A new report from Drone Industry Insights says the commercial industry can expect progress globally. DRONEII Editor Ed Alvarado writes that around the world, drone regulations – and the regulatory framework – are evolving rapidly. "This is a very welcome development given that the drone industry sees this as the most important driving factor. The movement on drone regulation in 2022 is global. In Korea, significant movement towards urban air mobility is underway: continuing the progress made this year with trial flights and the government committment to an early implementation of passenger VTOL aircraft.

A video of House Speaker Nancy Pelosi seeming to slur her speech at an event tore through the internet, gaining steam on Facebook. Share after share, it spread to the point of going viral. The altered video from May 2019 was a slowed-down version of the actual speech the California Democrat gave but was being promoted as real. Even though Facebook acknowledged the video was fake, the company allowed it to stay on the platform, where it continued to be reshared. That exponential resharing was like rocket fuel to the manipulated video.

The system also predicts extreme weather events with the help of artificial intelligence (AI) and machine learning," said a release from IIT, …

Adobe's PDF format has entrenched itself so deeply in US government document pipelines that the number of state-issued documents currently in existence is conservatively estimated to be in the hundreds of millions. Often opaque and lacking metadata, these PDFs – many created by automated systems – collectively tell no stories or sagas; if you don't know exactly what you're looking for, you'll probably never find a pertinent document. And if you did know, you probably didn't need the search. However a new project is using computer vision and other machine learning approaches to change this almost unapproachable mountain of data into a valuable and explorable resource for researchers, historians, journalists and scholars. When the US government discovered Adobe's Portable Document Format (PDF) in the 1990s, it decided that it liked it.

The rapid digitalisation accelerated due to the pandemic has brought numerous benefits like improved business agility and customer experiences. But there have also been negative effects like increased vulnerability to cybersecurity threats for your data and applications. A cyberattack is a malicious and deliberate attempt to breach the computer and information systems of an individual or organisation, disrupting the victim's network for personal gain. One of the biggest concerns with the development of Artificial Intelligence is the probability that attackers will weaponise AI and use it to expand and boost their cyberattacks. Cybercrime and cybersecurity landscapes are changing rapidly and boosting AI developments for enhancing cybersecurity will be a major gamechanger to protect against cyber-attacks.

Designing manufacturing processes with high yield and strong reliability relies on effective methods for rare event estimation. Genealogical importance splitting reduces the variance of rare event probability estimators by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is difficult when applied to deterministic systems where the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. However, this random perturbation strategy may be effective for some systems while failing for others, preventing variance reduction in the probability estimate. This work seeks to address this limitation using a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The proposed GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).

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).

Data processing and analysis pipelines in cosmological survey experiments introduce data perturbations that can significantly degrade the performance of deep learning-based models. Given the increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: 1) increased observational noise as represented by higher levels of Poisson noise and 2) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness. Without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy by 23% on data with higher observational noise. Domain adaptation also increases by a factor of ~2.3 the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. President Biden's administration has been much less aggressive with U.S. military air power in 2021 than former President Donald Trump was last year, with strikes falling 54% as of mid-December. "The biggest take-home is that Biden has significantly decreased US military action across the globe," reads a report released Wednesday by Airwars, a not-for-profit organization that tracks military actions and civilian causalities across the world. It added that the drop in strikes has resulted in "far lower numbers of civilians allegedly killed by the US strikes."

So, using satellite photos of three cities and drawing upon methods used to manipulate video and audio files, a team of researchers set out to identify new ways of detecting fake satellite photos, warn of the dangers of falsified geospatial data and call for a system of geographic fact-checking. In 2019, the director of the National Geospatial Intelligence Agency, the organization charged with supplying maps and analyzing satellite images for the U.S. Department of Defense, implied that AI-manipulated satellite images can be a severe national security threat. When applied to the field of mapping, the algorithm essentially learns the characteristics of satellite images from an urban area, then generates a deepfake image by feeding the characteristics of the learned satellite image characteristics onto a different base map -- similar to how popular image filters can map the features of a human face onto a cat. Next, the researchers combined maps and satellite images from three cities -- Tacoma, Seattle and Beijing -- to compare features and create new images of one city, drawn from the characteristics of the other two. Low-rise buildings and greenery mark the "Seattle-ized" version of Tacoma on the bottom left, while Beijing's taller buildings, which AI matched to the building structures in the Tacoma image, cast shadows -- hence the dark appearance of the structures in the image on the bottom right.