Why is the algorithm "Gradient Descent" the "only one that you need to know" ?

[Chip (OP)]

This refers to how a LLM's weights/parameters get tweaked during the intensive period over many months of reruns knows as "Training" or "Pretraining" (when the tweaks are no longer perceptible, the model is then deemed "trained" and is static so then its off for manual human verification (RLHF) and into action (Inference because it "infers").

Now the components that deploy gradient descent are "Loss" (who works out what made and quantifies them, the crappiest tweaks going backwards through what is known as the Feed Forward Neural Network of many "layers" (of Transformation), known as "Backpropagation", and applied the tweak in the opposite direction of the same magnitude  this whole automated process is the mechanism of ML or Machine Learning.

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Could you define gradient descent for our listeners? It's funny, Ilya Sutskever when he's on interviews, he's like, "There is only one algorithm, the algorithm is gradient descent. Everything else is just irrelevant compared to." So what happens right now is you build these ninety-six or one hundred and twenty layer deep neural nets, and all it is is a wholtioje bunch of random connections that do absolutely nothing useful, and the you give it fifteen trillion training examples.

When you see this, say that, or when you see this paragraph, this is the next token. And if you're guessing wrong, you get punished through gradient descent.

So the error, how far off you are, is your punishment, and it passes back through all the layers and it calculates an error and it blames each neuron or each connection.

How responsible were you for this terrible answer? And if you're way wrong, you get moved, and you move in the direction that helps you get the answer right.

So it's this incredibly laborious search process, and that movement of each synaptic connection is a gradient.

You just hill climb to the best possible state, and then it magically after about one hundred million dollars of compute or in Richard's case I guess four hundred million, it magically starts thinking through this one algorithm gradient descent.