The Path Toward AGI, According to Google's DeepMind — With Colin Murdoch

0:00A top executive at the heart of Google's deep mind efforts to advance artificial intelligence joins us right after this. LinkedIn presents.

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1:32We're joined by a very special guest today. Colin Murdoch is here. He's the chief business officer at Google's DeepMind. We're going to talk a little bit about theory. We're going to talk about practice and how Google is trying to take the cutting edge in artificial intelligence and productize it. Colin, welcome to the show. Alex, it's fantastic to be here. I'm really looking forward to this conversation. Why build artificial general intelligence? I mean, this is something that is a stated goal of DeepMind, to build AI on par with human intelligence or even something that surpasses it. Why do it?

2:08That's right. Well, I mean, just stepping back for a moment, I think it's really important to think about what artificial general intelligence is or AGI is, because you'll be familiar with a lot of AI systems today. You build an AI system to solve a particular problem, and that works exceptionally well. We've seen huge breakthroughs in fundamental AI research, which has driven really important impact in the world through this form of AI. What we hope, though, with artificial general intelligence is to build a system that can solve multiple different problems. So one AI system that can solve multiple different problems.

2:42And much like us humans, Alex, that means we can take learnings and the AI AGI system can take learnings from one setting and apply it to a new setting. And we expect that, therefore, means that this AGI system can create more creative and transformational solutions. And we know this is possible, actually, because humans are a form of artificial, well, not artificial, in fact, real general intelligence. And look at the incredible things that we've been able to achieve. And that's why we at DeepMind think it's really worthy to pursue, to develop a form of artificial general intelligence that will help, hopefully, tackle some of society's biggest challenges.

3:18things like climate change and problems and questions in healthcare. That's actually the core of what we're up to. And it's incredibly important. I think incredibly interesting. So what do you, what type of research and advancements need to happen in order to get us closer to that point? Well, we are still in early days, but let me give you some examples of what's currently working, what we call generalization. And I can talk about some of the areas of active research we think are going to need for us to get there. So just stepping back for a moment, what I mean by the ability to generalize, and you'll hear this stuff a lot in this field, is the ability to take learnings from one setting and apply them in another setting.

3:59And we recently developed, for example, an algorithm called MuZero, which was originally developed actually to play the games of chess and Go. And then we realized we were able to take this algorithm and apply it to, I wouldn't say, the game of YouTube video compression. Now, it's maybe funny I say that, but we were able to take an algorithm developed for games that was a master in chess and use that to dramatically reduce the bandwidth requirement to stream YouTube videos. We did that by understanding that actually a video is a series of individual pictures. If you imagine the transition between each of those pictures, it's like a step in a game.

4:33That gave us the insight that this algorithm would generalize from playing chess and go to YouTube video compression. And of course, generative AI is actually a great example as well. So these new tools where you're able to interact with them in a way that is kind of fairly conversational and get a really surprisingly powerful response. That is one way we're beginning to see this move towards more general intelligence. And maybe I could just jump in and tell you a little bit about how, for example, that recent work in genitive AI is allowing us to move closer to AGI. Yeah, let's do that. That's fascinating.

5:17Fantastic. So what's maybe surprising to know, if you've been following the field in genitive AI, you've really seen it burst onto the scene in the last 18 to 24 months. is that some of the underlying breakthroughs were developed about five years ago. What's happened in the last 18, 24 months, though, these systems have been really, really scaled up. And by that, I mean the size of the model, the number of parameters in the model which contain model power has grown dramatically. And these systems have been trained on kind of larger and larger data sets. And what's happened is that by scaling up these models, we've seen these emergent capabilities appear.

6:00And by that I mean, it's not always possible to predict exactly what capabilities will appear. But we've had these new capabilities appear that have demonstrated really powerful generalizability. So you can then take a system that's been trained in this way, and you can ask it to summarize a document or write you an email. And it wasn't necessarily trained expressly on these tasks. but is able to achieve these tasks because of the training process and the scaling up has happened. And that's been, as I'm sure you've been following and many others have been following, that's been a massively important breakthrough in the last 18 to 24 months.

6:37But these systems are still not complete. They still get things wrong. They maybe can't plan in the right way. They maybe can't remember what you did yesterday and help you today. So things like memory, the ability to remember between episodes, planning, the ability to imagine a whole range of different future scenarios and plan effectively in that setting. Those are two areas of active research that are really important. And at a kind of zoomed out level, another is what we call kind of concepts and transfer learning. So as humans, we're able to build this kind of deep conceptual understanding.

