Greg: I think both are incredibly important. The way models work is, you may get something that is verbatim that you can go through and trace back to an actual document. But statistically, you might not. And as a result of that, it may be pulling in many different documents together to give you one kind of concise, summarized answer. And being able to trace through, here's what the AI said, and where the data is located, is much, much easier when you actually have the data. And so the two go hand in hand. Right now, OpenWaldo does not contain facilities to trace. There are other systems out there. So, trace, for example, exists, and over time, this is just going to continue getting better and better. But I want to really reiterate that you can't always resolve an answer from an AI to a single, like here's where that came from. It's most probably going to come from hundreds, if not thousands or hundreds of thousands of documents and tokens and parameters that are all kind of statistically giving you an answer or leading you, leading the answer to a particular direction. So AI models are statistical masterpieces in how they work.
Greg: And they're very non-deterministic. So, as many people probably found, you can ask a model the same question many times and get different responses. But you can also ask a model the same question and sometimes get the exact same responses. I was building an AI agent a while ago. And one of the questions that I would just say is Tell me a joke. And I found that it would tell me the same joke several times in a row. And then sometimes. So in the agent, I actually put in something that basically said, you know, if you get the same response, don't accept it; send it back over to the AI and give it information that you got the same response, and you shouldn't get the same response. Even if I'm asking the same stupid question over and over, I want the AI to be able to reiterate and think about it or give me a different perspective or reconsider. Well, the thing about non-deterministic systems. Sometimes you get the same answer multiple times. So anyway. But sometimes you don't.
Addison: I mean, I love this answer, Greg, and I'll just take it and make it personal because, you know, here we are. We're a couple of talking heads on a webinar, and I love talking to you. And I've talked to you at conferences in the past. We've got a good rapport. And you and I are capable of talking about an awful lot of things. We had three minutes to go before this webinar. True story. We were talking about karaoke right before go-live, and the people behind the scenes are freaking out because they think the webinar is going to go live and we're going to be talking about karaoke. Did you and I get any advanced training before this webinar about, hey, please, you two stay on topic? We did. People want us to be on topic for this webinar. We get training too. And I like to think that as a couple of autonomous, intelligent people, we're capable of having a sense of occasion. And here's what we're talking about. But then also the other part of that is: where did the answer come from? And this is something that's personally very important to me as a professional analyst. Right. Because you, Greg, have challenged me on where that insight came from, especially on things that are not common knowledge, right? How did you reach that conclusion? And I'm able to show you: here's the survey, right? Or here's the methodology. Here's the way we built that. Here's where we got the list. Here's the other corroborating evidence. Kevin has seen it and, you know, heard us talk about how, when we go and measure a new thing, we try to do it with three different methodologies to see if we can get the same answer three different ways.
Addison: This is really important to me as an analyst, because that's going to be our niche. And I think that's a niche that potentially survives in the era of AI. Suppose the reason you want to work with an analyst is just to justify the decision that you've already made. I'll tell you what, you don't need to work with us for that. You can go to any AI agent that you like and say, " Give me a justification for this, and it will do it for you. And you'll say, " Hey, I asked the AI, and it told me to do it. You don't need an analyst for common knowledge. I think you do need an analyst who's going to give repeated, different perspectives on what you want; that's uncommon knowledge. Who says, I've looked at this, and I think there's a lane here that's underappreciated, and this is important to our line of business. I think it's important to your line of business. And when I talked about the perspective of enterprises in control and where these things are deployed, there's a lot of careful methodology that went into that, that you can audit, that we can audit, and we can take you through it. This is important, and the reason that you work with a company like us, or with a company like yours, is to get that kind of thoughtful leadership. It's an important question, and it's one that's important to me personally, and I kind of appreciated the chance to give a personal answer.
Kevin: Absolutely. So, you know, running low on time, obviously, I know you two, we could make this a six-hour talk, and we still have.
Addison: We might start talking about karaoke. I don't know.
Kevin: But, you know, I would like to get a couple more questions, and one, you know, looking forward, I hope to see a beautiful future for OpenWaldo. But what has to happen for OpenWaldo to become something enterprises rely on, rather than an interesting open-source project that they may watch from the sidelines? You know, what are the real nitty-gritty solutions this is going to give?
Addison: I'll go first on that just because Greg has been hogging things, but also because it's my job to give industry context. Right. And we already laid out what the state of the market is with AI, right. And the state of the market is: I want to adopt AI, and I'm spending money on it. I think that's a chip shot. People are there. And that part is common knowledge. But B, there's still a gap between how I'm using AI and what all the promises of AI are right now. And that is related to seeing this very live debate around AI risks and regulations and accountability, and how do I protect the downside there? And yeah, what is the difference between open weight and open-source, and how do I have something that's auditable that I can move forward? Those are the clear guidelines that we see in the market right now: OpenWaldo. The answer, it certainly seems to me, is an answer to a lot of these things. And now I'll pass it over to Greg, because you can start distinguishing your product from any others that are out there. But that's the industry. The way we at Intersect360 Research see it today.
