Select AI for the Future of Food
10:04PM Jul 14, 2020
Good afternoon. Good evening and welcome to the AI for Good Global Summit always on all yours off. We hope that you your family, your friends and your colleagues are all keeping healthy and safe. My name is Fred Werner from the ITU, the International Telecommunication Union. And it's a pleasure for me to introduce today's webinar. Na vi to you is the United Nations specialized agency for information and communication technologies. And we're also the organizers of the AI for Good Global Summit alongside with x five foundation. And in partnership we're 36 un sister agencies ACM and co convened with Switzerland. And the goal of AI Summit is to identify practical applications of AI to advance the Sustainable Development Goals and scalable solutions for global impact. And like much of the world, the air summit has gone digital, and we're moving forward with digital weekly online programming, allowing us to reach more people than any than ever before. And this week's webinar, we could consider it as part two, the AI for the future food breakthrough session that would have taken place in Geneva where it not for the virus. And before I introduce today's moderator, I'd like to go over a few housekeeping issues. First of all, we disabled your microphone, so please use the chat and q&a function if you wish to communicate. It's for the job and responsibility of the moderator to identify questions from the from you participants and ask them to report to the panelists and we're counting on your active participation to grade A very interactive session. And speaking of being interactive, I have a first challenge for you, could you please let us know where you're connecting from, and simply use the chat function, make sure it's selected, chat to everyone and type in where you're connecting from here I'll go first I'm connecting from Geneva, Switzerland, okay, Geneva, Washington, DC, Dallas, Brazil, Prague, Virginia, Germany, Botswana, Montreal, Ohio, Pennsylvania, Rome, South Africa, Kiev, Stockholm. While we really have a truly global audience here today, which is great. So without further ado, I'd like to introduce today's moderator. Her name is Caroline Coulter. And she's a senior associate at X Prize and she leads her research on the future of food systems. So Caroline, I'd like to welcome you to the show and the show is all yours. Welcome.
Thank you, Fred. Welcome, everyone to the today's webinar, where we will discuss how to leverage AI technologies to create More regenerative, inclusive, nutritious and resilient food systems. I'm joined today by our amazing food brain trust who you will meet very soon. But first, the purpose of this webinar is to introduce the topic areas in which we're seeking project submissions from the public. As part of the AI for Good food breakthrough track, a team of thought leaders known as the food brain trust, are using their expertise in data and AI to help drive global initiatives in the area of food and food systems. So before we dive in, I'd love for our brain trust members to introduce themselves. Lauren.
Good morning. Good afternoon. Good evening. What an amazing global audience we have. My name is Lauren freeze. I lead a strategic advising firm called future table focus on future food systems, especially how we can best use technology and innovation to improve our food systems for our regenerative food. Systems nutritious food systems and equity. I have a background in international development spent many years in rural Uganda working with small farmers and other international development actors and spent several years at the World Economic Forum as well working on public private partnerships. So it's a delight to be with you all today and to be part of this very interest.
Thank you, Lauren. I like to next introduce Marin.
Good morning and evening, everybody, wherever you are. This is Marin I'm joining in private as well. from the Netherlands. I first and foremost, consider myself an activist the circular economy food but I'll probably work at a non at the intersection of open innovation and the circular economy of food. I have a background in industrial design engineering, and I later joined the circular economy MBA at Bradford School of Management in the early days of the program when a circular economy food was still unexplored territory. So ever since I focused my work on helping set out a vision for the future of fruit, and in that capacity, I'm delighted to be here with all of you today.
Thank you man, I'd like to turn it to Emma.
Hi everyone, my name is Emma Chow joining today from the Isle of Wight which is in England, if you haven't heard of it before. It happens to be where the Ellen MacArthur Foundation is based. And I've been working here for the last two years, focusing on food now leading what we call our food initiative, which we launched just over a year ago in June 2019, following the release of the cities and circular economy for food report, so this initiative is all about working with pioneering actors from across the system, and being engaged in platforms like this to help influence innovation to accelerate the transition to a healthy food system. That is regenerative and restorative by design. And we no longer even have the concept of waste altogether. So I'm looking forward to our discussion today and also seeing the applications and submissions coming in, in the following weeks.
Thank you, Emma. And finally, I'd like to turn to Michael to introduce himself.
So Happy Tuesday. joining you today from sunny California. I lead a variety of global workplace programs at Google, including our food at work program, as well as our sustainability program for Roos. In my role, I'm responsible for providing great food experiences for Googlers around the world, 55 countries. And our organization is very thoughtful and intentional about what we do with foods. And as you can imagine, when you work for an organization like Google, you get involved in a variety of conversations around the use of machine learning AI, big data in the in food, food systems, sustainability, health and well being so I'm excited to be here with you today. To get involved in ultimately amazing submissions that are hope that will be the result of this kickoff today. Back to you, Caroline.
