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Fox News
42 minutes ago
- Fox News
Next-gen missile shown off in first Pacific test as US expands long-range arsenal
In a milestone moment, the Army Friday conducted a live-fire test of its precision strike missiles in Australia, the first ever west of the international dateline. The test, seen in footage obtained by Fox News Digital, marks a significant advancement in the region's long-range strike capabilities. The precision strike missile (PrSM) has an unclassified range of 300 miles and can hit moving targets on land or at sea. The test saw a U.S.-manufactured PrSM launched from a High Mobility Artillery Rocket System (HIMARS) vehicle owned by the Australian Defence Force. It was a show of force between the U.S. and Australia at a time of increasing tension in the Indo-Pacific. "This is just one of the key steps we're taking throughout the region to deter conflict, while ensuring that our soldiers have the best capabilities available," said Army Secretary Dan Driscoll, who observed the test at the Mount Bundey Training Area in Australia. "The PrSM allows our forces to hold land and maritime regions at risk, which gives adversaries pause and increases deterrence." The PrSM is the U.S. Army's newest addition to its long-range precision fires (LRPF) portfolio, a triad of advanced strike systems that includes HIMARS-launched missiles, the mid-range capability platform and the Dark Eagle hypersonic missile. While HIMARS has already proven itself in combat zones like Ukraine, where its ability to rapidly fire and evade counterattack has made it a prized system, the integration of the PrSM into this platform significantly enhances its strategic utility. The mobile launcher can be deployed from C-17 and C-130 aircraft, a U.S. Navy landing craft and even from ships at sea, a capability tested in joint drills with the U.S. Navy and Marine Corps. Unlike the 90-mile-range Extended Range Guided Multiple Launch Rocket System (ER GMLRS), which fires six missiles per HIMARS, the PrSM packs two missiles per launcher and can reach more than triple the distance. Asked why the missiles were an important part of preparation for a potential war in the Indo-Pacific, Driscoll told reporters, "I think if you look at the way conflict is unfolding now, what is not being rewarded is large, massive presences with static locations and big footprints and signatures. What is being rewarded is the ability to be agile, hide your signature and move quickly." Fielding of HIMARS continues across the U.S. Indo-Pacific Command, with the 25th Infantry Division in Hawaii recently receiving 16 launchers, a first for a light infantry division tasked with jungle and archipelagic warfare. Officials say they are looking to increase munitions production with key allies. The test comes after it was revealed the Pentagon privately pressed Australia to define how it might help if war broke out over a Chinese invasion of Taiwan. Australia responded by stressing it would not commit troops in advance of any conflict. Australia does not permit permanent foreign military bases, but the U.S. is expanding its rotational presence at Australian sites. Australia and the U.S. recently led a major joint exercise in Sydney involving 30,000 troops from 19 countries. It also comes at a time when Washington is reconsidering whether to sell nuclear-powered, Virginia-class submarines to Sydney through the Australia-UK-US (AUKUS) deal. The three nations would jointly design a new class of submarines, with Australian production beginning in the 2040s.
