SpaceX backs Anthropic with data centre deal amidst Musk's OpenAI lawsuit Anthropic has reached a deal to tap the computing resources of Elon Musk's SpaceX, marking a detente with its one-time critic and a boost for both companies in the high-stakes artificial intelligence race. Under the agreement announced on Wednesday, Anthropic will use the full computing power of SpaceX's Colossus 1 facility in Memphis, Tennessee, which houses more than 220,000 Nvidia processors and will give the Claude chatbot maker 300 megawatts of new capacity within a month. That's enough electricity to power more than 300,000 homes - as the Dario Amodei-led company seeks to boost the capacity of its Claude Pro and Claude Max AI assistants for subscribers. The tool allows AI systems to review work between sessions, spot patterns, and update files that store user preferences and other context. Available as a research preview, "dreaming" comes with software for managing agents, or AI programmes that perform tasks with little human involvement.

When you purchase through links in our articles, we may earn a small commission. The Alliwava GH8 is a good example of how much performance is possible today in the smallest of spaces. The Alliwava GH8 is a good example of how much performance is possible today in the smallest of spaces. With the Ryzen 9 8945HS, it not only offers powerful CPU performance, but also added value for AI applications thanks to the improved NPU. In doing so, it leaves many competitors behind in terms of connectivity and cooling management.

Since 2021, China has reportedly poured $100bn into support for AI datacentres. Since 2021, China has reportedly poured $100bn into support for AI datacentres. Beijing's AI policy is focused on real-life applications but Chinese companies are beginning to articulate their own grand visions S tanding on stage in the eastern China tech hub of Hangzhou, Alibaba's normally media-shy CEO made an attention-grabbing announcement. "The world today is witnessing the dawn of an AI-driven intelligent revolution," Eddie Wu told a developer conference in September. " Artificial general intelligence (AGI) will not only amplify human intelligence but also unlock human potential, paving the way for the arrival of artificial superintelligence (ASI)."

Amazon is set to invest up to $50bn to expand artificial intelligence (AI) and supercomputing capacity for United States government customers, in one of the largest cloud infrastructure commitments targeted at the public sector. The e-commerce giant announced the investment on Monday. One gigawatt of computing power is roughly enough to power about 750,000 US households on average. "This investment removes the technology barriers that have held the government back", Amazon Web Services (AWS) CEO Matt Garman said. AWS is already a major cloud provider to the US government, serving more than 11,000 government agencies.

OpenAI has signed a $38bn (£29bn) contract with Amazon to access its cloud computing infrastructure, as the start-up continues its run of major partnerships to secure computing power . In 2025, the ChatGPT maker has signed deals worth more than $1tn with Oracle, Broadcom, AMD and chip-making giant Nvidia. Its latest deal reduces its reliance on Microsoft. As part of the seven-year agreement, OpenAI will gain access to Nvidia graphics processors to train its artificial intelligence models. The deal follows a sweeping restructure of OpenAI last week which saw it convert away from being a non-profit and changed its relationship with Microsoft to give OpenAI more operational and financial freedom.

Nvidia's $100 billion investment is meant to help OpenAI build data centers with a capacity of at least 10 gigawatts of power -- equipped with Nvidia's advanced chips to train and deploy AI models. Nvidia will invest as much as $100 billion in OpenAI to support new data centers and other artificial intelligence infrastructure, a blockbuster deal that underscores booming demand for AI tools like ChatGPT and the computing power needed to make them run. The companies announced the agreement Monday, saying they'd signed a letter of intent for a strategic deal. The investment is meant to help OpenAI build data centers with a capacity of at least 10 gigawatts of power -- equipped with Nvidia's advanced chips to train and deploy AI models. The money will be provided in stages, with the first $10 billion coming when the deal is signed, according to people familiar with the matter. Nvidia is making the investment in cash and will receive OpenAI equity as part of the deal, said the people, who asked not to be identified because the talks were private.

In collaborative systems with complex tasks relying on distributed resources, trust evaluation of potential collaborators has emerged as an effective mechanism for task completion. However, due to the network dynamics and varying information gathering latencies, it is extremely challenging to observe and collect all trust attributes of a collaborating device concurrently for a comprehensive trust assessment. In this paper, a novel progressive trust evaluation framework, namely chain-of-trust, is proposed to make better use of misaligned device attribute data. This framework, designed for effective task completion, divides the trust evaluation process into multiple chained stages based on task decomposition. At each stage, based on the task completion process, the framework only gathers the latest device attribute data relevant to that stage, leading to reduced trust evaluation complexity and overhead. By leveraging advanced in-context learning, few-shot learning, and reasoning capabilities, generative AI is then employed to analyze and interpret the collected data to produce correct evaluation results quickly. Only devices deemed trustworthy at this stage proceed to the next round of trust evaluation. The framework ultimately determines devices that remain trustworthy across all stages. Experimental results demonstrate that the proposed framework achieves high accuracy in trust evaluation.

The integration of emerging uncrewed aerial vehicles (UAVs) with artificial intelligence (AI) and ground-embedded robots (GERs) has transformed emergency rescue operations in unknown environments. However, the high computational demands often exceed a single UAV's capacity, making it difficult to continuously provide stable high-level services. To address this, this paper proposes a cooperation framework involving UAVs, GERs, and airships. The framework enables resource pooling through UAV-to-GER (U2G) and UAV-to-airship (U2A) links, offering computing services for offloaded tasks. Specifically, we formulate the multi-objective problem of task assignment and exploration as a dynamic long-term optimization problem aiming to minimize task completion time and energy use while ensuring stability. Using Lyapunov optimization, we transform it into a per-slot deterministic problem and propose HG-MADDPG, which combines the Hungarian algorithm with a GDM-based multi-agent deep deterministic policy gradient. Simulations demonstrate significant improvements in offloading efficiency, latency, and system stability over baselines.

This is the latest confirmation that the "great man" theory of history continues to thrive in Silicon Valley. As such, it joins a genre that includes Walter Isaacson's twin tomes on Steve Jobs and Elon Musk, Brad Stone's book on Jeff Bezos, Michael Becraft's on Bill Gates, Max Chafkin's on Peter Thiel and Michael Lewis's on Sam Bankman-Fried. Notable characteristics of the genre include a tendency towards founder worship, discreet hagiography and a Whiggish interpretation of the life under examination. The great man under Witt's microscope is the co-founder and chief executive of Nvidia, a chip design company that went from being a small but plucky purveyor of graphics processing units (GPUs) for computer gaming to its current position as the third most valuable company in the world. Two things drove this astonishing transition.

The emergence of large language models (LLMs) opens new frontiers for unmanned aerial vehicle (UAVs), yet existing systems remain confined to predefined tasks due to hardware-software co-design challenges. This paper presents the first aerial intelligent agent capable of open-world task execution through tight integration of LLM-based reasoning and robotic autonomy. Our hardware-software co-designed system addresses two fundamental limitations: (1) Onboard LLM operation via an edge-optimized computing platform, achieving 5-6 tokens/sec inference for 14B-parameter models at 220W peak power; (2) A bidirectional cognitive architecture that synergizes slow deliberative planning (LLM task planning) with fast reactive control (state estimation, mapping, obstacle avoidance, and motion planning). Validated through preliminary results using our prototype, the system demonstrates reliable task planning and scene understanding in communication-constrained environments, such as sugarcane monitoring, power grid inspection, mine tunnel exploration, and biological observation applications. This work establishes a novel framework for embodied aerial artificial intelligence, bridging the gap between task planning and robotic autonomy in open environments.