Thank you so much. Thank you, everyone. And we have a lot to cover today. So I'll jump right in and start with Marin and Marin, we started this journey on May 7, we hosted a panel discussion similar to this one, exploring the potential for AI innovations on food and agriculture sectors. Can you describe a little bit of the journey of this track up until this point and what kind of work you and your fellow braintrust members have been doing to get us to the stage?
Absolutely. Thank you for that. Yeah, so it actually started with X PRIZE recently publishing its food impact roadmap, which is focused on a healthier and more inclusive food system within planetary boundaries, which I By the way, highly recommend anybody that's interested in the subject to have a look at following this during the main session. x rays and it is AI for Good Webinar with Lauren myself. Along with other parallel analysts and Caroline, you were there too. We talked about and we explored how AI can help us shape the food landscape of tomorrow. And in all honesty, it simply tasted like more. So even during that first conversation, we broadly agreed on the necessity of a system level shift. And we hence brought together this braintrust his team of experts and I would dare to call all of them activists don't know if they agree that I've all individually been working to shape a better food future within our industries within our ecosystems. And as we come together over the past few weeks, we have been crystallizing our joint vision for the future of food, which Lauren will briefly describe in a minute. And in doing so we focused on the role of technology and particularly AI NML. In this system level transformation. And so together with with Boren, Michael and Emma, we have identified three broad teams, which we believe that these teams represent key leverage points, key areas that can contribute to a larger system that will shift, a shift in the food systems toward division that Lauren will will lay out for you now.
Thank you so much, Marian, for this. And with that, I'll turn to Lauren, to tell us more about the vision behind this break food breakthrough track and what are the some of the goals that you're trying to achieve as a brain trust?
Thank you, Carolyn. And thanks, Meredith. Let me just start by saying what we mean by food systems that might be an unfamiliar term to some of you. So what you may have heard is farm to fork and that refers to this chain of events and logistics that bring food from where it's grown to where it's 10 that's also sometimes called the value chain. Sometimes people call that the value web, or food web when it's interconnected in a different way and actually has a more circular system. So that's what we mean by the food system, all the different parts of that web that make that work. And also Jason tissues, like a freshwater climate to farmers, livelihoods, food, safety, those sorts of things. So we want to start by by defining that term for you. So when we think about our current food systems, in many ways, they're extraordinary. They have been trying to meet the challenge of feeding about a billion people right now. They've been designed towards efficiency. They're enormously complex logistically. So there are many ways in which we have designed food systems to meet our needs up until this point, but I think we have come to increasingly realize that we have designed for a set of priorities that are not going to serve us into the future. We've designed for four calories to feel full Rather than nutrition, and we have about half of the world's population, malnourished, either undernourished or over and nourish, we've designed for yield rather than regenerative systems. And yet, about 25% of global greenhouse gases are the result of agriculture and associated land use and about 70% of our water resources are associated to agriculture, right? We've we've prioritized absolute growth, rather than equitable engagement. And yet we understand that physical and economic access to nutritional diet isn't is an equity issue at its core. So we were thinking through how do we actually redesign food systems, and that's really our North Star. The way I would articulate it, the way we put it together is that we want to accelerate the transition for food systems that are designed for positive social, ecological and nutritional impacts. And all of this in the spirit of the Sustainable Development Goals. So he attributes about I mentioned ecological prioritization of regenerative impacts around land at forests water, biodiversity, social stewardship, so shared prosperity and risk issues of equity, which are deeply at the center of food systems, health and nutrition with the production to nutrition, enabling greater access to and consumption of healthy food. And then I'll land on this final point around resilience. And I think this is an issue that has come really to the fore during this scope to pandemic where we have understood that we need to build resilience in our food systems, we need to make them more able to adapt to the sorts of shocks and threats that can really disrupt the global food systems and impact our ability to access food and in a safe way. So I'll put it there. I'll turn it back to Carrie.
Thank you so much, Lauren, for that overview of the vision and, and with that I want to pick up on on what Marian mentioned and what Lauren you were talking about, and We have three themes, as Marian mentioned in this food breakthrough track. And I'd like to turn to Emma to talk to us about the first theme for this track. And the title of that theme is how much might we use AI and ml to establish the correlation between growing practices and the nutritional value of food and create that connection between the two. So, Emma, if you can help us unpack this theme for our participants and explain it further for us? That'd be great.