Yahoo
an hour ago
- Yahoo
Meta names Shengjia Zhao as chief scientist of AI superintelligence unit
Meta CEO Mark Zuckerberg announced Friday that former OpenAI researcher Shengjia Zhao will lead research efforts at the company's new AI unit, Meta Superintelligence Labs (MSL). Zhao contributed to several of OpenAI's largest breakthroughs, including ChatGPT, GPT-4, and the company's first AI reasoning model, o1. 'I'm excited to share that Shengjia Zhao will be the Chief Scientist of Meta Superintelligence Labs,' Zuckerberg said in a post on Threads Friday. 'Shengjia co-founded the new lab and has been our lead scientist from day one. Now that our recruiting is going well and our team is coming together, we have decided to formalize his leadership role.' Zhao will set a research agenda for MSL under the leadership of Alexandr Wang, the former CEO of Scale AI who was recently hired to lead the new unit. Wang, who does not have a research background, was viewed as a somewhat unconventional choice to lead an AI lab. The addition of Zhao, who is a reputable research leader known for developing frontier AI models, rounds out the leadership team. To further fill out the unit, Meta has hired several high-level researchers from OpenAI, Google DeepMind, Safe Superintelligence, Apple, and Anthropic, as well as pulling researchers from Meta's existing Fundamental AI Research (FAIR) lab and generative AI unit. Zuckerberg notes in his post that Zhao has pioneered several breakthroughs, including a 'new scaling paradigm.' The Meta CEO is likely referencing Zhao's work on OpenAI's reasoning model, o1, in which he is listed as a foundational contributor alongside OpenAI co-founder Ilya Sutskever. Meta currently doesn't offer a competitor to o1, so AI reasoning models are a key area of focus for MSL. The Information reported in June that Zhao would be joining Meta Superintelligence Labs, alongside three other influential OpenAI researchers — Jiahui Yu, Shuchao Bi, and Hongyu Ren. Meta has also recruited Trapit Bansal, another OpenAI researcher who worked on AI reasoning models with Zhao, as well as three employees from OpenAI's Zurich office who worked on multimodality. Zuckerberg has gone to great lengths to set MSL up for success. The Meta CEO has been on a recruiting spree to staff up his AI superintelligence lab, which has entailed sending personal emails to researchers and inviting prospects to his Lake Tahoe estate. Meta has reportedly offered some researchers eight- and nine-figure compensation packages, some of which are 'exploding offers' that expire in a matter of days. Meta has also upped its investment in cloud computing infrastructure, which should help MSL conduct the massive training runs required to create competitive frontier AI models. By 2026, Zhao and MSL's researchers should have access to Meta's 1 gigawatt cloud computing cluster, Prometheus, located in Ohio. Once online, Meta will be one of the first technology companies with an AI training cluster of Prometheus' size — 1 gigawatt is enough energy to power more than 750,000 homes. That should help Meta conduct the massive training runs required to create frontier AI models. With the addition of Zhao, Meta now has two chief AI scientists, including Yann LeCun, the leader of Meta's FAIR lab. Unlike MSL, FAIR is designed to focus on long-term AI research — techniques that may be used five to 10 years from now. How exactly Meta's three AI units will work together remains to be seen. Nevertheless, Meta now seems to have a formidable AI leadership team to compete with OpenAI and Google. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
CNET
2 hours ago
- CNET
How Can You Turn Your Thoughts Into a Visible Ideas Board? I Spoke to an AI Company About it
After more than a year of researching, reviewing and writing about artificial intelligence, I've been wanting to learn more about AI as a thinking and planning platform. While it's a great tool for streamlining work, it's also being used in more creative ways, and I've been curious about my own thinking style. Why do I see connections and visualizations when someone speaks? Why do I often feel like I'm not just listening and digesting information, but instead, piecing through what's being said and translating it into a deeper connection point? "Problem solving, thinking… It's a journey you go through where you need to be able to have that freedom to explore," Stephen Chau, the co-founder of AI platform Cove, tells me. The story of Cove began with Chau and his co-founders Andy Szybalski and Michael Chu experimenting with AI, only to realize that something was missing. AI needed to work more like a human collaborator -- and that means thinking in flexible and responsive ways, while able to support complex, ongoing projects. So why do most AI tools still feel limited to linear, transactional chatbot interactions? Chau and his co-founders "we were having a lot of fun experimenting with AI," he tells me. "But the more we built, the more we saw fundamental limitations in chatbots." This insight sparked the creation of Cove: a flexible, visual workspace designed to match the natural ways humans think and work together, with AI as an active partner in the process. I was reminded of Flora, a creative operating system that contains a beautiful and fluid setup for ideation. It mimics a designer's sketchbook or filmmaker's storyboard. Cove, in contrast, is for thought clarity -- more architectural, but still beautiful and clean, and focused on sharpening what's been built and creating insight along the way. I dug into Cove's functionality and, more importantly, why it matters right now in a rapidly evolving age of AI. What is Cove, and how does it use AI? Cove's best feature, and one it has in common with many AI tools, is that it responds and adapts in real time. Chau shared that Cove pulls