[snake_doc]

Holy unnecessary use of terminology to explain a reverse graph traversal. “Loss”, “gradients”, “differentiating”— no! stop!

This must be what AI hype actually is. Complete incoherent language to explain a very straight forward concept.

This is just: LLMs judging intermediate node outputs, and reverse traversing the graph while doing so until it modifies the original prompt.

[currymj]

i am old enough to remember the opposite: people would try to sell deep learning to the mainstream ML community by pointing out that backprop is just message-passing on a Bayesian network with modified sum/product operations.

[snake_doc]

Valid point, but at least that was mathematics. This paper isn’t even math, it’s a data control flow masquerading as math.

[porridgeraisin]

Interestingly, backpropagation is the natural way I'd describe this process, not reverse graph traversal.

Background difference I suppose.

> This must be what AI hype actually is. Complete incoherent language to explain a very straightforward concept.

True, a lot of papers overdo the jargon just for hype purposes. My favorite funniest example is this one from Google Research (and universities) (have linked the paper review video below)

https://youtu.be/Pl8BET_K1mc

See the YouTube chapter about "Multidiffusion" (around 38minutes)

They spent multiple paragraphs formulating an "optimisation problem" which when peeled down amounts to taking the mean, just to be able to superficially cite their own previous paper.

Quite the sorry state of things.