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What LLMs might learn from Cyc? Remember Cyc?
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Todays discussion delves into the hybrid approach to AI advocated in the article, discussing how integrating the strengths of LLMs with symbolic AI systems like Cyc can lead to the creation of more trustworthy and reliable AI.
This podcast is inspired by the thought-provoking insights from the article "Getting from Generative AI to Trustworthy AI: What LLMs Might Learn from Cyc" by Doug Lenat and Gary Marcus - it can be found here.
The authors propose 16 desirable characteristics for a trustworthy AI, which include explainability, deduction, induction, analogy, theory of mind, quantifier and modal fluency, contestability, pro and contra argumentation, contexts, meta-knowledge, explicit ethics, speed, linguistic and embodiment capabilities, as well as broad and deep knowledge.
They present Cyc as an AI system that fulfills many of these traits. Unlike LLMs, which are trained on vast text corpora, Cyc is based on a curated knowledge base and an inference engine that enables explicit reasoning chains.
Cyc's expressive logical language allows it to represent and understand complex relationships and reasoning chains, and it utilizes specialized reasoning algorithms to enhance computational efficiency, processing contexts to organize knowledge and argumentation.
Read further here.
Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.
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