The generative AI models used in classified environments can answer questions but don't currently learn from the data they see. The Pentagon is discussing plans to set up secure environments for generative AI companies to train military-specific versions of their models on classified data, has learned. AI models like Anthropic's Claude are already used to answer questions in classified settings; applications include analyzing targets in Iran. But allowing models to train on and learn from classified data would be a new development that presents unique security risks. It would mean sensitive intelligence like surveillance reports or battlefield assessments could become embedded into the models themselves, and it would bring AI firms into closer contact with classified data than before. Training versions of AI models on classified data is expected to make them more accurate and effective in certain tasks, according to a US defense official who spoke on background with .

Meta's flailing virtual reality social experience is being discontinued in June. It's part of Meta's broader moves to slim down the business that became its namesake. Pour one out from your digital bottle, because Meta is shutting down the virtual reality experience of Horizon Worlds. Meta sent an email blast to Horizon Worlds users today stating that the social VR world will officially end on its Quest VR headsets; starting March 31, Horizon Worlds will no longer be in the Quest store. Some Horizon-specific perks, including Meta Credits, avatars, and some digital clothes and in-world purchases, will also be removed.

Nvidia CEO Jensen Huang defends DLSS 5 against user backlash, calling critics "completely wrong" about the generative AI graphics technology's function. PCWorld notes the controversy stems from concerns that DLSS 5 applies an "AI skin" over game models rather than true enhancement. Huang clarifies DLSS 5 offers developers controllability at the geometry level, describing it as real-time neural rendering that infuses photorealism into pixels. In just a day, Nvidia's DLSS 5 technology has become the hot button for most of the PC and gaming world. Now Nvidia's chief executive has weighed in, claiming that everyone is "completely wrong" about the technology. At a question-and-answer session at Nvidia's own Game Technology Conference, Nvidia chief executive Jensen Huang said that "as I have explained very carefully, DLSS 5 fuses controllability of the of geometry and textures and everything about the game with generative AI," he said. Huang went on to say of the controversy: "They're completely wrong." Nvidia's DLSS 5 has sparked controversy because it essentially applies a generative AI filter to computer graphics. Nvidia describes DLSS 5 as a "real-time neural rendering model that infuses pixels with photoreal lighting and materials," and a "GPT moment for graphics -- blending hand-crafted rendering with generative AI".

Yellowstone, Yosemite, and Grand Canyon all make the list, but aren't number one. Yosemite National Park came in at number five on the National Parks Service list. Breakthroughs, discoveries, and DIY tips sent six days a week. In 2025, the parks received 323 million recreation visits, according to new data release by the National Parks Service. The data includes visitors to National Parks, National Historic Sites, National Memorials, National Seashores, National Parkways, and other designated public lands.

Support journalism that doesn't flinch . Support journalism that doesn't flinch . Elon Musk leaves a meeting with House Republicans in the basement of the US Capitol building on March 5, 2025 in Washington, DC. Get your news from a source that's not owned and controlled by oligarchs. Tennessee teenagers are suing Elon Musk's company xAI over allegations that its artificial intelligence tool Grok undressed photos of them as minors--the latest challenge against the wealthiest living person's chatbot .

After the rise (and fall) of reservation scalping, DoorDash and a host of apps are fighting to book you a seat at the country's most exclusive restaurants. At The Eighty-Six in Manhattan, exclusivity is the point. The luxe, 11-table steakhouse is the sort of place that lavishes caviar and aged mimolette cheese on its potatoes, and crows that your market-price duck was raised by one Dr. Taylor Swift has reportedly dined there in a Miu Miu skirt. Reservations are a scarce commodity that the restaurant, and New York law forbids you from selling one. "Access is the main asset," wrote food writer Helen Rosner in a recent New Yorker review of The Eighty-Six. "The product is the door, and what a door!

After grapefruit-sized hail hit Missouri, more images may help improve severe storm forecasting. A CoCoRaHS volunteer submitted this photo that displays a hand holding three large and uniquely shaped hailstones from June 14, 2023. Breakthroughs, discoveries, and DIY tips sent six days a week. Tuesday March 10th was a particularly punishing day of bad weather for the residents of Kansas City, Missouri. That evening, hailstones as large as grapefruits bombarded homes, businesses, and vehicles in the area, causing widespread damage to the community.

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This paper proposes a novel deep reinforcement learning (RL) architecture, called Value Prediction Network (VPN), which integrates model-free and model-based RL methods into a single neural network. In contrast to typical model-based RL methods, VPN learns a dynamics model whose abstract states are trained to make option-conditional predictions of future values (discounted sum of rewards) rather than of future observations. Our experimental results show that VPN has several advantages over both model-free and model-based baselines in a stochastic environment where careful planning is required but building an accurate observation-prediction model is difficult. Furthermore, VPN outperforms Deep Q-Network (DQN) on several Atari games even with short-lookahead planning, demonstrating its potential as a new way of learning a good state representation.

Neural networks have a smooth initial inductive bias, such that small changes in input do not lead to large changes in output. However, in reinforcement learning domains with sparse rewards, value functions have non-smooth structure with a characteristic asymmetric discontinuity whenever rewards arrive. We propose a mechanism that learns an interpolation between a direct value estimate and a projected value estimate computed from the encountered reward and the previous estimate. This reduces the need to learn about discontinuities, and thus improves the value function approximation. Furthermore, as the interpolation is learned and state-dependent, our method can deal with heterogeneous observability. We demonstrate that this one change leads to significant improvements on multiple Atari games, when applied to the state-of-the-art A3C algorithm.