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Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling.
Symbolic Neural symbolic—is the current approach of many neural models in natural language processing, where words or subword tokens are both the ultimate input and output of large language models. Examples include BERT, RoBERTa, and GPT-3. Symbolic[Neural]—is exemplified by AlphaGo, where symbolic techniques are used to call neural techniques.
In the philosophy of artificial intelligence, GOFAI ("Good old fashioned artificial intelligence") is classical symbolic AI, as opposed to other approaches, such as neural networks, situated robotics, narrow symbolic AI or neuro-symbolic AI. [1] [2] The term was coined by philosopher John Haugeland in his 1985 book Artificial Intelligence: The ...
Neural vs. symbolic AI. The history of artificial intelligence is one of an almost sectarian struggle between opposing approaches to solving the challenge of creating machines that could learn and ...
Today, artificial intelligence is mostly about artificial neural networks and deep learning. In fact, for most of its six-decade history, the field was dominated by symbolic artificial ...
Generative artificial intelligence (generative AI, GenAI, [166] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 167 ] [ 168 ] [ 169 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 170 ...
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