We assume that artificial intelligence (AI), which is a simulation of human intelligence, and nature intelligence (NI), which is a simulation of nature intelligence, can complement each other.
Improving artificial intelligence (AI) involves several strategies and techniques. Here are some key methods:
1. Data Quality and Quantity
2.Model Architecture
3.Feature Engineering
4. Algorithmic Improvements
5. Human-AI Collaboration
6. Explainability and Transparency
Regarding the idea of complementing AI with nature intelligence (NI), this is an intriguing concept. Nature intelligence refers to the intelligence observed in natural systems, such as the behavior of animals, plants, and ecosystems. Here’s how AI and NI can complement each other:
- Biomimicry: AI can learn from natural systems to develop more efficient algorithms and models.
- Sustainability: NI can provide insights into sustainable practices that AI can adopt.
- Resilience: Natural systems are often highly resilient to changes and disruptions. AI can benefit from these principles to create more robust and adaptable models.
A. Einstein once said that natural intelligence had defeated the human brain.
Concerning the idea of complementing AI with natural intelligence (NI), we propose the following approach in 3 consecutive steps:
1-Find the most effective solution to a given social problem via an efficient AI algorithm.
2-This solution should be tested by NI nature intelligence to have an idea of its evolution over time.
In other words, predict the stability and convergence of the solution and the possibility that it will continue to thrive over time.
This is done directly via B-matrix chain statistical techniques.
3-Depending on the result of step 2, whether positive or negative, the solution proposed by AI can be accepted, refused or postponed.
To be continued.
Markov chains have been widely used in generative AI.
They serve as a basis for generating sequences of data points based on transition probabilities between states.
Markov chains are assumed to be only a subset of the B-matrix statistical chains that model the intelligence of nature as we know it via universal physical laws.
It is therefore logical to expect that the latter could be used to improve artificial intelligence.
To be continued.
1--I. Abbas, How to transform B-Matrix chains into Markov chains and vice versa, ResearchGate, IJISRT journal, December 2020
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