an out-of-control Chinese rocket crashed to Earth over the Indian and Pacific oceans. There had been fears that pieces of the 23-tonne Long March 5B booster could come down over a populated area, but experts had said the probability of this was extremely low. Nevertheless, NASA hit out at China by accusing Beijing of not sharing the'specific trajectory information' needed to calculate where possible debris might fall. Elsewhere at the weekend, a 10ft (3m) piece of space junk – thought to be from one of Elon Musk's spacecrafts – crashed into a farmer's property in Australia at around 15,500mph (25,000km/h). The object, believed to be part of the SpaceX Crew-1 craft, was found in a sheep paddock by a farmer living on a large property in the Snowy Mountains in New South Wales.