Saturday, July 15, 2023

AI: Google DeepMind CEO Demis Hassabis On How The Goog Will Get Back In Front Of The Parade (GOOG)

The Verge has fielded a team to report on AI that puts some larger organizations to shame.

From The Verge, July 10:

Google invented a lot of core AI technology, and now the company’s turning to Demis to get back in front of the AI race for AI breakthroughs.

Today, I’m talking to Demis Hassabis, the CEO of Google DeepMind, the newly created division of Google responsible for AI efforts across the company. Google DeepMind is the result of an internal merger: Google acquired Demis’ DeepMind startup in 2014 and ran it as a separate company inside its parent company, Alphabet, while Google itself had an AI team called Google Brain. 

Google has been showing off AI demos for years now, but with the explosion of ChatGPT and a renewed threat from Microsoft in search, Google and Alphabet CEO Sundar Pichai made the decision to bring DeepMind into Google itself earlier this year to create… Google DeepMind.

What’s interesting is that Google Brain and DeepMind were not necessarily compatible or even focused on the same things: DeepMind was famous for applying AI to things like games and protein-folding simulations. The AI that beat world champions at Go, the ancient board game? That was DeepMind’s AlphaGo. Meanwhile, Google Brain was more focused on what’s come to be the familiar generative AI toolset: large language models for chatbots, editing features in Google Photos, and so on. This was a culture clash and a big structure decision with the goal of being more competitive and faster to market with AI products.

And the competition isn’t just OpenAI and Microsoft — you might have seen a memo from a Google engineer floating around the web recently claiming that Google has no competitive moat in AI because open-source models running on commodity hardware are rapidly evolving and catching up to the tools run by the giants. Demis confirmed that the memo was real but said it was part of Google’s debate culture, and he disagreed with it because he has other ideas about where Google’s competitive edge might come into play.

Of course, we also talked about AI risk and especially artificial general intelligence. Demis is not shy that his goal is building an AGI, and we talked through what risks and regulations should be in place and on what timeline. Demis recently signed onto a 22-word statement about AI risk with OpenAI’s Sam Altman and others that simply reads, “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.” That’s pretty chill, but is that the real risk right now? Or is it just a distraction from other more tangible problems like AI replacing a bunch of labor in various creative industries? We also talked about the new kinds of labor AI is creating — armies of low-paid taskers classifying data in countries like Kenya and India in order to train AI systems. We just published a big feature on these taskers. I wanted to know if Demis thought these jobs were here to stay or just a temporary side effect of the AI boom.

This one really hits all the Decoder high points: there’s the big idea of AI, a lot of problems that come with it, an infinite array of complicated decisions to be made, and of course, a gigantic org chart decision in the middle of it all. Demis and I got pretty in the weeds, and I still don’t think we covered it all, so we’ll have to have him back soon.

Alright, Demis Hassabis, CEO of Google DeepMind. Here we go.

This transcript has been lightly edited for length and clarity

Demis Hassabis, you are the CEO of Google DeepMind. Welcome to Decoder.

Thanks for having me.

I don’t think we have ever had a more perfect Decoder guest. There’s a big idea in AI. It comes with challenges and problems, and then, with you in particular, there’s a gigantic org chart move and a set of high-stakes decisions to be made. I am thrilled that you are here.

Glad to be here.

Let’s start with Google DeepMind itself. Google DeepMind is a new part of Google that is constructed of two existing parts of Google. There was Google Brain, which was the AI team we were familiar with as we covered Google that was run by Jeff Dean. And there was DeepMind, which was your company that you founded. You sold it to Alphabet in 2014. You were outside of Google. It was run as a separate company inside that holding company Alphabet structure until just now. Start at the very beginning. Why were DeepMind and Google Brain separate to begin with?

As you mentioned, we started DeepMind actually back in 2010, a long time ago now, especially in the age of AI. So that’s sort of like prehistory. Myself and the co-founders, we realized coming from academia and seeing what was going on there, things like deep learning had just been invented. We were big proponents of reinforcement learning. We could see GPUs and other hardware was coming online, that a lot of great progress could be made with a focused effort on general learning systems and also taking some ideas from neuroscience and how the brain works. So we put all those ingredients together back in 2010. We had this thesis we’d make fast progress, and that’s what happened with our initial game systems. And then, we decided in 2014 to join forces with Google at the time because we could see that a lot more compute was going to be needed. Obviously, Google has the most computers and had the most computers in the world. That was the obvious home for us to be able to focus on pushing the research as fast as possible.

So you were acquired by Google, and then somewhere along the way, Google reoriented itself. They turned into Alphabet, and Google became a division of Alphabet. There are other divisions of Alphabet, and DeepMind was out of it. That’s just the part I want to focus on here at the beginning, because there was what Google was doing with Google Brain, which is a lot of LLM research. I recall, six years ago, Google was showing off LLMs at Google I/O, but DeepMind was focused on winning the game [Go] and protein folding, a very different kind of AI research wholly outside of Google. Why was that outside of Google? Why was that in Alphabet proper? 

That was part of the agreement as we were acquired was that we would pursue pushing forward research into general AI, or sometimes called AGI, a system that out of the box can operate across a wide range of cognitive tasks and basically has all the cognitive capabilities that humans have.

And also using AI to accelerate scientific discovery, that’s one of my personal passions. And that explains projects like AlphaFold that I’m sure we’re going to get back to. But also, from the start of DeepMind and actually prior to even DeepMind starting, I believe that games was a perfect testing or proving ground for developing AI algorithms efficiently, quickly, and you can generate a lot of data and the objective functions are very clear: obviously, winning games or maximizing the score. There were a lot of reasons to use games in the early days of AI research, and that was a big part of why we were so successful and why we were able to advance so quickly with things like AlphaGo, the program that beat the world champion at the ancient game of Go.

Those were all really important proof points for the whole field really that these general learning techniques would work. And of course we’ve done a lot of work on deep learning and neural networks as well. And our specialty, I suppose, was combining that with reinforcement learning to allow these systems to actively solve problems and make plans and do things like win games. And in terms of the differences, we always had that remit to push the research agenda and push things, advanced science. And that was very much the focus we were given and very much the focus that I wanted to have. And then, the internal Google AI teams like Google Brain, they had slightly different remits and were a bit closer to product and obviously to the rest of Google and infusing Google with amazing AI technology. And we also had an applied division that was introducing DeepMind technology into Google products, too. But the cultures were quite different, and the remits were quite different....

....MUCH MORE including the pod.