A soldier carries a drone during a military parade in Washington on June 14, 2025. The Pentagon's largest-ever budget request earmarks $75 billion for drones and technologies to counter them, mainly for a massive increase for a little-known office working with U.S. commandos to test and evaluate various systems, according to defense officials. The drone-funding proposal includes $54.6 billion for the Defense Autonomous Working Group, or DAWG, from just $225.9 million this year. That would appear to be the largest single year-over-year boost of any defense program or office, meaning it's likely to draw particular congressional and public scrutiny in an already eye-catching $1.5 trillion request that's 42% larger than this year's budget. The big boost for the Pentagon's little-known drone unit comes as the U.S. and Israeli war against Iran illustrates how drones can help level the playing field against even the world's most well-funded armed forces.

Things to Do in L.A. Tap to enable a layout that focuses on the article. A drone delivered her lethal dose of fentanyl in a church parking lot. The Drug Enforcement Administration was among agencies involved in the investigation. This is read by an automated voice. Please report any issues or inconsistencies here .

Meta to capture U.S. employee mouse movements and keystrokes to train AI NEW YORK - Meta is installing new tracking software on U.S.-based employees' computers to capture mouse movements, clicks and keystrokes for use in training its artificial intelligence models, part of a broad initiative to build AI agents that can perform work tasks autonomously, the company told staffers in internal memos. The tool, called Model Capability Initiative (MCI), will run on work-related apps and websites and will also take occasional snapshots of the content on employees' screens, according to one of the memos, posted by a staff AI research scientist on Tuesday in a channel for the company's model-building Meta SuperIntelligence Labs team. The purpose, according to the memo, was to improve the company's AI models in areas where they struggle to replicate how humans interact with computers, like choosing from dropdown menus and using keyboard shortcuts. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Michael Dell and Susan Dell became the first donors to give more than $1 billion to the University of Texas at Austin, funding a massive AI-native hospital and research campus.

We present a novel deep recurrent neural network architecture that learns to build implicit plans in an end-to-end manner purely by interacting with an environment in reinforcement learning setting. The network builds an internal plan, which is continuously updated upon observation of the next input from the environment. It can also partition this internal representation into contiguous sub-sequences by learning for how long the plan can be committed to - i.e. followed without replaning. Combining these properties, the proposed model, dubbed STRategic Attentive Writer (STRAW) can learn high-level, temporally abstracted macro-actions of varying lengths that are solely learnt from data without any prior information. These macro-actions enable both structured exploration and economic computation. We experimentally demonstrate that STRAW delivers strong improvements on several ATARI games by employing temporally extended planning strategies (e.g.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Elon Musk speaks at the SpaceX Hyperloop Pod Competition II in Hawthorne, California, in 2017. Elon Musk speaks at the SpaceX Hyperloop Pod Competition II in Hawthorne, California, in 2017. Cursor is a Silicon Valley startup using AI to automate coding as Elon Musk's firm seeks foothold in the AI market SpaceX said it has secured an option to either acquire code-generation startup Cursor for $60bn later this year, or pay $10bn for their new partnership, as it pushes deeper into the lucrative market for AI developer tools. Along with OpenAI and Anthropic, Cursor is one of several Silicon Valley startups that has drawn waves of developers by using artificial intelligence to automate coding, a business where AI companies have found early commercial traction. The deal could give xAI, the Grok chatbot maker that SpaceX merged with in February, a stronger foothold in the AI coding market where it has so far lagged rivals.

We developed task-optimized deep neural networks (DNNs) that achieved state-ofthe-art performance in different evaluation scenarios for automatic music tagging. These DNNs were subsequently used to probe the neural representations of music. Representational similarity analysis revealed the existence of a representational gradient across the superior temporal gyrus (STG). Anterior STG was shown to be more sensitive to low-level stimulus features encoded in shallow DNN layers whereas posterior STG was shown to be more sensitive to high-level stimulus features encoded in deep DNN layers.

Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while keeping each client's data local and confidential. However, traditional federated models often assume homogeneity in the degradation processes across clients, an assumption that may not hold in many industrial settings. To overcome this, this paper proposes a personalized federated prognostic model designed to accommodate clients with heterogeneous degradation processes, allowing them to build tailored prognostic models. The prognostic model iteratively facilitates the underlying pairwise collaborations between clients with similar degradation patterns, which enhances the performance of personalized federated learning. To estimate parameters jointly using decentralized datasets, we develop a federated parameter estimation algorithm based on proximal gradient descent. The proposed approach addresses the limitations of existing federated prognostic models by simultaneously achieving model personalization, preserving data privacy, and providing comprehensive failure time distributions. The superiority of the proposed model is validated through extensive simulation studies and a case study using the turbofan engine degradation dataset from the NASA repository.