What did the machine learning engineers and research scientists initially think that you were there to contribute? And what did you learn from being in the room while the underlying systems were still being developed, and, and what ultimately changed?

Yeah, I mean, when I first moved over, pr- I, I'm sure they were like, "Who is this guy? What is he doing here? Why did you bring him?" [chuckles] Yeah, so the...

There's, there's kinda, like, two moments.

So I moved over.

I was in the, Google's, uh, cloud division working on Google Cloud, and there was this director who moved over to, to Google.

Uh, his name's Blaise Aguera y Arcas, and he kinda came and presented, and he was like, "Hey, I'm gonna start to do this group.

We're gonna really focus on deep learning, a lot of computer vision things." And he did this presentation.

But how do you think about that in relationship to what you talked before about, um, in terms of the universal and trying to get to the universal principle? How does research change when the object that you're studying is less a bounded product than a vast collection of possible relationships and use cases? Is it still just going back to that universal, or is there something kind of a-added to that?

Are the specialties you need in researching for AI, are they different than what you might find in a, a different context? Uh, is it really that interest and knowledge of AI that is the important thing versus, you know, the, the methods and the research specialties?

Yeah.

It's been one of the more fun things is that you run into not just research specialties.

For each person, yeah, we're either weighing do they have specialty in AI? Do they have specialty at the intersection of AI and something else, or do they have a specialty outside of AI, but we really need an AI? And so what I'll say is like, there are some people on our team that have been working on AI for, for years and they, they have a deep expertise, and especially at Microsoft, like they just know how it's shown up in our products.

They have the history.

They're really, really, um, knowledgeable in that way.

Then we've brought on people who say, have had a lot of experience with, especially generative AI in, um, like Gen Z use cases.

Are there others that stick out, e- either about AI or human-centered thinking or research generally?