> People on GPT-3 are working in a way that is academically valid (able to make results that are meaningful to a community) but from engineering it is like building a bridge with one end or a tall tower that carries no load.

To me it seemed like the opposite. They are essentially working without any hypothesis of how their model actually works, without any model of the way it actually learns or the way it produces the results that it does, and instead placing blind trust in various metrics that are improving.

They are treating this as an engineering problem - how can we make the best human-sounding text generator - and not like a traditional research problem. GPT-3 has not taught us anything about anything except "how to generate text that seems human-like to humans". We have no firm definition of what that means, we have no idea of why it works, we have no idea of any systematic failures in its model, we know next to nothing about it, other than its results on some metrics.

Imagine the same applied to physics - if instead of inventing QM and Relativity or Mechanics, physicists got it in their head to try to feed raw data into a black box and see how well it predicts some observed movements.

In fact, this would be a pretty interesting experiment: how large would a deep learning model that could accurately predict what mechanics predicts get, given only raw data (object positions, velocities, masses, colors, surface roughness, shape, taste etc.)? Unfortunately, I don't think anyone has been interested in this type of experiment, because it is not useful from an engineering (or profit) perspective.

I think most people are still aware of the problem. I've seen a few people go off the rails, but most people have been well aware that "GPT-3 is having a conversation with me!" isn't itself particularly interesting.

On the other hand, we should acknowledge that humans are also structurally wrong for most of the domains we work in. A general-purpose neural network isn't a great tool to diagnose cancer, certainly - but it doesn't have to be great to exceed some radiologist's general-purpose light detectors. I think GPT-3 starts to edge into the territory of demonstrating Dreyfus was substantially wrong, and recognizably computer-like architectures are fully capable of doing abstract reasoning.

(That's not to knock on Dreyfus! Other voices in his era were optimistic to an absurd degree, and "come on guys our computers aren't that smart" was a very necessary response.)