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Comment by xboxnolifes | Hacker News Reader

When I was little, zebras were described to me as black and white stripped horses. Without even seeing one, I'm sure anything who has seen a horse could then merge those two concepts to create a close to accurate picture of what a zebra is.

If AI is supposed to resemble a human mind with ability to learn, then it must be able to learn from a blanker slate. You don't teach the human before it is born, and in this comparison an AI is born when you finish it's model and set its weights using the training set. If you test it with the training set, you aren't testing ability to comprehend, just regurgitate what it was born with

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You can look at a medieval bestiary to see how people thought animals might look based on descriptions alone. Like these lovely elephants

https://britishlibrary.typepad.co.uk/digitisedmanuscripts/20...

Honestly, these are some of my favorite stories and I think more ML people need to learn more about mythology (I say this as a ML researcher btw). Because once you go down this path you start to understand how "Rino" == "Unicorn". You have to really think about how to explain things when you're working with a limited language. Yeah, we have the word "rino" now, but how would you describe one to someone who has no concept of this? Maybe a cow with one big horn? Is "like a big fat tough skin horse with a big horn coming out of its head" accurate? And then apply your classic game of telephone[0]. It is also how you get things like how in Chinese a giraffe is "long neck deer"[1] (that doesn't work for all things in Chinese and there's another game of telephone (lol, maybe I was too harsh on the British in [0]) and well... you can imagine things get warped like crazy).

There's so many rabbit holes to go down when trying to understand language, vision, reasoning, and all that stuff.

[0] (Jesus England... this is what you call this game?!) https://en.wikipedia.org/wiki/Chinese_whispers

[1] https://translate.google.com/?sl=en&tl=zh-CN&text=giraffe&op... ----> https://translate.google.com/?sl=zh-CN&tl=en&text=%E9%95%BF%...

If you trained an image generator, removing all instances of zebras from the training set, you could ask it to output images of a black and white striped horse and it would likely succeed. Then you could fine tune an image recognition model (also with all zebras removed from the training set) on the generated image set to associate it with the word zebra. If you then showed it a bunch of images of actual zebras there’s a really good chance it would succeed.

AI is not supposed to resemble a human mind. It's just supposed to be useful to us.

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