Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that is routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas data compression techniques are concerned with reducing dimensionality, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data is available. An implementation of the method is available in a companion repository (https://github.com/NREL/Phase-space-sampling).

Those updates can be anything from software and firmware in other parts of a system, which can alter performance, power and thermal gradients, to new algorithms in the AI system or system of systems. Modifications to an algorithm can change the data flow within a system, stressing different parts of a system differently and at different times. These systems are supposed adapt to whatever is optimal for a particular user or device or application, so each system will adjust itself to the optimum performance or energy consumption based upon internal variations. That makes it almost impossible to predict how a system will behave, and it makes it much harder to pinpoint problems when a system is in use. The challenge going forward will be to build tools and systems that can both measure and predict all of these possible permutations, and to build sensors and analytics into systems that can at least give engineers some idea of how the hardware is behaving.

If human societies do nothing, in just a few decades, the planet could warm to levels it hasn't reached in at least 34 million years, leading to more melting glaciers and floods than ever before -- as well as the dire effect of urban heat waves. In 2021, in the U.S. alone, there were already 18 extreme climate-related disasters with losses exceeding $1 billion each, according to the National Oceanic and Atmospheric Administration. When looking at the world's natural calamities on a consequence and frequency scale, floods and earthquakes have a more devastating effect on people and property, but they occur less frequently than heat waves, which generally take the form of urban heat islands (UHIs). These are also known as heat pockets, which are found across cities' downtown areas, where temperatures are higher than the peripheries. With urbanized areas warming up fast, many more populations globally are bound to face the deadly consequences of the heat-island effect, highlighting urban public health disparities.

Every day, artificial intelligence opens up amazing opportunities all over the world. Today, artificial intelligence technology is being implemented in companies to improve decision making, business processes and obtain valuable insights. Artificial Intelligence has great potential to save our planet: detecting energy emission reductions, optimizing energy production, monitoring deforestation, predicting extreme weather conditions, cleaning oceans and protecting inhabitants, and much more. In this article, we will take a closer look at some AI solutions that save our planet. Also, you can find examples of AI startups, which effectively invested in AI technology for saving the planet.

Solar energy is now the cheapest form of electricity in history. Unfortunately, significantly increasing the grid's fraction of solar energy remains challenging due to its variability, which makes balancing electricity's supply and demand more difficult. While thermal generators' ramp rate -- the maximum rate that they can change their output -- is finite, solar's ramp rate is essentially infinite. Thus, accurate near-term solar forecasting, or nowcasting, is important to provide advance warning to adjust thermal generator output in response to solar variations to ensure a balanced supply and demand. To address the problem, this paper develops a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning. Specifically, we develop deep auto-regressive models using convolutional neural networks (CNN) and long short-term memory networks (LSTM) that are globally trained across multiple locations to predict raw future observations of the spatio-temporal data collected by the recently launched GOES-R series of satellites. Our model estimates a location's future solar irradiance based on satellite observations, which we feed to a regression model trained on smaller site-specific solar data to provide near-term solar photovoltaic (PV) forecasts that account for site-specific characteristics. We evaluate our approach for different coverage areas and forecast horizons across 25 solar sites and show that our approach yields errors close to that of a model using ground-truth observations.

At the end of last month, a set of principles and advice on ethics in the use of Artificial Intelligence (AI) was known, adopted for the first time jointly and unanimously by the 193 member states of the General Council of the UNESCO. Beyond the uniqueness of its universal character, it is about Unesco launched a guide to improve the relationship between humans and robots and combines ethical issues to a warning voice that has been heard for a long time. There are already several international political organizations that have been warning about the need to provide an ethical component to what is undoubtedly the most notable advance in applied science of our time. In fact, in November but from '19 the European Union (EU) had published its Ethical Guidelines for a reliable artificial intelligence whose proposal revolves around the collateral effects, or unforeseen risks, that the implementation of disruptive technologies like this can generate. Likewise, in April of this year we learned about the European Commission regulation regarding the use of algorithms able to learn and make decisions.

Wind power is a powerful tool for reducing carbon emissions that cause climate change. The turbines, however, can be a threat to birds and bats, which is why experts are looking for--and finding--ways to eliminate the danger. The US government has allocated $13.5 million to look for solutions. But, already a Boulder, Colorado company has produced a camera- and AI-based technology that can recognize eagles, hawks and other raptors as they approach in enough time to pause turbines in their flight path. Their tool, called IdentiFlight, can detect 5.62 times more bird flights than human observers alone, and with an accuracy rate of 94 percent.

The machine learning community has recently had increased interest in the climate and disaster damage domain due to a marked increased occurrences of natural hazards (e.g., hurricanes, forest fires, floods, earthquakes). However, not enough attention has been devoted to mitigating probable destruction from impending natural hazards. We explore this crucial space by predicting building-level damages on a before-the-fact basis that would allow state actors and non-governmental organizations to be best equipped with resource distribution to minimize or preempt losses. We introduce PreDisM that employs an ensemble of ResNets and fully connected layers over decision trees to capture image-level and meta-level information to accurately estimate weakness of man-made structures to disaster-occurrences. Our model performs well and is responsive to tuning across types of disasters and highlights the space of preemptive hazard damage modelling.

A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm (DSA), is proposed in this paper. This algorithm is inspired by the searching for food sources and foraging behaviors of the duck swarm. The performance of DSA is verified by using eighteen benchmark functions, where it is statistical (best, mean, standard deviation, and average running time) results are compared with seven well-known algorithms like Particle swarm optimization (PSO), Firefly algorithm (FA), Chicken swarm optimization (CSO), Grey wolf optimizer (GWO), Sine cosine algorithm (SCA), and Marine-predators algorithm (MPA), and Archimedes optimization algorithm (AOA). Moreover, the Wilcoxon rank-sum test, Friedman test, and convergence curves of the comparison results are used to prove the superiority of the DSA against other algorithms. The results demonstrate that DSA is a high-performance optimization method in terms of convergence speed and exploration-exploitation balance for solving high-dimension optimization functions. Also, DSA is applied for the optimal design of two constrained engineering problems (the Three-bar truss problem, and the Sawmill operation problem). Additionally, four engineering constraint problems have also been used to analyze the performance of the proposed DSA. Overall, the comparison results revealed that the DSA is a promising and very competitive algorithm for solving different optimization problems.

The rise of computation-based methods in thermal management has gained immense attention in recent years due to the ability of deep learning to solve complex 'physics' problems, which are otherwise difficult to be approached using conventional techniques. Thermal management is required in electronic systems to keep them from overheating and burning, enhancing their efficiency and lifespan. For a long time, numerical techniques have been employed to aid in the thermal management of electronics. However, they come with some limitations. To increase the effectiveness of traditional numerical approaches and address the drawbacks faced in conventional approaches, researchers have looked at using artificial intelligence at various stages of the thermal management process. The present study discusses in detail, the current uses of deep learning in the domain of 'electronic' thermal management.