AI systems that employ “evolutionary algorithms that are difficult to understand” can be said to be constantly learning and constantly seeking out new data and feedback in order to improve their understanding of the complexities of the real world.
One example of this is an AI system that monitoring a factory line for changes in the production process that may impact the quality of the product. By constantly collecting data and feedback about the factory line, the AI system can quickly adapt to changes and evolve its understanding of the manufacturing process. This would ultimately result in a more efficient and effective production process, as well as a higher quality product.
Another example of using AI to “evolutionary algorithms that are difficult to understand” is by constantly testing and refining its understanding of the world through experimentation. For example, an AI system could be employed to test different marketing strategies for a new product. By constantly experimenting with different marketing approaches, the AI system can quickly adapt and evolve its understanding of what works and what doesn’t work in order to eventually find the most effective marketing strategy. This would ultimately result in more sales and a higher ROI for the company.
Ultimately, the goal of using AI to “evolutionary algorithms that are difficult to understand” is to create systems that are constantly learning and evolving. By constantly collecting data, feedback, and experimenting, AI systems can quickly adapt and improve their understanding of the complexities of the real world. This would ultimately result in more efficient and effective systems that are better able to meet the needs of their users.
References:
https://www.forbes.com/sites/cognitiveworld/2019/08/06/evolutionary-algorithms-are-the-key-to-true-ai/#54f054366fbc
https://towardsdatascience.com/evolutionary-algorithms-987480bdd340
https://www.geeksforgeeks.org/evolutionary-algorithms/