Draft:Natural Intelligence

• Comment: Notability has not been proven - the only references are a WP:PRIMARY link to a company's website, and a press release (WP:PRSOURCE). Both cannot count towards notability. MolecularPilot ^{🧪️✈️} 07:46, 16 November 2024 (UTC)

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Natural Intelligence (NI) refers to computational systems inspired by biological processes that have evolved over millions of years. Unlike artificial intelligence (AI), which relies on data-driven models and statistical learning, natural intelligence aims to replicate the inherent capabilities of biological systems, such as navigation and decision-making, in a robust, efficient, and adaptable manner.:.

Origins and Development

The concept of natural intelligence emerged as an alternative to traditional AI approaches, particularly those dependent on data-heavy models like deep learning. Drawing inspiration from biological brains, NI emphasizes innate specializations observed in nature.

A notable figure in this field is James A. R. Marshall, a professor at the University of Sheffield. Marshall's pioneering research into insect cognition and decision-making, including studies on mechanisms such as cross-inhibition in honeybee swarms, has influenced algorithms that replicate nature's problem-solving methods. His work informed the founding of Opteran Technologies, a company developing natural intelligence systems for autonomous machines.

Comparison with Artificial Intelligence

Natural intelligence differs from artificial intelligence in several key ways:

• Training Requirements: NI systems replicate pre-evolved biological solutions, requiring little to no training compared to AI's extensive data and training needs.
• Energy Efficiency: Inspired by lightweight biological brains, NI systems consume significantly less computational power.
• Robustness: Designed to handle noise and dynamic environments effectively.
• Explainability: Mimicking natural systems allows for a clearer and more interpretable decision-making process.

Applications

Natural intelligence has found applications in areas requiring lightweight, energy-efficient, and resilient solutions, including

• Autonomous robots and drones operating in unstructured environments.
• Vehicles in GPS-denied areas.
• Warehouse logistics and disaster response systems

Future Prospects

As research progresses, natural intelligence is poised to redefine autonomy by enabling systems to adapt to complex, unpredictable environments efficiently. Companies like Opteran Technologies are at the forefront of this movement, leveraging nature’s time-tested algorithms to create scalable and sustainable solutions.