Fine as talcum powder, sharp as glass and seemingly everywhere, these super-fine particles coated the astronauts like soot and permeated their crew cabin, where it became more than a mere nuisance. Not only did it interfere with their equipment, it irritated their nostrils and eyes, giving some a mild allergic reaction. Nevertheless, between 1969 and 1972, Apollo astronauts brought back nearly 385 kilograms of lunar rock, pebbles and powder back to earth. Today, NASA deems interplanetary dust and dirt -- also known as regolith -- one of the greatest risks to long-term space settlements. On the Moon, it wears away at multi-million-dollar instruments and lodges into the slightest crevasses, weakening seals on pressure suits and causing hardware to malfunction.

Scenario-based methods for the assessment of Automated Vehicles (AVs) are widely supported by many players in the automotive field. Scenarios captured from real-world data can be used to define the scenarios for the assessment and to estimate their relevance. Therefore, different techniques are proposed for capturing scenarios from real-world data. In this paper, we propose a new method to capture scenarios from real-world data using a two-step approach. The first step consists in automatically labeling the data with tags. Second, we mine the scenarios, represented by a combination of tags, based on the labeled tags. One of the benefits of our approach is that the tags can be used to identify characteristics of a scenario that are shared among different type of scenarios. In this way, these characteristics need to be identified only once. Furthermore, the method is not specific for one type of scenario and, therefore, it can be applied to a large variety of scenarios. We provide two examples to illustrate the method. This paper is concluded with some promising future possibilities for our approach, such as automatic generation of scenarios for the assessment of automated vehicles.

Every major tech company is dedicating resources to breakthroughs in artificial intelligence. Personal assistants like Siri and Alexa have made AI a part of our daily lives. Meanwhile, revolutionary breakthroughs like self-driving cars may not be the norm, but are certainly within reach. As the big guys scramble to infuse their products with artificial intelligence, other companies are hard at work developing their own intelligent technology and services. Here are 30 artificial intelligence companies, according to Forbes, you should know.

Google has responded to European Commissions white paper on artificial intelligence (AI) on how to regulate and accelerate the adoption of responsible and ethical AI. In February this year, the European Commission launched a consultation on Artificial Intelligence and asked citizens and stakeholders to provide their feedback by June 14. Google said on Friday that it supports the Commission's plans to help businesses develop the AI skills they need to thrive in the new digital economy. "Next month, we'll contribute to those efforts by extending our machine learning check-up tool to 11 European countries to help small businesses implement AI and grow their businesses," the tech giant said in a statement. Google Cloud already works closely with scores of businesses across Europe to help them innovate using AI.

The author of Robots Are People Too responds to Tobias Buckell's "Scar Tissue." "Scar Tissue" is an emotionally resonant tale about healing through robot rearing--but it was only half of the story about Rob's creator, Advent Robotics. What we read was the part Cory could see: Rob as a growing child, nurtured by Cory. But in the story, Tobias Buckell offers us a little hint about everything happening at Advent: "Every time [Advent's robots] get on that charger, they're not just powering up their onboard battery--they're taking in their experiences and uploading data to our servers to have it examined." That's the part that worries me, as artificial intelligence applications may be able to leverage the data to manipulate Cory and other people--just as technology, PR, and marketing companies try to do in our lives today.

Prime Minister Narendra Modi has said India is facing a "long battle" ahead in its efforts to defeat the pandemic as the country set a new record for daily coronavirus infections. United States President Donald Trump has said the US is "terminating" its relationship with the World Health Organization (WHO), saying the agency has not made coronavirus reforms. The WHO and 37 countries launched the COVID-19 Technology Access Pool, an alliance aimed at making coronavirus vaccines, tests, treatments and other technologies available to all countries. More than 5.9 million cases of coronavirus have been confirmed around the world, according to data from Johns Hopkins University. Some 365,000 people have died, while more than 2.4 million have recovered.

The U.S. is using every tool at its disposal to defeat the novel coronavirus, including artificial intelligence. American laboratories are harnessing AI to discover new therapeutics. The Food and Drug Administration approved an AI tool to help detect coronavirus in CT scans. And the White House led an initiative to create a database with more than 128,000 articles that scientists can analyze using AI to help understand the virus better and develop treatments.

Testing large covariance matrices is of fundamental importance in statistical analysis with high-dimensional data. In the past decade, three types of test statistics have been studied in the literature: quadratic form statistics, maximum form statistics, and their weighted combination. It is known that quadratic form statistics would suffer from low power against sparse alternatives and maximum form statistics would suffer from low power against dense alternatives. The weighted combination methods were introduced to enhance the power of quadratic form statistics or maximum form statistics when the weights are appropriately chosen. In this paper, we provide a new perspective to exploit the full potential of quadratic form statistics and maximum form statistics for testing high-dimensional covariance matrices. We propose a scale-invariant power enhancement test based on Fisher's method to combine the p-values of quadratic form statistics and maximum form statistics. After carefully studying the asymptotic joint distribution of quadratic form statistics and maximum form statistics, we prove that the proposed combination method retains the correct asymptotic size and boosts the power against more general alternatives. Moreover, we demonstrate the finite-sample performance in simulation studies and a real application.

The news: The United Nations is endorsing a computer simulation tool that it believes will help governments tackle the world's biggest problems, from gender inequality to climate change. Global challenges: In 2015, UN member states signed up for a set of 17 sustainable-development goals that are due to be reached by 2030. They include things like "zero poverty," "no hunger," and "affordable and clean energy." How could the tool help? Called Policy Priority Inference (PPI), the software uses agent-based modeling to predict what would happen if policymakers spent money on one project rather than another.

Amazon's controversial facial recognition technology has incorrectly matched more than 100 photos of politicians in the UK and US to police mugshots, new tests have revealed. Amazon Rekognition uses artificial intelligence software to identify individuals from their facial structure. Customers include law enforcement and US government agencies like Immigration and Custome Enforcement (ICE). It is not the first time the software's accuracy has been called into question. In July 2018, the American Civil Liberties Union (ACLU) found 28 false matches between US Congress members and pictures of people arrested for a crime.