C3.ai intends to list its Class A common stock under the ticker symbol "AI" on the New York Stock Exchange. The number of shares to be offered and the price range for the proposed offering have not yet been determined. The offering is subject to market conditions, and there can be no assurance as to whether, or when, the offering may be completed or as to the actual size or terms of the offering. Morgan Stanley, J.P. Morgan and BofA Securities are acting as lead book-running managers for the proposed offering. Deutsche Bank Securities is acting as a book-running manager for the proposed offering.

Trump-backed candidate Dr. Ronny Jackson reacts to the big win in Texas and says wearing a mask amid COVID-19 should be a'personal choice' instead of a mandate. Dr. Ronny Jackson, the incoming Texas congressman and former White House physician to President Trump, called out the left for failing to demand a rigorous cognitive screening for President-elect Joe Biden, saying "there's something going on" with the Democrat's mental acuity. Jackson, in Washington for congressional orientation, recalled to Fox News this week the intense pressure he faced to administer a cognitive test to Trump as pundits and mental health professionals openly speculated that the commander-in-chief could have early-stage dementia or a decline in mental health. Trump scored a perfect 30 out of 30 during the cognitive test as part of his annual physical and Jackson announced the results in 2018 at a White House press conference, where he also famously quipped that Trump might "live to be 200 years old." Now Jackson, a retired Rear Admiral in the U.S. Navy, is raising the alarm about the "huge double standard" of the press for not demanding the same type of screening for Biden, who would be 78 at the inauguration.

… techniques, genomic and microbial data, and machine learning to improve the management of foodborne illness and better protect public health.

The Centers for Medicare & Medicaid Services (CMS) announced seven finalists who will advance to the final round of the Artificial Intelligence Health Outcomes Challenge. This multi-stage competition launched last year with more than 300 entities proposing AI solutions for predicting patient health outcomes aimed at revolutionizing healthcare for potential use by the CMS Center for Medicare and Medicaid Innovation. In this last stage of the competition, the seven finalists will further develop algorithms that demonstrate how AI tools can be used to predict unplanned hospital and skilled nursing facility admissions and adverse events, and also will develop predictive algorithms for a standard target to be selected by CMS. CMS will announce the Grand Prize winner (who will receive up to $1 million in prize money) and Runner-Up (who will receive up to $230,000 in prize money) by the end of April 2021.

It's no secret that Los Angeles is notorious for its traffic jams, typically ranking first in studies of the nation's traffic hot spots. Estimates suggest that Angelinos spend an extra 120 hours a year stuck in them. While a nightmare for drivers, the L.A. transportation system does have its advantages if you're devising a new system to quickly predict and potentially redirect that traffic. Researchers from across the U.S. Department of Energy's (DOE) Argonne National Laboratory set out to do just that under the umbrella of a larger project on the design and planning of mobility systems led by collaborators at DOE's Lawrence Berkeley National Laboratory (LBNL). Using an artificial intelligence (AI) technique called machine learning, the team leveraged Argonne's supercomputers to digest traffic patterns from nearly a year's worth of data taken from 11,160 sensors along the large California highway system.

Perhaps no other technology animates the imagination of defense policymakers and analysts as much as artificial intelligence (AI), or more precisely, a subfield of AI called machine learning. The Pentagon is no exception, with the Trump administration having pushed an AI agenda for the military, including through the creation of a Joint Artificial Intelligence Center (JAIC) in 2018. But while military gains from AI technologies are substantial, the way policymakers involved in military AI hard sell its potential often gives observers pause. Speaking virtually at a think tank event on November 6, JAIC's director Lieutenant General Michael Groen compared the military risks the United States faces today to 1914, when World War I broke out, marking the beginning of industrialized warfare. BreakingDefense quotes Groen as saying that "the Information Age equivalent of… lancers riding into machine guns" is using traditional command and control (C2) systems against an adversary equipped with AI. Groen, a Marine Corps intelligence officer whose tours of duty included Iraq and who became JAIC head on October 1, also pointed out the inefficiency of current processes integrating intelligence to kinetic action, in terms of a persistent lag between collation and analysis and engagement even in asymmetric conflicts.

You've seen them over sporting events, music festivals and political rallies; aerial drone displays have lit up night skies since the technology's 2012 debut at Ars Electronica Futurelab. No longer a niche entertainment spectacle, these light shows can feature massive swarms that are often presented right alongside traditional fireworks displays. And as small drone technologies continue to advance, these swarms are sure to grow. Drone swarm technology, in varying applications, has been pursued for years by government agencies like DARPA, where it's being investigated for military stuff, as well as private research institutions, such as University of Colorado Boulder where it's being explored as a search and rescue tool. Getting robots to not only sense the others around them but to actively work in concert, in pursuit of a common goal, enables the group to accomplish tasks that no individual could do on their own.

Facebook says it's using AI to prioritize potentially problematic posts for human moderators to review as it works to more quickly remove content that violates its community guidelines. The social media giant previously leveraged machine learning models to proactively take down low-priority content and left high-priority content reported by users to human reviewers. But Facebook claims it now combines content identified by users and models into a single collection before filtering, ranking, and deduplicating it and handing it off to thousands of moderators, many of whom are contract employees. Facebook's continued investment in moderation comes as reports suggest the company is failing to stem the spread of misinformation, disinformation, and hate speech on its platform. Reuters recently found over three dozen pages and groups that featured discriminatory language about Rohingya refugees and undocumented migrants.

The Tempest Multi-Function Radio Frequency System radar from Leonardo UK should be able to handle 10,000 times more data than existing systems.