Yes, so we're very interested to see solutions that indeed provide this correlation between how we grow food and the nutritional content, the density, the value that's contained within that food. And as Lauren just mentioned, there's a whole range of how we can actually grow food. And unfortunately, we're currently in a system where most of the food that is produced is in is grown and convention. Always that tend to be very depleted of destructive and damaging, including damaging to soil health, which has a connection to the nutritional density. That's something that is not clear today, and has widespread evidence and solutions. So that's one of the reasons why we're, we're interested in that area. And we want to see the shift towards more regenerative practices. So we think about the underlying why for different stakeholders. So there needs to be a health rationale, an economic rationale, an environmental rationale. And we can look at parallel industries even and solution sets and an often having the health case and nutritional case is something that speaks to you as an individual. It speaks to the food companies who are trying to provide nutrition, the best nutrition for people around the world. speaks to cities and authorities who are trying to take care of citizens. So we believe that if this case is very clear, and that link between growing food and a regenerative way, taking care of soil health, taking care of biodiversity, which also means the diversity of what we're eating right now we eat a tiny, tiny sliver of the thousands, hundreds of thousands of edible species, varieties of food that's out there. And so if we're able to make that clear link, then we hope that we'll be able to create clear incentives to see a shift across all the different food system actors who play a role in shaping what food is available for us around the world and as a consequence, helping to harness the power of food. So it's actually a force for good and digital capabilities and technologies and what we hope comes Through this challenge, can showcase and accelerate all of that. So hopefully that was a solid overview of why we chose this theme as one of the three, and the importance that we see in enabling the shift.
Thank you. This was very, this was a very nice overview. And I'd like to follow up on that and ask you an additional question about this theme is, what do you expect submissions to look like? Everyone that's listening now is interested to apply and, and, and pitch their projects. So what would a project under that theme look like or achieve in order to win?
Yeah, yeah. So just to dig into that a bit more. We're looking for approaches and technologies that can allow us to understand the implications of how something is grown on its nutritional value. And we're welcoming submissions. build on the quantitative or build the quantity of evidence rather than evidence based on growing practices that translate into the most nutritious products for human consumption.
Wonderful, thank you so much. And with that, I think we can definitely expand on that in the q&a. But I'd like to cover the second theme with Michael. And the second theme asks, How might we use AI to design resilient food systems that better anticipate adapt to and mitigate shocks and shocks could be any type of shock, whether it's climate or market shocks, etc. So Michael, please, please unpack this theme for us and so we can understand it better.
Yeah, I think we're all living in today. living through the pandemic, it is clear that there are so many disruptions in our system. And you can ask the question, weren't there any signal Prior that would have given exactly sufficient foresight of us coming our way, and how we might actually have more proactively address the growing risks, mitigating the risk, and subsequently when the pandemic half if including that all the consequences on the food systems around the world, how might you better ultimately upgrading disparate supplies in connection with ultimately the demands? So the second challenges we need to think through? How might you use machine learning and AI to build, design and support resilient food systems that are much better prepared for probably more and more shocks coming to local, regional and global food systems? Or if you think about it's one large rep of systems, how much you use data, and subsequent machine learning and AI to be better prepared, and when it actually happens? How can you better deal with the challenges as a result of as of today? I do believe that our leads are clear mismatches between the available supply and the demand that is very different today in different parts of the system. So the question is, how might we better use machine learning and AI to better prepare, mitigate and address the need for resilient systems?
Thanks, Michael, can you share a bit and yes, Lauren early May
I just compliment what Michael has said, because I would say all of the three themes that we've chosen are important and urgent. And this one may be even more urgent than others. I think as we see COVID-19 clearly impacting all of our sectors, including our food systems. We will see disruptions in a few months that are occurring today. That will have huge implications. For the future, that if we can start already to use this type of power of artificial intelligence and machine learning, we may be able to respond better in the future. I speak, for instance, about the ability for farmers to access inputs right now, the seeds, the fertilizer to be able to plant things to grow for the next season. The value chains that are disruptive to get those inputs in place right now in some cases, may actually affect the the yield the ability to produce a few months down the line. I'm using that as one example of many to illustrate that this is not only about sensing, I think Michael took this broader view. It's not only about sensing, pandemic coming, for instance, but also around anticipating what might happen within our food system based on the data that we're able to see right now. So thanks, Michael. Just wanted to complement that and add that horse
into learning. Great. Thank you both and Michael I also wanted to ask you about the specifics like, what are what are the projects look like to win that theme or to apply for that theme? Lauren gave one example. But what what are other examples that can apply to the scene?
I think one of the challenge that we're faced with in the most magical world of food systems is that there are so many different data sources out there that are not connected. So I think one hopeful outcome of this challenge might be, are there ways to basically access available presence exist existing data sources, in ways where you can immediately look at the power of multiple data sources brought together and generate insights that are very, very helpful to deal with both the anticipation as well as the mitigation to think critical over here so we're looking for ultimately the application of machine learning and AI on existing data? sources. So this is not just about from imagine that but knowing that different data sources all over the world are ultimately not connected. That's bringing them together through ultimately the use of technology. And then showcasing of what's the benefit of that might be. So not just theoretical, but immediate saying, Here's how you can do it.
