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The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1–5 min) but low spatial resolution (≥2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with deep learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps. [ABSTRACT FROM AUTHOR]

Technology always leaping more forward than before this can happen because there's a many inovations and work of mankind, as example we can take it from the development of Artificial Intelligence with this kind of development is a subject of a Research and Development from Technologies itself that can make work more easier than before and of course Artificial Intelligence always related to Big data and computing power that can think and make conclusions like human being, on the side of Law this development is a pretty game changer such as on Indonesia itself there was a website called a HukumOnline who has already launching an platform LIA (Legal Intelligence Assistant) that using an A.I based for the public, for next example we can take it from America that using an A.I too called "Do Not Pay" in short this Apps is a lawyer based on A.I, but on February 2023 this Apps only used for ticketing Case only In the case example above, of course, questions will arise about how AI is positioned in the criminal justice process in Indonesia, therefore this journal will explain the subject matter. [ABSTRACT FROM AUTHOR]

This article contemplates and advocates for the use of Artificial Intelligence (“AI”) through Large Language Models (“LLM”) in legal practice. The author ultimately addresses the need to orient LMMs within varying legal contexts including academia, private practice, as well as the U.S. court system. Additionally, the author emphasizes the inevitability of AI and LLM systems infiltrating legal practice, and the reality that the industry must acknowledge and accept these systems to regulate and to provide better while still ethical legal services. Large Language Models: AI’s Legal Revolution, begins by walking the reader through the history of technological innovation of AI, all the way to modern LLM systems. This in turn lays a foundation for understanding what exactly the product is that should shape the legal landscape, and why we should be paying better attention to it. The article then compares and contrasts the current LLM products on the market, including a discussion of their capabilities in the context of legal work. Finally, the article discusses the different practical areas of law where LLMs can prove to enhance the legal industry, how so, and the benefits that LLMs may bring to the landscape of law in the twenty-first century. [ABSTRACT FROM AUTHOR]

As China's ascendancy in Artificial Intelligence (AI) potentially surpasses that of the United States in specific dimensions, this research article critically assesses the existing and proposed AI policies of the United States, incorporating recommendations from the U.S. National Security Commission on AI (NSCAI). The research uses dual-scoring metrics based on the NSCAI-proposed AI stack and an original stakeholder metric to provide quantifiable variables to assess the potential impact of a policy, capturing feasible policy proposals. This analysis creates a practical tool for policy analysts and researchers to evaluate AI policies, which includes a tiered policy structure based on overall scores from the dualscoring metrics. Given the significance of AI in future national development, our model aims to aid policymakers in discerning the merit and feasibility of specific AI policies, thereby facilitating informed policymaking and development. [ABSTRACT FROM AUTHOR]

Seasonal snow-derived water is a critical component of the water supply in the mountains and downstream regions, and the accurate characterization of available water in the form of snow-water-equivalent (SWE), peak SWE, and snowmelt onset are essential inputs for water management efforts. Arising from recent advancements in artificial intelligence (AI) and machine learning (ML), we introduce a large-scale ML SWE model leveraging publicly available data sources and open-source software. The model demonstrates the application of a limited feature space in a relatively simple ML architecture without the need for process-based formulations to effectively estimate spatially continuous SWE at a daily temporal resolution. Beginning with in situ SWE measurements (i.e., SNOTEL), lidar-derived terrain features, and temporal variables, we employ localized feature engineering and optimization via gradient-boosting decision trees to identify regionally unique drivers of snowpack dynamics and use the optimal features to train regionally independent artificial neural networks to estimate regional SWE at a 1 km spatial resolution. The model results yield respectable skill in reconstructed 1 km gridded SWE magnitudes in a hindcast simulation of the 2019 water year that is independent of the training and testing data. Comparing model estimates to over 6200 observations, the model demonstrates a weighted RMSE of 15.4 cm, Kling-Gupta Efficiency metric of 0.86, and a percent bias of 0.71% across 23 snow-influenced regions in the western U.S. The model simulation produces peak SWE estimates within 10 cm for twenty of the twenty-three regions, demonstrating capability in effectively capturing regional snow accumulation processes. The demonstration of low-error ML workflows capable of providing near-real-time, spatially continuous SWE estimates at a high spatial resolution provides proof-of-concept and a foundation to effectively update snow state variables that drive water supply forecasts in snow-dominated regions. • A new Machine Learning-based SWE estimation model for the Western United States. • Utilization of open-source tools and up-to-date in-situ observations to estimate SWE. • Reconstructed SWE magnitudes exhibit respectable skill in a hindcast simulation. • Output may serve as a supplemental state-of-snowpack input to hydrologic models. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an emerging technology with widespread applications. The National Defense Strategy highlights the importance of AI to military operations for the United States to retain an advantage against its near-peer competitors. To fully realise this advantage, it will be necessary to integrate AI not only at the tactical level but also at the operational level of war. AI can be integrated into the complex task of operational planning most efficiently by subdividing it into its component operational functions, which can be processed by narrow AI. This organisation reduces problems to a size that can be parsed by an AI and maintains human oversight over machine supported decision-making. [ABSTRACT FROM AUTHOR]

