 I have the pleasure of welcoming and introducing the first presentation this afternoon. The speaker to start us off is Scott Nism. Daily Catch is talking about the use of AI for food narratives in small scale aquaculture systems. Hi, I'm Scott Nism. It's my pleasure to be a part of this incredible event focused on the application of artificial intelligence for a digital planet. I'm here today to explain how our company Daily Catch is using AI to improve economic outcomes for fishermen and other primary producers around the world. I'll be available to answer questions after this presentation and look forward to engaging with you all. Before I introduce the company, allow me to share a bit of my background. Prior to co-founding Daily Catch, I spent a dozen years working at the intersection of technology, natural resources, food and agriculture, both within the US government and in the private sector. I have an academic background in natural resources law and policies have spent much of my career focused on ocean, coastal and fisheries policy. My co-founder Chris Rodley is the founder and CEO of Snap IT in Nelson, New Zealand. He has spent over 14 years in business and startup leadership and has twice been a finalist for Innovator of the Year in New Zealand. For the last decade, Chris has worked extensively on government and fisheries technology projects and implementation around the world. With that, let me turn introducing Daily Catch. Daily Catch was designed to solve a problem. Fishermen and other primary producers are some of the hardest working people on the planet. And yet their immense efforts are often under-compensated. The existing food and fisheries system creates no economic incentive to deliver transparency and in fact, largely incentivizes a lack of transparency. At the same time, we see government efforts to impose electronic monitoring requirements in fisheries being met with a fair degree of reticence by the regulated community. The range of objections from privacy to cost to government and efficiency are levied against government oversight and so these policies continue to languish. Moreover, we have a situation in which consumers have become almost totally disconnected from their food. They have no idea that many of their fishermen are struggling to make ends meet and the lack of transparency in the system means that the general public has no clue about the economic, social and environmental impacts of food production. Thankfully, we're seeing a fundamental shift in society's attitudes towards food and agriculture. We're seeing a shift in consumer preferences as millennial and Generation Z become the dominant consumer groups and we're seeing shifts in corporate and political behavior toward more sustainable practice. Even with these emerging trends, there's still a problem. How do we get producers to recognize the value of what they're doing? How can we empower them to view transparency as a business advantage? Enter daily catch. We're pioneering transparency as a service to help food producers realize their true value. We're incorporating a range of inputs from live stream video to censor data to convert producers' transparent practices into compelling marketable content. We believe food producers are natural influencers with truly authentic stories to tell. Until now, they struggled to take advantage of the digital economy. With daily catch, we're giving producers the tools they need to take advantage of the creator economy and find their thousand true fans. What does this mean in practice? It means that we take raw data from live stream cameras, temperature and saline sensors and satellites and convert that data into useful and interesting information. In essence, we give producers the power to bring the public along for the ride and the ability to delight potential customers through engaging data visualization shared through social media. Most importantly, we create a sense of community by allowing direct engagement between fishermen and customers, both through our food focused daily catch social platform and through integrations into other social and marketplace platforms. So how does AI fit into the equation? We recognize that fishermen and other primary producers are very busy people. We also understand that some of them may not be that tech savvy. Recordingly, we're employing AI to automate tasks to make it easier for fishermen to take advantage of the digital economy. Our system translates raw transparency data from video feeds and other hardware sensors into engaging in educational content that we believe will give fishermen a business advantage in the marketplace, a way to distinguish themselves by doing the right thing. In early trials in New Zealand, we found that producers were eager for new ways to engage with potential customers. We've seen similar interests in other test markets, including South Florida, with producers eager to seize on the power of social media to gain more direct access to their customers. And that's what it's ultimately all about, leveraging the power of transparency, network effects and AI to help fishermen succeed in the digital economy and ultimately to incentivize behaviors that lead to a sustainable and digital blue planet. Thank you for your time. Thank you very much, Scott. Thanks for sharing your video and transparency. I guess this is one of the biggest questions we're working on and intelligent capture to enable producers to share their stories with the world. And what's your consideration about this being a double-edged sword, especially with a poorly informed city-based community? Do you think it is a double-edged sword and do you think we need greater standards potentially to allow people to tell such stories in a more realistic environment? Well, thank you for the opportunity to state. You know, early on in my journey here, I did struggle