7:12And that actually forms a kind of really strong foundation for us to take knowledge that we generated in one setting and transfer that to another. So concepts and transfer learning, planning and memory are all really active areas of research, which I think will help us push the next crunch here. And actually, by the way, we haven't necessarily reached the limit of making these models bigger either. No one's quite sure where that limit is. And so that's also a really important active area of research, just making these things bigger and bigger. where will that where will that go and where will that land and what more can we get from that yeah we've had jan lacuna on the show and i've been speaking with jan for since 2015 2016 so seven or eight years on this point about what intelligence is from the eye of an artificial intelligence researcher and he's always said that it's the ability to predict and to plan and it is very telling right now that the research now is is all about teaching these ais to predict into planet.

8:09In fact, speaking about the Gemini, the new Gemini model, I'm pretty sure people from DeepMind have talked about, I think I'm going to just cite this, that the algorithm should be better at planning and problem solving. So that seems to be where we're going. So first of all, I'm going to get, you know, I have a few questions for you about Gemini, but just let's talk about it on a broad level. How do you teach an AI to plan and predict? So there's a whole range of different active research tasks here. And to be clear, there isn't an answer yet, which is why it's still active research. But one of the ways we motivate this research is by making sure we have tasks that require planning.

8:50So we spend a lot of time and investment in building a whole suite of different evaluations and tasks, which then provide the target, if you like, for our research and our research programs to focus on. And that's a really interesting definition of intelligence. And one of the definitions of intelligence that we use at DeepMind and was actually created by one of the founders of Google DeepMind, Shane Legg, is intelligence is the ability to perform well across a range of different tasks. And I really like that definition because I think it's very descriptive and it's very easy to operationalize into a research program.

9:26And so this sense of building multiple different evaluations and tasks that provide then a way for us to measure our performance and progress against, whether it's planning or adding memory, is really central to actually the way we conduct research. And then behind that, it's a creative process. So what you're trying to do is bring together people from a whole range of different disciplines, from neuroscience, from different areas of AR research, to come together and have ideas about how we can make progress, and then use the incredible engineering talent we have and the compute resources we have to experiment and take steps forward.

10:05That's how we do it at a kind of meta level. And DeepMind started largely with some breakthroughs in gaming. So how is that applicable? Because, you know, I think about predicting and planning, and it seems like if you're playing, you know, sophisticated games like Go, then that's basically going to take you in that direction. Yeah, it's a great point. because games are a fantastic proving ground for these algorithms. They're fantastic because they're actually hard for humans. They have ability for us to measure how good performance is. There's normally a score of some sort, so we can benchmark the algorithm's performance versus the human's performance.

10:46And there's a whole raft of different existing games out there that can push and pull the algorithm, the AI's capability in different directions. And by the way, you can develop new games. And I think maybe the third, maybe the final point is that games can run faster than clock time. So you can do many, many iterations in a kind of simulated game much, much faster than you can experiment in the real world, which is why there's such an incredible proving ground and development ground. You're absolutely right for these algorithms. And we continue to invest deeply in kind of game-like environments for exactly those reasons.

11:21Maybe one other important point. They're also a really useful way of testing algorithms out to test their limits, and we can check their technical safety to make sure they're doing what we expect them to do. So they're a nice way of developing and testing an algorithm before they break out into the real world. And maybe a nice example here, actually. We often use this technique of first developing an algorithm in a game, to your point, to do planning. and there's an example here in robotics. If you try and train an algorithm directly on a real robot, it's going to take you a long time because a robot can take quite some time to complete the task.

12:01And in the beginning, it may be all over the place, maybe like a young child learning to walk. What we do is we create a simulation of that robotic environment, a robotic arm stacking blocks. And we train the algorithm in that simulated environment until it gets good in that simulated environment and we take that algorithm and then we apply it to the real robot stacking blocks we discover it's actually then pretty good out of the blocks and in the real world the robot can then begin to build on the training there so the way that i picture this happening is like it feels like most of the general public has started to get a chance to like start talking with ai via these large language models so you know when i when i try to conceptualize like what this might look like down the road, I start thinking that when I'm speaking with a chat GPT or a Bard or a Bing, it starts to remember who I am.

