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Q When you think about that, is continuous learning the next breakthrough that you're most excited by?

A I think there's quite a few things that are missing. There's, there's continual learning. I think there's a lot of, uh, I think a lot of mileage in looking at different memory systems. Um, at the moment we have these long context windows, which are kind of a bit brute force. You just put everything in them. Um, I think there's, there's, there's a lot of, uh, interesting, probably architectures to be invented there. Um, and then there's stuff like, uh, longterm planning, you know, hierarchical planning. These systems are not very good at planning at long time horizons, you know, many years into the future, uh, which we is, you know, with our minds we can do. So, um, there's quite a lot of, uh, problems I think that are still left to overcome. Maybe one of the biggest is consistency. So, you know, I sometimes call these systems jagged intelligences because they're really amazing at certain things, uh, when you pose the question in a certain way. But if you pose a question in a slightly different way, they can actually still fail at quite elementary things. So a general intelligence shouldn't be that sort of jagged.

AI assessment note: “I think there's quite a few things that are missing. There's, there's continual learning.”

Q Is that closer than you thought? Has that changed over time?

A Not really. I mean, actually, when you, when you, uh, it's funny, um, my co-founder, Shane Legg, who's chief scientist here, um, uh, when we started out DeepMind back in 2010, he used to write blog posts sort of predicting about, um, Uh, when AGI would happen, and bearing in mind in 2010 when we started, almost nobody was working in AI, and everyone thought AI, uh, basically didn't work. No, and, but they're still there on the internet for people to check, and, uh, we used to do this extrapolation of compute and algorithmic, uh, progress, and basically we predicted around 20 years it would take from when we started out, and I think we're pretty much on track.

AI assessment note: “we predicted around 20 years it would take... we're pretty much on track”

Q a lot of your research for years, and we see many very good quality open models. How do you think about the future of open? I have many portfolio companies that kind of use frontier models, and then they use that to set a benchmark, and then they use open models to kind of get as close as possible, but with more cost effectiveness. What does that future look like?

A Yeah, I think it's probably similar to what we're seeing today. I mean, we're, we're big supporters of, of open science and, and open models, and we've done many, many things obviously from, from the original transformers to, to alpha fold, you know, these are all, uh, things we've sort of given out into the world and to help the, the, the, the research community. And we plan to continue to do that, especially in applied domains, you know, scientific domains, applying AI to science, which is obviously my passion. Um, but, uh, I, I think increasingly, um, you know, what you're gonna see is the open source models are probably one step back from the absolute frontier. Um, you know, it usually takes about six months for the open source community to sort of re-implement and figure out what those ideas are. Um, but we are also, uh, pushing hard on a kind of suite of open source models called Gemma, which are, you know, we're determined to kind of make best in class for their sizes. So specifically for small developers or, Um, academics, uh, or, or the, you know, the beginnings of a startup. I think they're perfect for that, and also edge computing too. So we're very interested in open source models for certain types of, um, uh, applications.

AI assessment note: “open source models are probably one step back from the absolute frontier”

Q So what will that do? Because that's another big meme that people talk about.

A So what do you, well, it would help with the energy crisis and Climate crisis, because, um, if you had sort of cheap, uh, superconductors, you know, then you can transport energy from one place to another without any loss of that energy, right? So you could potentially put solar panels in the Sahara desert and then just have a, the, the, the, the superconductor, you know, uh, funneling that into Europe where it's needed. At the moment you would just lose a ton of the power to heat and other things on the way. So then you need other technologies like batteries and other things to store that. Cause you can't, you can't just pipe it to the place that you want without, without, without being incredibly inefficient. So, uh, but also materials could help with things like batteries too, like, but come up with the optimal battery. I don't think we have the optimal battery designs, um, that maybe we can do things like a combination of materials and, and, and proteins. We can do things like carbon capture, you know, modify, uh, algae or other things to, to do carbon capture, uh, better than, um, uh, our artificial systems. Um, I mean, even the one of the most famous and most important chemical, chemical processes, the harbor process to make fertilizer and ammonia, you know, to take nitrogen out of the air, um, was, was, was something that allows modern civilization. Uh, but there might b…

AI assessment note: “it would help with the energy crisis and Climate crisis, because”

Q a lot of your research for years, and we see many very good quality open models. How do you think about the future of open? I have many portfolio companies that kind of use frontier models, and then they use that to set a benchmark, and then they use open models to kind of get as close as possible, but with more cost effectiveness. What does that future look like?