Great. Thank you. And that point on data actually takes us to the last theme, the third theme of this track, and I turned to Marin and the theme. The third theme asks, How might we use AI to leverage these diverse data sets that Michael was talking about, but instead have to create more resilient systems to develop a more complete picture on the social ecological and nutritional impact of the food choices that we make every day? So Marin, please, if you can explain this topic, and Give us some examples, that would be great.
Absolutely. And I'm going to ask you to close your eyes and think with me, you're standing in front of the shelf at a farmers market. Or you're choosing your food in an app, or as a consumer, or you're creating a recipe or a formulation, choosing between options as a chef or a product developer in the food industry. Every one of these, in each of these situations, all of us, we have a voice, we have the power and we have an opportunity, an opportunity to vote for the world that we want. However, it's currently hard, almost impossible to vote for the world we want. You'd have to have a PhD to understand the impact socially, ecologically, and nutritionally of your choices while standing in front of that shelf, sitting at the bench up or standing at the countertop and even if We believe that there is a gap in data and an information on the impact in availability and an access. Or if we look at the same thing from the bright side, there is an opportunity here. And that's why we're calling for projects and solutions that can help provide a more holistic, complete and systemic overview of the impact of our choices. By bringing together the necessary data sets across the spectrum of social as we mentioned, economic, ecological and nutritional impact, organizing them, making this data available and making this accessible and understandable. Because we believe that the availability of such reliable transparent and unbiased data and information can have a catalytic effect on the transition of our food system from a more monolithic use. driven the neural system like Lauren talked to towards this holistic regenerative food system that we envision.
Wonderful. And I'm imagining submissions in this, in this theme could vary from data collection to data visualization. Can you elaborate further on what these submissions could look like? What what is what is the limit here? To apply?
I don't know. I, I wonder if there's a limit. surprises, I would say, but we're looking for solutions approaches and technologies that create more holistic systemic understanding of the impact of the food choices we make. That makes us understand and enables us to make well informed choices in the trade offs, for instance, that we accept, and helps us avoid the perverse sometimes negative effects that we can have because we miss out on Certain system indicators. So we're looking for projects that that have access, or indeed, combine comprehensive data sets, some data sets to demonstrate the potential of this in AI applications.
Thank you so much. So so far, we've gone through the three different themes. We've expanded on them further, and I invite you all to ask more questions about the themes in the chat. We'll transition to the q&a in a bit. But before we do that, I want to turn to some more of the logistical aspects of the submissions and turn to Lauren and ask, what are some of the principles that you invite people to consider when preparing to submit a project? What are you looking for, other than the project itself?
Sure, well, we've been talking across these three themes which are the what but the How also matters, it matters immensely. And so let me offer a few of the principles almost the ethos with which we offer and these these prompts and invite your submission. So the first is that we're taking a systems approach. These are systems issues, that we should be tackling underlying challenges and not just symptoms of those challenges. The second is that we're not just about reducing harm, we're actually trying to create a positive Good, that's why you hear us talking about regeneration. We're not just talking about zero carbon emissions, we're actually talking about carbon drawdown as an example. So we encourage you to think through that positive future that Mary was mentioning, how do we actually reverse course where we need to reverse course and get there. A third aspect of this ethos is around agency and inclusion. They have ownership transparency of algorithm design. All of these are at the center of equity. And there are some data gaps there's access to data challenges, following access to their own data, gender disaggregated data. These are the sorts of elements that are at the center of the type of project that we will consider to be inclusive that we will consider to be meeting those the principle of equity. And so as you're thinking about your approach, if you, for instance, design teams would encourage you to take a human centered design approach really designed around the people, or certainly the problem you're trying to solve for and the people who own and represent that problem, and would encourage you to have diverse teams across different sectors or multi stakeholder if you know, you have people from industry, people from policy, farmer, etc. Where you can recognize that not all teams will see gender diversity, and also racial diversity really want to get the power and the promise that a range of perspectives can bring to the design of these proposals. Thanks
Thank you so much. And with that, I'd like to turn to our hosts it you and ask them to share a slide with the three topics just so they're in front of us. And while while we get that done, I have the pleasure of introducing Amir Benny foe to me, he's the chief innovation and growth officer at XPrize and Amir, please introduce yourself to the crowd.
