Hey, everyone. Thanks for joining us. We've got an I/O special, Sergey Brin. We're talking all things Google. Thanks for taking the time to chat.
Thank you, Logan. And you and I are all the time in chat spaces on all kinds of products, but it's nice to hang out in real life.
Yeah, it is the-- my California experience is always-- it's incredibly fun to-- I spent a bunch of time with Koray yesterday and today. And he's just-- it's like you feel the warmth and the humanity of AI progress when you spend time in person with everyone. So it's been a ton of fun. But we're sitting here at I/O, and I think the general sentiment from the world, and I think also from the team internally, is incredibly great day for Google, a ton of progress across models, across all of our products. What's your take? What's your reaction? Obviously, there's a lot of stuff we still need to do, but where's your head at?
Yeah, I think it was definitely a phenomenal set of announcements. Honestly, I probably didn't even know about 30% of them or so. There's only so much time. And I'm deep in the weeds on Gemini, and I didn't even know about the virtual fit, for example, for products in Google Search. I didn't realize we were shipping that. So there were many things that also even surprised me. That was great. I think the reception has been great. There are so many things though. I think it takes a while for people to explore them, wrap their heads around them. And obviously, we're very busy delivering all of them right now. And it's a lot of energy across the way, just making sure things are actually shipping, shipping smoothly, that people are able to sign up for Ultra, have all those new features, and so forth.
Yeah, I feel like I/O is the start of lots of work for lots of other people. It's like at the finish line for some teams, and then it's like the starting line for a bunch of other teams. How do you think about-- obviously, we launched more-- there's a bunch of Gemini announcements. Gemini Diffusion, we'll talk more about later. Deep Think, which is continuing to push the frontier on reasoning models. I see you in the reasoning strike chat all the time pushing on people to keep pushing the frontier, which I love to see. How do you think about-- maybe how you think about your focus but also just generally the DeepMind team's focus around like Veo and Imagen. And we have a whole suite of generative media models. We just announced a Lyria, our music model, and then also at the same time, the core Gemini main model. Do you work on the Gen media stuff at all, or is it mostly laser-focused on Gemini at the moment?
Mostly, I'm on Gemini, the core text model primarily because I think that that's what will lead to, for example, self-improvement, will help US code and develop the science behind AI even better. So that's the number one focus that I have. At the same time, the generative media is so amazing.
That is just so superhuman. With a text model, there's some math problems or whatever that I might be able to solve that it gets wrong or something like that, or stumbles on a piece of code. Although, that's less and less frequent. And I actually end up now relying on Gemini to do some of my coding math, and so forth. But nevertheless, it's in the range of human.
Given my artistic talent, there would be zero chance of me ever coming up with either an image or a video. Just the amount of work that that would-- I imagine would take if I was an expert videographer, 3D renderer, or whatever, special effects person, that's got to be a month of solid work to get the thing that I can get in a few minutes. And obviously, it's visually so compelling that just sucks you in. You can't escape it.
The audio piece with Veo makes it feel-- I historically have-- personally, I think generating videos is awesome and stuff, but it's always felt slightly gimmicky to me. And I think when I saw audio in Veo 3 on stage yesterday, I think it like-- that was the moment for me where I was like, OK, this is actually-- so many people are going to be able to-- just because also practically, historically, you could generate a video, but then you'd have to go, and where does audio come from? How do you sync everything up? And now you can make humans talking and having a conversation, and it just does it all well. And it blew my mind.
Yeah, you're right. I've been a huge fan of that. I'm a pretty visual person, personally. I'm not a very, I guess, audio person. But over the years, especially with Google Glass, the moment we added some sound, that's just like-- it adds so much richness to have sound.
It's like you're better off adding audio than adding 3D, for example. Although some of the 3D stuff's cool if you played with the big wearable thing.
But anyhow, yeah, it's just an incredible change in perception when you get audio working. And I knew I saw the model training the last month or two. And I just saw it from checkpoint to checkpoint. And I knew that, wow, this is just going to feel different.
Yeah, it'll be interesting to see how the fusion of those capabilities-- because it does seem like there's a lot of similarities to mainline Gemini-- mainline Gemini model. And obviously, we landed native audio support in the mainline Gemini model at I/O and in Veo as well. And I was having a conversation with Tulsee this morning about just how-- are those similar breakthroughs? Are they different? It sounds like it's actually technologically very different from a technology standpoint. But it is cool that we have other rails to do this innovation and, ideally at all, upstreams back to Gemini in some way?
