From the Harvard Business Review, December 27:
TRANSCRIPT
AZEEM AZHAR: Hi, I’m Azeem Azhar, founder of Exponential View and your host on the Exponential View podcast. When ChatGPT launched back in November, 2022, it became the fastest growing consumer product ever and it catapulted artificial intelligence to the top of business priorities. It’s a vivid reminder of the transformative potential of the technology. And like many of you, I’ve woven generative AI into the fabric of my daily work. It’s indispensable for my research and analysis. And I know there’s a sense of urgency out there. In my conversations with industry leaders, the common thread is that urgency. How do they bring clarity to this fast-moving noisy arena? What is real and what isn’t? What, in short, matters? If you follow my newsletter, Exponential View, you’ll know that we’ve done a lot of work in the past year equipping our members to understand the strengths and limitations of this technology and how it might progress. We’ve helped them understand how they can apply it to their careers and to their teams and what it means for their organizations. And that’s what we’re going to do here on this podcast. Once a week, I’ll bring you a conversation from the frontiers of AI to help you cut through that noise. We record each conversation in depth for 60 to 90 minutes, but you’ll hear the most vital parts distilled for clarity and impact on this podcast. If you want to listen to the full unedited conversations as soon as they’re available, head to exponentialview.co. My next conversation started in the front seat of a self-driving car. I joined Alex Kendall, the co-founder and CEO of Wayve. It’s an autonomous driving startup and we went for a test drive on some really unpleasant roads in North London. Alex’s team is behind some impressive work using generative AI and synthetic data to train autonomous systems that can safely navigate roads and human behavior in complex environments. I was really impressed by the experience. This car driving itself navigated some quite difficult roads, cyclists all over the place, people crossing without looking, a huge bevy of police and ambulances and roadworks. The types of things you often see in London. You’ve got to watch the video for yourself. We had a couple of GoPros recording us and our reactions. The link to that video is in the show notes. Now, Alex and I go under the hood of Wayve self-driving systems. We discuss the evolving business model in the car industry and what needs to happen to bring improved safety through AI to cars on our streets, and potentially one day self-driving vehicles as well. Enjoy. Alex, we’re now back in your office and I have to say I am still buzzing and reflecting on that amazing experience, that 15, 20 minute drive we did around some pretty tough streets in London in one of your vehicles powered by your AI system. Is that the normal experience? Is that how you find people responding to their first drive in a Wayve?
ALEX KENDALL: It’s magic every time. I try and get out in the car every week and stepping out of it… Every time you go for a drive you see something new, whether it’s different weather, different road layouts. We had some crazy interesting scenarios today and it’s always a treat to see how the AI learns and grows over time. And when you see a new behavior for the first time or something like that. I mean, most people have had their ChatGPT moment with AI, but for me getting in a physical car and seeing it interact in the real world, there’s nothing like that. It’s really special.
AZEEM AZHAR: I like the way you’ve used that actually, the ChatGPT moment, because when I first used ChatGPT and we’re recording this nearly a year to the day from the launch of ChatGPT, it really was a moment and I came away, the way scientists say, rethinking my priors. And I would say that having been in the car for 20 minutes, sitting next to the safety driver that you have to have for legal reasons, watching where his hands were and they were near the wheel, but off the wheel, while we drove through those really difficult streets and running across situations which I could imagine someone who had been driving for only three or four years would’ve really, really struggled with, and the car handled them with real great aplomb. So does that feel… Am I rethinking my assumptions about this technology after that? Well, right now I am, but it’s really fresh in my mind, so yes. So maybe it is a bit like a chat gpt moment. Maybe it is. But let me ask you, what was the journey that took you from a researcher to deciding that this is something that you wanted to build, that was even had potential, that the time frames were right for it?
