Discovering underlying principles and predicting outcomes is two sides of the same coin in that there is no way to confirm you have discovered underlying principles unless they have some predictive power.
Some had tried to come up with other criteria to confirm you have discovered an underlying principle without predictive power, such as on aesthetics - but this is seen by the majority of scientists as basically a cop out. See debate around string theory.
Note that this comment is summarizing a massive debate in the philosophy of science.
If all you can do is predict an outcome without being able to explain how then what have you really discovered? Asking someone to just believe you can predict outcomes without any reasoning as to how, even if you're always right, sounds like the concept of faith in religion.
The how is actually just further hypotheses. It's turtles all the way down:
There is a car. We think it drives by burning petrol somehow.
How do we test this? We take petrol away and it stops driving.
Ok, so we know it has something to do with petrol. How does it burning the petrol make it drive?
We think it is caused by the burned petrol pushing the cylinders, which are attached to the wheels through some gearing. How do we test it? Take away the gearing and see if it drives.
Anyway, this never ends. You can keep asking questions, and as long as the hypothesis is something you can test, you are doing science.
In the vein of "can a biologist fix a radio" and "can a neuroscientist understand a microprocessor", see https://review.ucsc.edu/spring04/bio-debate.html which is an absolutely wonderful explanation of how geneticists and biochemists would go about reverse-engineering cars.
The best part is where the geneticist ties the arms of all the suit-wearing employees and it has no functional effect on the car.
When you read about a wizard using magic to lay waste to invading armies, how much value would you guess the armies place in whether or not the wizard truly understands the magic being used against them?
Probably none. Because the fact that the wizard doesn’t fully understand why magic works does not prevent the wizard from using it to hand invaders their asses. Science is very much the same - our own wizards used medicine that they did not understand to destroy invading hordes of bacteria.
Exactly! The magic to lay waste to invading armies is packaged into a large flask and magical metal birds are flown to above the army. There the flask is released from the birds bellies and gently glides down. When the flask is at optimum height it releases the power of the sun and all that are beneath it get vaporized. A newer version of this magic is attached to a gigantic fireworks rocket that can fly over whole mountain ranges and seas.
it's still an extremely valuable tool. just as we see in mathematics, closed forms (and short and elegant proofs) are much coveted luxury items.
for many basic/fundamental mathematical objects we don't (yet) have simple mechanistic ways to compute them.
so if a probabilistic model spits out something very useful, we can slap a nice label on it and call it a day. that's how engineering works anyway. and then hopefully someday someone will be able to derive that result from "first principles" .. maybe it'll be even more funky/crazy/interesting ... just like mathematics arguably became more exciting by the fact that someone noticed that many things are not provable/constructable without an explicit Axiom of Choice.
but in usual natural sciences we don't have proofs, only data and models, and then we do model selection (and through careful experiments we end up with confidence intervals)
and it seems with these molecular biology problems we constantly have the problem of specificity (model prediction quality) vs sensitivity (model applicability), right? but due to information theory constraints there's also a dimension along model size/complexity.
so if a ML model can push the ROC curve toward the magic left-up corner then likely it's getting more and more complex.
and at one point we simply are left with models that are completely parametrized by data and there's virtually zero (direct) influence of the first principles. (I mean that at one point as we get more data even to do model selection we can't use "first principles" because what we know through that is already incorporated into previous versions of the models. Ie. the information we gained from those principles we already used to make decisions in earlier iterations.)
Of course then in theory we can do model distillation, and if there's some hidden small/elegant theory we can probably find it. (Which would be like a proof through contradiction, because it would mean that we found model with the same predictive power but with smaller complexity than expected.)
// NB: it's 01:30 here, but independent of ignorance-o-clock ... it's quite possible I'm totally wrong about this, happy to read any criticism/replies
Isn’t that basically true of most of the fundamental laws of physics? There’s a lot we don’t understand about gravity, space, time, energy, etc., and yet we compose our observations of how they behave into very useful tools.
And much of the 20th century was characterized by a very similar progression - we had no clue what the actual mechanism of action was for hundreds of life saving drugs until relatively recently, and we still only have best guesses for many.
That doesn’t diminish the value that patients received in any way even though it would be more satisfying to make predictions and design something to interact in a way that exactly matches your theory.
We were using the compass for navigation for thousands of years, without any clue about what it was doing or why. Ofcourse lot of people got lost cause compasses are not perfect. And the same will happen here. Theory of Bounded Rationality applies.
Some had tried to come up with other criteria to confirm you have discovered an underlying principle without predictive power, such as on aesthetics - but this is seen by the majority of scientists as basically a cop out. See debate around string theory.
Note that this comment is summarizing a massive debate in the philosophy of science.