 There's three specific SDGs that we really want to focus on on erudite. First off is the obvious one quality education. We know that direct one-to-one instruction is the most effective form of learning, however, at an average cost of anywhere between $40 to even $100 an hour for private tutoring or a workshop if you want to learn new coding skills to gaining the math that you need to even get into high school. That is a financial barrier for most of us in the world. It is a financial barrier for the 1.8 billion global youth, most of whom live in developing countries, as well as the 1.6 billion living in poverty today. So our main goal is to make sure that we are lowering those financial barriers and making quality education accessible and affordable and scalable. The second SDG goal is equity. So we want to make sure that regardless of your background, your socioeconomic status, your gender, that you can connect with other experts around the world and across your communities to really tap in to that collective wisdom. And that shouldn't matter who you are and what you look like. And then third SDG that we really focus on on erudite is that of decent work. So we believe that by cultivating this expert network for you, that not only will you gain academic growth but access to meaningful work and to increase your earning power that will in turn result in economic mobility for communities. We actually have two approaches to data bias, one from a societal perspective and one actually from a very technological perspective, the latter of which is driving our platform. So to start with a societal perspective, when our learners are engaging with the erudite system, we're using NLP to create up-to-date maps of their knowledge and their skills. We call these, everyone's, each individual's talent map. That talent map is very unique to you and it's very specific to each individual. And what we can do with that is raise that knowledge and skills that we're collecting up to this higher level that everyone can tap into. So in that way, that bias of education, that academic privilege that maybe someone from a higher socioeconomic status would have, that becomes unsilowed and funneled into that collective knowledge base so that anyone can tap into that. And so that's very important and how this transforms is that when we do identify experts for you as a learner, often that collaboration in other systems is not scaffolded properly. So the expert and the learner are left alone together and then what happens next? Nothing, that dialogue is unsupported. So with us, we provide coaching to those experts to pull down that collective knowledge base so that they can respond well to that learner in need. Now we do have an approach to data bias from a technology perspective and that is actually we do want our NLG to be high biased and the reason why is that we want those platinum nuggets of knowledge to be scaled to others and so we do need to have a bias approach to identifying what silver, gold and platinum so that we can ensure that if you do have a question as a learner, if you have knowledge gains that need to be filled, that we're filling those with high quality instruction. What do you think the value of this sort of convening is? For erudite, the big value is knowledge sharing. So we are a knowledge sharing company and so we want to make sure that we're embedding that mission within every fiber of our organization and that is to make sure that we're participating in congregations like this to be able to meet other companies across the AI industry who are working on similar pressing challenges. For example, healthcare is actually very relevant to us as an education and learning company because you can imagine both fragmented markets. The buyer is rarely ever the user and so we learn quite a bit in terms of how AI is supporting healthcare and how we can translate that to other industries like our own which we're tackling like learning and education.