In Alex Garland’s recent sci-fi TV series Devs, Silicon Valley engineers have built a quantum computer that they think proves determinism. It allows them to know the position of all the particles in the universe at any given point, and from there, project backwards and forwards in time, seeing into the past and making pinpoint-accurate forecasts about the future.
Garland’s protagonist, Lily Chan, isn’t impressed. “They’re having a tech nerd’s wettest dream,” she says at one point. “The one that reduces everything to nothing — nothing but code”. To them, “everything is unpackable and packable; reverse-engineerable; predictable”.
It would be a spoiler to tell you how it all ends up, but Chan is hardly alone in criticising the sometimes-Messianic pronouncements of tech gurus. Indeed, her lines might as well have been written by the entrepreneur and business writer Margaret Heffernan, whose book Uncharted provides a robust critique of what she calls our “addiction to prediction”.
Our fervent desire to know and chart the future — and our exaggerated view of our ability to do so — forces us, she says, into a straitjacket whenever some authoritative-sounding source makes a prediction: the future’s laid out, we know what’ll happen — it’s been forecast. Only by kicking this habit, she argues, “do we stop being spectators and become creative participants in our own future”.
That’s something of a lofty goal, but as we’ll see, the consequences of misunderstanding predictions can be far more immediate. In pandemics, it can end up killing thousands of people.
Heffernan does get to pandemic disease in the latter part of her book, but before that, she provides some cautionary tales that are useful to readers way beyond her targeted “business book” audience. Take, for instance, the 2013 prediction by researchers at the Oxford Martin School that “by 2035, 35% of jobs will have been taken by machines”. As Heffernan notes, this was an impossibly specific quantity: exactly this number of years in the future, exactly this percentage of jobs will be done by robots. When you think about it, such specificity is absurd, but it didn’t half grab the media’s attention, playing on people’s quite reasonable fears about the coming age of automation. The resulting media discussion, Heffernan says, “projected inevitability onto what was no more than a hypothesis”.
There are subtler manifestations of the prediction addiction. In science, for example, researchers — and I include myself in this — often deploy the word “predict” in a way that doesn’t comport with its everyday usage. Variable X predicts variable Y, they say, even though both were measured at exactly the same time. What they mean is that, if you didn’t know anything about Y, you would have some information about it if you knew X. But this “prediction” can be very weak: usually just “a bit better than chance” rather than “with a strong degree of accuracy”. By the time this translates to the public, often via hyped press releases, it’s frequently been imbued with a great deal more certainty than is warranted by the data.
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