alt Hacker NewsI don't understand this ontology thing. What's the difference between this and a memory system plus skills to collect your enterprise's APIs and CLIs etc?
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Ontology is a so-called white-box approach for AI: all dependencies are clearly stated and formalised. If something does not work as you wish, unlike LLMs, in ontology world you can clearly diagnose where the system fails. I understood the NVIDIA's idea this way: we take unreliable LLM and make a "reliable" ontology using it. If you look back into history, Prolog , was exactly a sort of "memory system" or, better put, a human-friendly database. Ontology is a further development of this concept.
One issue in a business is what does all the data actually mean? What are the various definitions and how do they relate to each other. What's a customer? Should that include people that have bought things, or anyone who is potentially buying? Do different systems use the same definitions, what are the columns, etc.
An ontology in this kind of context means a more precise machine readable spec of this and how it all relates to each other. You can totally build one by hand. The idea here is I think that it tries to figure it out itself and can take corrections over time, so you can ask natural language questions and get more reliable results rather than hoping an llm works out what "customers this month" means the same way each time over a wide span of data rather than having a much clearer API to work against.
A memory system is managed by the RL trained behavior, these systems use specific prompts and ordinary software to hardcode a policy. The RL version is usually more efficient, but it can't be improved or made more repeatable.
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This is more about streamlining the Date Engineer / Data Scientist role. Business Intelligence, in other words.
Before: domain expert wants to ask a question about the data, but which of these hundred tables actually has the right “revenue per User” column? Ask a DE to help slice the data and bless the queries.
After: ask your agent. The agent looks at the ontology and can usually find the best blessed data. To enable this you need to write down all the tribal knowledge that lived in the DE/DS head; agents can take the first pass at proposing and updating metadata.
Under the covers the DE and DS roles switch to reviewing the underlying ontology, instead of working directly with business stakeholders on one-off queries.