This content can also be viewed on the site it originates from. On a cloudless morning last May, a pilot took off from the Niagara Falls International Airport, heading for restricted military airspace over Lake Ontario. The plane, which bore the insignia of the United States Air Force, was a repurposed Czechoslovak jet, an L-39 Albatros, purchased by a private defense contractor. The bay in front of the cockpit was filled with sensors and computer processors that recorded the aircraft's performance. For two hours, the pilot flew counterclockwise around the lake.

Artificial intelligence is used in everything from the Internet of Things to fighting cybersecurity attacks, so those cutting-edge skills will be in high demand for quite some time to come, which makes them a perfect choice for turbocharging your mid-level tech career. The Machine Learning Master Class Bundle can take your intermediate skills and boost them in a number of different directions, including data science and gaming. If you have some math and Python experience, then go straight for the main overview, "Machine Learning For Absolute Beginners," which will provide hands-on learning with nine actual projects. And if you're interested in data science, which every industry from medical to tech relies on, then follow up with "Data Visualization With Python: The Complete Guide." On the other hand, if math gives you problems, take "Mathematical Foundations For Machine Learning & AI" which can demystify it for you.

The U.S. military and China's People's Liberation Army are both pursuing artificial intelligence capabilities which could give them a leg up in future conflicts. PLA investment in AI is now on par with the Pentagon's, experts say. "Supported by a burgeoning AI defense industry, the Chinese military has made extraordinary progress in procuring AI systems for combat and support functions," according to a recent report from the Georgetown University Center for Security and Emerging Technology. The People's Liberation Army is most focused on procuring AI for intelligence analysis, predictive maintenance, information warfare, and navigation and target recognition in autonomous vehicles, said the study, "Harnessed Lightning: How the Chinese Military is Adopting Artificial Intelligence," by analysts Ryan Fedasiuk, Jennifer Melot and Ben Murphy. Additionally, laboratories affiliated with the Chinese military are actively pursuing AI-based target recognition and fire-control research, which may be used in lethal autonomous weapon systems, according to the authors.

Hate speech is a major issue in social networks due to the high volume of data generated daily. Recent works demonstrate the usefulness of machine learning (ML) in dealing with the nuances required to distinguish between hateful posts from just sarcasm or offensive language. Many ML solutions for hate speech detection have been proposed by either changing how features are extracted from the text or the classification algorithm employed. However, most works consider only one type of feature extraction and classification algorithm. This work argues that a combination of multiple feature extraction techniques and different classification models is needed. We propose a framework to analyze the relationship between multiple feature extraction and classification techniques to understand how they complement each other. The framework is used to select a subset of complementary techniques to compose a robust multiple classifiers system (MCS) for hate speech detection. The experimental study considering four hate speech classification datasets demonstrates that the proposed framework is a promising methodology for analyzing and designing high-performing MCS for this task. MCS system obtained using the proposed framework significantly outperforms the combination of all models and the homogeneous and heterogeneous selection heuristics, demonstrating the importance of having a proper selection scheme. Source code, figures, and dataset splits can be found in the GitHub repository: https://github.com/Menelau/Hate-Speech-MCS.

Polarization in America has reached a high point as markets are also becoming polarized. Existing research, however, focuses on specific market segments and products and has not evaluated this trend's full breadth. If such fault lines do spread into other segments that are not explicitly political, it would indicate the presence of lifestyle politics -- when ideas and behaviors not inherently political become politically aligned through their connections with explicitly political things. We study the pervasiveness of polarization and lifestyle politics over different product segments in a diverse market and test the extent to which consumer- and platform-level network effects and morality may explain lifestyle politics. Specifically, using graph and language data from Amazon (82.5M reviews of 9.5M products and product and category metadata from 1996-2014), we sample 234.6 million relations among 21.8 million market entities to find product categories that are most politically relevant, aligned, and polarized. We then extract moral values present in reviews' text and use these data and other reviewer-, product-, and category-level data to test whether individual- and platform- level network factors explain lifestyle politics better than products' implicit morality. We find pervasive lifestyle politics. Cultural products are 4 times more polarized than any other segment, products' political attributes have up to 3.7 times larger associations with lifestyle politics than author-level covariates, and morality has statistically significant but relatively small correlations with lifestyle politics. Examining lifestyle politics in these contexts helps us better understand the extent and root of partisan differences, why Americans may be so polarized, and how this polarization affects market systems.

The estimated economic cost of all fatalities due to traffic crashes in 2018 was approximately $55 billion in the United States (CDC, 2020). Such a huge cost warrants continued investigation into the contributing factors of crash fatalities and the implementation of effective countermeasures for improving traffic safety. Traditional safety studies have generally focused on identifying correlations between crashes and roadway features. Due to a lack of substantial driving behavior information in conventional historical crash datasets, these studies can seldom identify driving behaviors that contribute to crashes. Moreover, traditional studies require crash data spanning an extended period of time.

We introduce AugLy, a data augmentation library with a focus on adversarial robustness. AugLy provides a wide array of augmentations for multiple modalities (audio, image, text, & video). These augmentations were inspired by those that real users perform on social media platforms, some of which were not already supported by existing data augmentation libraries. AugLy can be used for any purpose where data augmentations are useful, but it is particularly well-suited for evaluating robustness and systematically generating adversarial attacks. In this paper we present how AugLy works, benchmark it compared against existing libraries, and use it to evaluate the robustness of various state-of-the-art models to showcase AugLy's utility. The AugLy repository can be found at https://github.com/facebookresearch/AugLy.

A robot on Mars has bitten off more than it can chew while collecting rock samples on the red planet. Perseverance was sent to Mars last year by US space agency Nasa to find out more about our nearest planetary neighbour. The Mars rover has been successfully collecting small samples of rock to bring back to researchers on Earth. But in late December, it ran into trouble after pebbles fell into its machinery, causing it to malfunction. Nasa's Perseverance begins its Martian journey Nasa reveals new Mars rover name!

The Spanish AI Agency will be responsible for the development, supervision, and monitoring of projects within the framework of the National AI Strategy, as well as the projects promoted by the European Union – in particular those related to the regulatory development of artificial intelligence and its potential uses. Although the specific competences of the Spanish AI Agency have not yet been specified (since the creation of the body must be approved by law), we will keep a close eye on these developments, considering the high penalties foreseen under the AI Regulation and the supervisory powers that could be granted to this new authority.

A group of scientists at the U.S. Department of Energy's Ames Laboratory has developed computational quantum algorithms that are capable of efficient and highly accurate simulations of static and dynamic properties of quantum systems. The algorithms are valuable tools to gain greater insight into the physics and chemistry of complex materials, and they are specifically designed to work on existing and near-future quantum computers. Scientist Yong-Xin Yao and his research partners at Ames Lab use the power of advanced computers to speed discovery in condensed matter physics, modeling incredibly complex quantum mechanics and how they change over ultra-fast timescales. Current high performance computers can model the properties of very simple, small quantum systems, but larger or more complex systems rapidly expand the number of calculations a computer must perform to arrive at an accurate model, slowing the pace not only of computation, but also discovery. "This is a real challenge given the current early-stage of existing quantum computing capabilities," said Yao, "but it is also a very promising opportunity, since these calculations overwhelm classical computer systems, or take far too long to provide timely answers."