Imagine the money that could be made or the status acquired if algorithms could correctly predict the merit of cultural works:
The budget for the film was $180m and, Meaney says, “it was breathtaking that it was under serious consideration”. There were dinosaurs and tigers. It existed in a fantasy prehistory—with a fantasy language. “Preposterous things were happening, without rhyme or reason.” Meaney, who will not reveal the film’s title because he “can’t afford to piss these people off”, told the studio that his program concurred with his own view: it was a stinker.
The difference is the program puts a value on it. Technically a neural network, with a structure modelled on that of our brain, it gradually learns from experience and then applies what it has learnt to new situations. Using this analysis, and comparing it with data on 12 years of American box-office takings, it predicted that the film in question would make $30m. With changes, Meaney reckoned they could increase the take—but not to $180m. On the day the studio rejected the film, another one took it up. They made some changes, but not enough—and it earned $100m. “Next time we saw our studio,” Meaney says, “they brought in the board to greet us. The chairman said, ‘This is Nick—he’s just saved us $80m.’”…
But providing a service that adapts to individual humans is not the same as becoming like a human, let alone producing art like humans. This is why the rise of algorithms is not necessarily relentless. Their strength is that they can take in that information in ways we cannot quickly understand. But the fact that we cannot understand it is also a weakness. It is worth noting that trading algorithms in America now account for 10% fewer trades than they did in 2009.
Those who are most sanguine are those who use them every day. Nick Meaney is used to answering questions about whether computers can—or should—judge art. His answer is: that’s not what they’re doing. “This isn’t about good, or bad. It is about numbers. These data represent the law of absolute numbers, the cinema-going audience. We have a process which tries to quantify them, and provide information to a client who tries to make educated decisions.”…
Equally, his is not a formula for the perfect film. “If you take a rich woman and a poor man and crash them into an iceberg, will that film always make money?” No, he says. No algorithm has the ability to write a script; it can judge one—but only in monetary terms. What Epagogix does is a considerably more sophisticated version, but still a version, of noting, say, that a film that contains nudity will gain a restricted rating, and thereby have a more limited market.
The larger article suggests algorithms can do better at predicting some human behaviors, such a purchasing consumer items, but not so good in other areas, like critical evaluations of cultural works. There are two ways this might go in the future. On one hand, some will argue this is just about collecting the right data or enough data. Perhaps we simply aren’t looking at the right things to correctly judge cultural products. On the other hand, some will argue that the value of an object may be too difficult for an algorithm to ever figure out. And, even if a formula starts hinting at good or bad art, humans can change their minds and opinions – see all the various cultural, art, and music movements just in the last few hundred years.
There is a lot of money that could be made here. This might be the bigger issue with cultural works in the future: whether algorithms can evaluate them or not, does it matter if they are all commoditized?