Thank you very much, Karolina. And it's a pleasure to be here. So my name is I'm your benefit me I work for XPrize as Chief Innovation Officer, but also I'm the Program Chair of the content for AI for Good Summit. It's been a pleasure to see the development of the breakthrough. Breakthrough tracks and today we announced the the breakfast facts for the foot systems. Our goal is to give a chance to every one of you to propose either a project or a startup or a documentary or grant research. Whatever makes makes sense. fields into these three topics that was highlighted by our presenters today. Topic number one is how might we use AI and machine learning to establish the correlation between growing practices and a nutritional value of foods? topic number two is how might we use AI to design resilient food systems that better anticipate, adapt, or migrate sharks. And topic number three, just announced a few minutes ago is how might be used AI to leverage diverse datasets to develop a more complete picture of the socio ecological and nutritional impact of food choices. So you can choose any of these three topics and submit a proposal. The goal of this proposal is not to launch a startup or to pitch an idea is for you to get a chance to have the brand trust and all the access that we have gathered to look at these proposals and help refine them between August 14 and September 5, which is a time frame. They're going to be Looking at going to give those tests again for precision. But as you propose those those subjects, the breakfast and the experts will look at them. And we're going to be choosing three topics and three themes. So nine total that we end up at the AI for Good Summit is some tablets have a chance to be workshopped and discussed, the goal would be that those topics will be launched as a project in 2021. So this is an opportunity to have a global observation on those ideas have the support of the brain trust and the foot and AI experts around that and have a chance to September when the AI for Good Summit happens to have them workshop and be discussed and get the support needed to be launched later on. So this is a very comprehensive set of goals. And we think and we have done in the past over the past years, and he has been successful. And this is the time for you to give it give us your opinions. ideas about what topics you choose and what themes or project you think are worth building and developing on a global level, and get a chance to have the brain trust in the expert to support you in doing so. The deadline to submission, there's a link after that for for the URL to get your submissions. And you can snap a picture of this or type it down or write it down. The submission deadline is August 14. So we have pretty much about a month for you to think about it. Maybe ask questions. And as you submit the braintrust will look at these submissions between August 14 and September 5. And then in the meantime, connect with you and give you some support in refining them. Our goal is to select again, three main themes to be presented the AI for Good Summit and during that time, we'll have three days of workshop for each finalist or winner. If you Call them to get a chance to be lunched. Happy to answer more questions. This is, this has been a pleasure. And we're delighted to have the braintrust guide us through this journey, and to find the intersection of AI and data can impact global food systems.
Thank you so much, Amir. So you've heard that you have a month to submit ideas and projects under the three different themes. We are getting a lot of questions already in the chat. So I'd love to turn it back to our panelists with some questions. And the first one is a pretty high level question to get into the specifics of what is the holistic food system we've been talking about that for for almost half an hour now and maybe a nice overview of what we considered that a holistic food system can help and if I have any volunteers from the panel, please raise your hands are all very capable of answering that one Marin, please. I'll
speak to then and happy if the rest of the Pinterest chimes in. But typically, we tend to look at systems analysis, we tend to actually divide them into subsystems. And by either trying to understand or trying to design for, or optimize the subsystems. Our conviction is that we're optimizing the food system. Unfortunately, that is actually not how, what is called complex adaptive systems, just like nature systems, just like nature work. Actually, what happens then is you might often have perverse effects and I think Lauren alluded to this with for instance, the focus on yield. If we think about focus on yield if we only have a monolithic focus on you, but we do not look at the social impact of optimizing maximizing yield? Or we do not look at, for instance, what are you seeing back, then we create a food system that, for the indicators that we are tracking might have a very positive impact. But for some that we aren't tracking or aren't tracking yet, because we've also evolved in our understanding actually has perverse effects. And food that is interesting because for instance, biodiversity wasn't really on the map as much until recently, until the publication's basically last year until the UN ga last year. So we had been working to optimize things for water use and optimize for soil for instance, but we weren't necessarily looking at biodiversity, one can hypothesize that there are other indicators that we're not yet looking at today. So when we're trying to establish like what is a good well functioning system, we want to make sure that the indicators that we look are, are as complete as possible and take as wider range as possible. And if you think about the role of the system, the role of the system ultimately is to provide nutrition, but also to provide livelihoods to provide flavor and experience a means of bonding and a social identity. So the food system in that sense is very, very broad. And thus, we need a very broad, holistic set of indicators that we track and who wants to chime in.
And you go, I was gonna, I was gonna
tag on in case you Okay,
let's see. Let's see. So for me, I think of it From a health standpoint, like what is a holistically healthy food system, and it's being able to provide everyone nutritious food, but we also the way that food is grown, the way we manage the waste and byproducts is healthy as well because the reality is today, we can live this irony of enjoying a perhaps we see it as a nutritionally healthy plate of food, a salad. But actually, the salad leaves have been grown in ways that are using finite resources to grow and using pesticides are polluting the air and having negative health impacts on the farm workers. So that simply isn't going to allow people and nature to thrive long term. So the great thing is we can design systems that are optimize and maximize all these co benefits. And rather than looking at it as either wars, how do we create and design systems that maximize the ends?
