I was impressed. No. I was impressed. Good good one. Thanks.

first time. Close.

Okay then. Hello. I'm Rome Baharo. I'm a long time entrepreneur in the intersections of media and technology. And if it's okay, I'd like just to jump right in to the the presentation of Black and Wiki because it's really the best way to get to know me, actually. Is it okay if I go ahead and share my screen? Okay. Here we go. Alright. Everyone can see my screen okay?

Alright. Well, it's a just to know, it's a really it's a big honor for me to present this to you all, and I've said this before. I was so intimidated about joining this community for the longest time. So I I I really do, appreciate this, this opportunity to share you. I'm very excited. This is Aikiwiki sent. That's iKey. The name does come from Aikido, so that's where iKey comes from. I like to call this the win win protocol for the World Wide Web. And oops. Where am I? Here we go. So IkiWiki is, it's a platform for consensus building, and it's comprised of two components. One, there's a methodology. I call it non dual consensus theory. And it's a it's a computational system, and I call that nine by three narrative logic. I give a live walk through of how Akiwiki works. It's very psychological. It's lots of fun. I've given it to hundreds of people. We don't have time for a full presentation. So right? But in the walk through, individuals can attempt to game Akiwiki. Right? But I'm just gonna reveal all the system processes, and I'm gonna try to move through it. I can get through it in twenty minutes. But OkuWiki is essentially for any kind of critical conversation consensus building, brainstorming, you name it. It has applications in in all these areas. And this is I thought attorneys would hate AkkiWiki, and it turns out lawyers are the biggest supporters of it. So this really has wonderful applications, and a lot a lot of applications are way over my head even. But, basically, how it works in Akimiki consensus building, voting is is never used to determine an outcome of a consensus or any decision whatsoever. There's no moderators. There's no admins. There's no third party who's gonna arbitrate this outcome. There's no AI that's gonna do it. And in principle, legal contracts could be negotiated without an attorney. So, basically, all resolutions and decision making is just made user to user directly. And in this conversational game of Oku Wiki, players work through conflict and disagreement by collaborating to obtain permissions to edit the document and write the consensus. So I make a lot of strong claims in this presentation. I really encourage everyone's skepticism, but you really have to go through the demonstration to see it. But, basically, the the claim is is that, you know, a mutually resolving consensus can be reached in a way that's both psychological and computational without voting, no thumbing up or downs, no third party. And the consensus itself is gonna be comprised of everyone's best behaviors. And this is what I give people the opportunity to gain in a one on one demonstration. Just about me for me, this has been a journey of a lifetime. This has been actually twenty two years of of research overall. This was discovered by accident. Right? I had zero background in anything that led to this, but it was just interesting long story, but a lot of research has gone into this. Eight years of online field research and, into very public toxic Wiki wars in Wikipedia. I have the battle scars to prove it. And there's certain things you can only learn, certain kinds of data. You have to have it done to you, right, to see where a lot of problems emerge and how they emerge. This is, what I'm sorry. He was. This was my science adviser since 02/2007, and he passed away December 2023. I really owe a lot to him. He was very gracious with his time, and he was very encouraging with this accidental discovery that I made. And he's also famous, so some of you might know who he is. AkiWiki Parley is coded right now, so everything I'm showing you in this demonstration, right, it is in our coded pilot right now. And basic oops. Basically, in Akiwiki, a consensus is built from text. Right? So if we have an article, we the first step in Akiwiki is just bringing that article into Akiwiki and giving it a title. And there's basically five different ways of looking at this article, and I'm gonna show you these because these are important functions. So the first thing the platform is gonna do is provide a front page view to this article. And the front page view, this is a very psychological system. So the front page view is where all the stakes are depending on the context. Right? It could be a very large audience, maybe, you know, hundreds of thousands or millions are gonna read it. It could be a law. It could be something on the