Sergey Brin: Lessons from Google Glass + Why Every Computer Scientist Should be Working on AI

It's a race. It's a race to develop these systems. Is that why you came back to Google?

Um I mean I think as a computer scientist uh it's a very unique time in history. Like uh honestly anybody who's

history. Like uh honestly anybody who's a computer scientist uh should not be retiring right now should be working on AI. That's what I would just say. I mean

AI. That's what I would just say. I mean

there's just never been a greater sort of problem and opportunity a greater cusp uh of technology. Um so I don't I wouldn't say

technology. Um so I don't I wouldn't say it's because of the race. uh although we fully intend that Gemini will be the

very first AGI clarify that uh but uh to be immersed in this uh incredible technological revolution I

mean it's unlike you know I went through sort of the web 1.0 thing. It was very exciting and whatever. We had mobile, we had this, we had that. But uh I think

this is scientifically uh far more exciting and I think uh I think ultimately the impact on the world is going to be even greater in as much

as you know the web and mobile phones have had a lot of impact. Um I think AI is going to be vastly more transformative.

So what what do you do dayto-day?

I think I torture people like uh Demis um who's amazing by the way. He

tolerated me crashing this uh fireside.

Um I'm in the you know I'm across the street uh you know pretty much every day. Um and they're just uh uh people

day. Um and they're just uh uh people who are working on the key Gemini text models on the pre-training on the post-training mostly those I

periodically delve into some of the multimodal work. uh V3 as uh you've all

multimodal work. uh V3 as uh you've all seen.

Um but I tend to be uh pretty deep in the technical details. Um and that's a luxury I really enjoy fortunately because guys like Demis are you know

minding the shop.

Um and uh yeah that's just where you know my scientific interest is. It's

deep in the algorithms and how they can evolve. Okay let's talk about the

evolve. Okay let's talk about the products a little bit. some that were introduced recently. Um, I just want to

introduced recently. Um, I just want to ask you a broad question about agents, demis, because when I look at other tech companies building agents, what we see in the demos is usually something that's

contextually aware, has a disembodied voice, is often interacted uh with you often interact with it on a screen. When

I see Deep Mind and Google demos, often times it's through the camera. It's very

visual. We there was an announcement about smart glasses today. So talk a little bit about if that's the right read, why why Google is so interested in

having an assistant or companion that is something that sees the world as you see it. Well, it's for several reasons,

it. Well, it's for several reasons, several threads come together. So as we talked earlier, we've always been interested in agents. That's actually

the the the heritage of deep mind actually. We started with agent-based

actually. We started with agent-based systems in games. We are trying to build AGI, which is a full general intelligence. Clearly that would have to

intelligence. Clearly that would have to understand the physical environment, physical world around you. And two of the massive use cases for that in my opinion are a truly useful assistant that can come around with you in your

daily life, not just stuck on your computer or one device. It needs to we want it to be useful in your everyday life for everything. And so it needs to come around you and understand your

physical context. Um, and then the other

physical context. Um, and then the other big thing is I've always felt for robotics to work, you sort of want what you saw with Astra on a robot. And I've

always felt that the the bottleneck in robotics isn't so much the the hardware, although obviously there's many many companies and and working on fantastic hardware and we partner with a lot of them, but it's actually the software

intelligence that I think is always what's held um robotics back. But I

think we're in a really exciting moment now where finally with um these latest versions especially 2.5 Gemini and more things that we're going to bring in this kind of VO technology and other things I

think we're going to have really exciting uh algorithms to make robotics finally work in in in its and you know sort of realize its potential which could be enormous. So I think this and

and then in the end AGI needs to be able to do all of those things. So for us and that's why you can see we always had this in mind that's why Gemini was built from the beginning even the earliest versions to be multimodal and that made

it harder at the start because it's harder to make things multimodal than just text only. But in the end I think we're reaping the benefits of those decisions now and I see many of the Gemini team here in the front row of the correct decisions we made. They were the

harder decisions but we made the right decisions and now you can see the fruits of that with all of what you've seen today. Actually, Sergey, I've been

today. Actually, Sergey, I've been thinking about whether to ask you a Google Glass question. Oh, far away.

What did you learn from Glass that Google might be able to uh apply today now that it seems like smart glasses have made a reappearance? Wow. Yeah. Uh

great question. Um I learned a lot. I

mean, that was um I definitely feel like I made a lot of mistakes with Google Glass. I'll be honest.

Glass. I'll be honest.

Um I am still um a big believer in the form factor. So I'm glad that we have it

form factor. So I'm glad that we have it now. Uh and now it's like looks like

now. Uh and now it's like looks like normal glasses doesn't have the thing in front. Uh I think there was a technology

front. Uh I think there was a technology gap honestly. Now in the AI world, the

gap honestly. Now in the AI world, the things that these glasses can do to help you out without constantly distracting

you, that capability is much higher. Uh

there's also just um I just didn't know anything about consumer electronics supply chains really and how hard it would be to build that and have it be at

a reasonable price point um managing all the manufacturing so forth. Um this time we have great partners that'll are helping us build this.