Right, thank you so much. Lauren. Do you want to add to that
there's very little one can add to Marin and Emma describing holistic food systems, I will only say, and I'll take a cue from there to close your eyes. And imagine that you're going to where your food was produced. Okay, you can open your eyes. How many of you imagined a farm? Probably boats. How many of you imagine the ocean? How many of you imagine a vertical farm or urban farm I also want to invite us to think about the ways in which we can reimagine production or we can expand our understanding of production. oceans are another extraordinarily important part of our ecosystem and food from the sea, for instance, is part of this. So expand your thinking, if you haven't already done so around what food systems are in and holistic nature.
Wonderful, thank you all so much for this. And I want to tag on to this. Some of your answers. You mentioned indicators for what is considered to be a holistic food system. And one question came in specifically for Lauren and Emma, because you mentioned goals, global goals, such as the SDGs. that are that are, we're looking for to as we designed this and as we detail all the fields, and the question is, what are the specific targets within these goals? And maybe we don't have the targets right now. But can you point participants in the direction to drill deeper into these goals and understand the numeric quantitative indicators that they should be working on? Through their submissions.
we're learning, I'll take that one and Mo, please feel free. I think that we are open to where your project may achieve goals in line with the Sustainable Development Goals and the targets underneath those goals. For those of you who are familiar with the Sustainable Development Goals, you'll know that number two is zero hunger. That might be the one that seems most obvious. But actually, if you look a little deeper, not too much deeper, you'll see that we probably touch 10 plus of the Sustainable Development Goals and how we've designed and described these challenges. And so I don't think we want to be prescriptive, just to say that where you can demonstrate that there's alignment with some of these broader targets, we welcome them, and where you can also demonstrate that you're in alignment with some of the principles that we've also said here today. Whether or not you're explicitly giving us a SDG target, around regeneration around nutrition. I think people will take that into consideration as well, you can show some demonstrable contribution to, again, the evidence base or the positive incentives that would push us towards some of those desires aspects of our future.
Adding to learn how SAS in my world hours things through, on the one hand, How big can you think because the challenges are global, they're truly gigantic. But at the same time, you have to break it down into ultimately addressable steps. So thinking all the solutions that you're going to be thinking of and ultimately proposing is to find this balance between their global challenges that manifests themselves in different ways in different parts of our ecosystem. And at the same time, you have to Movie bites the elephant, one bite at a time. And don't get stuck into a solution or a suggestion, Jeff might, in your own mind be too small, or just be applicable to a specific region or a specific country. Because you'll learn by doing xrv help to find ultimately actionable solutions that will give us some more insight upon we can then build to get to ultimately the broader global set of challenges because there are different.
Great, thank you, Michael, I'd like to stay with you on this next question, because it we've mentioned data as an essential part of all of these themes. And one question came in looking for suggestions for solutions to the lack of accessibility to that data and they mentioned the digital divide and countries. So how can we address these issues through AI when data sometimes Isn't there or there's this digital divide between communities within country or between countries.
Challenge is complex. But I would always say from start with what can be addressed. So I think what we're trying to address here are the challenges in the food system. And then you can intersperse them or relate in with all these other set of very real, very relevant challenges as well. But the moment you're trying to solve multiple problems concurrently, it becomes very, very wicked very overwhelming. And I think we're all at risk of just getting stuck in acknowledging how complex it is. So instead of just saying there are multiple challenges, let's find the one for which we have the tools available to us today. So we can start to work at least on that challenge versus to spend time on articulating I mean, what else is out there? And if only we would have those elements as well, the world would be a better place.
Thank you. If anyone would like to add to that, please feel free. We have a lot more questions to go through and when once specifically touched on the themes that we discussed, and one question is, how did we select these themes? And we talked a little bit about that. But if we can get more into the details of why these three themes specifically what did we leave out if anything? How did we make these difficult choices? Any volunteers Lauren?
merit I was looking at you but I
first saw through.
We started by identifying our Northstar what what are we actually trying to achieve? through the power of this platform, the brilliance of all of you XPrize foundation it you What can we generate through this process that we may not be able to generate otherwise? And that's where we really set out that that broad vision and broad set of goals that I mentioned. Then we said, Well, okay, but how do we actually take bites out of that is Michael said you can't address the whole. And even though I think the themes that we proposed are rather large, we said, Where is there a, a missing element in the transition towards this improved food system that could be aided by AI and ml, and that it's not happening yet, for whatever reason? Maybe it's not happening because the incentives are not right. Maybe it's not happening because the data are not there, or we're not actually learning from those data to incorporate that into our food system design. So we took as a group, we took a long time. list of potential questions and have hashed through them and came up with with the set that you've seen in front of you today. I will say also, and I think I'm just scanning a little bit through the chat as we talk. The four of us are currently the face of this brain trust. We do not yet have the diversity of thought of experience that we need. And we're building that I just want to say that explicitly because there are some who are asking about farmers in the group, maybe some other my co Bray trust members are farmers. Clearly we need to continue to build out so that we can have a robust response to the submissions that come in.