blockchain, but the front page view represents the stakes. And I'm gonna turn that consensus points on, and you could see that this article now has a zero, one, or two after each consensus point, and a consensus point is just a single coherent idea in a conversation. And this is what is replacing a voting algorithm, zero one or two. It we it is used as a as a pair of consistent logic, but we also use this as a system organizing principle. And I'm gonna show you how this works. The icons could be enter anything, but in our closed pilot, I discovered that people preferred the zero, one, or two, that the icons are too confusing. But, basically, how it works if you come across, like, a comment or a section of a proposal or whatever it is, a piece of content and you accept it, you believe it's true, it's agreeable, you select one. And when you select one, some tags are gonna come up, and the tags are there to bring more context to what it is that you mean when you say, oh, yeah. This is true. Now the tagging gets more sophisticated than I'm showing you right now. I'm just showing, you know, the basic mechanics. If you think something is false, it's not true, you would select two. But this is where the methodology in the beginning is a little counterintuitive because fault in this system does not mean a mistake or a wrong answer. It's a certain quality and we wanna understand what someone means when they say, hey. That's not true. Well, is it false because maybe it's just something that's subjective or fiction or could be something creative? Could also be misleading. So this is two, and if you're not sure, it's zero. Any type of ambiguity in the system is a zero. Any type of speculation is a zero. Even a question itself is a zero. So this is essentially the language of the system right here. And I'd like this you know, I don't have time just to hang on each slide, so I hope I'm not moving too fast. This is the emerging view or the draft view of the same article. This is basically where the consensus is at in real time and notice that there's a format applied to this article. At the top is what the consensus is arriving at to be verifiable or true. Underneath that, however, is what the consensus is finding to be misleading, misinformation, a misconception, anything like that. And underneath that is a a a a, sorry, subtitle for what the consensus is still trying to figure out all the open questions happening inside the consensus. And this, format does get applied to the front page view after the first consensus. I like to call this contextual completeness, which might anger some mathematicians. But, basically, what this means is that at the top of every document is a summary of what has found to be verifiable or true within the consensus. Underneath that, there are all the misconceptions, and underneath that are all the open questions. And the idea is that, you know, just knowing what's true is not good enough anymore. Sometimes we really do need to know what the bullshit also looks like, and most most importantly, open questions. So all the document is published into this kind of format. And this is the discussion forum view of the same article. So every article has essentially three dedicated conversation rooms. These are basically three different kinds of algorithms. The consensus room is very important because this is where permissions are assigned to make a change inside the system, and the rules here are strict but simple. So for example, if you're saying something is true, you have to be prepared to explain how you came to that conclusion that it's true, and your answers really can't contain any unresolvable contradictions. You can't avoid the question. You can't be an asshole. Right? But if someone was to say introduce an let's say even lying or manipulation into their into their answers, the conversation would get moved here. If someone has an unresolvable contradiction or they're avoiding questions, the conversation gets moved into the speculation. So, basically, any direction that a conversation can go in, one of these three rooms can accommodate it. So this is the discussion forum view, and each consensus point on an article is the title of its own discussion thread. Right? So if I click on this consensus point, here's the consensus point at the top. Here's the conversation thread right below. So this is the discussion forum view. This is the narrative view, and this is where we get to a lot of the fun stuff in Akioiki. One of the things that the narrative view can do is, in real time, start to tell a story about how the consensus process itself is going. This is a a totally reliable story. It's told in real time, and this is not relying on an AI to do this. This is just an output of the computational system itself. And no you know, all the microagreements, all the micro