A Yeah, I think it's probably similar to what we're seeing today. I mean, we're, we're big supporters of, of open science and, and open models, and we've done many, many things obviously from, from the original transformers to, to alpha fold, you know, these are all, uh, things we've sort of given out into the world and to help the, the, the, the research community. And we plan to continue to do that, especially in applied domains, you know, scientific domains, applying AI to science, which is obviously my passion. Um, but, uh, I, I think increasingly, um, you know, what you're gonna see is the open source models are probably one step back from the absolute frontier. Um, you know, it usually takes about six months for the open source community to sort of re-implement and figure out what those ideas are. Um, but we are also, uh, pushing hard on a kind of suite of open source models called Gemma, which are, you know, we're determined to kind of make best in class for their sizes. So specifically for small developers or, Um, academics, uh, or, or the, you know, the beginnings of a startup. I think they're perfect for that, and also edge computing too. So we're very interested in open source models for certain types of, um, uh, applications.

AI assessment note: “open source models are probably one step back from the absolute frontier”

Q Brilliant. Don't put the burgers down too close to the roller coaster. Um, but you know, obviously as a media company, I go through any media platforms and I don't know what's real or fake. I'm always having to ask what's real or fake. Who is that arbiter of verification?

A Yeah. Well, I think there are, I mean, ultimately it's gotta be government, I think. But, um, Um, you know, the kinds of technical bodies that would, um, be able to do the technical work would be like maybe the AI safety institutes. You know, there's a very good one in the UK that, uh, uh, you know, was set up under Prime Minister Sunak, and I think it's doing great work, and then there's one in the US, and maybe some of the leading countries that have the best research should also have an equivalent body that is staffed with high quality researchers too, um, that can actually evaluate And audit these kinds of systems, uh, against certain benchmarks and, um, I kind of like independently check whether they are, uh, meeting the right standards.

AI assessment note: “ultimately it's gotta be government, I think. But, um, Um, you know, the kinds”

Q What are you thinking about that you're not reading about or seeing anyone talk about?

A Um, I think it's more, so I think a lot of people are worrying about the economic questions around AGI, uh, that we talked about earlier, but I, I worry a lot about the philosophical questions around it. Like when it comes, let's say, should we get the technical right? Let's assume we get the economical economics part of it, right? Both of those are hard. Then there's a philosophical question of what is meaning? What is purpose? Um, we'll find out maybe what consciousness is. Um, what does it mean to be human? I think that's Uh, what's coming down the road, and I think we need some great new philosophers to help us, to help us, uh, navigate that.

AI assessment note: “I worry a lot about the philosophical questions around it”

Q What are the biggest bottlenecks when you look today? You know, in, in the documentary you said you just never have enough compute. What are the biggest bottlenecks when you look at where we are today?

A I think compute is the big one, not just for the obvious reason of scaling up, uh, your ideas and your systems as, as you know, the scaling laws as they're called, you know, keeping on building bigger and bigger, um, architectures with more and more parameters. Um, and as you do that, you get more intelligent systems. But the other thing you need a lot of compute for is for doing experiments. So, um, The computers , the cloud is our workbench, basically. So if you have a new idea, a new algorithmic idea, but you want to test it, you kind of got to test it at a reasonable scale, otherwise it won't hold when you actually put it into the main system. So, um, you need quite a lot of compute if you have a lot of researchers with lots of new ideas.

