Why Reliable AI Systems Favor Simple, Proven Choices (Full Transcript)

[00:00:00] Speaker 1: Luca, we heard a little bit about reliability, latency, redundancy. I'm curious, any perspectives from your side around like how some of these things go into the way that we're like training and thinking about bringing these models to market and assembly?

[00:00:10] Speaker 2: So for the beginning, it's very easy for from the R&D perspective, it's very easy to get things wrong. We still get some things wrong. And that's why we have a close partnership with your guys to kind of iterate over them really quickly. But I feel like the biggest role is coming from just spending a lot of time with customers and understanding how your models perform and just going back to first principles and then designing systems. Thing is, sometimes we really want to have some very elegant and cool solution.

[00:00:40] Speaker 1: Hey, like, why don't we do speech to speech right away that we wanted to do that in the beginning, because it's pretty cool.

[00:00:46] Speaker 2: But at the end of the day, it always just comes down to what is the simplest way I can answer to this problem. And usually it just we have published a few papers, the technology we used it, some of it is very, very novel. Some of it is actually from like 2015s to like early 2020s, because they are very reliable. It's easy to make sure that, hey, we know the performance of the systems, but we also know all the bad things you can do. And that amount is very, very small, right? So yeah, it's very iterative approach we have to take. And some unfortunately saying no to cool things that that's the way we can ensure reliability from our perspective.