The crush of “big data”: To Know, but Not Understand

This is an interesting long-form. The link appears below the quote but it was essential to highlight this passage:

The problem — or at least the change — is that we humans cannot understand systems even as complex as that of a simple cell.

It’s not that were awaiting some elegant theory that will snap all the details into place. The theory is well established already:

Cellular systems consist of a set of detailed interactions that can be thought of as signals and responses. But those interactions surpass in quantity and complexity the human brains ability to comprehend them. The science of such systems requires computers to store all the details and to see how they interact.

Systems biologists build computer models that replicate in software what happens when the millions of pieces interact. It’s a bit like predicting the weather, but with far more dependency on particular events and fewer general principles.

To Know, but Not Understand: David Weinberger on Science and Big Data – The Atlantic

Mathematical models cannot “predict” chance events, which explains the relentless (and sometimes) futile efforts to pour money into a “science” that forms the stuff for “science fiction”. For example, efforts to understand anti ageing in humans. Or attempts at understanding rejuvenation using stem cells. A spin off benefit from 3D printing organs would replace the need for human donors (subsequently) but investing good money to go after bad science like restoring youth requires a re-think.

It’s much like the AI and machine learning models- we are trying to “simplify” complexity without understanding its deeper constructs. It is difficult, if not impossible, to construct the understanding of “whole”. We must accept those limitations and focus instead on how individual pieces interact- it is only then we will get the entire picture.

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