AI assessment note: “I think compute is the big one, not just for the obvious reason of scaling”

Q We said about plateauing of scaling rules. Everyone talks about a commoditization of models in terms of capabilities. Do you think we see that? Or do you think we see ones to continuously accelerate ahead of the others?

A Yeah. I feel like, uh, maybe, you know, the, the, the, the, Three or four leading labs now, which we're one. I think the gap is sort of, um, starting to pull away because, uh, a lot of these tools also, of course, help you build the next generation. So things like coding tools, math tools, and it's getting harder and harder. I would say to kind of eke out the same, uh, gains from just the same ideas. So I think those labs that have capability to, you know, invent new algorithmic ideas Are going to start having bigger advantage over the next few years as, as the, the last set of ideas are sort of, um, you know, all the juices being wrung out of them.

AI assessment note: “the gap is sort of, um, starting to pull away”

Q Speaking of regulatory, AI safety is a big topic and a big concern. I think it was, again, I watched it last night over dinner, which was a great watch, which is obviously the documentary. And I think it was Stephen Hawking, he said, we must get it right. Because we might not get another chance. Do you think that's right?

A Yeah, I do think that's right. I think that is the, the, the, the stakes, uh, that, that, uh, you know, we have to deal with and, um, you know, there's two things I worry about. One is, uh, the misuse of these systems by bad actors and they can be repurposed. These are dual purpose technologies. They can be used for incredible good in science and health as we just discussed, but they can also be repurposed for harmful ends by a bad actor. So that's one issue. Second issue is a technical one, making sure These systems as they get more powerful, not today's systems, but maybe in a year or two's time when they become more agentic, more autonomous as we get towards AGI, um, can they be kept on the guardrails that we want? Um, and I think regulation, the right kind of regulation could help here in terms of making sure there's at least sort of minimum standards from all of the, uh, uh, leading providers, but it needs to ideally be a kind of international, uh, standards.

AI assessment note: “Yeah, I do think that's right. I think that is the”

Q You're in London. I'm in London. I'm very proud to be in the UK. You have been, I'm sure, pushed or prodded at every turn to move to the US. Why have you stayed?

A Well, um, I should ask you that question too, but I think, uh, I think I saw in London when we started DeepMind as a place that, and the UK in general, and Europe in such, to some degree, there's incredible talent here. You know, we've always had, I don't know what it is, three or four of the top 10 universities in the world with Cambridge and Oxford, Imperial, or UCL, these kind of universities. So we're producing, um, kind of the envy of the world, really, these amazing graduates and PhD students. Um, we have incredible scientists here. We've got rich heritage of that for all the way from, you know, Turing and, and Hawking and Darwin, uh, Newton. So, you know, we have this incredible history of, of, of scientific breakthroughs and having great thinkers. So I felt we had all the ingredients, uh, and the talent and great engineers here, but it just hadn't been galvanized into, uh, an ambitious startup idea, deep tech, Startup idea. And, and that's what I, but I, I felt it was possible. And I felt that there was actually less competition here for that sort of talent. And we could even draw in the best talent from the top, uh, European universities. And that's what it was like in the early days of DeepMind. So I think it was a huge structural advantage for us. And then the final thing is maybe being a bit away from the valley. There is some disadvantage in that. You're not plugge…

AI assessment note: “I felt that there was actually less competition here for that sort of talent.”

Q So my mother's got multiple cirrhosis, so it's like something, it's the thing that I'm always most excited about. The thing I worry about is actually kind of drug discovery, the process of getting it through all the trials, and knowing that it takes a decade before my mother will actually get any benefits from it. How do we solve that?

