[Image: Sunrise, via].

A short item in The Economist last month suggested that town planners could simply bypass their own aesthetic responses to a landscape and turn instead to an algorithm to design “scenic” locales.

Researchers at the Warwick Business School, we read, “have adapted a computer program called Places to recognize beautiful landscapes, whether natural or artificial, using the criteria that a human beholder would employ.” Acting as a kind of sentient Hallmark card, Places has been “optimized to recognize geographical features. [Head researcher Chanuki Seresinhe] and her team taught the program to identify such things as mountains, beaches and fields, and various sorts of buildings, in pictures presented to it.”

Most of the results are not surprising. Lakes and horizons scored well. So did valleys and snowy mountains. In artificial landscapes castles, churches and cottages were seen as scenic. Hospitals, garages and motels not so much. Ms. Seresinhe’s analysis did, however, confirm one important but non-obvious finding from her previous study. Green spaces are not, in and of themselves, scenic. To be so they need to involve contours and trees.

While this sounds ridiculous on its face, suggesting a saccharine world of endless Viagra ad backdrops, the article includes an unexpected detail at the end that makes the whole thing seem much stranger.

There, The Economist points our attention briefly to “an idea promulgated 30 years ago by Edward Wilson, an evolutionary biologist at Harvard University. He suggested that the sorts of landscapes people prefer—and which they sculpt their parks and gardens to resemble—are those that echo the African savannahs in which Homo sapiens evolved. Gently undulating ground with a mixture of trees, shrubs and open spaces, in other words (though, ideally, without the accompanying dangerous wild animals).”

This newfangled computer program, then, could be accused of simply repeating the observational landscape prejudices of our own pre-human ancestors. It’s as if we have been carefully stewarding into existence a world of thinking machines and semi-autonomous neural networks—only to find that they don’t think like envoys of the future, like inscrutable alien subjectivities set loose inside silicon.

Rather, they are earlier versions of ourselves, like a patient hospitalized for dementia becoming more childlike as they age. Not after, but before. Paleoalgorithmica.