“A professor of mine once taught a class on a Tuesday, only to read a paper the next day that invalidated what he had taught. So he went into class on Thursday and told the class, “Remember what I told you on Tuesday? It’s wrong. And if that worries you, you need to get out of science."

Science is always in this draft form.”

Permanent incompletion is one of the unique features of science. No matter how accurate our model of the universe appears to be, further experimentation, hypothesis testing and repetition has always produced greater predictive accuracy. There is never a reason to think science is ‘finished’. There is always something else to do.

To draw contrast with an alternative philosophy, consider the following diagram:

For me, the salient point in this diagram is that science does not ‘End’. It’s an infinite loop — obsessed with obtaining a more accurate understanding of the universe.

This perpetual cycle has an interesting side-effect: As methods for data collection and data processing have increased, the models we have developed to understand the universe have, unsurprisingly, become increasingly complicated. Thus, as the amount of data increases, the more we have to externalise the processing of this data to computers. Our models of the universe are no longer thought, they are computed.

If science continues its perpetual cycle of mass data collection and deeper modelling, we will eventually generate data so baffling that only computers will ‘understand’ it. Science will reach a point where the human brain can not comprehend the complexity of its conclusions.

Arbesman cites evidence that this is already happening:

“A computer program known as Eureqa that was designed to find patterns and meaning in large datasets not only has recapitulated fundamental laws of physics but has also found explanatory equations that no one really understands. And certain mathematical theorems have been proven by computers, and no one person actually understands the complete proofs, though we know that they are correct.”

I would argue this has actually been happening for quite a while. In the 1970‘s Richard Feynman famously popularised the impenetrability of quantum mechanics to the human mind. Yet, despite its incomprehensibility, quantum mechanics remains one of the most accurately predictive axioms in science. It may be deeply strange, but it provides an accurate, testable model of the universe. And that, I think is the real danger with increasing data complexity: Testability.

Science permits the presence of ideas that a human can not understand. What it does not permit are ideas that can not be tested.