 Thanks for joining us here in Geneva for the AI for Good Global Summit 2018. My guest here in this studio is Chris Fabian, he's co-founder of UNICEF Ventures. Chris, thank you for joining us. Thanks so much for having me today. So Chris, tell us about UNICEF Ventures. It's interesting because you invest in innovative companies that develop solutions that will help fix the world. Yeah, that's one way of putting it. I think we've tried to find the strength of UNICEF, which has always been to fight for the world's most marginalized people. People who are left behind by commerce or by the way that governments develop their programs. And UNICEF has always worked to work with those communities and help those people have access to opportunity and choice. And we see that in the world of technology, we can do the same thing, but maybe do it even faster and maybe a little bit better. But we can't do that from Geneva or New York. We can't develop technologies and approaches to fixing problems that are far away from sitting in an office room that's air conditioned and very comfortable. And what our team does is we try to use a very typical sort of venture capital methodology to find and invest in the best of breed tech companies, but companies that are registered in the countries that UNICEF works in, in Burundi, a company looking at using data science to solve the problem of malnutrition in Malawi, a company that's using drones to get emergency supplies to where they're needed more quickly. And we think that by, and we've seen that it's true, that by finding those companies and those entrepreneurs and investing in them and giving them the networks that we have access to. We can not only build great products, but we can build great products that solve actually terrible problems that are sort of approaching all of us. Do you find that it's a better approach to try to solve an issue from, to use a bottom up approach in a sense that you have a problem on the ground, a real life problem. You mentioned some African countries, for instance, having a Burundi, I think you mentioned, having a specific problem and using technology to solve this issue. Do you think that's the right approach? So in my previous life, I worked in private sector and tech startups. And I've never done a good company where I tried to solve somebody else's problems. So the things that I've worked in that were successful were because I had an actual issue that I wanted to fix. And I think the same thing is true everywhere in the world. The people who I know in Silicon Valley who are working on some of the best and coolest technology are also using it to find like a better restaurant seat than their friends faster than anybody else can. The people who I see working in open source tech or AI in Nairobi, for example, may actually see the contrast turned up on the problems around them a little bit more, which means they're building for more real world problems. I think that in general, the problems that are going to face us all in the next 20 years are not problems of nation versus other nation, but they're problems of wealthy versus poor. And you can see those problems expressed in rich countries now that have pockets of big disparity. If you look outside of an urban area and you see where all the shopping malls are closing because nobody's going shopping anymore, where you look at reservations of indigenous people who have no access to services that you can get in a city just 20 kilometers away, those aren't national problems. Those are problems of poverty. And I think that as we try to build to solve those problems, we need to work with the people who feel them and see them the most because they're going to have the most constraints, which really leads to the most creativity. And the only thing they don't have is often the access to capital and the access to those networks that you find so readily in Silicon Valley to help them scale their projects larger than a few people or give them a language of business rather than a pure social enterprise. So let's focus on artificial intelligence itself. Do you invest in AI based solutions currently and which ones? Which ones do you think are really good to help deliver the SDGs? So when we think about AI, AI is a tough set of words or a tough acronym anyway, but we really think of sort of complicated math on top of big data sets. We have a substantial platform investment as a team in a data science platform that gives us access to a tremendous amount of data and the ability to model on top of it. But we've also invested through the Venture Fund and several startup companies. And one that we talked to just yesterday on stage here is called Kometrica. Kometrica is working in Kenya and they're using basically feature extraction from images to identify kids who are malnourished. And it's interesting because that same approach could be used, I don't know, in a gym, in a country where people have a lot of access to opportunity and the time to go and exercise to take a photo and see your body mass index change over time and you get gym benefits or insurance benefits. But we've actually chosen to focus on a place where you have a huge amount of malnutrition as well as stunting where children don't grow physically to their full physical capacity but also mentally. The brain doesn't develop as well if you don't have the right nutrients. And to see if you could use that same type of science to look at kids' pictures and see how they were on the sort of nutritional scale. And what we see coming out of that project is that it actually is possible to use techniques that are developed globally but actually to have them applied and created in Kenya, for example, which has a thriving tech ecosystem for Kenyan issues. And then our job as a fund, vis-à-vis our portfolio companies, is to take that and help them create a global business out of it. So that's the type of issue that we try to look at when we're talking about artificial intelligence. Chris, you were here last year during the launch, really, of the AI for Good Global Summit. Do you find that there is a difference in tone or in terms of the direction of the conversation around AI this year? Yeah, I think last year we launched, now we're like halfway to outer space. We see, last year I thought there were a lot of conversations happening in isolation and it felt to me like there were two different communities. There was a set of UN folks and there was a set of tech folks. I don't see that as much this year and what I see rather than like a bunch of little disparate points and conversations happening is that you're starting to see collections of conversations that are centering around the same set of problems and a group of approaches to solve those problems. And that I didn't see as much last year because it was the first time that we'd done this. I think what we're going to see over the next year is that those clusters start to develop some commonalities, some maybe common principles or approaches, methods or technologies. And what I really hope for in the coming year is that we see the application of those and we start to see scale. It's great to solve a problem in one area for one community but I think our role as the UN has to be about scale. And I think that a lot of the companies that we work with also look to us, whether it's on normative guidance or else on just access to opportunity for their products and we can guide that by looking and making sure that things are open source, that things are transparent, that the data sets are based on true human data sets not just the data sets of the rich. And that can help make stronger product for everybody. So I actually look forward to a very rich discussion next year as well and it's been a pleasure to be here again. Chris, thank you very much. Thanks so much.