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Comment by maxweylandt | Hacker News Reader

I've seen in readings (and replicated myself on a set of embeddings derived from google books/news) the capital cities:

Berlin - Germany + France = Paris , that sort of thing

again,

    capital(germany, berlin).
    capital(france, paris).

is clearer.

Someone once told me you need humongous vectors to encode nuance, but people are good at things computers are bad at, and vice-versa. I don't want nuance from computers any more than I want instant, precise floating point calculations from people.

I think you are missing the difference between a program derived from training data and logic explicitly created. Go ahead and proceed to continue doing what you are doing for all words in the dictionary and see how the implementation goes.

It depends whether you want your system to handle all of natural language and give answers which are correct most of the time (but it isn't easy to tell when it's wrong), or to handle a limited subset of natural language and either give answers which are demonstrably correct (once it's fully debugged or proven correct), or tells you when it doesn't know the answer.

These are two opposing approaches to AI. Rule induction is somewhere in between - you use training data and it outputs (usually probabilistic) human-readable rules.

What’s Hamburg - Germany + France?

In Qwen3-Embedding-0.6B that's Marseilles

And Munich - Germany + France is Strasbourg

It’s kinda interesting just because France is a bit more centralized than Germany.

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