zlacker

Maybe it's a bit on the nose but I had his article summarized by Anthropic's Claude 2 100k model (LLMs are good at summarization) for those who don't have time to read the whole thing:

The article discusses generative AI models like ChatGPT and contrasts them with knowledge-based AI systems like Cyc.

Generative models can produce very fluent text, but they lack true reasoning abilities and can make up plausible-sounding but false information. This makes them untrustworthy.

In contrast, Cyc represents knowledge explicitly and can logically reason over it. This makes it more reliable, though it struggles with natural language and speed.

The article proposes 16 capabilities an ideal AI system should have, including explanation, reasoning, knowledge, ethics, and language skills. Cyc and generative models each have strengths and weaknesses on these dimensions.

The authors suggest combining symbolic systems like Cyc with generative models to get the best of both approaches. Ways to synergize them include:

Using Cyc to filter out false information from generative models.

Using Cyc's knowledge to train generative models to be more correct.

Using generative models to suggest knowledge to add to Cyc's knowledge base.

Using Cyc's reasoning to expand what generative models can say.

Using Cyc to explain the reasoning behind generative model outputs.

Overall, the article argues combining reasoning-focused systems like Cyc with data-driven generative models could produce more robust and trustworthy AI. Each approach can shore up weaknesses of the other.

May he rest in peace.