Very compete response. I think if you if you analyze the three the three questions that we asked, there are one inside systemic because as Lauren said, We identified a Northstar. And we want to make sure that the interventions are systemic in nature. But another insight in all honesty, they're also though very ambitious. They're realistic for someone to submit a proposal in, in the next few weeks, and for us to work on in the next month and year to come. We started off with even more audacious broader challenges. But if everything is missing from the data set, all the way to definition, technology, etc. Then it was just too big of a piece of the elephant was all of its, its back end to bite off and it was a little bit too much to put on the grill.
I'm starting to wonder what sort of food Michael does provide his Googlers biting off elephants over there, Michael
Great. So we have theme specific questions. Now we started with some broad questions, but now we were getting questions about the one about the application of AI. And the questions How can AI apply to local production instead of like mass scale global production?
Yeah, it's a great question because it sheds light on the different scales of food systems. And we really need that diversity and local is important in terms of connecting what's available near consumption hubs, like studies, for instance, let's let's eat what's being grown nearby if it's available and appropriate. And for that reason, I think there are lots of opportunities to be applying AI to designing those systems. To measuring impacts and and specifically when it comes to the first question, in particular, that's about growing practices and nutritional health, if we look at the linkage between soil soil is the profile of soil varies from place to place around the world. So by its very nature, the way that you use those tools will need to be tailored to the places aren't need to have capabilities to tailor to different places, based on the characteristics
if I, if I if I met
I think it's actually embedded in the questions if you if you think about all three
And this is why we wrote the North Star, our vision is systems cannot be healthy. If they do not operate at all scales, they're not this nested hierarchy that operates at all skills because indeed, we have a question that is linking growing methodologies to nutritional content that brings scale into the picture, then there is because one of the things that we've been doing is we've been growing our food in a way where we force an ecosystem to output only one crop out of the ecosystem year in year out, rather than regarding that ecosystem as exactly that as a system that produces more than just one single mono crop. And to get that system to produce these outputs, we therefore had to add all these inputs we had we were impoverishing the soil we created runoff etc. And you can see how the system breaks down. If we think about resilience in the second question, a system is only resilient if it operates on skills, actually, that diversity is what creates resilience. And then again, if we look at the impact, how can a food system I would wonder, have a positive social impact if it is only reliant on mass scale and centralized production systems, I think it's an all three of them, if you if you take a closer look
wonderful, thank you for that. And I want to jump to theme two, we receive the question about the most restricting global policies to the deployment of wide scale solutions, such as the one we're asking, asking for relating to the second theme, especially with countries having different metrics on the social, ecological and even nutritional impacts of food. So for example, that question specifies that some additives could be legal in one country but illegal in another country. So how can those submissions that are trying to create that link, abide by or be in line with these changing global policies.
my starting point would be to start small make it relevant in a country or in a region. And don't consider all the constraints that you might face if you take a global, because the solutions are going to be diverse, they're going to be locally relevant. And I've seen very few solutions that actually work either globally. And if they do, they probably work at a very slow pace. So all these different considerations that you have to take into consideration. But I'd need to start with how can you use data? How can you use machine learning and AI to ultimately for the second challenge to create more resilient food systems? And that's where it really starts. And just to acknowledge that there are clear regional differences, different policies, but don't be constrained by the difference between the focus on what you can do in your specific area of choice.
Great, thank you.
Does anyone want to add to that before we jump to the next one? Right, great. I wanted them to theme three. And this question is interesting because I think it touches us all every day. And the question is, how can AI help nutrition be more clear for everyone and this person who has this question specifically asks about some foods that are healthy one day and unhealthy the next in the media and what we hear? So for example, like eggs are good one day eggs are bad the next day. So how can AI support a better understanding of what is nutritious once and for all?
We've got some smart people on the line asking the hard questions, which is terrific.
I will offer an observation and a challenge. The observation is that I think what this question is getting at is How can we establish a scientifically rigorous evidence base behind what we're doing? And I think that that is what we want to pursue. We want to have science, we want to have evidence at the core of how we, how we design solutions moving forward. My challenge is to put that question right back to you. How could you incorporate some kind of solution within the project that you propose to us? This is tremendously difficult. nutritional advice and studies are deeply influenced in various ways. And so I think it's a, it's a very important question. I would propose to have the answer, and let's grapple with it together.