A I think we'll get to that point soon. First of all, what we're doing is, you know, after we did the alpha fold project to do protein folding, um, then we spun out a company called Isomorphic Labs, which is doing extremely well, and that is supposed to, you know, the idea there is we're focusing on solving the rest of the drug discovery process, which is a lot of chemistry, designing the compounds, uh, checking it's not toxic, and all the different properties you need for, for drugs to be safe. Um, I think we'll have that whole Drug design engine ready in, you know, the next five plus five to 10 years, then you're right. The next problem is the clinical trials still take many, many years, right? Um, and, but I think AI can help there in terms of, um, maybe simulating, uh, parts of, uh, the human, uh, metabolism, um, also stratifying patients to make sure that certain patients get exactly the right type of drug that's suitable for their, uh, genomic makeup. Um, and so I think AI can help there too, but I think the real revolution will come when a few, maybe a dozen or so AI drugs get through the whole process. Uh, and then the government and the regulatory body see that, and they have enough data to sort of, uh, back test the predictions of those models. And then maybe what we can do will be in the future where maybe 10 further years where, um, we can really just trust the predic…

AI assessment note: “skip out some steps, perhaps like the animal testing is not needed anymore.”

Q I'm pulling out the magic wand again, but this time applied to European technology. What would you do to implement a growth mindset, a ability to build that trillion dollar company that we don't have today?

A I think in the UK, I mean, this may apply to other European countries too. I think unlocking what pension funds Can invest in or just for the kind of growth stage. I think we're brilliant at doing the startup idea and getting it to a certain level like we did with deep mind. But then if you really want to cross that sort of chasm into the trillion dollar, uh, global, you know, player, then where are the billion dollar rounds going to come from? Uh, where you can really take on those that, you know, the existing incumbents. And I think that certainly was missing 10 years ago when I was doing fundraising for deep mind and Um, I think it's still kind of missing today. Just that kind of level of ambition and, and the amount the capital markets can, can support.

AI assessment note: “unlocking what pension funds Can invest in or just for the kind of growth stage”

Q quickly and caught up slash overtaken other providers. I think I'm tweeting. I think you liked it, but I basically tweeted, um, what I used and how it's changed over time. DeepMind now is my number one for research for new shows. It wasn't that way before. What has led to the acceleration and progression of DeepMind in a way that it wasn't maybe there two to three years ago?

A Yeah, well, we made some organizational changes, so I think we've always had the deepest and broadest research bench at Google and at DeepMind. I mean, if you look at the last decade, uh, or plus, you know, 15 years, but I would say about 90% of the breakthroughs that underpin the modern AI industry were done by either by Google Brain or Google Research or DeepMind. So one of our groups, um, if you think of like AlphaGo and reinforcement learning, and of course Transformers, You know, these are all the key breakthroughs. So I would back us to sort of, um, make those breakthroughs in the future, uh, if there are any missing ones. Um, and I think we've basically helped put together all the talent from around the company, sort of pushing in one direction. Uh, and then we talked earlier just about, you know, compute resources. It was also about combining all of our resources together so we could build the biggest models rather than having two or three versions, uh, around the company. So I think a lot of it was assembling together all the ingredients we already had, and then kind of pushing with relentless sort of focus and, and, and pace, um, acting almost like a startup really, uh, to get back to the frontier and, and be ahead in, in many areas.

AI assessment note: “we made some organizational changes... combining all of our resources together”

Q Speaking of regulatory, AI safety is a big topic and a big concern. I think it was, again, I watched it last night over dinner, which was a great watch, which is obviously the documentary. And I think it was Stephen Hawking, he said, we must get it right. Because we might not get another chance. Do you think that's right?

A Yeah, I do think that's right. I think that is the, the, the, the stakes, uh, that, that, uh, you know, we have to deal with and, um, you know, there's two things I worry about. One is, uh, the misuse of these systems by bad actors and they can be repurposed. These are dual purpose technologies. They can be used for incredible good in science and health as we just discussed, but they can also be repurposed for harmful ends by a bad actor. So that's one issue. Second issue is a technical one, making sure These systems as they get more powerful, not today's systems, but maybe in a year or two's time when they become more agentic, more autonomous as we get towards AGI, um, can they be kept on the guardrails that we want? Um, and I think regulation, the right kind of regulation could help here in terms of making sure there's at least sort of minimum standards from all of the, uh, uh, leading providers, but it needs to ideally be a kind of international, uh, standards.