Um so that's another step forward. Uh what else can I say? I do

forward. Uh what else can I say? I do

have to say I miss the the um airship with the wings suiting sky divers for the demo.

Honestly, it would have uh been even cooler here at Shoreline Amphitheater than it was up in Moscone back in the day. But maybe we'll have to we should

day. But maybe we'll have to we should probably polish the product first this time.

Ready and available and then we'll do a really cool demo. So that's probably a smart move. Yeah. What I will say is I

smart move. Yeah. What I will say is I mean look we've got obviously an incredible history of glass devices and smart devices so we can bring all those learnings to today and very excited about our new glasses as you saw but

what I' what I've always always talking to our team and Sham and the team about is that I mean I don't know if Serge would agree but I feel like the the universal assistant is the killer app

for smart glasses and I think that's what's going to make it work apart from the fact that it's all the tech the hardware technology is also moved on and improved moved a lot is this. I think I feel like this is the actual killer app,

the natural killer app for it. Okay.

Briefly on video generation, I sat uh in the audience in the keynote today and was like fairly blown away by the level of uh improvement we've seen from these models and I I mean you had filmmakers

talking about it in the presentation. I want to ask you Demis um

presentation. I want to ask you Demis um specifically about model quality. If the

internet fills with video that's been made with artificial intelligence, does that then go back into the training and lead to a lower quality model than if

you were training just from human generated content? Yeah. Well, look, we

generated content? Yeah. Well, look, we we you know, there's a lot of worries about this so-called like model collapse. I mean, video is just one

collapse. I mean, video is just one thing, but in any modality, text as well. There's a few things to say about

well. There's a few things to say about that. First of all, we're very rigorous

that. First of all, we're very rigorous with our data quality management and curation. We also, at least for all of

curation. We also, at least for all of our generative models, we we attach synth ID to them. So there's this invisible AI actually made watermark

that um is pretty very robust has held up now for you know a year 18 months since we released it. And all of our images and videos are embedded with this watermark.

that we can detect and and we're releasing tools to allow anyone to detect uh uh these watermarks and know that that was an AI generated um uh image or video. And of course that's

important to combat deep fakes and misinformation, but it's also of course you could use that to filter out if you wanted to whatever was in your training data. So I don't actually see that as a

data. So I don't actually see that as a big problem. Um, eventually we may have

big problem. Um, eventually we may have video models that are so good you could put them back into the loop as a source of additional data, synthetic data it's

called. And there you just got to be

called. And there you just got to be very careful that you're you're actually creating from the same distribution that you're going to model. Um, you're not distorting that distribution somehow.

Uh, the quality is high enough. We have

some experience of this in a completely different main with with things like Alphafold where there wasn't actually enough real experimental data to build the final alpha fold. So we had to build an earlier version that then predicted

about a million protein structures and then we selected it had a confidence level on that. We selected the top three 400,000 and put them back in the training data. So there's lots of it's

training data. So there's lots of it's very cutting edge research to like mix synthetic data with real data. So there

are also ways of doing that. But on the terms of the video sort of generator stuff, you can just exclude it if you want to. At least with our own work and

want to. At least with our own work and hopefully other um gen media companies follow suit and um put robust watermarks in also obviously first and foremost to

combat uh deep fakes and misinformation.

Okay, we have four minutes. I got four questions left. We now move to the

questions left. We now move to the miscellaneous part of my question. So

let's see how many we can get through and as fast as we can get through them.

Um let's go uh to Sergey with this one.

What does the web look like in 10 years?

What does the web look like in 10 years?

I mean, go one minute. Boy, I think 10 years because of the rate of progress in AI is so far beyond anything we can see, not just the web. I mean, I don't know.

I don't think we really know what the world looks like in 10 years. Okay,

Demis.

Well, I think I think that's a good answer. I do think the web I think in

answer. I do think the web I think in nearer term, the web is going to change quite a lot. If you think about an agent first web, like does it really need to, you know, it doesn't necessarily need to see renders and things like we do as as

humans using the web. So, I think things will be pretty different in a few years.

Okay. Uh, this is kind of an under over question. Uh, AGI before 2030 or after

question. Uh, AGI before 2030 or after 2030?

Uh, 2030. Boy, you really kind of uh put it on that fine line. I'm going to I'm going to say before. Before. Yeah.

Dennis, I'm just after. Just after.

Yeah. Okay. Um, no pressure, Dennis.

Exactly. But I have to go back and get working harder. Is that I can ask for

working harder. Is that I can ask for it. He needs to deliver it. So, Exactly.

it. He needs to deliver it. So, Exactly.

Stop sandbagging.

We need it next week. That's true.