If I can add to that, Lauren, it feels to me and I'm not a nutritionist. I'm not a dietitian. I don't play one on television, is that the true science is actually Pretty well established. But what we get confused with is the noise, probably about two to 5%, where different diet proponents are just creating noise or attention for their beliefs. That's the core nutritional science, I think is very well broadly accepted. So I would actually believe that the role of AI ml is not so much about the further enhancement of the nutritional science, but more which data sources that are evidence based and are supported by key universities around the world could we use in order to actually work move forward with the challenges posed to us today?
And I want to build on that question too. And it's, it touches on a concept maybe that a new one on personalized nutrition, so can we take it even further Can we use AI and ml? And do you know of cases where it's already being used to personalize nutrition either for a person or an ecosystem? What is a good crop for that ecosystem? specifically?
I think I think there there are actually two ways of looking at this. One is indeed personalization. And yes, even though the broad science of nutrition is pretty well understood. If we look at personalized nutrition, we're still making huge strides. And yes, there are some startups, for instance, that are working on the microbiome of pro athletes, and trying to analyze the microbiome of pro athletes and identify what are some specific markers and nutrients. On the other end site, there's also the other end of the spectrum, which is if we look at the resources that are at our availability, and this is also due to the fact that we use Only those 12 crops. But beyond that we actually know less than 5% of the plant world, the plant kingdom, so to say, we know less than 5% of the phytonutrients to plant nutrients that are available within the food that that's readily available all around. It's grown everywhere. So there's a there's an opportunity on one hand side to have more personalized outcomes. But there's also an opportunity to start to better use nature and the diversity of nature as a source of solutions for a healthier nutrition. And not just a source, not just a singular source today, we often go to we discover that that's taken. I'm not saying this is healthy By any chance, but caffeine is a phytonutrient. We all know it, we all know its properties. So therefore, the word starts to scale coffee. What we can go to tomorrow is to say well, there's a Phyto neutral Caffeine, and it can be found in this humongous set of ingredients. And therefore, we can also look at Now which one of those is actually locally relevant from an ecosystems point of view. But also from a cultural point of view. I think there is still a lot to be discovered, and we don't have all the answers. There's 110 of you on the line. Now, we hope you have huge ecosystems. That's why we're here. We want your help.
Great, thank you for that, Marian. And we're almost at time. So I have one last question for the group. And it's specifically about how do we assess the effectiveness of the submissions that we get? And I think that's a great question to end with. What are we comparing these submissions against? What is the benchmark that we're using as the brain trust? What will look good or bad in terms of the submission that we'll get? And I think that's a great question. And
so any volunteer
Also two things. One is surprises. What can you offer from your brilliant perspective? And the second is how can what you propose the tangible in some of the ways that Michael suggested.
Any other thoughts on that last one?
Yeah, it's it's hard to give a set of, again, indicators, because the questions are broad. And the proposals that we might expect that we're hoping to get ought to be very broad. One of the things that we will be looking at is also the timeline, the feasibility within that timeline, which doesn't mean that an ID that is submit might not be brilliant, but we will also be looking at If this is one that can really be addressed within the forum in September and in the following 12 to 18 months and have an impact at that scale. But I mean, I'm speaking totally out of school here. But if you're if your submission isn't selected as a winner, it doesn't mean that it isn't being seen. And if it is really interesting breakthrough, but just doesn't fit the timeline. I think at least I could speak for myself, we'll we'll find a way of collaborating on it and making helping to contribute to making it happen.
Thank you so much. So we're right on time. I want to thank all my panelists for a great discussion that we had today, Mary and Lauren Michael and Emma was really great having you and of course, Amir, thanks for jumping in there. Thank you to ITU for hosting us and hosting this webinar. And we look forward to receiving all your project submissions. And with that, I'd like to turn it back to Fred to close this out.
Thank you very much, Karolina, and thanks for a great session. On behalf of ITU, I'd like to thank all the panelists and for your great job moderating, and for all the participants are connecting, and maybe a couple of housekeeping issues. So we have an action packed week still to come. Tomorrow we have a session on AI for Africa. And we're going to I won't spoil the surprise, but I highly recommend you connect and see how that's working. And on Thursday, we also have a session where we'll be launching the global data portal pledge. And this is a project that's coming under the global initiative for AI and Data Commons. And we have two last minute surprise speakers there. One is a Nobel Peace Prize winner, Mohammed Yunus and also Anya Calderon, who's the executive director of the Open Data charter. So if you're interested in this topic, you're interested in data On how data collaboration can help solve big problems, I think you'll be interested in that session as well. And I highly recommend you tune in on Thursday. And with that, I'd like to, again, thank everyone on the panel. thank our sponsors or partners, X ray is ACM co convener, Switzerland. And thank you very much and I hope to see you tomorrow on Thursday as well. Goodbye. Thank you.