Greg: I'm going to approach this from a very different perspective, and focus more on the challenge with OpenWALDO and where it is today, and how people can be part of this. So, people aren't standing on the sidelines. People are learning. So the way that I see it is we have people on one side who totally understand open-source. They understand the value. They, they rely on the value of it. They've been running software infrastructures at large organizations, enterprises, etc., or hobbies and home labs for decades. They get open-source, and we got on the other side. We have a lot of people that have been really driving the research and science of AI and building labs, building capabilities. And it's a fairly small population of people. But it's growing, and it's growing quickly. So we have the open-source people, and we have the AI people, and there's not much overlap between the two. And OpenWaldo sits at the intersection point of those two communities. So on one side, I'm talking to the open-source people, which is my background. My legacy has been on that side. So I'm talking with the open-source people on the value of an open-source infrastructure, the same that we've been leveraging and relying on for decades, but now also for this new amazing thing that's going to profoundly change how everybody does their job and, potentially, lives in a good way. I'm not a doomer at all, in a good way.
Greg: And then on the other side, I'm learning as much as I can about AI. I have to, as I am running this open-source project around AI, learning about building models and learning about the math and the statistics that need to go into it and whatnot. But I'm also communicating with a lot of the people on that side now, educating them on the need for open-source and how this will make things better. For them, the way open-source works is that it levels everybody up together, and it becomes a resource that even direct competitors can leverage, work with, and collaborate on. So they're not, they're not collaborating on, excuse me, they're not competing on the general knowledge. They're competing on their value add and it gives them and again levels everybody up and gives everybody a better opportunity,, to, to focus on what they do best. So OpenWALDO really drives this by building a community and building a capability for everybody to come together. And just to give an example of one area that I think that this might be super impactful to businesses, not only are we going to go train and run our own models or fine-tune our models, which is a huge potential use case.
Greg: We kind of briefly touched on it, but training an existing open-weight model with your specific corpus of data is incredibly valuable, and OpenWaldo provides tooling to make that much, much simpler. But if you look at it from a whole other side, which is companies that may or may not be even using AI, the example that I always give and sometimes people don't like the example, but I love the example- and I got the mic right now, so I'm going to talk about it. For decades now, companies have been thinking about marketing, SEO, and search engine optimization. And one potential gotcha with search engine optimization is that the algorithms are always changing by the search engines. You're always changing your website, hoping that something gets slurped up by a web crawler, gets indexed and cached, such that when somebody does a search, your site shows up and ranks well in AI. We're approaching it almost the exact same way, where companies are just putting a load of data on the Internet and hoping and praying that that data gets slurped up by some AI indexer and gets included in the model. You have no ability to say, " Here's what I want." You're basically just saying, I'm going to throw a whole bunch of stuff up there and hope for the best. And it's a difficult situation, but what if those companies can just say, " Here's our entire corpus of everything we have for the company? And all of our product information, all of our company information, the services that we offer, why we are great, etc., all their marketing material," and they put it directly into the corpus.
Greg: And then every AI that slurps up from that corpus now has information about every company, and that information is auditable. And one thing I didn't mention is everything in the corpus. There's one requirement. It has to be distributable. You can't put copyrighted material into the corpus. It has to be distributable. Right. So you have to be able to allow others to take that same data and go and do what they want with it. So, incredibly important notion of open-source in general. And this gives us a platform to do this together. And now that all these companies can come together and start saying, well, I want to make sure I've got more information on my product than our competitors. So they're going to start building more in the corpus and providing more information in that corpus about them, about their. And all of a sudden, the problem of data doesn't go away, but it surely gets better. Now all of a sudden, we've got really good data in that corpus that companies can be using to train, and AI labs using to train, audit, and verify. And I'm going to now put the mic down. Not really a drop. Just very gently put it down.
Addison: I have nothing to add. That was perfect.
Kevin: This is what I'm saying. This could be a six-hour webinar. Maybe next time. But obviously a lot of great information. We're, we're running out of time here.
Greg: I have a plug before you close.
Kevin: Please go right ahead.
Greg: So Addison and I are going to be sitting down together again at Supercomputing (SC26) this year. If you're not familiar with Supercomputing, you need to check it out. Supercomputing is one of the coolest conferences in existence. And Addison and I are gonna be sitting down and doing a panel. We're going to be having discussions like this. We're going to be taking more information, more questions from the crowd. And we really are a lot of fun on stage together. I'm just saying. So you all should come and join. If you're headed to Supercomputing, definitely join us. If you're not going to Supercomputing, you're missing out. You should.
Addison: Greg's going to prompt me to tell a joke while we're up there. I'm pretty sure I told him. I told him I have a couple of dad jokes at the ready. Absolutely.
Addison: All right. I'm supposed to ask someone: where do people learn more about OpenWaldo? What do you want, people? Where do you want people to go?
Greg: The easiest way is to go to OpenWALDO.org. No hyphens, no spaces. So I don't think you need to say spaces anymore. It's a URL, but no hyphens. No dots except for ".org". But anyway, OpenWALDO.org. You'll find it. Come and join. We've got a Slack instance. Super friendly people. We're all talking about AI. We're talking about building AI. And you can learn, you can, you can level up and be part of the cool kids club.
Kevin: And don't we all want to be part of the cool kids club?
Addison: We're the commercial for the cool kids. Oh.
Greg: Actually, that's probably okay. Well, you guys are cooler than me, so.
Kevin: Well, again, I'll just say that the website is OpenWALDO.org. If anybody has any questions, concerns, or comments, please head there. There is a Contact Us section. Greg, Addison. Thank you both. We covered a lot here today. And I think this is a really interesting discussion.
Addison: Thanks, Kevin. Thanks, Greg.
Greg: Thank you. Addison.
Kevin: Bye, everyone.