I don't

Okay. Can you hear me can you hear me right now? Mhmm.

You can see okay. So, basically, the the this narrative view gives you can reveal all the microagreements happening at the consensus, all the microdisagreements. Every possible thing that can happen in the chaos of a consensus process is told in a reliable narrative format. Now the reason why Akiwiki can account for all possible things that can happen is because Akiwiki in terms of its mechanism design only serves one purpose, which is distributing conflict into resolution. It has no other purpose other than that. And because we are dealing with the context of conflict resolution, there are naturally occurring narrative arcs or narrative themes that happen in conflict resolution. I didn't make them up. These are universal to all cultures all over the world. And what this enables us to do is create a customizable narrative theme for each consensus depending on the culture, depending on the context. You could even have a Star Wars narrative theme around a consensus process if you want. And I'm gonna show you what these narrative events are. The first possible thing that can happen in Akiwiki is kind of what I've started to to show you already, is we start by uploading a consensus article, an idea into Akiwiki, and it begins in zero unknown. But you could see that even though computationally it's zero unknown. If I wanted to, I could give this a literary theme because every story in the world also begins in some kind of mystery, and I could call this many different things from a literary perspective. Right? I can give it a lot of bad titles even, but computationally, it's always exactly the same. The second possible thing that can happen in AkioWiki is someone comes into this unknown, and they select the truth as they see it. I'm gonna call this the journey begins as its literary theme. Now the next possible thing that can happen after that is someone else comes in, and they make a different truth selection than the other person. A contradiction gets introduced. I'm gonna call this king versus king as a literary thing just thinking like chess. Psychologically, though, it's truly truth versus truth. You have two people who both believe they're essentially right in some way. It's just not possible both of them can be based on how they're explaining what they believe to be true to themselves and each other. Now in a perfect world, what would happen next? Well, in a perfect world, one of these two individuals would acknowledge that they had a mistake or they had a misunderstanding of some kind, and they changed their tagging. Right? Where they selected, tag two, they changed it to one. Now in this system, this is a very important event because this is how we truly measure what we will call a rational user, someone who could acknowledge that they made a mistake of some kind. There was a misunderstanding on their part. And when someone makes this acknowledgment, we're going to award this person a permission to rewrite the consensus point. I'm gonna call that the great liberation as a literary theme, but this only happens in a perfect world. It's really interesting when it doesn't happen because we're gonna learn a lot. The first thing we know that if it does not happen, we know that one of these two people in this paired conversation must be making a contradiction of some kind. For certain, both of them could be, but absolutely one of them must be. We just don't know which one yet. And because there's this unresolved contradiction in the conversation, psychological stakes start to build. And this next narrative event covers mirrors. So this is when we can predict making people making personal attacks and being an asshole or being a bully, things like that. The conversation becomes personalized, and we have a digital signature for it when it happens. Another event that can occur as these contradictions remain unresolved is people can try to introduce deception, manipulation, perhaps even try to subvert the system itself to avoid the unresolvable contradiction. Another event that could, of course, happen is just two people could just decide to come to a resolution or an agreement for any reason we can never think of, but they just need to work out the details. And the last possible thing that can happen in the consensus process is that this consensus point has gone through this process and it's changed, and minds have changed along the way, and we have a complete story about everything that's happened. So these are the nine narrative events. You could call them gazillions of different things. Right? But computationally, they're always the same. I love this one. This is Ernest Hemingway via chat GPT of the nine narrative events. This is what it just looks like in our system right now just to show you. Right? These are discussion rooms. This is the yeah. Consensus editing booth, and we have a very complex decision tree. I'm just showing a few pages. So, basically, in our community consensus building, one assumption that we make is that honesty is very important in the consensus process and that people are more likely to be honest in the process if they believe everyone is equal. So in AkiWiki, no one will ever know who they're speaking to. We're gonna change everyone's name. The the usernames are gonna be randomized. And they someone will discover a consensus document from the front page view, and they can select zero, one, or two. Right? Each individual one. And they they make this discovery, and they do all this work in the user workstation. User workstation one lets the person know which narrative event they're in, where they're at in the conversation, and it's gonna give them a prompt like leave a comment, select a consensus point, or hey. If you've made a mistake, I'll give you a permission to edit. They just put their responses right there in the box. Formatting codes, which unfortunately, I ran out of funds. So the version now does not have form basic codes for formatting. This blue button means something like, hey. I admit that I had a misunderstanding. I admit that I made a mistake, or, hey. I'm sorry. I was being the asshole. This red button means the opposite. It means I believe the person I'm speaking to, they have a misunderstanding of some kind. They made a mistake or they're being an asshole. The purple button is there to invite the person you're speaking with, fuel resolution of some kind depending on the prompt. This is the zero one or two select icon. That's the inner button, and that's it. These are the only tools that we need to go through this process. Someone selects one, the