AI assessment note: “Yeah, I do think that's right. I think that is the stakes”

Q I'm pulling out the magic wand again, but this time applied to European technology. What would you do to implement a growth mindset, a ability to build that trillion dollar company that we don't have today?

A I think in the UK, I mean, this may apply to other European countries too. I think unlocking what pension funds Can invest in or just for the kind of growth stage. I think we're brilliant at doing the startup idea and getting it to a certain level like we did with deep mind. But then if you really want to cross that sort of chasm into the trillion dollar, uh, global, you know, player, then where are the billion dollar rounds going to come from? Uh, where you can really take on those that, you know, the existing incumbents. And I think that certainly was missing 10 years ago when I was doing fundraising for deep mind and Um, I think it's still kind of missing today. Just that kind of level of ambition and, and the amount the capital markets can, can support.

AI assessment note: “unlocking what pension funds Can invest in or just for the kind of growth stage”

Q What are the biggest bottlenecks when you look today? You know, in, in the documentary you said you just never have enough compute. What are the biggest bottlenecks when you look at where we are today?

A I think compute is the big one, not just for the obvious reason of scaling up, uh, your ideas and your systems as, as you know, the scaling laws as they're called, you know, keeping on building bigger and bigger, um, architectures with more and more parameters. Um, and as you do that, you get more intelligent systems. But the other thing you need a lot of compute for is for doing experiments. So, um, The computers , the cloud is our workbench, basically. So if you have a new idea, a new algorithmic idea, but you want to test it, you kind of got to test it at a reasonable scale, otherwise it won't hold when you actually put it into the main system. So, um, you need quite a lot of compute if you have a lot of researchers with lots of new ideas.

AI assessment note: “I think compute is the big one, not just for the obvious reason of scaling”

Q When you think about that, is continuous learning the next breakthrough that you're most excited by?

A I think there's quite a few things that are missing. There's, there's continual learning. I think there's a lot of, uh, I think a lot of mileage in looking at different memory systems. Um, at the moment we have these long context windows, which are kind of a bit brute force. You just put everything in them. Um, I think there's, there's, there's a lot of, uh, interesting, probably architectures to be invented there. Um, and then there's stuff like, uh, longterm planning, you know, hierarchical planning. These systems are not very good at planning at long time horizons, you know, many years into the future, uh, which we is, you know, with our minds we can do. So, um, there's quite a lot of, uh, problems I think that are still left to overcome. Maybe one of the biggest is consistency. So, you know, I sometimes call these systems jagged intelligences because they're really amazing at certain things, uh, when you pose the question in a certain way. But if you pose a question in a slightly different way, they can actually still fail at quite elementary things. So a general intelligence shouldn't be that sort of jagged.

AI assessment note: “I think there's quite a few things that are missing. There's, there's continual learning.”

Q We said about plateauing of scaling rules. Everyone talks about a commoditization of models in terms of capabilities. Do you think we see that? Or do you think we see ones to continuously accelerate ahead of the others?

A Yeah. I feel like, uh, maybe, you know, the, the, the, the, Three or four leading labs now, which we're one. I think the gap is sort of, um, starting to pull away because, uh, a lot of these tools also, of course, help you build the next generation. So things like coding tools, math tools, and it's getting harder and harder. I would say to kind of eke out the same, uh, gains from just the same ideas. So I think those labs that have capability to, you know, invent new algorithmic ideas Are going to start having bigger advantage over the next few years as, as the, the last set of ideas are sort of, um, you know, all the juices being wrung out of them.

AI assessment note: “I think the gap is sort of, um, starting to pull away”

Q How do you think about a world post LLMs? You have different people with different views. You're Jan Lacoons with very different views.

