<ul><li>Researchers have developed a computing architecture for autonomous learning that resembles traditional (von Neumann) computing with numbers but performs operations on high-dimensional vectors.</li><li>The architecture includes a high-capacity memory for vectors, similar to random-access memory (RAM) for numbers, and is inspired by models of human and animal learning.</li><li>This approach, which aligns with ideas from psychology, biology, and traditional computing, provides insights into how brains compute and can potentially be applied to enable learning by robots.</li><li>To realize the vision of computing with minimal material and energy usage, further development of a mathematical theory and large-scale experiments are required.</li></ul>

Autonomous Learning with High-Dimensional Computing Architecture Similar to von Neumann's

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