 Welcome to the AI for Good Global Summit 2018. I'm delighted to be joined by Celine Hawiah, who is a partner at PWC UK. And Celine, you need the work, the firm's work on innovation and sustainability. Thank you for joining us. Thank you. So, Celine, you were a panelist earlier today on a session dedicated to how AI can transform our future. It's very important in the context of the fourth Industrial Revolution. We are going through at the moment, isn't it? Yeah, absolutely. It was a fantastic panel. And the angle that I took was a focus on how AI can be used to solve some of the most urgent challenges that we have in our generation at the moment, which are around the health of our planet. So our planet has never been under so much strain, whether it's climate change, the health of our oceans, the fact that we're in the middle of a six-math extinction crisis right now with one of five species facing extinction, and the health of air quality, water, et cetera. And so part of what we need to do is think about how we can transform our human systems, our cities, our industrial supply chain, agriculture, water and transport networks, et cetera, transform them using new technologies so that we don't create the negative impacts that past industrial revolutions have created. And AI, as one of the, as the pervasive technology of the fourth Industrial Revolution, is going to be critical to delivering lots of those productivity gains. Can you give us concrete examples of how we can use AI to alleviate those problems? Yeah. I mean, the starting point, I think, is to remember two key characteristics of AI. So AI helps to increase productivity, which is exactly what we need when we need to improve all of those systems that I mentioned, optimize our energy grids and optimize our water use, et cetera. And the second is that AI enables scientific discovery, right? Whether that speeds up the process of learning and understanding and scientific discovery and therefore it will get us to answer sooner around things from climate modeling to new clean fuels, whether that's around fusion or new biotechnologies for things like water purification or air purification, other things. So AI unleashes productivity and discovery, which will help us address earth challenges. But in a more practical sense, there's a number of emerging applications we're already seeing. So let's take transport. We look at our cities and our transport networks in our cities. We're already seeing the emergence of eco-driving algorithms or platooning of cars, of optimal navigation systems which reduce congestion, air pollution and greenhouse gases. AI obviously is fundamental to autonomous vehicles, which as they roll out, will start to enable better mobility on demand services. So transport is one. Then let's take energy. The future of energy has three Ds, digital, decarbonized and decentralized. And AI is essential to all of those. AI will enable us to aggregate a grid and manage the demands of a grid of a very distributed sets of renewable resources, which is what we need in the future when we need to decarbonize. There's numerous examples we can take across sectors. Agriculture, again, we're already heavily using precision agriculture, which is AI-infused. So there's a great opportunity, but it doesn't come without its risks. We'll talk about the risks a bit later. I want to talk to you about the timing for all this, because AI is not new. It was invented in the 1950s, I guess. But we've been talking about it a lot for the past five, maybe 10 years. Are we at a very critical time right now for the use or the good use of AI? Absolutely. I mean, it's a unique moment in time, which is exciting. There's a number of factors that have come together. Big data with millions and trillions of sensors around us. We have maturing of algorithms, so things like unsupervised learning. We also have a democratization of algorithms. We now have open source algorithms that are available for people to use. We have advances in compute, including deep learning chips and all sorts of other techniques and GPUs, et cetera. So these advances are basically taking us into a point where we now have startups and industry having access to the AI tools that previously only a handful of research labs had. And so now a few data scientists can make a very big impact in a business. And earlier, you alluded to the risks of AI. What did you mean exactly? There's been a lot of discussion around the unintended consequences of AI and the broader risks of AI. And I think the starting point is whenever we think about an opportunity and AI can create, how it can increase the productivity or create strategic business value or create economic growth in a nation, we always have to design that future, bearing in mind some of the social and environmental impacts it could also have. Because there always are consequences we might be able to guess now, but also unintended consequences later. I'll give you a very simple example. We spoke about autonomous vehicles, autonomous trucking being one of the first aspects of that that will be rolled out and over three million job losses in the US as that happens. And the economic harm to the towns along those highways that used to support the truckers. So there's always an unintended consequences. Autonomous vehicles are great in many respects and for the environment have some fantastic savings in terms of greenhouse gas savings. But we have to think about the other side effects as we think about any opportunity. OK, Celine, thank you very much. Thank you.