tags are right there. Someone selects two, the tags are right there. Someone selects zero, the tags are right there. So, the prompt though, after someone tags the consensus point, they're also prompted to leave a comment, and they're also prompted to also tag their own comment. Right? So someone will look at their comment, and most of the time, they would naturally think what they're saying is also true, so they would tag their own comment as true. The when they have completed their inputs into the system, the system is gonna have a dialogue with them. It could be in the form of a chatbot. Basically, what the system is gonna do is you if you could see here in the red, I'm over exaggerating the narrative theme just to make a point here. This is not an AI that's doing this. All the system is doing is taking the user inputs and giving it back to the user, but in the context of the narrative event that they're in, and that's all it's doing. So someone else comes in, right, and they're gonna make a different selection, and the system will walk this person through in the exact same way as it did the other person. And, basically, this is like a meeting of the minds. Two individuals have been paired in this consensus process, and the system is just gonna continue to walk them through. Now we don't have time to do the full and complete walk through right now. But basically, these all of these problems are worked out between the users. The system is never gonna make any decision. So as the users begin to engage in what is essentially a conflict over an editing permission, this is an important point because the permissions to publish, edit, or make any changes in the consensus article are only awarded to people who begin with a conflict or a disagreement of some kind. Right? That's the way into the system. All resolution must have a beginning in this conflict, and if there's no conflict, there's no resolution. And in this moment, users have to make a decision about what kind of relationship they're gonna have with the consensus process itself. So for example, in this example, we have a Bob and a Joe, and either Bob or Joe could just leave the conversation for any reason. They could think it's stupid, dumb, it's boring, it's not getting anywhere. But if because this is not a voting based algorithm, if they leave the consensus before resolution, all their tagging just gets muted back to zero. Now either Bob or Joe could acknowledge that they had a mistake or made a misunderstanding of some kind, and if either Bob or Joe acknowledges a misunderstanding, both Bob and Joe are gonna win a permission to rewrite the consensus point together. Or, of course, Bob or Joe could just have a good faith disagreement, and maybe they just need to hash it out a little bit more, or either Bob or Joe could choose to be an asshole of some kind. But these are the only possible relationships that people can have with the consensus process, and the game has already started. And this is where, you know, I I call this a conversational game theory. I do wanna stress this has not been formalized, this game theory, even though ChatCBT thinks it hasn't formalized. But what we can demonstrate at this stage is convergence or abandonment. So I ask you to consider. If we have two people who are paired in a conversation and they're both naturally collaborative, even though they disagree, they're just naturally collaborative. Both of those people will be a part of every consensus point all the time. We could pair two people who are essentially assholes. And if we pair two assholes together, their own decisions and behaviors are gonna keep themselves away from the editing process. But what happens if we pair someone who is naturally collaborative with someone who's essentially just being a competitive asshole? In this system, the entire mechanism design is win win conflict resolution. That is the dominant strategy. And the individual who's applying, you know, basically, more competitive strategies, They're just gonna create a lot of work for themselves in the system for no payoff. So their options are just to, you know, adopt the dominant strategy or just leave the process entirely. And in this process, the most powerful thing in Akioiki is asking a question. The question is very powerful. This is a very Socratic system. And, this is what Akioiki is continually doing. It's, it's guiding people in the consensus process to confront what is like a meta decision. Are you gonna walk away from this consensus process and essentially lose? The only way to lose an Okie Wiki is someone just leaves. Right? Are you gonna walk away, or are you just going to acknowledge that you had a misunderstanding and win a permission and stay in the rational consensus? So, something like in this game, right, these bloop these buttons that I had, something like I admit I had a misunderstanding or I'm accusing the person I'm speaking with as having a misunderstanding. These are reoccurring patterns in conversation. Again, they're universal. It might seem confrontational, but these are essential for getting to resolution very quickly. So the system will just continue to walk people through this these narrative events, but people can try to game the system. I think it's human nature to try to game a system. And, if someone tries to game the question itself, someone can tag that in a different way. So if I, you know, and I can look at if I ask someone a question, I'm gonna tag my question as a question. I'm a send it over to them. They're gonna tag my question as a question. They're gonna give me their answer, and they're gonna tag their answer as an answer and send it back to me. But if I was to say, hey. You know, you're a big, fat, smelly person question, they can just tag that as a personal foul. This is another contradiction in the system that me and this other user now have to work out. The system is not sure which discussion forum that our conversation goes in. So, basically, in this system, tension builds when users are are gonna try to gain the system away from its purpose. And the more tension, the more work that gets created for them. It just it's an insurmountable amount of work. If player if players try to attempt to gain the system towards its purpose, though, everything is right there. I think it's very easy to do. And people in disagreement have the same power over each other. It's up to each user to hold themselves and the person they're speaking with accountable to the rules of the consensus room. And this is why we have the house I call it the house of resolution in this example. Fairly. This narrative of yes?