12:55It starts to be able to accomplish tasks for me. It starts to be able to help me plan. Is that sort of like the next step here? Is that where this research is building toward? That's right. So you're able to converse with these dialogue agents today, as you've discovered, and you can have actually quite a meaningful and important conversation, but it might have not remembered what you did last week for example they've got limited kind of context windows as you may have heard it called but what you really want it to remember as you've noted what did i do yesterday what did i last week what's my preference when it comes to kind of looking at a given film for example because next time i asked to watch a film and it wants to know what i watched before and maybe what my preferences were so that ability to remember more about our previous interactions actually becomes really important as you want i mean yeah these things have like the memory of a goldfish it's like you sit you're talking with it and then five minutes later it's like hey just remind me what what you said like it totally forgets so that's like one step yeah yeah absolutely right it's a really important area for us to kind of expand the memory of these systems sometimes we refer to this as episodic memory so they remember episodes important episodes in the past so they are able to bring to bear that important understanding.

14:11Then when it comes to planning about the future, these systems need to be able to stop and reason about the right sequence of steps to take. So, for example, I want to plan a holiday. I want to go here, then I want to go here, and then I want to go there. And the series of things I want to do may change over time. I may get up on a flight. If you can ask one of these systems today, they can come out with a pretty good response on some of these things, but they aren't able to plan based on what you've done in the past and what would a reasonable kind of itinerary be that's changing over time. I'm actually going to hold it over my family very soon.

14:53This is very live on my mind. I don't think the AI systems can really get to a level that I would really want them to at this point. Right. And so we think about where this is going. We talked a little bit about being able to predict and plan. And we talked about, we sort of hinted at multimodality, right? Like having a model that's generalized. So being able to do text, but also like a human, we can talk, we can read, we can see, we can process. And most of these models have just been text or computer vision or computational. And it does seem like the next step is really going to be bringing them all together.

15:29That seems like a massive technological feat. But my understanding is that that is something that's being worked on inside Google with this new Gemini model. I mean, those two descriptions that I just read are both Gemini. So talk a little bit about what Gemini is and how it's going to take us on that road. So Gemini is one of our latest research programs. And you're absolutely right. One of the really important areas it's touching on is what we call multimodality. It's a bit like the human senses you just described. We can kind of use all our human senses together and combined to achieve the goal we're setting out to.

16:05So it will bring in things like text. It will bring in things like images. We're able to input those things, but also output both those things. So you might have a question about something you can see. You can share that image, and you can also ask a question about it. You may want to then adapt something in that image by saying, please edit this element of the image, and it can do that for you. So bringing together these different modalities is something that is a really cool and important part of that Gemini program, as well as the kind of memory and planning architectures that we discussed earlier.

16:39And maybe a final component is we're hoping to develop models of different sizes and scales. So there'll be kind of different sizes of these Gemini models, which can then be applied to different use cases depending on what's important. I mean, has there been anything about training Gemini that's surprised you? Or is this kind of like where you think it's supposed to, where, yeah, it should have been going the whole way? I'm not deep in the Gemini research program myself. But what I would generally share is that it's not Gemini-specific, is that when it comes to training these large models, I think people in general have been surprised that as you make these models bigger, they get more capable and they start to demonstrate these capabilities that you wouldn't necessarily have planned or expected.

17:26And in the field, this is generally referred to as these emergent capabilities. And I'm not sure if we fully got to the end of that process yet. So there's a kind of almost a constant state of surprise as these new capabilities emerge. Right. So I was speaking with some folks at Google and trying to figure out like what to ask you about. And someone brought up talking about modes of training. So I'm curious, I want to ask you about the deep mind approach versus this new approach that's maybe not new, but definitely is gaining share in people's minds called constitutional AI. So I'm just going to read you what constitutional and our listeners, what constitutional AI is from a recent New York Times article.

18:08And I want to get your take on whether that's the right way to train these models. So it says constitutional AI begins by giving an AI model a written list of principles, a constitution, and instructing it to follow those principles as closely as possible. A second AI model is then used to evaluate how well the first model follows its constitution and corrects it when necessary. I'm curious what you think about this approach and whether that's something that Google would consider employing. And if not, why not? This is kind of, I would generally think about this approach and other approaches like this as a way of ensuring these models are behaving in the way that we want them to behave.

18:50And we do definitely think about that very deeply. It's very important to everything we do. And there are different ways to do that. One way is actually by having an AI system like the one you've described provide feedback to the model that you're training about whether it's behaving in the way that the designers would like that system to behave. And that's certainly something that's all part of the overall approach. Another important way, actually, is that you have humans providing feedback to the model. This is a process called ROHF that folks might be familiar with, where human races interact with these models and are observing the constitution and provide feedback to the model on whether and how well the model is performing against that constitution.

