We'd end up doing nothing at all.
However, to your point, I'm willing to bet that our need to use heuristics and approximations has more to do with the mathematical inability to reverse-engineer chaotic systems than anything. It's not so much that we just don't have the time to build a more complete picture. It's that it's actually impossible to do so.
Mind you, the universe could be infinite as well, which would throw a bit of a wrench into it. All of which is to say that there may not be an algorithm the universe follows, even in principle.
I would say the universe follows algorithms just fine! Example
(H) + (O) + 'ignition source' = H2O.
The problem isn't algorithms, the problem is 'problem space'.
4 billion or so years ago, life forms were likely pretty easy to follow algorithmically. Then one evolved slightly and got an edge on the other ones, then another evolved and took the requirements to survive further. A random change here protected one life form this way, another that way.
Fast forward 4 billion and you see that the number of different algorithms is in the trillions. And we don't have nice code classes either. This is spaghetti. One change here affects something over there. Move that and you die. Move this and you can eat poison and surive.
It doesn't matter if it does or doesn't, we don't have perfect knowledge of the universe or quantum events, therefore the universe is still unpredictable regardless if it is deterministic or not.
> Why are 'trends' are not cut and dry, and algorithms are?
> Is there something special about a cell that is not blueprintable and manufacturable?
It comes down to strategy.
The dominant historical strategy for computer functioning is exact, precise and reproducible instruction following of a tuned, high performance state machine. It is almost opposite of our untuned but general biological nature. For almost any species, survival is based on adaptation where our computer machinery has no inner process guiding it's adaptation, only we as technologists attempting to adapt the computer's strategy to the world. You could argue that business use of technology is the survival characteristic.
Biology on the other hand produces "suboptimal" systems that are very practical or relatively general, with many mechanism to absorb and compensate for errors or mistakes. We are just now teaching computers to adapt and compensate for specific situations with the hopes of generalizing the technique and it's a hard problem to solve. Not impossible, but we need to develop different methodologies for handling natural complexity.
I agree there probably isn't something fundamentally different as both systems coexist in the physical world, there is just lifetimes of practical learning and effort that must be done to merge the two strategies into a cohesive way forward.
(edit, spelling & gramar)
There isn't a human alive who hasn't been taught most of what they know by other humans.
In the West we dedicate the first couple of decades of human life to this, with extensions for gifted individuals who are better at learning than the average.
Expecting an AI to understand the world on its own without formal teaching is equivalent to expecting an AI to recapitulate the entirety of human intellectual development within the span of a single research program.
It may be a realistic expectation at some point in the future, but it's certainly not realistic right now.
This was my first thought. Self-taught humans aren't that great at dealing with the real world either, so expecting self-taught computers to be awesome at it seems a little unfair.