Oh, yeah. It gets it is right. Again, so how so this, this is why we have this narrative event. The this is just all the tension that builds up can just be resolved, really quickly and people just get to resolution. This whole system I just showed you does not require an AI, but it's really incredible when we add one. So there's a lot of things to do. The whole system is designed to be interoperable with everything. I'm not planning on Twitter to, you know, to adopt this, but if they did, you hardly see it. Everything is on the back end. And we have Parley, which is right now, we can demonstrate the computational system between two people. The next version, Consensus Dojo, which applied for the interoperability grant is for five to 25 people. And, basically, we just real quick, we do this through a sensory consensus delegates, and we can accommodate more people that way. And that's it. I'm done.

Voila.

Was it was it twenty minutes? Was it twenty minutes on the

No. Yeah.

fair word. That's a good

no. No. That's a that's a very good question. When we when we have right now, we demonstrate Aku Wiki for two people. The solution to what you just described emerges when we have a group of people. So there's something called consensus ranking. And so, I mean, that's how it's resolved. It I I it's I don't know.

too much to go into this mode, but that's an excellent question. And that essentially won't happen. We could we have a consensus ranking for that.

Oh, okay. Okay. Sure. Alright. So so, basically, right the next version of iKey Wiki, I think it it it could accommodate up to a 100, but we're we're we're targeting five to 25 people. So, basically, the way Akimiki works, if someone is applying a competitive strategy, right, they're they're being an asshole of some kind. Forgive my language. Right? They're they're being a bully. All these things, the other person can tag. And and maybe the other person is wrong. Maybe they're just being paranoid. But either way, the person has to account for them in the conversation. It's it create they're basically introducing contradictions when they're doing that. If they're applying a competitive strategy, there there's no competition in Oku Wiki. Right? So they're they're all they're doing is creating a lot of work where where it's going to and they can continue to do that work. The system is never gonna punish them. Where it what to your point, though, so what happens if you have someone who's collaborative? If they're collaborative, they're building a consensus ranking. And if, basically, if someone is applying a competitive strategy, someone else can come in and take their place. Right? So that so where that person left off in conversation and where and if they're continuing to go in a more competitive direction, their conversation can be isolated. When we add an AI, it gets even more incredible because the AI can take over the conversation with them. We can just let them spin their wheels and completely isolate them from the conversation.

Okay.

So there's there's there's just no pay there's there's just no payoff for it ever.

Cool. Thanks so much, Rome. This was really, really cool and, just exciting. I'm I'm really, like I'm trying to imagine you, like, working on this for so long and kind of working with I take it like the Wikipedia community as sort of the main, like, inspiration and research, like, for inspiring the development of the system. And I guess I have so many questions that I wanna, like, chat with you again about this stuff soon. But I think for now, can you give us a little bit more background on the whole system, like, about the like, when you did the Wikipedia research? Like because the system feels, like, more necessary than ever. I feel like, of course, like, we're in increasing polarized times, and, like, people are so, at this point, like, used to bullying each other on the Internet. And it it just feels like you've been working in this space for so long. I'm curious, like, your insights about and, like, hope for the future. Like, does it does it feel like, you know, when you were thinking, conceiving this idea, you were like, oh, the Internet, you know, doesn't have to, you know, be this way. It can be better. And now, you know, it feels like you said twenty years later, you're like it feels like it's the problem's gotten worse, and, like, you know, how can we think about implementing your extremely thorough, like, solution in a world that just, like where the problem has gotten so much worse?

