Computers, Scientific Insight, and Naches

When thinking about how knowledge grows and changes over time for The Half-Life of Facts, I explored how computers are beginning to aid humans in scientific discovery. One example of this is in the realm of helping us to uncover hidden knowledge, such as when computers assist us in combining papers in the vast scientific literature to create new findings (I discuss this a bit in a Q&A here). But in the case of most of these computational aids, while algorithms help sift through data and scientific papers, stitching together ideas or helping with analyses, we still understand the results of these discoveries.

But is this drawing to a close? Will there come a day when computers make discoveries that we, as humans, can't even understand? In a recent piece over at Slate, I explore whether there are hints of this already and what this might mean:

A computer program known as Eureqathat 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. As the mathematician Steven Strogatz has argued, these could be harbingers of an “end of insight.” We had a wonderful several-hundred-year run of explanatory insight, beginning with the dawn of the Scientific Revolution, but maybe that period is drawing to a close.

So what does this all mean for the future of truth? Is it possible for something to be true but not understandable? I think so, but I don’t think that that is a bad thing. Just as certain mathematical theorems have been proven by computers, and we can trust them, we can also at the same time endeavor to try to create more elegantly constructed, human-understandable, versions of these proofs. Just because something is true, doesn’t mean that we can’t continue to explore it, even if we don’t understand every aspect.

But as I've argued in this piece and elsewhere, what we also need is the perspective of naches: a Yiddish word that means pride and joy, but often vicariously so. One has naches in one's children getting married or graduating from college. These aren't your achievements, but you helped make them possible. And the same thing is true in the computational realm. We might not entirely understand what they discover, but that's okay. They are our machines. And we can still have naches in their insights.

It's time to bring some naches into science.

Read the rest of the Slate piece here. And for further reading: From Insight to Naches

Top image:terren/Flickr/CC