Azeem Azhar

But within a few years, passengers were complaining about the time it took, the comforts on board and the food that they were being served.

And so the paradox of negative space is that progress makes the gaps stand out much more.

And I think for many people who are using large language models today through these chatbots, there are these concrete contrasts.

Models have got faster.

They've become more reliable, better at using tools.

They are hallucinating less.

You can more reliably get them to search the web and extract information for you.

But they still lack a whole range of capabilities, whether it is long-term memory about you, whether it's actually actively learning from your experiences.

And you also get a sense that maybe they don't generalize as well as a real intelligence would.

And so you end up in this quite odd space.

And let me give you that example.

If you've got an AI system that is unreliable, so say 10% of the time it makes errors, you're unlikely to put it into any kind of automated workflow.

You'll want to sit on top of it because one time in 10, it's going to make a muck up.

Now, when that error rate drops to 1%,

you'll feel much more confident about putting it into some kind of automated system, automated workflow, hundreds of times a minute, thousands of times an hour, tens of thousands of times a day.

But that 1% hallucination rate will show up time and again.

Or consider

a series of individual steps chained one to another.

Imagine you've got a process with 25 steps.

Well, a 1% hallucination rate means that each step succeeds 99 times out of 100, but across a chain of 25, it will mean one in five times that chain will fail.