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The Brain: Key to More Efficient AI

Bylgarcia

A team of researchers from the University of Surrey (United Kingdom) has developed a novel Artificial Intelligence model directly inspired by the functioning of the human brain. This breakthrough, which promises to significantly improve AI performance and efficiency, could be a key step for the next generation of intelligent systems.

The Challenge of AI Efficiency

Current Artificial Intelligence models, especially large language models (LLMs), require an enormous amount of energy for their training and operation. This is because their artificial neural networks are too dense, with massive connections that are not always 100% efficient.

In contrast, the human brain is capable of performing complex tasks using a fraction of that energy. This extraordinary biological efficiency was precisely the inspiration for the Surrey team.

The Solution: Mimicking Brain Structure

The study, published in the prestigious journal Neurocomputing, proposes a neural network architecture that mimics the way the brain organizes its connections, using a more dispersed and efficient structure instead of traditional dense connections.

“Our work demonstrates that intelligent systems can be built much more efficiently, reducing energy demand without sacrificing performance,” explained Dr. Roman Bauer, a Senior Lecturer at the University of Surrey and one of the project leaders.

Breakthrough with Impact on the IT Sector

This approach could have significant repercussions in the world of technology and computing:

  • More Sustainable AI: Lower energy consumption and reduced carbon footprint of data centers.
  • Improved Performance: Resources are utilized more effectively, allowing for more complex processing.
  • More Accessible: An energy-efficient AI could be integrated into smaller and more affordable devices.

Towards a More Human and Sustainable AI

In short, the University of Surrey’s study represents a fusion between neuroscience and computing, opening new avenues for the development of technologies closer to the biological functioning of the brain.

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