Well, it's you know, I've been talking about this for a long time. It's so funny. I couldn't get anyone to pay attention to it until things just got so bad on the Internet. Then all of a sudden, people went, oh my god. We we really need this. By the time I got to you know, I I've had to develop this, and I've had to prove it to myself first and foremost. Because when I made this discovery, it was by accident. I wasn't sure if I was lucid or crazy or something. You know, I didn't have enough knowledge. That's why meeting, professor Fallon was very important. So, so first, there's the methodology. I, by myself, had to be clear on what the methodology, how it was, how it worked. And I this would happen by me testing it in just live conversations where there was no platform support for the methodology. So I knew that I wanted my final thing in my own research was participating in a Wiki war. I was fascinated by Wiki wars. And when I got to that place, I wanted to test the effect of the methodology on a platform that does not support the methodology. And I was actually able to make a change in the consensus article. I got so brutally harassed. It was unbelievable. It would but it just turned out to be the most heinous of all experiences. Like, it it was horrible, but it was so illuminating because I had to whittle down behaviors. Right? Like, there was a consistency in behaviors, and I really had to distill them. So, unfortunately, I had to go through mirrors and shadows for years and years to find what is the reoccurring pattern that that emerges. The the Wikipedia could be its own interesting seminar, by the way. I mean, just seeing how Wikipedia itself has gained all those things. So, yeah, I mean, there's there's so much there. There really is. But after after I I tested the methodology on Wikipedia, then I knew I was ready to start kind of coding a platform around the methodology. And I didn't finish the algorithm until 02/2020. COVID gave me the first vacation I had in, like, ten years. Right? So so I was able to finish the finish the algorithm. And Jim Fallon was the first person one of the first people I showed it to because he said, hey. When you're ready, take it to me, and you have to see if I can game it. If I can't gain it, you'll you'll know you'll have it. You'll you'll know you'll have something. So, yeah, that's, I mean, that's a quick summary of of that part of the background.

Thinking about many aspects of what you're saying, I've been thinking about what I call social truths, which seems very similar to some parts of what you're doing. That is distinguish unknown consensus, dicensus, you know, consensus versus marginal versus contentious issues. It seems to me we cannot always reach a consensus. In the sense, you say, you know, if people refuse to acknowledge another position, they'll be, set aside at a certain point. Isn't it important to record that also as well? This is the dissenting opinion as they have in courts. Oh, yeah. The the the, you know, sometimes was going too far in some ways, but he made the interesting point that going against Galileo was the reasonable consensus with the information at the time, except it was wrong. The the the I I believe strongly in efforts to try to get people towards reasonable consensus whenever possible, but there are cases where it doesn't work.

That that that's that's a great that's a that's a great point. So the, you know, the first agreement that can be built is an agreement about what our disagreement is about. Right? So that would be the that would be the first step. Yeah. Yes. And Totally. I I had to I had to when I was developing this, and this might sound obnoxious, but I had to coin a new word because I could not find a word in the English language for this principle. I call it symbiquity. Right? So symbiquity and just be patient with me as I define it. But symbiquity is essentially, like, environmental information that's equally distributed to everyone. So, you know, if you have two people on a battlefield who just, you know, wanna go fight, go fight it out, but it's a really hot summer day, you know, they're going to be existing in a field of agreement. Right? It's a really hot day. We're meeting by the tree to have our fight. So the principle here is that we already exist in a field of agreement. It's really only the small details that we wind up disagreeing on, and this is what nondual consensus theory is about. So the first thing that we would seek to build a consensus on is what do we disagree about? And then we just take it from there.

The okay. Go ahead.

Yeah. For sure. And in that way, it's it's not that dissimilar from Wikipedia. Right? There would be a version that when they could discover what the original, you know, idea was that initiated the consensus. Right? So, basically, everything that happens is recorded. And there's you know, in the narrative view of everything that's happened, you could this is why the AI is so incredible. Right? Because you could just ask the AI also, Could you show me where this has started or where these conversations or who which conversations in particular change things? So everything is recorded. Everything, you know, is is easy to find and navigate.

