Steve Hsu

We have agents that are capable of replacing, you know, something like 80%, maybe 90% of the calls that come into a call center.

I think they feel about it the way that someone who was a blacksmith or a buggy whip maker felt when they saw their first automobiles rolling down the street, right?

They could tell something was happening and they don't like it, but what else can they do?

You don't just want to turn this thing on and then discover like, oh, overnight it got into some bad loop and pissed off 100,000 customers, right?

One of the things that I think the general public doesn't understand is like, how much rigorous statistical testing is required to know what are the tail risks associated with a deployment of autonomy?

That could be automated customer service.

That could be a driverless vehicle.

Combined with the people there are just more pro-technology.

You say like, oh, we're going to have AI at the hospital.

The average Chinese person is not thinking like,

Oh, what about my privacy?

Or what if the AI makes a mistake?

So I think everyone who's experimented with large language models knows that they hallucinate.

So they will sometimes give you a very confident but completely wrong answer.

And that answer will always be typical of the kinds of things that it saw in its pre-training.

So if you ask an LLM, is there a flight that reaches Paris from London that lands around four o'clock?

It will give you an answer, but it might not be real.

It will seem like a real answer, like, oh, Air France has one that lands at 412, right?

And maybe in the past it did, but maybe right now it doesn't, right?

Typical large language model hallucination.