A For me, I don't think it's, uh, you know, I kind of disagree with Jan on a few things in terms of, Um, I think there might be this, there's a fifty-fifty chance there's some things maybe missing that we still need to make breakthroughs in, perhaps their world models, um, uh, these kinds of, uh, approaches, but my betting is, uh, pretty strongly is we've seen how successful these foundation models have been. They can do incredibly impressive things. I don't think that's going to go away. We're still sealing, seeing, you know, gains from the, from returns from the scaling laws. Um, so My, I think the only question really is when you think about a future AGI system is, you know, is an LLM foundation model going to be the key component only, or is it the total system? Right? So I just think it's, it's a question of, um, uh, you know, is there anything else needed? Not, is it not, I don't think it's going to get replaced. I think it's going to get built on top of these foundation models, just like the way we do with our world models.

AI assessment note: “I don't think it's going to get replaced. I think it's going to get built”

Q So my mother's got multiple cirrhosis, so it's like something, it's the thing that I'm always most excited about. The thing I worry about is actually kind of drug discovery, the process of getting it through all the trials, and knowing that it takes a decade before my mother will actually get any benefits from it. How do we solve that?

A I think we'll get to that point soon. First of all, what we're doing is, you know, after we did the alpha fold project to do protein folding, um, then we spun out a company called Isomorphic Labs, which is doing extremely well, and that is supposed to, you know, the idea there is we're focusing on solving the rest of the drug discovery process, which is a lot of chemistry, designing the compounds, uh, checking it's not toxic, and all the different properties you need for, for drugs to be safe. Um, I think we'll have that whole Drug design engine ready in, you know, the next five plus five to 10 years, then you're right. The next problem is the clinical trials still take many, many years, right? Um, and, but I think AI can help there in terms of, um, maybe simulating, uh, parts of, uh, the human, uh, metabolism, um, also stratifying patients to make sure that certain patients get exactly the right type of drug that's suitable for their, uh, genomic makeup. Um, and so I think AI can help there too, but I think the real revolution will come when a few, maybe a dozen or so AI drugs get through the whole process. Uh, and then the government and the regulatory body see that, and they have enough data to sort of, uh, back test the predictions of those models. And then maybe what we can do will be in the future where maybe 10 further years where, um, we can really just trust the predic…

AI assessment note: “we're focusing on solving the rest of the drug discovery process”

Q Brilliant. Don't put the burgers down too close to the roller coaster. Um, but you know, obviously as a media company, I go through any media platforms and I don't know what's real or fake. I'm always having to ask what's real or fake. Who is that arbiter of verification?

A Yeah. Well, I think there are, I mean, ultimately it's gotta be government, I think. But, um, Um, you know, the kinds of technical bodies that would, um, be able to do the technical work would be like maybe the AI safety institutes. You know, there's a very good one in the UK that, uh, uh, you know, was set up under Prime Minister Sunak, and I think it's doing great work, and then there's one in the US, and maybe some of the leading countries that have the best research should also have an equivalent body that is staffed with high quality researchers too, um, that can actually evaluate And audit these kinds of systems, uh, against certain benchmarks and, um, I kind of like independently check whether they are, uh, meeting the right standards.

AI assessment note: “ultimately it's gotta be government, I think. But... maybe the AI safety institutes.”

Q If I could give you like a magic wand that was only applicable to AI safety, sadly, uh, what would be your implementation idea program that you would put in place with this magic wand?

A Yeah, I think we need some kind of, um, uh, international body, maybe similar to the atomic agency, something like that, That perhaps the, the AI safety Institute sort of feed into, and the research community has to also do this and be involved in like, what are the right set of benchmarks to check what types of traits, what types of capabilities, uh, maybe there are other safeguards too, like, um, you know, it's, it wouldn't be desirable to have, uh, AI systems, um, output tokens that are not human readable. So, you know, in some kind of machine language that We couldn't understand. I think that would, you know, uh, introduce a new vulnerability. So there's quite a few sort of things like that, which I think most of the leading labs, uh, would agree, uh, probably not best to do. Um, and then these, uh, these bodies would, uh, you know, these institutions would test against those things. And I think that would give the public confidence and, um, and, you know, academia could be involved as well, as well as civil society that these, uh, systems are going to get incredibly powerful, um, have been, Independently, uh, checked and audited.

