Fox News chief national security correspondent Jennifer Griffin has the details from the Pentagon on'America Reports.' The United States may have shot down one of Communist China's spy balloons, but America's problem of surveillance from foreign powers is far from destroyed. Many Americans were alarmed as we watched the Biden administration take a dangerously lazy approach to taking down the Chinese spy balloon. We are right to be upset about this, and demand action in response, but that balloon is just a small piece of Communist China's massive surveillance effort targeting you, our military and American businesses. The harsh reality is that the Chinese Communist Party has been surveilling the United States for years using drones purchased with your taxpayer dollars and operated by your federal government.

Jets can be flown by A.I. and can even take off, land and participate in dogfights. Yes, you read the headline correctly. The United States Defense Department recently confirmed that artificial intelligence successfully flew a jet similar to an F-16 for 17 hours straight. The jet was flown over a series of 12 flights back in December 2022 at the Edwards Air Force Base in Kern County, California. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER The Defense Department used an experimental plane called the Vista X-62A for the flights.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An explosion in eastern Syria on Wednesday killed at least three people, according to reports. A war monitoring group said the blast was likely caused by a drone strike that targeted Iran-backed militiamen. No group claimed responsibility for an attack in the area and reports about what had happened were sketchy.

The cybersecurity firm Darktrace has warned that since the release of ChatGPT it has seen an increase in criminals using artificial intelligence to create more sophisticated scams to con employees and hack into businesses. The Cambridge-based company, which reported a 92% drop in operating profits in the half year to the end of December, said AI was further enabling "hacktivist" cyber-attacks using ransomware to extort money from businesses. The company said it had seen the emergence of more convincing and complex scams by hackers since the launch of the hugely popular Microsoft-backed AI tool ChatGPT last November. "Darktrace has found that while the number of email attacks across its own customer base remained steady since ChatGPT's release, those that rely on tricking victims into clicking malicious links have declined while linguistic complexity, including text volume, punctuation and sentence length among others, have increased," the company said. "This indicates that cybercriminals may be redirecting their focus to crafting more sophisticated social engineering scams that exploit user trust." However, Darktrace said that the phenomenon had not yet resulted in a new wave of cybercriminals emerging, merely changing the tactics of the existing cohort.

AI could benefit society, but it could also become a monster. An artificial intelligence boom is taking over Silicon Valley, with hi-tech firms racing to develop everything from self-driving cars to chatbots capable of writing poetry. Yet AI could also spread conspiracy theories and lies even more quickly than the internet already does – fueling political polarization, hate, violence and mental illness in young people. It could undermine national security with deepfakes. In recent weeks, members of Congress have sounded the alarm over the dangers of AI but no bill has been proposed to protect individuals or stop the development of AI's most threatening aspects.

In an effort to inspire the next generation of space explorers, NASA has partnered with Minecraft to develop "worlds" that are based on its ambitious Artemis Program. Minecraft gamers can now try taking part in NASA's Artemis Program with new Artemis-themed worlds. They were developed through a partnership between NASA's Office of STEM Management and Microsoft, which actually owns Minecraft. Gamers can experience the various tasks that are required for the mission, from building a rocket and launching it to establishing a lunar base. It starts with "Artemis: Rocket Build" mission, where kids will learn about rocket engineering by building and launching a rocket.

We study how in-context learning (ICL) in language models is affected by semantic priors versus input-label mappings. We investigate two setups-ICL with flipped labels and ICL with semantically-unrelated labels-across various model families (GPT-3, InstructGPT, Codex, PaLM, and Flan-PaLM). First, experiments on ICL with flipped labels show that overriding semantic priors is an emergent ability of model scale. While small language models ignore flipped labels presented in-context and thus rely primarily on semantic priors from pretraining, large models can override semantic priors when presented with in-context exemplars that contradict priors, despite the stronger semantic priors that larger models may hold. We next study semantically-unrelated label ICL (SUL-ICL), in which labels are semantically unrelated to their inputs (e.g., foo/bar instead of negative/positive), thereby forcing language models to learn the input-label mappings shown in in-context exemplars in order to perform the task. The ability to do SUL-ICL also emerges primarily with scale, and large-enough language models can even perform linear classification in a SUL-ICL setting. Finally, we evaluate instruction-tuned models and find that instruction tuning strengthens both the use of semantic priors and the capacity to learn input-label mappings, but more of the former.

As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. We analyze the emissions of several AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) relative to those of humans completing the same tasks. We find that an AI writing a page of text emits 130 to 1500 times less CO2e than a human doing so. Similarly, an AI creating an image emits 310 to 2900 times less. Emissions analysis do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.

When robots are deployed in the field for environmental monitoring they typically execute pre-programmed motions, such as lawnmower paths, instead of adaptive methods, such as informative path planning. One reason for this is that adaptive methods are dependent on parameter choices that are both critical to set correctly and difficult for the non-specialist to choose. Here, we show how to automatically configure a planner for informative path planning by training a reinforcement learning agent to select planner parameters at each iteration of informative path planning. We demonstrate our method with 37 instances of 3 distinct environments, and compare it against pure (end-to-end) reinforcement learning techniques, as well as approaches that do not use a learned model to change the planner parameters. Our method shows a 9.53% mean improvement in the cumulative reward across diverse environments when compared to end-to-end learning based methods; we also demonstrate via a field experiment how it can be readily used to facilitate high performance deployment of an information gathering robot.

Recent work has utilised knowledge-aware approaches to natural language understanding, question answering, recommendation systems, and other tasks. These approaches rely on well-constructed and large-scale knowledge graphs that can be useful for many downstream applications and empower knowledge-aware models with commonsense reasoning. Such knowledge graphs are constructed through knowledge acquisition tasks such as relation extraction and knowledge graph completion. This work seeks to utilise and build on the growing body of work that uses findings from the field of natural language processing (NLP) to extract knowledge from text and build knowledge graphs. The focus of this research project is on how we can use transformer-based approaches to extract and contextualise event information, matching it to existing ontologies, to build a comprehensive knowledge of graph-based event representations. Specifically, sub-event extraction is used as a way of creating sub-event-aware event representations. These event representations are then further enriched through fine-grained location extraction and contextualised through the alignment of historically relevant quotes.