Those steps are both necessary and usually take decades to build understanding.

But the activity of "attempting to find explanations" is specific.

The attempt to formulate laws (the "better faring hypoteses") can bring to "universal laws" or to more humble "patterns". It will contribute to the scientific effort - it does not exhaust it.

Next time we get the urge to complain that GPT-n is just applying patterns seen in the training corpus we should remember humans don't do much better. We are just language agents with rich feedback from outside. We can't even write software top-down in one go without running the code.

> patterns

Except we consciously (intentionally) work on those matchings.

> are

Opinions should be offered in proper places. Polling is not debating.

Incidentally, that opinion is also offensive. Not just some tossed "All X are Y, goodnight", but a loaded one.

If you happen to conjure thoughts and let them roam wildly, well don't. Make an effort. Critical thinking as a conscious intentional effort must have plenty of literature. The "feedforward" output, the hallucination, is not a dish for service.

Corporate wants you to find the difference...

That is to say, the two things you mention are the same process. "Identifying the best ways of smelting ore to obtain the metal through trial and error" is the easy part, when you get to pick low-hanging fruits in a field. But as the easy options get cleared out, continuing improvements requires increasingly complex, sophisticated methods - that's where the process transitions towards "trying and understanding the nature of materials". It's still the same process, but couple layers of abstraction up from the original "how to get better stick than neighbor and beat them instead of getting beaten".

The difference, while moving in "interdependent" directions, is in the purpose: obtaining some sufficient information on how things work versus an actual consideration of the nature of things.

It is not really (fully the same process), because you could (in theory) "early stop" when you have achieved technically sufficient competence - the description of the optimal process -, before the jump to the understanding. Something works - that is the technical side; why it works is the scientific side.

Note that the approach has controversial sides: if you took Newton's "hypoteses non fingo", He refused that jump explicitly:

> hypotheses, whether metaphysical or physical, or based on occult qualities, or mechanical, have no place in experimental philosophy

I.e.: Newton proposed an «experimental philosophy» which stopped short of a "true understanding of the reasons behind phenomena", in order to avoid contexts in which solidity (at his time) could not be expected. "Understanding" as "identifying universal laws" was the best that seemed affordable.

I wanted to address that in the second paragraph, which I ultimately deleted before submitting, because I couldn't phrase it right. But since you brought it up: I'd consider this an effect of specialization.

In the process of improving your object-level "how things work" goals, you end up generalizing and stacking increasingly complex theoretical models. Soon enough, you end up with people working high up the stack - not knowing or caring about the initial goals. Those people end up growing the "mound of knowledge" both upwards and sideways. The work is sort of self-justifying, but really, it's also self-similar. Where early materials science may have been driven by, say, desire for better/cheaper weapons, soon enough, you have people doing materials science because they desire to solve a puzzle. Whether the practitioners are smelting different combinations of ores to find one that will win them the war, or they're mixing up different kinds of equations to figure out a clean solution to a theoretical conundrum - it's the same process, same motivation. And it always involves play.

The kind of methodical, boring approach, with hypotheses and control groups and peer-reviewed papers? That's the boring part you have to do after play.

See also (with no implied judgement in this context): software developers that lose sight of (or care little about) business goals, and instead aim for theoretical markers of what "good code" is, and/or solve abstract puzzles of algorithms and architecture. Or the MBAs that view companies as abstract money-printing processes, running them by the rulebook that's entirely independent of whatever it is the business is actually doing or selling. Both are cases of growing complexity creating a new field of work that's independent of what brought it into existence.

> an effect of specialization

It is also a specific purpose, a deliberate endeavour. We are in front of a world, we have to digest its phenomena, we try to understand it - it is a primal process, i.e. mental digestion. In some ways it could be argued that the "scientific drive" is anterior to the technical one: first you assess, then you act. Consistently, science started as "natural philosophy".

It is relevant, in the framework of "scientific drive as (development from the natural process of) mental digestion", that terminologically "science" is not just "the realm where false statements start to exist (and try to be avoided)" ("discriminate" - ex scindere), but also, before that, an activity of sorting, of "separating" conceptual elements - mental digestion. The "scient" (as a participle of scire, used as "to know") is a "distinguishing" mind.

We not only «desire to solve a puzzle» - it is our nature (as per the above) as minds dealing with a phenomenical world we need to grasp, as part of the activity of "seeing" it, already before action.

«Play[ing]» is a process that both allows to refine knowledge of the world, physically (in the physical play), and to refine the grasping of the world, mentally (in the mental play).

> growing complexity

Or just abstraction, enabled by a refined framework (following which, other areas become primary, topical - in the synthetic effort which is part of the above said process).