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The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the mid-1990s. [4]
Symbolic AI in the 1960s was able to successfully simulate the process of high-level reasoning, including logical deduction, algebra, geometry, spatial reasoning and means-ends analysis, all of them in precise English sentences, just like the ones humans used when they reasoned. Many observers, including philosophers, psychologists and the AI ...
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.
In fact, for most of its six-decade history, the field was dominated by symbolic artificial intelligence, also known as “classical AI,” “rule-based AI,” and “good old-fashioned AI.”
Symbolic AI is a top-down approach where the computer is given a set of rules—written by humans—and then must learn how to apply those rules to specific examples or circumstances.
The physical symbol system hypothesis claims that both of the following are also examples of physical symbol systems: Intelligent human thought: the symbols are encoded in our brains. The expressions are thoughts. The processes are the mental operations of thinking. English language: the symbols are words. The expressions are sentences.
AI, as an infrastructure for alignment, enables humanity to transcend the boundaries of fragmented perception, aligning systems of thought, action, and meaning into coherence.
Critics such as Noam Chomsky argue continuing research into symbolic AI will still be necessary to attain general intelligence, [395] [396] in part because sub-symbolic AI is a move away from explainable AI: it can be difficult or impossible