A.I. will be pervasive in our future, but still struggles in predictions and forecasts

Thoughts at Vox

Somewhat related: the story behind the chaos at OpenAI

Excerpt to first link: "Being able to predict the future seems nice. I would’ve liked to know that my S&P 500 index funds would peak in mid-February and then fall off a cliff in April.

It would’ve been helpful for my reporting in the lead-up to the inauguration to know just how far the Trump administration would go to attack foreign aid.

And while I’m at it, I’d like some sense of where mortgage rates are going, so I can better judge when to buy a house.

The art of systematically making these kinds of predictions is called forecasting, and we’ve known for a long time that some people — so-called superforecasters — are better at it than others. But even they aren’t Nostradamuses; major events still surprise them sometimes. Superforecasters’ work takes time and effort, and there aren’t many of them.

It’s also hard for us mortals to emulate what makes them so effective. I wrote a whole profile of one of the world’s best superforecaster teams, called the Samotsvety group, and despite their tips and tricks, I didn’t leave the experience as a superforecaster myself.

But you know what’s sometimes better at learning than I am? AI models.

In recent years, the forecasting community has increasingly pivoted to trying to build and learn from AI-fueled prediction bots. More specialized fields have, of course, been doing this in various forms for a while; algorithmic trading in financial markets, for instance, where computer programs using various prediction tools trade assets without human intervention, has been around for decades. But using AI as a more general-purpose forecasting tool is a newer idea.

Everyone I spoke with in the field agrees that the top human forecasters still beat machines."