Ice cores in freezers, dinosaurs on display, fish in jars, birds in boxes, human remains and ancient artifacts from long gone civilizations that few people ever see – museum collections are filled with all this and more. These collections are treasure troves that recount the planet's natural and human history, and they help scientists in a variety of different fields such as geology, paleontology, anthropology and more. What you see on a trip to a museum is only a sliver of the wonders held in their collection. Museums generally want to make the contents of their collections available for teachers and researchers, either physically or digitally. However, each collection's staff has its own way of organizing data, so navigating these collections can prove challenging.

It seems like it should be pretty easy to get to Mercury. The little rocky planet is so much closer to Earth than distant destinations like Jupiter, where we've successfully sent multiple spacecraft. Plus, it doesn't have a crushing atmosphere like our nearest neighbor Venus. But, in fact, it's actually really difficult to reach the innermost planet of our solar system--which makes it that much more impressive that the ESA and JAXA's BepiColombo mission has almost reached Mercury, recently completing its final flyby of the planet before entering orbit next year. Reaching Mercury is such a challenge because "the gravitational pull of the Sun is very strong near Mercury, which makes it difficult for spacecraft to slow down enough to enter orbit around the planet," explains Lina Hadid, staff scientist at CNRS in France and principal investigator of one of BepiColombo's instruments.

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.

With the recent rise of neural operators, scientific machine learning offers new solutions to quantify uncertainties associated with high-fidelity numerical simulations. Traditional neural networks, such as Convolutional Neural Networks (CNN) or Physics-Informed Neural Networks (PINN), are restricted to the prediction of solutions in a predefined configuration. With neural operators, one can learn the general solution of Partial Differential Equations, such as the elastic wave equation, with varying parameters. There have been very few applications of neural operators in seismology. All of them were limited to two-dimensional settings, although the importance of three-dimensional (3D) effects is well known. In this work, we apply the Fourier Neural Operator (FNO) to predict ground motion time series from a 3D geological description. We used a high-fidelity simulation code, SEM3D, to build an extensive database of ground motions generated by 30,000 different geologies. With this database, we show that the FNO can produce accurate ground motion even when the underlying geology exhibits large heterogeneities. Intensity measures at moderate and large periods are especially well reproduced. We present the first seismological application of Fourier Neural Operators in 3D. Thanks to the generalizability of our database, we believe that our model can be used to assess the influence of geological features such as sedimentary basins on ground motion, which is paramount to evaluating site effects.

"An opportunity to pursue an alternate paradigm of research that explores data science methods in earth sciences." At its inception, most geoscientists believed that the oceans and landmasses were fixed and permanent, as the basic features of the earth's crust. Subsequently, geology underwent a conceptual revolution, geoscientists came to a consensus that indeed the earth was covered by rigid plates, thin in relation to the earth's diameter. These plates' creation, displacement, and destruction were consequential of the mid-ocean ridges, the areas of mountains and earthquake activity, and the deep ocean trenches. Evidently, the conceptual revolution has opened Pandora's box. There are more questions, questions that need answers.

The Moon’s polar regions are home to craters and other depressions that never receive sunlight. Today, a group of researchers led by the Max Planck Institute for Solar System Research (MPS) in Germany presents the highest-resolution images to date covering 17 such craters in the journal Nature Communications. Craters of this type could contain frozen water, making them attractive targets for future lunar missions, and the researchers focused further on relatively small and accessible craters surrounded by gentle slopes. In fact, three of the craters have turned out to lie within the just-announced mission area of NASA's Volatiles Investigating Polar Exploration Rover (VIPER), which is scheduled to touch down on the Moon in 2023. Imaging the interior of permanently shadowed craters is difficult, and efforts so far have relied on long exposure times resulting in smearing and lower resolution. By taking advantage of reflected sunlight from nearby hills and a novel image processing method, the researchers have now produced images at 1-2 meters per pixel, which is at or very close to the best capability of the cameras.

Armed with a newly minted undergraduate degree in geology, Jasper Baur is in the mining business. Not those mines where we extract metals or minerals; the kind that kill and maim thousands of people every year. As a freshman at upstate New York's Binghamton University in 2016, Baur started working with two geophysics professors, Alex Nikulin and Timothy de Smet, to look into employing instrument-equipped drones to speed the slow, hazardous task of finding land mines. Baur stuck with the research all the way through college; now a grad student in volcanology at Columbia University's Lamont-Doherty Earth Observatory, he is still pursuing it. "It seemed like a really relevant and impactful use of science," he said.

NASA has been forced to end its mission to drill down into the Martian soil after its unique geology proved too much for the InSight lander. The InSight probe was equipped with a probe -- dubbed the Mole -- which was going to drill up to 10 feet into the ground. However, the agency said that the soil's "unexpected tendency to clump" meant that the drill could never get enough purchase to function properly. It's the end of a long saga that began at the start of 2019 when the properties of Mars' soil proved tough to crack. After plenty of trial-and-error, and some help from InSight's robotic arm, the hardware only managed to reach a few centimeters into the ground.

As a geologist and professor I speak and write rather cavalierly about eras and eons. One of the courses I routinely teach is "History of Earth and Life," a survey of the 4.5-billion-year saga of the entire planet--in a 10-week trimester. But as a human, and more specifically as a daughter, mother, and widow, I struggle like everyone else to look Time honestly in the face. That is, I admit to some time hypocrisy. The now risible "Y2K" crisis that threatened to cripple global computer systems and the world economy at the turn of the millennium was caused by programmers in the 1960s and '70s who apparently didn't really think the year 2000 would ever arrive.

An important problem in geostatistics is to build models of the subsurface of the Earth given physical measurements at sparse spatial locations. Typically, this is done using spatial interpolation methods or by reproducing patterns from a reference image. However, these algorithms fail to produce realistic patterns and do not exhibit the wide range of uncertainty inherent in the prediction of geology. In this paper, we show how semantic inpainting with Generative Adversarial Networks can be used to generate varied realizations of geology which honor physical measurements while matching the expected geological patterns. In contrast to other algorithms, our method scales well with the number of data points and mimics a distribution of patterns as opposed to a single pattern or image. The generated conditional samples are state of the art.