In the described in-context learning setup, a neural network receives X,Y examples and predicts the Y corresponding to a new X.

“So basically your inputs into the neural network are X,”

“Y pairs.”

“x y x y x y that happened to be on the line and”

“then you do x and you expect the y and the neural network when you train it in this way actually does do does do linear regression and normally when you would run linear regression you have a small gradient descent optimizer that basically looks at x y looks at an error calculates the gradient of the weights and does the update a few times it just turns out that when they looked at the weights of that in context learning algorithm they actually found some”