from a mix of top AI models -- OpenAI, Anthropic, Gemini, Perplexity -- without requiring you to choose or toggle between them. The goal is for it to feel less like software and more like inviting a collaborator into your thought process. The way Cove works is simple: You tell it what you're working on, and instead of a single thread or linear chat, Cove opens into a canvas, one that shapes itself around whatever you're trying to figure out. As Chau described it, Cove creates a flexible workspace for you, filled with cards that break your project into manageable pieces. Enlarge Image Cove / Screenshot by CNET "One card might show the pros and cons of living in Palo Alto versus Vancouver," Chau explains. "Another might list moving considerations, immigration info or other related topics." These cards let you explore multiple ideas at once, compare options side by side and dive deeper through suggested actions on each card. This visual, interactive approach helps you think through complex problems in a way traditional chatbots don't. When you drag in content like PDFs, spreadsheets, links and images, those files become part of the workspace. Cove ingests and learns from your uploads, using them to inform its suggestions and build a board that evolves as your project does. Similar to a chatbot, this deepens the workspace's intelligence over time, making future insights more relevant and specific to your needs. But it's more like continuing a dialogue with a tool that remembers what matters to you. I see it as an integrated workspace. It doesn't require me to search for old threads, desperately trying to find what I was working through days prior (which is something I've repeatedly wished I could do with ChatGPT). Who should use Cove? What genuinely excited me about Cove is how it feels like it was made for "snowball thinkers" like myself -- rapid brainstorming with a need for visualization as my ideas evolve. Given how deeply personal and ongoing these projects can be, data privacy is fundamental. Since privacy is a common concern around AI tools, I asked Chau how Cove handles it. "A lot of what we're doing is leaning on these large LLM model providers and their fundamental policies," he tells me. "But we also want to make sure that from a user data privacy standpoint, we have the right policies as well." These policies include maintaining control over your data when you're using the app, such as a toggle to opt out of having your content used for AI training. As for who uses Cove, Chau suggested the "chief household officer" juggling one project after another. Education has also emerged as a significant use case, with students adopting Cove for collaborative research and iterative drafting. Entrepreneurs use it, too -- as a virtual co-founder and thought partner or a place to lay out brain dumps for team clarity. How to use Cove to brainstorm and share ideas Enlarge Image A Cove workspace I made about a Yosemite camping trip, featuring an AI-generated image, a map, a website and AI-generated text with itineraries and tips on stargazing. Cove/Screenshot by CNET Ready to try it for yourself? Here's how to go from idea to execution inside Cove, which exists on desktop and as a Google Chrome extension. Head to and click Get Started to sign up with Google or by creating an account. Cove uses an "infinite" canvas. Each idea is represented by a card, which can contain things like text, attachments, images and tables. These cards can be added endlessly to your canvas. There's also an option to paste URLs, and Cove will create cards with fetched content from the web, as well as things you upload, including PDFs, pitch decks and working docs -- or use empty text cards and generate content from there. Cove will generate images you describe in a new card, too. Using these cards, you can create a mind map of different ideas, where you're dragging cards around, zooming in and out and reorganizing them. It also has the capability to predict what you're going to need next, working in real time to create something that serves your initial prompt or purpose. Open the chatbot at the bottom right of the screen, where you can interact with the AI tool and use it to highlight cards that have been created, or select multiple and ask Cove's AI to connect them together and find patterns or ways of thinking. Now, use your cards to create AI-powered tools right in your canvas. Describe what you want and Cove will auto-generate an app based on your context or needs. You can customize it as much as you need and then deploy a new app within your workspace. Since Cove was made for collaboration, you can invite teammates into your workspace for real-time ideation and process feedback. (You have agency over who accesses your workspace -- think of it like a virtual desk.) There's also the option to share your app cards or choose to Publish to Gallery, which is how you can make your workflow public or to share a tool that others can duplicate. Lastly, revision history allows you to keep ideas evolving without losing work. Since everything is divided by your app cards, there's the possibility for focused edits or individual-turned-collaborative experimentation. For more information on how to use Cove, head to the support page on its website, and check out this demo video, which visually walks you through the process above. Chau sums up the vision simply: People using Cove are "working through a long-term problem." "Over time, we hope to use that information as frameworks for tackling those problems, so more users can benefit," he tells me. Ultimately, Cove creates space for non-linear output -- think of it less as a tool and more of a partner in thoughtful growth and discovery -- one that you can share with others who are equally interested in understanding the brain's layered, intricate process. Happy thinking.
