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Show HN: Semantic Calculator (king-man+woman=?)

calc.datova.ai

81–90 of 179 posts

Re: Show HN: Semantic Calculator (king-man+woman=?)

#82

data + plural = number data - plural = research king - crown = (didn't work... crown gets circled in red) king - princess = emperor king - queen = kingdom queen - king = worker king + queen = queen + king = kingdom boy + age = (didn't work... boy gets circled in red) man - age = woman woman - age = newswoman woman + age = adult female body (tied with man) girl + age = female child girl + old = female child The other…

Can you elaborate on what the unsolved problem you're referring to is?

Re: Show HN: Semantic Calculator (king-man+woman=?)

#83

Here's a challenge: find something to subtract from "hammer" which does not result in a word that has "gun" as a substring. I've been unsuccessful so far.

if I'm allowed only 1 something, I can't find anything either, if I'm allowed a few somethings, "hammer - wine - beer - red - child" will get you there. Guessing given that a gun has a hammer and is also a tool, it's too heavily linked in the small dataset.

Re: Show HN: Semantic Calculator (king-man+woman=?)

#86

Here's a challenge: find something to subtract from "hammer" which does not result in a word that has "gun" as a substring. I've been unsuccessful so far.

The word "gun" itself seems to work. Package this as a game and you've got a pretty fun game on your hands :)

Re: Show HN: Semantic Calculator (king-man+woman=?)

#90
post #64

data + plural = number data - plural = research king - crown = (didn't work... crown gets circled in red) king - princess = emperor king - queen = kingdom queen - king = worker king + queen = queen + king = kingdom boy + age = (didn't work... boy gets circled in red) man - age = woman woman - age = newswoman woman + age = adult female body (tied with man) girl + age = female child girl + old = female child The other…

For fun, I pasted these into ChatGPT o4-mini-high and asked it for an opinion: data + plural = datasets data - plural = datum king - crown = ruler king - princess = man king - queen = prince queen - king = woman king + queen = royalty boy + age = man man - age = boy woman - age = girl woman + age = elderly woman girl + age = woman girl + old = grandmother The results are surprisingly good, I don't think I could've do…

This is an LLM approximating a semantic calculator, based solely on trained-in knowledge of what that is and probably a good amount of sample output, yet somehow beating the results of a "real" semantic calculator. That's crazy!

The more I think about it the less surprised I am, but my initial thoughts were quite simply "now way" - surely an approximation of an NLP model made by another NLP model can't beat the original, but the LLM training process (and data volume) is just so much more powerful I guess...

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