Mike Carruthers

Well, it's so interesting to think about this because most people, as you pointed out, most people don't think about this.

This is not something people talk about.

We make the predictions, we follow the predictions, or we ignore the predictions, but we don't think about the process of predictions and the ramifications of that.

Well, I think everybody's had the, well, I've certainly had the experience when I was younger of like applying for a loan or applying for a credit card or something and thinking, well, they want to know, they're going to give me a credit card for the rest of my life, assuming I pay the bill, based on my current situation, which 20 years from now will be nothing like it is.

But they're using today's data.

Like you could be unemployed and apply for a loan and you'll never get it.

But in six months from now, you might have a job and you'll get the loan.

But there isn't much difference between now and six months ago, except that you now have a job.

But that has nothing to do with the future.

The more data you have, the better the prediction.

I think that's an assumption people make.

If you have a lot of information, if you have in your head or on your computer or in a book, that your predictions will be better, right?

Well, I have to say this conversation has really got me thinking because I've never thought of predictions this way.

But you well, now you've changed the way I think about them.

I've been speaking with Carissa Valise.

She's an associate professor at the Institute for Ethics in AI at the University of Oxford.

And her book is called Prophecy, Prediction, Power and the Fight for the Future from Ancient Oracles to AI.

And there's a link to that book in the show notes.

Carissa, thank you.

This was fun.