Decisions impacting human lives are increasingly being made or assisted by automated decision-making algorithms. Many of these algorithms process personal data for predicting recidivism, credit risk analysis, identifying individuals using face recognition, and more. While potentially improving efficiency and effectiveness, such algorithms are not inherently free from bias, opaqueness, lack of explainability, maleficence, and the like. Given that the outcomes of these algorithms have a significant impact on individuals and society and are open to analysis and contestation after deployment, such issues must be accounted for before deployment. Formal audits are a way of ensuring algorithms meet the appropriate accountability standards. This work, based on an extensive analysis of the literature and an expert focus group study, proposes a unifying framework for a system accountability benchmark for formal audits of artificial intelligence-based decision-aiding systems. This work also proposes system cards to serve as scorecards presenting the outcomes of such audits. It consists of 56 criteria organized within a four-by-four matrix composed of rows focused on (i) data, (ii) model, (iii) code, (iv) system, and columns focused on (a) development, (b) assessment, (c) mitigation, and (d) assurance. The proposed system accountability benchmark reflects the state-of-the-art developments for accountable systems, serves as a checklist for algorithm audits, and paves the way for sequential work in future research.

Transformer-based models for transfer learning have the potential to achieve high prediction accuracies on text-based supervised learning tasks with relatively few training data instances. These models are thus likely to benefit social scientists that seek to have as accurate as possible text-based measures but only have limited resources for annotating training data. To enable social scientists to leverage these potential benefits for their research, this paper explains how these methods work, why they might be advantageous, and what their limitations are. Additionally, three Transformer-based models for transfer learning, BERT (Devlin et al. 2019), RoBERTa (Liu et al. 2019), and the Longformer (Beltagy et al. 2020), are compared to conventional machine learning algorithms on three applications. Across all evaluated tasks, textual styles, and training data set sizes, the conventional models are consistently outperformed by transfer learning with Transformers, thereby demonstrating the benefits these models can bring to text-based social science research.

Many complex vehicular systems, such as large marine vessels, contain confined spaces like water tanks, which are critical for the safe functioning of the vehicles. It is particularly hazardous for humans to inspect such spaces due to limited accessibility, poor visibility, and unstructured configuration. While robots provide a viable alternative, they encounter the same set of challenges in realizing robust autonomy. In this work, we specifically address the problem of detecting foreign object debris (FODs) left inside the confined spaces using a visual mapping-based system that relies on Mahalanobis distance-driven comparisons between the nominal and online maps for local outlier identification. Simulation trials show extremely high recall but low precision for the outlier identification method. The assistance of remote humans is, therefore, taken to deal with the precision problem by going over the close-up robot camera images of the outlier regions. An online survey is conducted to show the usefulness of this assistance process. Physical experiments are also reported on a GPU-enabled mobile robot platform inside a scaled-down, prototype tank to demonstrate the feasibility of the FOD detection system.

About GANs Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs. Rooted in game theory, GANs have wide-spread application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of-the-art images, colorizing black and white images, increasing image resolution, creating avatars, turning 2D images to 3D, and more. About this Specialization The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.

In France, permanently constructed pools increase property taxes because they boost a property's value. Pools are taxed by size and according to local tax rates; the average 30-square-meter pool, or roughly 323 square feet, costs the owner about 200 euros in taxes per year. Property taxes are paid to local municipalities. A small minority of France's 67 million citizens own swimming pools, but they have become increasingly popular in recent years. There are over 3 million private swimming pools in France, and over 240,000 were built in 2021 alone, according to France's Federation of Pool and Spa Professionals, an industry lobbying group.

Iran's Islamic Revolutionary Guard Corps (IRGC) has seized an American sea drone in the Gulf and tried to tow it away, only releasing the unmanned vessel when a US Navy warship and helicopter approached, US officials have said. The incident on Tuesday marks the first time the Navy's Middle East-based 5th Fleet's new drone task force has been targeted by Iran. While the interception ended without incident, it comes amid growing tensions between the United States and Iran as negotiations over the tattered Iranian nuclear deal hang in the balance. The U.S. Navy prevented a support ship from Iran's Islamic Revolutionary Guard Corps Navy (IRGCN) from capturing an unmanned surface vessel operated by the U.S. 5th Fleet in the Arabian Gulf, Aug. 29-30. The IRGC's Shahid Baziar warship attached a line to the Saildrone Explorer in the central part of the Gulf in international waters late Monday night, said Commander Timothy Hawkins, a 5th Fleet spokesman.

The world around us is full of arguments and evidence for the benefit of artificial intelligence (AI) in our daily lives. But the specter of AI threats looms large in today's world. While there's plenty of fear around the future of human-level AI, there's debate over whether AI today is truly working in our best interest. But what many people don't know is that AI is already being used by cybercriminals to attack them at scale with cyber threats, like cognitive attacks, that are only possible with AI. One of the greatest threats that AI represents today is how it can be abused by cybercriminals, specifically in its capability to deceive people and trick them into engaging in actions with unwanted or underestimated consequences.

An Iranian ship seized an American military unmanned research vessel in the Gulf but released it after a US Navy patrol boat and helicopter were deployed to the location, the Pentagon said Tuesday. The US Central Command's 5th Fleet said a support ship from Iran's Islamic Revolutionary Guard Corps Navy, the Shahid Baziar, was spotted towing the seven-meter (23-foot) Saildrone Explorer unmanned surface vessel (USV) late Monday. The US naval drone, equipped with an array of sensors, radars and cameras, was in international waters collecting navigation and other unspecified data, the 5th Fleet said in a statement. When the Iranian vessel was seen towing the unmanned boat, US forces sent the USS Thunderbolt coastal patrol ship, which was operating nearby, to the scene. In addition, an MH-60S Sea Hawk helicopter based in Bahrain flew to the location.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. Navy stopped an Iranian ship from taking an American sea drone in the Arabian Gulf Monday night. The Iranian Revolutionary Guard Corps Navy was in the process of towing the drone, which belongs to the U.S. Navy's 5th Fleet at 11 p.m. local time when the American Navy immediately sent out the nearby Navy coastal ship USS Thunderbolt. The 5th Fleet also repeatedly called Iranian officials, who then let the drone go.

At a workshop hosted through the Air Force's military university on Aug. 26 in Montgomery, Alabama, students were shown a video of President Joe Biden addressing the UN while effortlessly switching between five languages including Mandarin and Russian. While Thomas Jefferson and John Quincy Adams were fluent in several languages, Biden, like most U.S. presidents, is only known to speak English. The video was a piece of synthetic media, more commonly known as a "deepfake." Created using a combination of machine learning and artificial intelligence, deepfakes are hyperrealistic videos that replace one person's likeness with that of another, or appear to show them doing something they never did. And as the technology improves, they get harder to detect.