with that, whether it's a double-edged sword. And my ultimate conclusion has been that, no, I think that there's something to be said about being able to see the process, even if it's warps and all. I think that we're in a position where for much of human history, we had a much closer connection to the provenance of our food, to the ways in which our food has been processed and brought to our tables. I think that our disconnection from that process has ultimately led to us living lives that have led to unsustainable practices, that have led to unsustainable habits, et cetera. So I think my ultimate conclusion is that if we can incentivize behaviors and also ensure that people have a much more direct sense of where their food comes from, they're going to understand and likely model and change behaviors on the basis of the types of activities that they want to incentivize. So I actually see it as being more of an action-forcing mechanism than as one that's going to facilitate and lead to proliferation of bad actions. Okay, thanks, Scott. I'm going to ask Anton Ellen Book, if he has a question. Thank you. Yes, and the question to Scott is related to the... So if I am a producer of not only fish now, but in the future also images or other data, sensor data, how will I ever become a beneficiary of other people's users of my data? So you talked about salinity. I know there are some interesting to get high resolution ensure salinity profiles with turbidity and other data. So how do these fishermen or fish of people, they are incentivized to share the data, but what will they have to be able to get in return? How do you work with? That is a technical question. So do you work with persistent identifiers to information they share and also like a more social economic question, what is the value of the data and how does that become transparent to a data provider? Thanks, Anton. I will answer the second one. That question is something that's really important. My ultimate goal with Daily Catch is not only to provide a platform for social interaction, that's really merely step one in this journey. The ultimate goal is to really be able to drive greater value to producers through their stories. And in my view, fishermen are frontline observers in the climate crisis and they're some of the most, but for their presence on the waters, we would be without large categories of data. So we're in a position where ultimately what we would like to do is be able to take and large quantities of data from either local specific action specific or actually global type of data sets and start to find the types of data returns and reports and information that we can share to adjacent and non-adjacent sectors that would be interested in that data. And my view is that that is the type of resource that is extraordinary valuable to numerous sectors around the world, but that is really requires there to be effectively profit sharing back to the communities and to the fishermen and others for the use of their data. So what we effectively then are doing is taking yet another element of the fishermen's story and that is the collection of data from their vessels, et cetera and giving them value for that as well. So we start with the stories, we start with kind of the lowest hanging fruit which is the images and the data visualizations from the individual and then we start to move towards higher and higher levels of complexity so that we can then start to share the resource and value back to that. Thank you, Anton. I guess Scott, there's an element here where you will start to build up patterns within the data to help people make decisions. Matt Walsh has got a question for you. Thank you. Yeah. How are you using High Scott thanks for your presentation? How are you applying image recognition in your model and how does that drive value back to the producers? I think that's one of the biggest issues sometimes with producers is that as you said, they put in an awful lot of effort and undertake a lot of risk but receive very small returns considering what their risks and fail particularly in the fishery sector. Sure, excuse me. So in terms of pattern recognition, you know, I say in the presentation that one of the goals is to really facilitate the ability of producers to identify the thousand true fans and what I envision for Daily Catch to become down the road is really almost kind of like a dating site for food. What we wanna be able to do is facilitate the kind of relationships between producers and consumers that you would have had or your grandparents or great grandparents would have had someone that is much akin to your digital high street or digital main street that you have formed a relationship based on your preferences, based on your value systems with a particular producer, one that you're more likely to over time than want to support and provide support even during lean time. So what we're trying to do is to take large data sets in terms of consumer preference over time, large data sets based on practices from the producer side and then marry those and find those types of those patterns that really are going to correspond to values and really going to correspond to repeat purchase behavior. And what we're going to be doing that is through our marketplace side of our site, but also pushing it out so to allow within the other social platforms to identify through those engagements as well, who is most likely to be able to purchase from or be interested in the types of products coming from a certain producer. Thanks very much, Scott. I must admit I can see that the connection between the consumer and the producer is largely broken by the way the market chain happens. So it's very exciting to see you building that idea of the thousand fans and so on and people realizing who produces that food and building those relationships. We need that.