AI systems learn by creating millions of internal variables (weights) that process input data and generate predictions, then use feedback from correct/incorrect outputs to continuously adjust these weights and improve accuracy over time, enabling tasks like facial recognition that would be impossible to search manually.
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How AI Learns From BILLIONS Of Faces 🤖👀 #shorts追加:
The problem with the database is eventually you reach a point where there's just too much information and you don't really know how to search and sort it properly. So, what you do is you train the computer to do it for you.
Okay, how? So, let's let's for example use facial recognition technology.
Let's just say you have like a billion faces, 1 billion faces.
Now, what you're trying to do is you're trying to differentiate these billion faces and you're trying to match it to the proper name, okay? You you have a face database, you have a name database.
And you're trying to align the two together, okay? You don't know how to do it, but so what you do is you train the computer to do it for you. And so, what you do is you create a system of weights.
Just a million variables, okay?
Variables.
And then the input, sorry.
Yeah.
The input are just the databases. All right? And then the output is uh yes or no. Oh, does it match or doesn't it match, okay?
Depending on the output, this information is fed back into the database and the weights.
Okay?
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