Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Following Tuesday's historic touchdown on the asteroid Bennu, NASA has released never-before-seen images of the OSIRIS-REx spacecraft kicking up rocks and debris on the space rock's surface. The images are from the point in time when OSIRIS-REx approached and touched down on the surface of Bennu, which is more than 200 million miles from Earth. "The spacecraft's sampling arm – called the Touch-And-Go Sample Acquisition Mechanism (TAGSAM) – is visible in the lower part of the frame," NASA wrote on its website.

A pandemic is raging with devastating consequences, and long-standing problems with racial bias and political polarization are coming to a head. Artificial intelligence (AI) has the potential to help us deal with these challenges. However, AI's risks have become increasingly apparent. Scholarship has illustrated cases of AI opacity and lack of explainability, design choices that result in bias, negative impacts on personal well-being and social interactions, and changes in power dynamics between individuals, corporations, and the state, contributing to rising inequalities. Whether AI is developed and used in good or harmful ways will depend in large part on the legal frameworks governing and regulating it.

Stunning images taken from the historic OSIRIS-REx mission show the moment the spacecraft touched down on the asteroid Bennu more than 200 million miles away from Earth to collect a sample of dirt and dust Tuesday night. On Wednesday NASA unveiled videos and images showing the moment the spacecraft pulled off the six-second touch-and-go (TAG) mission where it bounced off the asteroid's surface and picked up samples along the way. The triumphant $1.16 billion mission is the first American effort to take a sample from an asteroid with the hopes to unlock secrets about the origin of life on Earth. The sample will be returned to Earth in 2023. The images show how the spacecraft descended within three feet of the target landing spot dubbed Nightingale on the asteroid while avoiding boulders the size of buildings.

The shift to cloud networks and a wider attack surface brought about by new working practices during the COVID-19 pandemic have made traditional security strategies unfit for purpose, according to Steven Tee, principal solutions architect at Infoblox, speaking during a session at the Infosecurity Online event. He made the case that there needs to be much greater use of automated tools such as machine learning to effectively detect and combat cyber-attacks in the current age. Tee began by outlining the alarming increase and impact of cybercrime over recent years. "Cybercrime is a problem that either directly or indirectly affects everyone," he said. He noted that the average cost of a data breach in 2019 was almost $4m.

The European Parliament is among the first institutions to put forward recommendations on what AI rules should include with regards to ethics, liability and intellectual property rights. These recommendations will pave the way for the EU to become a global leader in the development of AI. The Commission legislative proposal is expected early next year. The legislative initiative by Iban García del Blanco (S&D, ES) urges the EU Commission to present a new legal framework outlining the ethical principles and legal obligations to be followed when developing, deploying and using artificial intelligence, robotics and related technologies in the EU including software, algorithms and data. It was adopted with 559 votes in favour, 44 against, and 88 abstentions.

Remember, in 2017, when Elon Musk said that in a few years, robots would move so fast that you will need a strobe light to see them? The modern age of robotics embodies some of the highest levels of engineering and human ingenuity. However, when people talk about these machines, they can be very opinionated. There sometimes seems to be no middle ground when people discuss robotics. Some people either think robots are amazing, or are worried they will take your job, or fear they will eventually over the world.

WASHINGTON – The U.S. space agency said Wednesday that it likely succeeded in collecting samples through a spacecraft touchdown on asteroid Bennu a day earlier, based on images that captured the activities. "Everything that we can see from these initial images indicates sampling success," Dante Lauretta, a University of Arizona scientist who leads NASA's OSIRIS-REx mission, told an online news conference, although noting that verification activities will continue. As part of the first U.S. mission to carry samples from an asteroid back to Earth, the OSIRIS-REx spacecraft on Tuesday unfurled its robotic arm to touch the surface of Bennu for six seconds, according to NASA. Images unveiled Wednesday showed the disk-shaped sample collector attached at the end of the arm making contact with the surface of the asteroid and crushing some rocks underneath it. The collector, which is 30 centimeters in diameter, then fired nitrogen gas to stir up and lift rocks and dust for capture.

Mars Rover Curiosity has been on the Red Planet for going on eight years, but its journey is nowhere near finished -- and it's still getting upgrades. You can help it out by spending a few minutes labeling raw data to feed to its terrain-scanning AI. Curiosity doesn't navigate on its own; there's a whole team of people on Earth who analyze the imagery coming back from Mars and plot a path forward for the mobile science laboratory. In order to do so, however, they need to examine the imagery carefully to understand exactly where rocks, soil, sand and other features are. This is exactly the type of task that machine learning systems are good at: You give them a lot of images with the salient features on them labeled clearly, and they learn to find similar features in unlabeled images.

When COVID hit the world a few months ago, an extended period of gloom seemed all but inevitable. Yet many companies in the data ecosystem have not just survived but in fact thrived. Perhaps most emblematic of this is the blockbuster IPO of data warehouse provider Snowflake that took place a couple of weeks ago and catapulted Snowflake to a $69 billion market cap at the time of writing – the biggest software IPO ever (see the S-1 teardown). And Palantir, an often controversial data analytics platform focused on the financial and government sector, became a public company via direct listing, reaching a market cap of $22 billion at the time of writing (see the S-1 teardown). Meanwhile, other recently IPO'ed data companies are performing very well in public markets. Datadog, for example, went public almost exactly a year ago (an interesting IPO in many ways, see my blog post here).

Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing predictive uncertainty is of key importance. Motivated by applications in the physical sciences, the widely-used Mat\'ern class of Gaussian processes has recently been generalized to model functions whose domains are Riemannian manifolds, by re-expressing said processes as solutions of stochastic partial differential equations. In this work, we propose techniques for computing the kernels of these processes on compact Riemannian manifolds via spectral theory of the Laplace-Beltrami operator in a fully constructive manner, thereby allowing them to be trained via standard scalable techniques such as inducing point methods. We also extend the generalization from the Mat\'ern to the widely-used squared exponential Gaussian process. By allowing Riemannian Mat\'ern Gaussian processes to be trained using well-understood techniques, our work enables their use in mini-batch, online, and non-conjugate settings, and makes them more accessible to machine learning practitioners.