Science faces a replicability crisis. This is well-known among scientists and even the general public. Various explanations have been proposed as to the cause of this crisis – some of them on this blog – but these proposals have usually been informal in nature. Recently, two scientists (Smaldino & McElreath, 2016) built a computational model to explain the replicability crisis.
Science is a framework for understanding the world. We observe a phenomenon, ask questions about it, and build models to describe and predict it. Crucially, this process is iterative. We’re constantly refining our experiments, theories, and models, in an effort to improve our understanding of some phenomenon. Instead of accepting the results of a study as “fact”, we ask whether those results can be replicated reliably under similar and different conditions, what theories can be extrapolated from those results, and how we might extend those results to new domains or new questions.
Iteration is how science fosters dialogue. Without it, science isn’t really science; it’s just a bunch of people shouting over one another, with nobody listening. Or even worse, it’s like a game of telephone, in which nobody checks whether the last person got the message right.
Anyone who’s ever used Siri has likely experienced the frustration of not being understood. There’s a fundamental – almost existential – panic that surfaces when someone else doesn’t know what you’re saying.
It’s an even bigger problem for people with accents other than “General American English”. Voice interfaces struggle with accents, from regional American accents to foreign-accented speech. But why?
Almost all visions of “the future” include computers that we can talk to. Something about language seems central to our understanding of intelligence, and often it is taken as a given that intelligent machines will be conversant with humans.
People like to label things.
One of the best-known examples of a classification scheme is our biological taxonomy, which is meant to show the relationships of different organisms to each other. But no taxonomy is set in stone; even our biological taxonomy has undergone many changes in the last century.
This got me thinking: how does our method for classifying organisms work? And what’s the point of a taxonomy, anyway?