From The Debrief, November 22:
Scientists Develop AI That Mirrors Human Neural Functions, Paving Way for Artificially Intelligent Brain-like Systems
Researchers at the University of Cambridge have developed a self-organizing, artificially intelligent brain-like system that emulates key aspects of the human brain’s functionality.
The study, published in Nature Machine Intelligence, reveals how imposing physical constraints on AI systems can lead to the evolution of brain-like characteristics, offering insights into human cognitive processes and the future of AI development.
When considered an integrated system, the human brain is a remarkable feat of nature. It is adept at optimizing information processing and energy efficiency to skillfully solve complex problems.
“Not only is the brain great at solving complex problems, it does so while using very little energy,” said Jascha Achterberg, a Ph.D. student at Cambridge’s MRC Cognition and Brain Sciences Unit and study co-author. “In our new work, we show that considering the brain’s problem-solving abilities alongside its goal of spending as few resources as possible can help us understand why brains look like they do.”
In the study, researchers successfully showed that by applying physical and energetic constraints to an AI system similar to those in human neural networks, they could develop an artificially intelligent brain-like system with organizational strategies and efficiencies akin to the human brain.
The Cambridge team’s AI system used computational nodes instead of neurons. Each node was assigned a location in a virtual space, mimicking the brain’s structure, where neuron proximity influences communication ease.
The system was then tasked with solving a maze navigation challenge. This challenge, often used in brain studies involving animals like rats, requires integrating various pieces of information to find the maze’s shortest path.
Initially, the artificially intelligent brain-like system doesn’t know the correct path through the maze. However, as it continued to run into dead ends, the system learned from its errors, adjusting the strength of its nodal connections and developing centralized node hubs for efficient information transfer....
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