Breakthroughs, discoveries, and DIY tips sent every weekday. An international team of physicists has developed a new methodology to aid NASA and other government agencies in their ongoing investigations into unidentified aerial phenomena (UAPs). The result is a novel strategy integrating a specially designed artificial intelligence program that was partially inspired by the physicists' own hunt for elusive dark matter. More popularly known as unidentified flying objects or UFOs, UAPs aren't necessarily considered as outlandish as they were decades ago. Setting aside the various theories that point to mysterious visitors from another planet, analysis increasingly centers on determining more worldly explanations.

Applying AI foundation models directly to geospatial datasets remains challenging due to their limited ability to represent and reason with geographical entities, specifically vector-based geometries and natural language descriptions of complex spatial relations. To address these issues, we investigate the extent to which a well-known-text (WKT) representation of geometries and their spatial relations (e.g., topological predicates) are preserved during spatial reasoning when the geospatial vector data are passed to large language models (LLMs) including GPT-3.5-turbo, GPT-4, and DeepSeek-R1-14B. Our workflow employs three distinct approaches to complete the spatial reasoning tasks for comparison, i.e., geometry embedding-based, prompt engineering-based, and everyday language-based evaluation. Our experiment results demonstrate that both the embedding-based and prompt engineering-based approaches to geospatial question-answering tasks with GPT models can achieve an accuracy of over 0.6 on average for the identification of topological spatial relations between two geometries. Among the evaluated models, GPT-4 with few-shot prompting achieved the highest performance with over 0.66 accuracy on topological spatial relation inference. Additionally, GPT-based reasoner is capable of properly comprehending inverse topological spatial relations and including an LLM-generated geometry can enhance the effectiveness for geographic entity retrieval. GPT-4 also exhibits the ability to translate certain vernacular descriptions about places into formal topological relations, and adding the geometry-type or place-type context in prompts may improve inference accuracy, but it varies by instance. The performance of these spatial reasoning tasks offers valuable insights for the refinement of LLMs with geographical knowledge towards the development of geo-foundation models capable of geospatial reasoning.

Despite being trained specifically to follow user instructions, today's language models perform poorly when instructed to produce random outputs. For example, when prompted to pick a number uniformly between one and ten Llama-2-13B-chat disproportionately favors the number five, and when tasked with picking a first name at random, Mistral-7B-Instruct chooses Avery 40 times more often than we would expect based on the U.S. population. When these language models are used for real-world tasks where diversity of outputs is crucial, such as language model assisted dataset construction, their inability to produce diffuse distributions over valid choices is a major hurdle. In this work, we propose a fine-tuning method that encourages language models to output distributions that are diffuse over valid outcomes. The methods we introduce generalize across a variety of tasks and distributions and make large language models practical for synthetic dataset generation with little human intervention.

We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages. This underexplored problem poses new challenges at the pre-writing stage, including how to research the topic and prepare an outline prior to writing. We propose STORM, a writing system for the Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking. STORM models the pre-writing stage by (1) discovering diverse perspectives in researching the given topic, (2) simulating conversations where writers carrying different perspectives pose questions to a topic expert grounded on trusted Internet sources, (3) curating the collected information to create an outline. For evaluation, we curate FreshWiki, a dataset of recent high-quality Wikipedia articles, and formulate outline assessments to evaluate the pre-writing stage. We further gather feedback from experienced Wikipedia editors. Compared to articles generated by an outline-driven retrieval-augmented baseline, more of STORM's articles are deemed to be organized (by a 25% absolute increase) and broad in coverage (by 10%). The expert feedback also helps identify new challenges for generating grounded long articles, such as source bias transfer and over-association of unrelated facts.

The Pentagon will look to develop new artificial intelligence-guided planes, offering two contracts that several private companies have been competing to obtain. The Collaborative Combat Aircraft (CCA) project is part of a 6 billion program that will add at least 1,000 new drones to the U.S. Air Force. These drones would deploy alongside human-piloted jets and provide cover for them, acting as escorts with full weapons capabilities that could also act as scouts or communications hubs, The Wall Street Journal reported. Boeing, Lockheed Martin, Northrop Grumman, General Atomics and Anduril Industries have all taken up the challenge. General Atomics supplied the Reaper and Predator drones the U.S. has deployed in numerous campaigns in the Middle East, and Anduril is a newcomer to the field, founded in 2017 by inventor Palmer Luckey, an entrepreneur who founded Oculus VR.

As wildfire activity reaches record levels, the tech integration company SAIC is developing artificial intelligence technology that can help predict when they'll happen, how to stop them, and how to keep folks safe. A long list of new products and developments made 2023 possibly the biggest year yet for artificial intelligence, with major tech companies breaking into action and everyday consumers becoming increasingly aware of the rapidly developing technology. "2023 was a banner year for AI in that we saw both investment and public interest explode," Samuel Mangold-Lenett, a staff editor at The Federalist, told Fox News Digital. "We also saw how AI can revolutionize every aspect of every major industry. From defense to finance to dating apps, AI proved it's here to stay."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Billionaire Elon Musk and OpenAI CEO Sam Altman are engaged in a raging battle over how much access the public should have to the technology behind artificial intelligence or AI. The most influential player in this space, however, is not Musk or Altman. A careful dusting for fingerprints reveals that it is none other than the president of the United States himself, Joe Biden.

Alice Globus, head of Nanotronics, said AI could minimize the damage done by recent malware attacks on hospitals and the Colonial Pipeline shutdown in 2021. The CEO of one of the most popular artificial intelligence platforms is warning that AI systems could eventually be capable of "superhuman persuasion." "I expect AI to be capable of superhuman persuasion well before it is superhuman at general intelligence," Sam Altman, CEO of OpenAI, the company behind the popular ChatGPT platform, said on social media earlier this month. He added that such capabilities could "lead to some very strange outcomes." Altman's comments come as fears over what rapidly developing AI technology might eventually be capable of have continued to grow, with some speculating that the technology might surpass the cognitive functions of humans.

Artificial intelligence will likely lead to seismic changes to the workforce, eliminating many professions and requiring a societal rethink of how people spend their time, prominent tech leaders said Tuesday.

The Wall Street Journal on Monday kicks off its annual Tech Live conference in Laguna Beach, Calif., with an emphasis on the fast-paced changes wrought by artificial intelligence across business, technology and policy-making.