AI assessment note: “I think we need some kind of, um, uh, international body, maybe similar to”

Q That there have been a lot of tricks that have been put on top of LLMs. Um, I hear often about scaffolding and orchestration and AI that can use a tool to search the web, but it won't remember what it learns. As soon as you close that session, it forgets. Is that just a limitation of the large language model paradigm?

A Well, look, I think there is, and I'm definitely a subscriber to the idea that maybe we need one or two more big breakthroughs before we'll get to AGI, and I think they're along the lines of things like continual learning, better memory, longer context windows, or Or perhaps more efficient context windows would be the right way to say it. So don't store everything, just store the important things. That would be a lot more efficient. That's what the brain does. Um, and better long-term reasoning and planning. Now, it remains to be seen whether just sort of scaling up existing ideas and technologies will be enough to do that. Uh, or we need one or two more, uh, uh, really big insightful innovations. I'm probably, if you were to push me, I would, I would be in the latter camp. Um, but I think, um, no matter what camp you're in, we're gonna need Large foundation models as the key component of the final AGI systems. Of that, I'm sure. So, I don't, I'm not subscriber to someone like Jan LeCun who thinks, you know, that there's sort of some kind of dead end. I think the only debate in my mind is, are they a key component or the only component? So, I think it's between those two, two options. And, and for me, we, this is one advantage we have of having such a deep and rich research bench. We can go after both of those things At maximum, with maximum, uh, force, both, you know, scaling …

AI assessment note: “we need one or two more big breakthroughs... along the lines of things like continual learning”

Q Is that closer than you thought? Has that changed over time?

A Not really. I mean, actually, when you, when you, uh, it's funny, um, my co-founder, Shane Legg, who's chief scientist here, um, uh, when we started out DeepMind back in 2010, he used to write blog posts sort of predicting about, um, Uh, when AGI would happen, and bearing in mind in 2010 when we started, almost nobody was working in AI, and everyone thought AI, uh, basically didn't work. No, and, but they're still there on the internet for people to check, and, uh, we used to do this extrapolation of compute and algorithmic, uh, progress, and basically we predicted around 20 years it would take from when we started out, and I think we're pretty much on track.

AI assessment note: “we predicted around 20 years it would take... we're pretty much on track”

Q quickly and caught up slash overtaken other providers. I think I'm tweeting. I think you liked it, but I basically tweeted, um, what I used and how it's changed over time. DeepMind now is my number one for research for new shows. It wasn't that way before. What has led to the acceleration and progression of DeepMind in a way that it wasn't maybe there two to three years ago?

A Yeah, well, we made some organizational changes, so I think we've always had the deepest and broadest research bench at Google and at DeepMind. I mean, if you look at the last decade, uh, or plus, you know, 15 years, but I would say about 90% of the breakthroughs that underpin the modern AI industry were done by either by Google Brain or Google Research or DeepMind. So one of our groups, um, if you think of like AlphaGo and reinforcement learning, and of course Transformers, You know, these are all the key breakthroughs. So I would back us to sort of, um, make those breakthroughs in the future, uh, if there are any missing ones. Um, and I think we've basically helped put together all the talent from around the company, sort of pushing in one direction. Uh, and then we talked earlier just about, you know, compute resources. It was also about combining all of our resources together so we could build the biggest models rather than having two or three versions, uh, around the company. So I think a lot of it was assembling together all the ingredients we already had, and then kind of pushing with relentless sort of focus and, and, and pace, um, acting almost like a startup really, uh, to get back to the frontier and, and be ahead in, in many areas.