ALEX KENDALL: Well, going back to the ChatGPT comment, if you ask people what AI is today, I think that’s what people gravitate to. And don’t get me wrong, that technology is mind-blowing and incredible. The first thing I asked ChatGPT when I was playing with it, I asked it the trolley problem, how do you navigate risk when you’re self-driving a car? It was interesting to see how it answered. But I think in 10, 20 years, if you ask people what AI is, they’re going to be referring to the embodied AI, the system that whether it’s the bipedal robot in their living room helping out with their domestic tasks or the autonomous self-driving car that just dropped them off or delivered their groceries or what have you. It’s the AI that’s going to be around us and able to support the lives we live in, accelerate what we do and free up our time and make our lives safer. This is where I think we’re going to go. And when we founded Wayve, the reason why we started was in order to build that future, it’s not going to be built by a robot that’s hand programmed to drive in it or to operate in a way that’s designed with a set number of rules and to operate in a set given way because the world is just too unstructured, unpredictable. You need to have a system that has the intelligence to understand that and make its own decisions. And there’s no better way of doing that than end-to-end deep learning, big machine learning models that can learn through data, learn things that are more complex than we can hand program as engineers. And I’d seen that during my research with Computer Vision. I’d been inspired by similar breakthroughs at the time, whether it was to AlphaGo, to be able to solve the hardest ball game in the world and be able to learn how to beat the world champion at it. The work that Google DeepMind did. These kinds of things at the time made me think, look, now’s the right time to go and build this in an embodied physical system and we can actually go and make this technology safe to be deployed in the physical world.
AZEEM AZHAR: Right. So that’s actually very helpful to understand because deep learning has been with us since a moment of inflection probably since 2010, 2011, although the theory and the first prototypes are somewhat older than that. And what it did was it challenged the assumption that in order to build AI systems, you would need to have lots and lots of rules, rules about the world and those rules and that approach is now called GoFi. Good old fashioned AI would construct, the joke was millions and millions of if, then, else statements, right? If you see a child, then brake, else continue as you were driving. And you have to construct so many rules, it becomes very complex. So was that approach, the approach that the autonomous vehicle industry started with when they used to have those DARPA challenges across the desert in the US? Is that how those systems were built 15 years ago?
ALEX KENDALL: Well, you make a good observation there. I mean, this has been the same pattern that’s played out, whether it’s the very first systems that could beat humans at chess or image recognition or even ChatGPT, as we talked about before. People used to build those technologies with rule-based systems and it was going to an end-to-end neural network that’s enabled them to hit this inflection point. But actually one of the very first autonomous vehicles in, I think it was 1989 or around that time, was an end-to-end learning approach. It was built by Dean Pomerleau and colleagues at Carnegie Mellon University in the US. And he was inspired because, what I’ve read about is, he wanted to build a system that could drive on the east and west coast of the US. It could generalize, it could scale. And so he built, it was a very small, I think, hundreds of parameter neural networks, a very, very small neural network, but it could get a vehicle at the time to do lane following. But after that, we had this AI winter where people couldn’t… There wasn’t, for many factors, compute resources, algorithmic maturity, things like this, it wasn’t possible to scale these systems. In the meantime, rules-based systems were the fashionable thing at the time. And in the 2005, 2007 era, the US government poured a lot of funding into those DARPA grand challenges as you described. They allowed academic research labs to scale up rules-based systems based on mapping and LiDAR technologies. And ultimately when Google ended up acquiring and funding one of them commercially, that’s what led to the first commercial self-driving car effort. And that’s now since we’ve seen a lot of offshoots from that project that have formed many of the existing commercial efforts today. But the result has been the prevailing commercial technology has been that traditional rules-based stack. So we’re not the first and it’s not a new idea to do end-to-end learning for self-driving. Many have tried along the way, but when we started in 2017, I think there are a number of reasons around timing that meant that it’s been possible to build and scale it now. And then with all of the breakthroughs and foundation models and generative AI, it’s just simply accelerated this. But certainly in 2017 when we started and there were multi-billion dollar efforts behind these rules-based approaches, and everyone thought, look, it’s going to scale and commercialize, it’s going to do that in a year, it’s all done, the market has won, I think there was a certain sense of contrarianism and bravery that we had to grasp in order to go and set off on this alternate path, which has been a very contrarian approach for the last few years.
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On the blog our interest in machine learning/deep learning/AI goes back to 2013 -2014:
