Submission declined on 16 November 2024 by KylieTastic (talk).
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| Submission declined on 16 November 2024 by MolecularPilot (talk). Neologisms are not considered suitable for Wikipedia unless they receive substantial use and press coverage; this requires strong evidence in independent, reliable, published sources. Links to sites specifically intended to promote the neologism itself do not establish its notability. Declined by MolecularPilot 11 hours ago. | |
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)
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.:[1].
Origins and Development
editThe 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[2].
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[3]. His work informed the founding of Opteran Technologies, a company developing natural intelligence systems for autonomous machines[4].
Comparison with Artificial Intelligence
editNatural intelligence differs from artificial intelligence in several key ways[5]:
- 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
editNatural intelligence has found applications in areas requiring lightweight, energy-efficient, and resilient solutions, including[6]
Future Prospects
editAs research progresses, natural intelligence is poised to redefine autonomy by enabling systems to adapt to complex, unpredictable environments efficiently[9]. Companies like Opteran Technologies are at the forefront of this movement, leveraging nature’s time-tested algorithms to create scalable and sustainable solutions.[10]
References
edit- ^ "Opteran". opteran.com. Retrieved 2024-11-15.
- ^ "Opteran". opteran.com. Retrieved 2024-11-15.
- ^ Dargan, James (2024-04-27). "World Leading Expert on Bio-inspired AI to Direct University of Sheffield's Centre for Machine Intelligence". AI Insider. Retrieved 2024-11-15.
- ^ "Opteran". opteran.com. Retrieved 2024-11-15.
- ^ "Opteran". opteran.com. Retrieved 2024-11-15.
- ^ "Opteran". opteran.com. Retrieved 2024-11-15.
- ^ "Opteran". opteran.com. Retrieved 2024-11-16.
- ^ "Opteran". opteran.com. Retrieved 2024-11-15.
- ^ "Home | Opteran". live-opteran-fe.appa.pantheon.site. Retrieved 2024-11-15.
- ^ "Opteran". opteran.com. Retrieved 2024-11-15.
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