AI meta taxonomy refers to the classification of different types or categories of artificial intelligence (AI) based on their characteristics and capabilities.
There are several ways to categorise AI, and different taxonomies may be used depending on the context and purpose of the classification. Here are some commonly used taxonomies for AI.
Based on task and application.
This taxonomy classifies AI systems based on the type of task they are designed to perform, such as image recognition, natural language processing, or decision-making. This classification can help to identify the specific strengths and limitations of different AI systems.
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Based on learning approach.
This taxonomy classifies AI systems based on how they learn, such as supervised learning, unsupervised learning, or reinforcement learning. This classification can help to understand the different approaches to training AI systems and their potential applications.
Based on functionality.
This taxonomy classifies AI systems based on their functionality, such as expert systems, neural networks, or genetic algorithms. This classification can help to understand the underlying mechanisms of different AI systems and their potential applications.
Based on cognitive level.
This taxonomy classifies AI systems based on their level of cognitive complexity, such as reactive systems, limited memory systems, or theory of mind systems. This classification can help to understand the different levels of intelligence exhibited by different AI systems.
Based on ethical considerations.
This taxonomy classifies AI systems based on their ethical considerations, such as transparent AI, explainable AI, or ethical AI. This classification can help to understand the potential ethical implications of different AI systems and their impact on society.
The choice of AI meta taxonomy will depend on the specific context and purpose of the classification, and different taxonomies may be more useful for different applications.
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