Regardless of formalization used, one on-going challenge for AI systems that model legal proceedings is accounting for contextual issues, particularly where judicial decisions are made in criminal cases. The law assumes a rational approach to rule application in deciding a defendant's guilt; however, judges and juries can behave irrationally. What should a model prize: efficiency, accuracy, or fairness? Exactly whether and how to incorporate the psychology of courtroom interactions into formal models or expert systems has only just begun to be examined in a serious fashion. Here, I outline data from the United States which suggest that trying to incorporate psychological biases into formal models of legal decision-making will be challenging. I focus on the use of neuroscience data in criminal trials, homing in on so-called group-to-individual (G2i) inferences. I argue that data which should be the most effective at swaying judicial decisions are in fact those most likely not to make a difference in the disposition of the case. I conclude that judges often assign culpability by ignoring what our best science regarding how human decision-making occurs. [ABSTRACT FROM AUTHOR]

Evaluation was not a thing when the first author was a graduate student in the late 1970s. There was an Artificial Intelligence (AI) boom then, but that boom was quickly followed by a bust and a long AI Winter. Charles Wayne restarted funding in the mid-1980s by emphasizing evaluation. No other sort of program could have been funded at the time, at least in America. His program was so successful that these days, shared tasks and leaderboards have become common place in speech and language (and Vision and Machine Learning). It is hard to remember that evaluation was a tough sell 25 years ago. That said, we may be a bit too satisfied with current state of the art. This paper will survey considerations from other fields such as reliability and validity from psychology and generalization from systems. There has been a trend for publications to report better and better numbers, but what do these numbers mean? Sometimes the numbers are too good to be true, and sometimes the truth is better than the numbers. It is one thing for an evaluation to fail to find a difference between man and machine, and quite another thing to pass the Turing Test. As Feynman said, "the first principle is that you must not fool yourself–and you are the easiest person to fool." [ABSTRACT FROM AUTHOR]

Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25–50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991–2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991–2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world. [ABSTRACT FROM AUTHOR]

This article examines the potential for a shift in defense logistics and the DOD's relationship with industry to meet the logistical demands of the modern battlespace. The concept outlines solutions that protect supply chains and manufacturing capabilities through increased agility, adaptability, and resilience. The article uses historical examples and a survey of technologies to make a case for change. It examines enabling technologies and offers an implementation strategy. Artificial intelligence (AI), robotics, big data resources, and ever-improving manufacturing methods comprise the key enabling technologies. The implementation strategy involves establishing a market ecosystem that adequately protects intellectual property and does not jeopardize major contributors to the US economy. The US can evolve its industrial base to meet future logistical demands that spur innovation and sustain competition to emulate industrial age capacity at information age speeds. This change effectively pivots defense logistics from supply management and provision to a deployable, war materiel producing system. The emergent paradigm creates a force structure and manufacturing capability adaptable to the entire spectrum of conflict in an on-demand capacity. [ABSTRACT FROM AUTHOR]

The article presents an interview with U.S. national security professional Robert O. Work who discusses competitive strategies formed by China against the U.S. According to Work, industrial and technical espionage (ITE), system destruction warfare and artificial intelligence (AI) used for military superiority by China are some of the threats to U.S. national security.

Despite a slow legislative start by President Trump, new federal and state regulatory health care changes will have important implications for pharmacists, P&T committees, and patients. [ABSTRACT FROM AUTHOR]

The article focuses on the robotics research in Australia. The author notes that Australia has robotics research community for the size of the population which is often third in the number of papers during major robotics gatherings after the United States and Japan. He added that the scenario is partly because to the needs of the economy of Australia for the creation of domains for the applications of robotics. He argues that Australia has natural advantages in the field of research for robotics over more populous nations.

The generalization error bounds found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. These bounds are intended for the entire input space. However, support vector machine (SVM), radial basis function neural network (RBFNN), and multilayer perceptron neural network (MLPNN) are local learning machines for solving problems and treat unseen samples near the training samples to be more important. In this paper, we propose a localized generalization error model which bounds from above the generalization error within a neighborhood of the training samples using stochastic sensitivity measure. It is then used to develop an architecture selection technique for a classifier with maximal coverage of unseen samples by specifying a generalization error threshold. Experiments using 17 University of California at Irvine (UCI) data sets show that, in comparison with cross validation (CV), sequential learning, and two other ad hoc methods, our technique consistently yields the best testing classification accuracy with fewer hidden neurons and less training time. [ABSTRACT FROM AUTHOR]

This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without manual intervention. The robot's software system relied predominately on state-of-the-art artificial intelligence technologies, such as machine learning and probabilistic reasoning. This paper describes the major components of this architecture, and discusses the results of the Grand Challenge race. © 2006 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The article focuses on the application of artificial intelligence in the future outer space explorations of the U.S. National Aeronautics and Space Administration (NASA). Artificial intelligence technology is believed to support decision-makings in space vehicles, particularly in detecting and tracking events of scientific interests. With onboard decision-making, the space vehicles of NASA could rapidly identify scientific explorations such as detecting planets and space environment, even if the spacecraft is at a great distance. INSET: Retrospective on 1998 Special Issue: "Autonomy in Space".

Presents information on the 15th National Conference on Artificial Intelligence (AI) held in Madison, Wisconsin from July 26 to 30, 1998. Observation on the state of the AI field; Relationship between science fiction and the AI field; Description of the AI applications at the conference.

The article discusses results of a test of face recognition software. The 2006 Face Recognition Vendor Test, sponsored by the U.S. National Institute of Standards and Technology, assessed 22 systems from around the world. Improvements over previous years' results were noted in four key areas. Processing speed increased significantly when compared to the 2002 tests. Quality was sufficient to establish a benchmark for three-dimensional face recognition. Recognition systems improved their ability to match faces under diverse lighting conditions. In terms of unfamiliar faces, recognition systems can outperform humans.