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> One might accept the progress machine learning has made in recent years and still assert that all this activity is not Science, but mere “commercialization” or “product engineering.” Fine. But to what end? The value of any governing structure or practice of science should be weighed pragmatically against the benefits it provides and the costs it imposes on a field. The goal should be the actual production of knowledge, not clinging to a set of canons about scientific process.

This reminds me of a section of Thomas J. Allen's _Managing the Flow of Technology_:

> The scientist's principal goal is a published paper. The technologist's goal is to produce some physical change in the world.

It strikes me that this aligns with the end of your quote above; the goal *of science* should be the actual production of knowledge. But for all of the progress made in AI over the past decade (or whatever), I'm not totally certain how much actual knowledge we've ended up with. Instead, I think we've produced changes in the world, and utility, and value.

This is all to say that we can criticize AI companies for not producing science while at the same time admiring them for the value they've created. Engineering is good! If I point at the Space Shuttle and say "that isn't science," that statement can (and to some extent should) be seen as a neutral semantic distinction, not a criticism of the space shuttle or any of the engineering that went into it.

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