This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by Refinitiv Lipper .

America's most sophisticated B-21 Raider stealth bomber program gains momentum with Spartan's successful flight, showcasing advanced AI and aerodynamic technology.

Two people reportedly are injured after a Navy parachutist crash-landed during a performance in San Francisco. U.S. Naval officials, on Monday, identified the two crew members who died last week in a Navy jet crash near Mount Rainier in Washington state, as two 31-year-old aviators from California. The fighter jet pilots were identified as Lt. Cmdr. Evans and Wileman died when their EA-18G Growler jet from the Electronic Attack Squadron out of Whidbey Island Naval Air Station crashed on a mountainside east of Mount Rainier on Tuesday afternoon. The wreckage of the jet was located resting about 6,000 feet up in a remote, steep and heavily-wooded area, and until Sunday, the status of the crew remained a mystery without a site assessment of the debris area.

In this work, we propose Table-LLM-Specialist, or Table-Specialist for short, as a new self-trained fine-tuning paradigm specifically designed for table tasks. Our insight is that for each table task, there often exist two dual versions of the same task, one generative and one classification in nature. Leveraging their duality, we propose a Generator-Validator paradigm, to iteratively generate-then-validate training data from language-models, to fine-tune stronger \sys models that can specialize in a given task, without requiring manually-labeled data. Our extensive evaluations suggest that our Table-Specialist has (1) \textit{strong performance} on diverse table tasks over vanilla language-models -- for example, Table-Specialist fine-tuned on GPT-3.5 not only outperforms vanilla GPT-3.5, but can often match or surpass GPT-4 level quality, (2) \textit{lower cost} to deploy, because when Table-Specialist fine-tuned on GPT-3.5 achieve GPT-4 level quality, it becomes possible to deploy smaller models with lower latency and inference cost, with comparable quality, and (3) \textit{better generalizability} when evaluated across multiple benchmarks, since \sys is fine-tuned on a broad range of training data systematically generated from diverse real tables. Our code and data will be available at https://github.com/microsoft/Table-LLM-Specialist.

A retired, senior Pentagon official has confirmed that UFO'mother ships' were spotted'releasing swarms of smaller craft' -- adding further mystery to the still-unexplained intrusions over multiple US military bases. His statements come amid the release of 50 pages of Air Force records related to provocative'drone' incursions, that one general calls'Close Encounters at Langley.' For at least 17 nights last December, swarms of noisy, small UFOs were seen at dusk'moving at rapid speeds' and displaying'flashing red, green, and white lights' penetrating the highly restricted airspace above Langley Air Force Base in Virginia. Senior ex-Pentagon security official Chris Mellon told DailyMail.com'Two of the notable aspects,' he said, 'are the fact our drone signal-jamming devices have proven ineffective and these craft are making no effort to remain concealed.'

Panicked locals in two cities north of Los Angeles, California, piled onto Amazon's Ring neighbors app to report UFOs that'zig zagged' and hovered over the weekend. Their reports of a'bright light' that looked like'a shooting star' but acted more like a'hovercraft' sparked shockwaves across social media -- alongside the emergence of eerie cell phone videos that purported to capture some of these six alleged craft. But a wide community of experts, including UFO researchers with Harvard's Galileo Project, told DailyMail.com The videos appeared to show drone swarms used in an LED light show thousands of miles away from California, based on landmarks and other visual cues, they said. And some of these UFO videos were paired with old and unrelated audio tracks passed off as the videos' own.

Large language models (LLMs) can store a vast amount of world knowledge, often extractable via question-answering (e.g., "What is Abraham Lincoln's birthday?"). However, do they answer such questions based on exposure to similar questions during training (i.e., cheating), or by genuinely learning to extract knowledge from sources like Wikipedia? In this paper, we investigate this issue using a controlled biography dataset. We find a strong correlation between the model's ability to extract knowledge and various diversity measures of the training data. $\textbf{Essentially}$, for knowledge to be reliably extracted, it must be sufficiently augmented (e.g., through paraphrasing, sentence shuffling) $\textit{during pretraining}$. Without such augmentation, knowledge may be memorized but not extractable, leading to 0% accuracy, regardless of subsequent instruction fine-tuning. To understand why this occurs, we employ (nearly) linear probing to demonstrate a strong connection between the observed correlation and how the model internally encodes knowledge -- whether it is linearly encoded in the hidden embeddings of entity names or distributed across other token embeddings in the training text. This paper provides $\textbf{several key recommendations for LLM pretraining in the industry}$: (1) rewrite the pretraining data -- using small, auxiliary models -- to provide knowledge augmentation, and (2) incorporate more instruction-finetuning data into the pretraining stage before it becomes too late.

The Pentagon revealed its new B-21 nuclear stealth bomber Friday in Palmdale, California. DEADLY STEALTH: This US bomber is why China suddenly wants to talk about nukes and AI. HIGH-TECH HEALTH: AI could help predict lung cancer risks in non-smokers. FORCE MULTIPLIER: US accelerates race for new military tech against China. PAYCHECK PROBLEMS: AI may be greater threat to wages than jobs, European study finds.

The Pentagon revealed its new B-21 nuclear stealth bomber Friday in Palmdale, California. The Air Force's super-secret B-21 Raider stealth bomber quietly made its first flight in California on Nov. 10, flying from the factory to Edwards AFB at not much more than tree-top level. Yes, right before China's President Xi Jinping visited San Francisco. All of a sudden China wants to start talks on AI and nuclear weapons. "The leaders affirmed the need to address the risks of advanced AI systems and improve AI safety through U.S.-China government talks," the White House stated on Nov. 15 after President Biden met with Xi. The B-21 has cloud technology and an open architecture for continuous software upgrades.

Rep. Rob Wittman, R-Va., joins'Fox News Live' to react to the United States Air Force's unveiling of its new B-21 raider stealth bomber, named'The Raider' for Jimmy Doolittle's famous bombing raid on Japan in WW2. With the launch of the Air Force's hypersonic missile off the coast of California earlier this month, the Navy's development of water-based drones over the summer and the recent unveiling of the B-21 Raiders, the U.S. military has made major technological advancements over the past year. The military unveiled the U.S. Air Force B-21 Raider in Palmdale, California. The B-21 Raider is the first new American bomber aircraft in more than three decades. In an email to Fox News Digital, a spokesperson confirmed the Air Force would transition its three-bomber fleet to a two-bomber fleet of B-21s and modernized B-52s.