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Explainable artificial intelligence (XAI) is the ability to understand and explain the reasoning behind AI decisions or predictions. XAI aims to improve trust, transparency, and accountability of AI systems, and to overcome the "black box" problem of machine learning.
Artificial intelligence (AI) is intelligence exhibited by machines, particularly computer systems. It is a field of research that develops and studies methods and software that enable machines to perceive, learn, and act in their environment.
Learn how artificial intelligence (AI) is used in various fields and domains, such as internet and e-commerce, agriculture, finance, government, health, education, and more. Explore the examples, challenges, and opportunities of AI applications with Wikipedia.
In artificial intelligence, symbolic artificial intelligence (also known as classical artificial intelligence or logic-based artificial intelligence) [1] [2] is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. [3]
Learn about generative AI, a type of AI that can create data using generative models, such as text, images, videos, and more. Explore the history, modalities, applications, and concerns of generative AI.
One can distinguish three levels at which fiction has impacted the development of artificial intelligence and robotics: Historically, fiction has been prefiguring common tropes that have not only influenced goals and visions for AI, but also outlined ethical questions and common fears associated with it.
Prompt engineering is the process of structuring natural language text that can be understood by a generative AI model. Learn about different techniques, such as chain-of-thought, chain-of-symbol, and few-shot learning, that enable in-context learning and improve reasoning ability.
Neuro-symbolic AI is a type of AI that integrates neural and symbolic architectures to address the weaknesses of each. Learn about the arguments, categories, and examples of this approach, as well as its relation to artificial general intelligence and cognitive science.
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