Alright. So when I speak to that, I mean, I, myself, have tested now remember, there's a methodology and then there's a platform. Right? So I myself have tested this where it was literally me versus 75 people. Okay? So so basically, I'd like, sometimes in the presentation, I'll say, it just takes one person to change the outcome of a consensus. So it was from my own testing that I that I came to the conclusion that this can at least accommodate up to a 100 people. That doesn't mean we couldn't, have more than that. It just means that we'd have to stack it. I wanna get this to the place where this could be thousands, tens of thousands of people. Right? But, this is where I need help. Right? So I'm getting I'm getting to the place where I've gone to the limits of my, you know what I mean, of my brain. I am looking for, like, a mathematician to collaborate with. This project was accepted into the National Science Foundation SBIR program. I have to reapply for phase one, but I, you know, I'm also looking for, you know, collaborators. But, yeah, this this is a very special project, and, honestly, it needs people smarter than me. So I where I want to take it, right, is I I wanna take this to where it's thousands of people just because and I think with it with I think that is a problem that AI could solve. That that one problem, I think AI might be able to do that one.

Oh, those

are those are great questions. I do think we wonder so there's the methodology. Right? And the methodology in principle doesn't need a platform. It you know, I use this all the time. Right? Like, my the birth of my son represents the you know, his birth certificate is the first contract, the first win win contract that was negotiated. So but I do would love to see this to the place where it can handle, like, live video chat and gamification. Right? I haven't got to that level in the development, and I again, I think that's an area where AI can help. And then what was I'm so sorry. Could you refresh on the second part? Because it was a really good question for some reason.

Oh, yeah.

Sure. Alright. So, you know, from my own research, now I'm you know, I I've been an independent researcher for a long time. So this process, from my experience, collapses groupthink. So polarization, to me, right, polarization emerges from groupthink. And right now, the Internet is just so incentive like, the a voting algorithm, a like button, to me, that's all the same thing. The this is what is increasing this kind of polarization. So now I I didn't present this this as a research study in this. I would love to do this paper, but just the effects of groupthink, how groupthink itself emerges, right, inside a consensus process. And, you know, it's it's simpler to collapse that. You know? We're not given the opportunity to have a third value in conversations. And, usually, it's the environment that people are having the conversations in that's really influencing their responses. Right? So we're assholes now on the Internet because there's a like button, basically. Right? So if the environment has basically, it can allow for this kind of, like, third value to emerge naturally, this it's a natural process. My experience, it it it doesn't happen anymore. There's just no incentive. Now this is something, of course, to really seriously test when we have a platform, you know, up and running with a lot of people on it. But that's that's my take on that.

K.

I will tell you one thing. I'll tell you one thing. I have shown this I'm from the, you know, from The US. I have shown this to hardcore, you know, democrats and hardcore republicans, and I love this. I'll show this to a republican, and they'll go, oh my god. The democrats need this so bad. I'll show this to to a democrat. Oh my god. The republicans need this so bad. I have shown AkiWiki to so many different types of ideologies. That's been a lot a big part of my research. And every single ideology that I have shown this to has been open and receptive to it, and they all think the other person needs it the most. That's, if that's optimistic.

So so so, basically, I, you know, I I always say this this is a I feel very fortunate to have discovered this by accident. Right? I wanna stress that I was not normally that's this smart. This working on this for such a long time has really made me a very, very, intelligent person going through this process over and over again, but I really think this project needs about a thousand people smarter than me. So I am not doing this to be the smartest person in the room or, you know, to be a, you know, big tech entrepreneur. I really am very open to collaboration. I this really needs a lot of bright minds involved. So however, we we can work together. I mean, I I I'm flying by the seat of my pants, right, as an entrepreneur. I applied for the Medigov interoperability grant, if anyone can put in a good word a good word. But I wanna continue to work with the Medigov community because I wanna once I have phase two developed, really going in and doing research, right, on this and then segueing that into the the NSF grant because the big picture here is to build one large global library of consensus articles. So I I I've met a lot of interesting like minds, a lot of freak minds like me here. Right? So I would love this to be home community, TalkyWiki. I don't wanna be obnoxious. I know everyone's got their own thing. But, yeah, just reach out to me, and and we'll figure it out.

Thank you. And and also, anyone is if if anyone wants to go on an individual walk through, takes about forty five minutes over Zoom. I love giving walk throughs. I could do one a day for the rest of my life. Reach out to me if you want a one on one Zoomer. Totally totally cool. And big honor for me to be here. It's so lovely to meet you all. I'm very happy to be a part of this community. Perfect.