What would it look like to combine Google search (shallow Knowledge Graph reasoning over an ultra-ultra-wide index) with LLM in-context learning (highly intelligent operations on a tiny index)?

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The future may lie in "cognitive layering" - shallow but broad knowledge as a foundation, with deep reasoning capabilities that can dynamically zoom in and perform complex operations on relevant subsets. Like how human experts combine broad awareness with focused analysis. 🧠

Surely were going to find out in the near future

Sounds like Jeff made up and answered a harder question (hand-waving), probably because the answer to your question is that Google is already doing that and it's all secret.

Perhaps read the two appendices of the Syntopicon of The Great Books explaining the methods used in the attempt to index, to .. 'bring together the topics', which seemed important at the time for the continuation of Western Civilization when it was first published in 1953.

The curation of what to put in the context will be key to unlock actual new knowledge and it will be shaped as a graph. With semantic rules and/or ontology to structure it. Here is how I think of it : You label the inputs as a knowledge graph You prompt the LLM to generate a subgraph of outputs You integrate the subgraph into the global knowledge graph. Repeat. Do that for argumentation for example and you unlock actual reasoning

I don't think we actually need a large memory (context) for this... Just a large database and the right search parameters

Do you consider LLM in-context learning "highly intelligent"??

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