19:36And actually, at the moment, that's a really important part of, I think, the core research process, because humans are actually very good at this. There's a kind of secondary benefit of that is that we are beginning to understand how we can begin to embed more and more human feedback into the model process. So I think in general terms, yeah, this is a really important part of how we approach research to make sure the model is well aligned with the sort of, if you like, constitution that the designers and the society ultimately would like these models to be behaving in accordance with. Colin Murdoch is here with us.

20:12He's the chief business officer at Google DeepMind. When we come back, we're going to talk a little bit about the business side of these models and especially how they're being applied within Google. Back right after this. Capital One's tech team isn't just talking about multi-agentic AI. They already deployed one. It's called Chat Concierge, and it's simplifying car shopping. Using self-reflection and layered reasoning with live API checks, It doesn't just help buyers find a car they love. It helps schedule a test drive, get pre-approved for financing, and estimate trade and value. Advanced, intuitive, and deployed.

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22:04And we're back here with Colin Murdoch. He's the chief business officer at Google's DeepMind. Colin, what is the state of the merger between DeepMind and Google Brain, which just came together to sort of be able to work together in a way that hasn't happened for years. That's right. So we've just formed Google DeepMind from the team that was at DeepMind and the team that was at Brain. And actually, these teams have been working together for quite some time in the background. I think the recent merger, if you like, to form this super unit has come at a really important time in the overall development of AI.

22:43We're kind of in this super era or golden era of AI development. And we just thought it was the right time now for that reason, start to bring together the talent, but also the compute and the resources so that we could make sure we were focused and organized in the right way for the next phase. And I've actually been at DeepMind, now Google DeepMind, for about nine years. And the pace and the change and the kind of frontier that we're working at means we're constantly needing to refine the way we organize to make sure that it fits where we are in the kind of technology evolution cycle. And, you know, it's going great.

23:21I'm really enjoying kind of getting to know the entire new team and we're making good progress. Right. And so it's so interesting because, you know, DeepMind's areas have been the gaming, working on protein folding, which we're about to talk about. Google Brain, you know, maybe more search related. So how much of your activities are now going to be focused on the core Google business versus some of this other type of research? I think what's interesting to know, even at D-Mind, and actually this is very close to my role, is that we have for a long time being taking the technology that's been developed in our fundamental research programs and apply that to Google's products and services.

24:02So that's actually been a cool part of both these groups and actually is now a fundamental part of what we do at Google DeepMind. So we're both advancing the state of the art in the technology, applying that to really big problems in science, and then using those breakthroughs to drive value and impact across these, you know, what's often bid in user products at Google. And that's absolutely right, Alex. It's fundamental to the new setup at Google DeepMind. So where do you see the bigger business opportunity? Is it going to be? I mean, you're the chief business officer. So is it going to be search?

24:36We talked a little bit about artificial general intelligence. I mean, you have AlphaFold right now that's out in market. Where's the future of the business on this front? So search is, of course, an incredibly important part of Google's portfolio. I expect it to continue to be a very important part of Google's portfolio. So we'll continue to do everything we can to drive value in search. Let me tell you about how I think about it, because this is a technology transfer process, And that's not easy going from research to real-world impact at any means at all, even when you're operating at Google DeepMind, working with Google.

25:11I think about it actually as a matching process. So on the one side, we have all this amazing research and these research breakthroughs, and there's a team of people that are developing those. And on the other side, we maintain relationships with all the great businesses and business units across the whole of Google, and in fact, the Alphabet group as a whole. so we can deeply understand what's important to them in moving their business forward. So we've got this set of solutions on one side and this set of problems on the other side. And then we try and match these two things together. I sometimes joke it's a bit like running a dating service where you're trying to match problems and solutions.

25:46So we go ahead and do that. And then we try that out. And if it works, we go ahead and launch. So there's a process there. That's what I want to share. It's quite important to share. There's quite a systematic process. It's one of matching technology solution and product problem as defined by the business. So that's just an important area. We've done a lot of work with YouTube as well. So, for example, I talked about that a bit earlier. We've worked with YouTube to help create better tooling for YouTube Shorts so you can more easily find the videos you want. We've worked with YouTube to reduce the bandwidth requirement to watch these videos.