Google DeepMind's Vision for AI, Search and Gemini with Oriol Vinyals from Google DeepMind

**Sarah** (0:05)
Hi, listeners, and welcome to No Priors. Today, we're talking to Oriol Vinyals, the VP of Research at Google DeepMind and technical co-lead for Gemini. His storied career in machine learning includes leading the AlphaStar team, which built a professionally competitive and pioneering StarCraft agent all the way to today, and we're really excited to get his historical perspective on where we are in machine learning. Welcome to the show, Oriol.

**Oriol Vinyals** (0:26)
Yeah, amazing. Thanks, Sarah, for the invitation, and likewise, thanks, Elad, for hosting me.

**Elad** (0:32)
Yeah, thanks for joining.

**Sarah** (0:33)
Last year was an eventful year at Google and DeepMind. How is that research effort organized now, and what do you think of the mission as internally?

**Oriol Vinyals** (0:41)
Yeah, so sure. I mean, I'm happy to obviously discuss the different phases that research organizations have gone through in the last many years. But focusing on last year, two major events happened. One was that the Gemini Project was formed as a result of having two parallel efforts on LLMs, mostly led by Google Brain and what we now call Legacy DeepMind. So earlier in the year, there was an effort to merge the two projects, and that's when Jeff and I came together and brought the two teams together to create the very first Gemini model, which was eventually released later in the year. Then the second big event was to take all the organizations that were doing AI research or AGI research and also form a singular organization. That's what today is called Google DeepMind, and it comes from Google Brain and Legacy DeepMind coming again together under one roof. Obviously Gemini being a very large and very important project within that organization, and really the goal of Gemini itself is to create an awesome core model to power the technology that of course LLMs today are powering all around the world, and we obviously expect this to unincrease.

**Sarah** (2:04)
How do you interact with the rest of the company and like Google as a business? And I feel like I have to ask you, does AI replace traditional search?

**Oriol Vinyals** (2:13)
So even running that from a research standpoint is super interesting, right? There's two major centers, one in California, one in London, given the organizations that we come from. So that is very interesting. In a way, we have the project running 24-7, which is helpful when you train these large models. And then you have to do a few things, right? One of the things we do, of course, is trying to build state-of-the-art technology, showing from sort of a research, knowing where the field is coming from and when it's going to, trying to really showcase from our own sort of intuitions and ambition what might come next, right? So a prime example of these was, for example, the long context that we released earlier in the year, right?
Millions of tokens now have been able to be processed by our models. But then, of course, we also sort of take into consideration all the different needs, right? From the different products that we work with. Google has a lot of product areas. So we try to focus, of course, initially, especially to form the project, we try to focus on critical projects. And you see that very much by how Gemini is first surfaced to users or to enterprises, right? So obviously, cloud and enterprise is very important, developers as well. Super cool to put these models in the hands of creative minds that are going to do things you didn't even anticipate these models could do. And then, very important, formerly known BART, now Gemini App, which is sort of the chat bot surface of our models. And then maybe the last very important piece indeed is Search, which is trying to integrate, of course, this technology into their product. And of course, has a lot of users. So it's extremely exciting to think, well, the decisions you make at modeling eventually, and eventually means just maybe a few couple of months after or so, will make it into the users that maybe are signing up for a beta, etc. So super exciting. And it's obviously connected. It's the core of the company, really, especially for the products that require very intelligent AI systems, like the ones we're creating today.

**Elad** (4:29)
How do you think about the various types of use cases that fall under chat-based model versus search-based models? Because I remember, I was at Google many years ago at this point. And at the time, a lot of the different types of search queries were broken into different chunks. There's navigational queries. You're trying to get to some other site, and it just kind of helps direct you there. There were sort of strong intent commerce queries. You know, you could kind of break it down. There's medical queries. And so you can kind of map out the world of different types of things that the user is actually trying to do with the search as the interface to get there. What do you think will move more towards chat? And what do you think remains in the domain of more traditional search-based approaches?