AI assessment note: “combining all of our resources together so we could build the biggest models”

Q How, how do we solve the energy crisis that comes with an AI revolution? What it means in terms of energy requirements is unprecedented. I know it's an incredibly hard question, which I'm delving from really hard question to really hard, but how do we solve that unprecedented need for new energy?

A Well, I think actually, um, AI will in the, in the medium to long run, uh, more than pay for itself, I think, in terms of energy costs. And so, you know, we work on all these projects of Like optimizing existing infrastructure, like optimizing the grid. I think we could probably get 30, 40% more efficiency out of our national grids. Um, and then there's like modeling the climate and weather, and we have all sorts of the best kind of weather modeling systems in, in, in the world. So that helps us work out where the effects are really happening to mitigate that. Uh, and then finally, the most exciting maybe is like these new breakthrough technologies like fusion, like new batteries, uh, superconductors that I think Uh, AI will be essential for helping us reach. And then I think we'll be in a completely new energy situation than we've ever been as humanity where, uh, and then that will of course help with things like the climate and environment. Um, and eventually also help us, um, get into space much more cheaply. Cause if you have a, you know, uh, an incredible energy source like fusion, um, then, uh, you have effectively unlimited rocket fuel because you can just, um, uh, still, uh, uh, catalyze seawater.

AI assessment note: “AI will in the, in the medium to long run, uh, more than pay for itself”

Q Where are we behind where you thought we'd be?

A Um, I think actually in most areas we are ahead of where I thought we would be. If you think about things like, um, the video models or, um, even now with our newest systems like Genie, their interactive world models, um, which I think is kind of incredible if you sort of step back and think about it. I think if you'd shown me that five, 10 years ago, I would have been pretty amazed. Um, so I think in most domains where we, we, we are ahead of where, um, the field thought, um, Um, there's still some big things missing though, like continual learning. These systems don't learn, uh, after you finish training them, after you put them out into the, into the world. You know, they're not very good at learning further things, and I think some critical capabilities are missing.

AI assessment note: “there's still some big things missing though, like continual learning.”

Q If I could give you like a magic wand that was only applicable to AI safety, sadly, uh, what would be your implementation idea program that you would put in place with this magic wand?

A Yeah, I think we need some kind of, um, uh, international body, maybe similar to the atomic agency, something like that, That perhaps the, the AI safety Institute sort of feed into, and the research community has to also do this and be involved in like, what are the right set of benchmarks to check what types of traits, what types of capabilities, uh, maybe there are other safeguards too, like, um, you know, it's, it wouldn't be desirable to have, uh, AI systems, um, output tokens that are not human readable. So, you know, in some kind of machine language that We couldn't understand. I think that would, you know, uh, introduce a new vulnerability. So there's quite a few sort of things like that, which I think most of the leading labs, uh, would agree, uh, probably not best to do. Um, and then these, uh, these bodies would, uh, you know, these institutions would test against those things. And I think that would give the public confidence and, um, and, you know, academia could be involved as well, as well as civil society that these, uh, systems are going to get incredibly powerful, um, have been, Independently, uh, checked and audited.

AI assessment note: “we need some kind of, um, uh, international body, maybe similar to the atomic agency”

Q Where are we behind where you thought we'd be?

A Um, I think actually in most areas we are ahead of where I thought we would be. If you think about things like, um, the video models or, um, even now with our newest systems like Genie, their interactive world models, um, which I think is kind of incredible if you sort of step back and think about it. I think if you'd shown me that five, 10 years ago, I would have been pretty amazed. Um, so I think in most domains where we, we, we are ahead of where, um, the field thought, um, Um, there's still some big things missing though, like continual learning. These systems don't learn, uh, after you finish training them, after you put them out into the, into the world. You know, they're not very good at learning further things, and I think some critical capabilities are missing.

AI assessment note: “there's still some big things missing though, like continual learning.”