Ambiguity pervades language. This ambiguity can be used strategically by speakers, but it’s also what makes language so challenging for machines to understand – and in some cases, it even leads to miscommunications between people, particularly over written communication.
A recurring question in both scientific and public discourse is whether any given property of an organism is innate or learned. This debate, usually framed in terms of Nature vs. Nurture, often centers around properties of human behavior and cognition: intelligence, language, morality, mathematics, and so on. But while this dichotomous framing perhaps seems obvious to us now, when did the question first arise? And is it really the best way to investigate these properties?
Bias is real – and often harmful. It’s been shown to manifest in hiring decisions, in the training of machine learning algorithms, and most recently, in language itself. Three computer scientists analyzed the co-occurrence patterns of words in naturally-occurring texts (obtained from Google News), and found that these patterns seem to reflect implicit human biases.
The term “p-hacking” has made its way into the public discourse surrounding science, particularly regarding the replicability crisis. But although the term suggests intentional malevolence on the part of the scientist, it’s actually a scenario that many well-trained scientists can fall into. So what is p-hacking, and why is it dangerous?
Humans are pretty creative. We adapt the world around us to fit our needs and develop tools to help with almost any task imaginable. Other animals use tools as well, but not to the same extent, and not with the same flexibility and ease.
Tools make us more efficient, and more intelligent (for the most part); they help optimize the relationship between our bodies, our minds, and the world we live in. Some researchers (Clark and Chalmers, 1998; Hutchins, 1995) even argue that tools, in some ways, extend our minds and our bodies. And, in fact, there’s some evidence from cognitive neuroscience that the brain considers tools – at least some of the time – part of the body.
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.