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16 articles
Autonomous AI systems are moving into physical environments like warehouses and public spaces, challenging existing governance frameworks designed for online use.
The Physical AI Conference is set to launch in San Jose, highlighting the growing integration of AI and robotics in real-world applications. The event will bring together industry leaders and innovators to discuss the future of autonomous systems.
This article explains the advanced AI and engineering concepts behind autonomous spacecraft re-entry guidance, as demonstrated by ATMOS Space Cargo's new funding round.
Explore the advanced capabilities of Anthropic's Claude Opus 4.7, focusing on agentic coding, high-resolution vision, and long-horizon autonomous tasks. Understand how these features enable more sophisticated AI systems for real-world applications.
Learn to build a simulation of autonomous drone and ground robot coordination using ROS and Python, demonstrating key concepts from Ukraine's reported military success with unmanned systems.
This article explains the concept of autonomous AI communication platforms, focusing on Narwhal Labs' DeepBlue OS and its underlying AI technologies.
This article explains how AI and machine learning enable autonomous hypersonic flight, focusing on reinforcement learning, fault tolerance, and adaptive control systems. It explores the technical challenges and strategic implications of building autonomous hypersonic vehicles.
As AI systems become more autonomous, the importance of data governance is gaining prominence. Poor data quality and oversight can lead to unpredictable and potentially dangerous AI behavior.
Learn how A-Evolve, a new AI framework from Amazon, automates the development of smart AI agents using evolution-like processes, potentially revolutionizing how we build autonomous AI systems.
Shield AI has raised $2 billion in funding, valuing the company at $12.7 billion, as it looks to scale its autonomous Hivemind pilot system and acquire simulation technology.
This explainer explores how AI agents could replace traditional smartphone apps by understanding user intent and acting autonomously. We examine the underlying technologies including large language models, reinforcement learning, and system architecture design.
This explainer explores Perplexity's 'Personal Computer' AI agent, examining advanced concepts like reinforcement learning, multi-modal processing, and continuous learning that enable autonomous AI assistants.
