You've probably got at least one on your person right now. They're built to fit into smartwatches and smaller things, and that small size hampers how well they can sense changes. Engineers in Florida have now come up with a new take on the accelerometer that is as much as 1 million times as sensitive as a typical smartphone accelerometer, and it maintains that sensitivity up to a car-crash-scale 100 gs. That combination of high sensitivity and large dynamic range in a cube that's just 3 millimeters on a side should make the new accelerometer particularly useful in things that move quickly in three-dimensions, such as military drones, microrobots, and self-guided projectiles, according its inventors. Ordinary MEMS accelerometers are made up of a moveable plate and a stationary plate, oriented perpendicular to each dimension measured.

NPR's Mary Louise Kelly speaks with Pennsylvania Attorney General Josh Shapiro about his office's intent to look into how the data of 50 million Facebook users got into the hands of the political data-mining firm, Cambridge Analytica.

Robots have moved into factories, warehouses, stores and even our homes. Tech startups are developing self-driving bulldozers, drones to inspect work sites and robot bricklayers. In this photo taken Jan. 26, 2018, Mike Moy, an assistant plant manager for Lehigh Hanson Cement Group, inspects a Kespry drone he uses to survey inventories of rock, sand and other building materials at a mining plant in Sunol, California. Robots are coming to a construction site near you. Tech startups are developing self-driving bulldozers, survey drones and bricklaying robots to help the construction industry boost productivity, speed and safety as it struggles to find enough skilled workers.

Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is structural information that can be used to predict complex chemical properties. In this work, we develop SMILES2vec, a deep RNN that automatically learns features from SMILES to predict chemical properties, without the need for additional explicit feature engineering. Using Bayesian optimization methods to tune the network architecture, we show that an optimized SMILES2vec model can serve as a general-purpose neural network for predicting distinct chemical properties including toxicity, activity, solubility and solvation energy, while also outperforming contemporary MLP neural networks that uses engineered features. Furthermore, we demonstrate proof-of-concept of interpretability by developing an explanation mask that localizes on the most important characters used in making a prediction. When tested on the solubility dataset, it identified specific parts of a chemical that is consistent with established first-principles knowledge with an accuracy of 88%. Our work demonstrates that neural networks can learn technically accurate chemical concept and provide state-of-the-art accuracy, making interpretable deep neural networks a useful tool of relevance to the chemical industry.

This farm in Arkansas may soon be the most scientifically advanced farm in the world. There's a farm in Arkansas growing soybeans, corn, and rice that is aiming to be the most scientifically advanced farm in the world. Soil samples are run through powerful machines to have their microbes genetically sequenced, drones are flying overhead taking hyperspectral images of the crops, and soon supercomputers will be crunching the massive volumes of data collected. Scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab), working with the University of Arkansas and Glennoe Farms, hope this project, which brings together molecular biology, biogeochemistry, environmental sensing technologies, and machine learning, will revolutionize agriculture and create sustainable farming practices that benefit both the environment and farms. If successful, they envision being able to reduce the need for chemical fertilizers and enhance soil carbon uptake, thus improving the long-term viability of the land, while at the same time increasing crop yields.

Five simple soft sensor methodologies with two update conditions were compared on two experimentally-obtained datasets and one simulated dataset. The soft sensors investigated were moving window partial least squares regression (and a recursive variant), moving window random forest regression, the mean moving window of y, and a novel random forest partial least squares regression ensemble (RF-PLS), all of which can be used with small sample sizes so that they can be rapidly placed online. It was found that, on two of the datasets studied, small window sizes led to the lowest prediction errors for all of the moving window methods studied. On the majority of datasets studied, the RF-PLS calibration method offered the lowest onestep-ahead prediction errors compared to those of the other methods, and it demonstrated greater predictive stability at larger time delays than moving window PLS alone. It was found that both the random forest and RF-PLS methods most adequately modeled the datasets that did not feature purely monotonic increases in property values, but that both methods performed more poorly than moving window PLS models on one dataset with purely monotonic property values. Other data dependent findings are presented and discussed. Preprint submitted to Arxiv March 14, 2018 1. Introduction Soft sensors for regression tasks have found wide utility in process engineering and process analytical chemistry [1, 2, 3]. A soft sensor is effectively a calibration used on time-series data. Here, we consider a soft sensor to be any algorithm that can be used to estimate a property value from several readily available but indirect measurements. The goal of implementing a soft sensor is typically to avoid the use of a physical sensor for variables that may require extensive time or work up to measure [3]. In the context of industrial chemical processes, these algorithms should meet several specifications.

Data is shaping almost every area of our lives. Agriculture has been slow to embrace new technology but even here it's beginning to have a big impact. There are now hundreds of companies offering everything from farm management and precision tools to bots and drones. Some tractors have computing power that would have turned Nasa's moon-landing mission green with envy. What started in farm equipment is moving into the field – at least in the developed world.

Health Catalyst introduced Touchstone at HIMSS18 and, in so doing, described it as a performance discovery, prioritization, benchmarking and recommendation tool. "Touchstone is built from the ground up on the latest AI and software from Silicon Valley," said Dale Sanders, President of Technology, Health Catalyst. "Touchstone's recommendation engine, which borrows from Amazon and Netflix, gives you not just comparative benchmarks but recommendations to improve your performance against benchmarks." The technology includes risk models based on artificial intelligence and anomaly detection algorithms that hospitals can use to pinpoint underperforming areas. Touchstone performs risk-adjusted benchmarking by culling data in claims, cost-accounting systems, EHRs, external benchmarks and operations to risk-adjust benchmarking, to "guide users to the data and analyses of greatest relevance to their work and to the organization's goals," the company said.

"Just because something works, doesn't mean it can't be improved." She has an innovative spirit and mind, and she wants to take her African nation of Wakanda to a new level. Wakanda is a world in which women play an equal and vital role, and can ascend to any position. Shuri possesses the genius to take vibranium -- the precious metal that fuels Wakanda's technology -- to drive new innovations that keeps this African country more advanced than all others. Technological change and advancement is often the focus of the future.

The International Monetary Fund (IMF) has joined criticism of Donald Trump's plan to impose a 25% tariff on steel imports and 10% on aluminium. The body warned that such a move would hurt the US as well as other countries. It said others could follow the US president's precedent by claiming tough trade restrictions were needed to defend national security. Canada, the largest supplier of steel to the US, said tariffs would cause disruption on both sides of the border. It is one of several countries that have said they will consider retaliatory steps if the president presses ahead with his plan next week.