Summarize the text below: 

Speaker A:
Awesome. Okay. We have these different ways that we validate our ideas, right? And so there's all these different things we're always testing. We're very big on anything that we implement is always run as like a test and we validate the impact and just make sure all the metrics we won't move. That's always like our starting point. And then once we find something that we think has traction, we then typically bring in our machine learning or our research science team to figure out, can we optimize this? And so we'll typically have some prototype idea and then they'll take it and try to productionize an AI model that really gets as much more optimization out of the idea that we can. And so we constantly have bandit systems running on top of all of our different models. We're looking for HDE effects and all of our different decision making that we're doing. And so when you open the lift app, when you put in a destination, for instance, we have models backing what the ETA prediction will be based on all these different features. We have like 165 features that go into of the three modes we show above the fold, which one do we show to you and which one do we preselect? And it's a combination of user specific features, time specific features, temporal specific features. And so what we're hearing a lot of is like, we have all these things going on. But I think the biggest thing that I face at my level and what my peers face is our models drift. Our models change decision making. New features come in and out that we're not aware of. And so all of a sudden, you'll see this sharp change in a business metric, and you go talk to the team, and it's really hard for them to say why the model is making a different decision. Like, our ETA just went up on average by, like, 22 seconds two weeks ago, and everyone was freaking out that a bunch of drivers weren't coming on the platform. And our models figured out that driver acceptance rate was, like, a new relevant feature, and it's constantly selecting which features to include. Otherwise, our models become really expensive just because of the amount of data we have, and we just decided to incorporate the feature. No one knows about this. We waste, like, a couple of days figuring out why our ETAs are, like, losing competitively versus Uber. I think, to answer your question, I think the biggest thing at our level is figuring out, okay, these models are great when we measure the initial experiment impact, but it's really hard to get visibility into w Do not say the word "change" in the response
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