Yeah, honestly, I think we've just taken a long time to ship the native audio in Gemini. It's been in there for--
Back in the summer, right?
No, no, the base model has had audio that it's trained on for at least a year. And I don't know. There's always-- I think honestly it's just-- there's just so much to do, so much to ship that nobody has, for whatever reason, gotten it out there, and native audio it in, native audio out. I think native audio in has been in there even longer. But to get through all the little hoops to make it really work well and so forth, I just think, for whatever reason, took a long time. But yeah, that's finally out there. I don't think that's done, as you say, the same way Veo does it-- I believe does the audio also through diffusion just like it does the video.
In fact, if you watch during the training run, you can actually see it generating videos that are-- a couple percent into it, it's these-- the shapes aren't quite right, and the words are kind of warbled and stuff like that. But then it takes shape and it develops until at the end of the run you have what you have, what you see today. So yeah, I'm pretty sure that that's diffusion-based audio. Yeah, diffusion is a really powerful technique. As you know, we shipped text diffusion for small early test run. I think that's one of the things that I'm grateful that we have the bench of machine learning researchers in there that we can pursue simultaneously different based techniques for across modalities.
Yeah, the results of Gemini Diffusion look super, super promising so far. I'm hopeful the model progress is there and it all fully works because the demo works-- we were talking off camera. The demo looks really good. So hopefully, the capability translates well and everything works from that perspective. But you mentioned this before about watching the training run. I actually haven't seen what this looks like. So what does it actually mean to watch the training run?
Oh, OK. Well, maybe you've seen for our text models, but we're able to test out the intermediate checkpoints in 10% of training, 20% of training, and so forth. And the model is weak at those points in time, but you can get a sense for the trajectory.
And so usually, especially if you have a big training run, that you have a lot-- you're using a lot of compute, and you have high hopes for it. You're going to test it out in various ways through many times throughout the run. So you're going to have a pretty good sense of what it's on trend for.
So that's true for the text models. That's true for the Diffusion video model for Veo. Yeah, all of these models have these intermediate results that you can take a peek at. And if you're really deep in there, you're for sure checking them because you're nervous and excited about what exactly it will produce.
Yeah, I need to come and hang out in the MK more and watch--
And watch some of them, yeah. One of the-- I was listening to Sundar's conversation with Dave Friedberg. And Sundar made the comment that even 15 years ago you and Larry and him were having conversations about-- and the team at Google were having conversations about what this future-facing AI moment would look like. And that it's eerily close to what you all were talking about 10 or 15 years ago. I'm curious, what are the things that have been most surprising to you about this moment? And we can ground it in products if you want to look at search or just the technology. Or what's been surprising, and what's been almost what you would have expected to have happened?
Yeah, I think from an intellectual standpoint, you can reason your way through the singularity. And famously, Ray Kurzweil did this, I don't know, however many decades ago.
It kind of-- I don't remember what date he said. It was 2037-- or I can't remember. He put some date on it based on his extrapolation. Today looks like maybe that was conservative. I don't know. But you can intellectually reason through it. I think, to see it happening is altogether different.
And I think when you're talking, whatever, 15 years ago, I wouldn't say you're joking around. You're truly talking about it, but you're imagining the science fiction future. But it's almost like a game. You're just chatting with the other folks who are interested in it. I think it's fun. But yeah, as I said, seeing it actually start to happen feels very different.
And of course, the way in which it happens is pretty surprising. And I can give you an example. The fact that language models seem to be, right now, the way that AI is developing, I don't think you necessarily would have known that 15 years ago. In fact, DeepMind-- in the past, and even now, to a certain extent, has bet a lot on this physical grounding, that it's important to have a physical world to ground on. And we're obviously doing experiments in that vein, like Genie and whatnot. But the fact that these language models have come as far as they have wasn't obvious. And an interesting side effect of that, especially with the thinking models, is that they are also surprisingly interpretable. You can look through the thoughts of one of these thinking models and how it's coming to the conclusions that it's coming to. You can't necessarily, without a huge number of tools, inspect the weights of the model and try to infer stuff from that. But you can understand a lot of its reasoning in very understandable terms. So I think that's kind of-- you wouldn't necessarily have guessed that 15 years ago.
