WEBVTT

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Okay, thank you very much for the delay and I will start quickly, I only have 10 minutes

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so that's really tough.

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My name is Richard Haigle.

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I'm one of the founders of Halloweld and Halloweld is the company behind the Blue Spice Media

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Week.

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Open Source Enterprise Week as XWiki, which are good colleagues here and fighters for Open Source

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in Europe.

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So, what I want to introduce you is our some thoughts.

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We have, we have in the evening, if we sit in Riggsburg and have some beers and it's about

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the relationship between Wiki, okay, we are Wiki manufacturer, right?

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Well, so everybody is talking about that.

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To bring you on the same page, I want you to bring you quickly back 20 years ago.

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So, 20 years ago we have had something, what I would call a Wikipedia shock.

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The shock needs, it means okay, we have something like collective intelligence,

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wisdom of the crowd, is a new paradigm in the way how you deal with knowledge.

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So, even the youngest people here in the room, maybe don't know what that means.

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So, people are really impressed that we have an easy to use Wiki as a central source of truth

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in the web and it works because people are working together, they collaborate, they generate user,

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they use a generate content and structures, by the way, not only content structures,

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as important as the content I think, and it's easy to use because you can work with natural language

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and with free text.

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It's made a difference in the way how we search for knowledge, okay, you're using Google

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but the second click mostly is Wikipedia.

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And you have a fast and centralized access to initial information.

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And this paradigm was transferred to the private sector, of course,

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and the companies also become used to have a wiki internally for internal information

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and sharing processes stuff like this is of the interest.

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We have seen a few challenges from the beginning.

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It was about how reliable is the information we see there.

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And where does the information come from?

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What are the sources or the references to that?

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Can we categorize everything and what is a good category?

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What is a good structure beyond a single page?

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And last but not least, is that what I see and that what we have as information?

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Is this relevant somehow?

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I give you, in my opinion, that has to be properly solved.

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All these problems have to be properly solved by the wiki media community.

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And now we come to something where is it not solved?

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Today, we see something like a jet GPT shock.

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Sorry, it's for me, it is a shock.

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Because you have now the way that you, like with a human,

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can take conversations with artificial agents, with a large language model,

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and you get impressive in a passive way, you get answers back in a quality,

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which you haven't seen so far.

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And in the background, there is a lot of mass data.

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And so people become used to look things up in jet GPT or in others,

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or deep seek of whatever, and get the information.

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If you know how those engines are working,

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it's incredible that people think they get a reliable information from them.

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That's the reason how jet GPT or the LLM's work,

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they do a lot of good stuff in talking with you,

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but they have no clue what they're doing.

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And you have no clue where the sources are, or what is,

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where does the content come from?

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But people believe this is how they get proper information,

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because it's a convenient way to deal with.

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And so that will not go away,

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so maybe we should talk where are the strengths of these tools,

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when there are oh, five minutes left,

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and where can we do something different?

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You know all the problems,

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Hellenization, you know,

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deterministic behaviors.

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So if you have a prompt and you put the prompt again and again,

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and again, you get different answers.

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Give it a try, ask that GPT,

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what is 2 plus 2, 10 times.

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And you will see the 9th results will surprise you.

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So, what we see now,

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because people say, oh, oh, oh, oh, no,

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Google is gone.

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Wikipedia is dead, so it's a replacement,

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not true at all.

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So what we see and I have learned over the last two years,

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there is a symbiotic relationship,

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so between those two technologies.

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Because, okay, first of all, everybody knows that,

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that engines where the models are trained with Wikipedia content,

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okay, that's given.

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But what we see also is that Wikis,

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as knowledge-based,

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can be a very reliable and valuable source for controlled internal information,

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and we can give this to the chatbot.

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So we can, or the ideas,

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we can enhance the reliability of the answers of a chatbot

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by using Wikis.

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How is that done?

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Okay, use the chatbot.

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Look, I have a pretty nice source for you in a Wikis,

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for instance, just get the information from there.

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And maybe I also use a search and enterprise search

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and say, make a pre-selection,

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what content should be part of the answer.

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So with that, it's easy now,

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you get from a controlled source,

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the information back,

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and with the search, you can also say,

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okay, that other references, that very sources,

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we have looked up.

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So now it became more and more interesting.

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You can add references,

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maybe you also give,

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for the answering machine,

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you give an ontology,

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you say, there are some relationships,

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so you work with guidelines or checklists,

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stuff like that.

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And in the end, of course,

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you can validate if that answer was good,

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and you can improve the source where it came from.

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One and a half years,

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nobody had an idea that will work.

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That is one practical example.

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That is,

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and sorry, is it's manual.

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From the Federal Ministry for Education and Research,

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in Germany, they have an internal source here for projects,

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a funding of projects,

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and what we have done is exactly here.

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You see there is a chat where they can ask

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question how they get the funding,

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and some more details,

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and you see here where it comes from,

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and you can look up the sources.

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Another example, a bakery.

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So there is a huge bakery,

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not a single one-man,

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one woman bakery,

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but a huge one with hundreds of people

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working there on large machines,

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and if that machine is breaking,

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maybe you need to know what is,

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how can I,

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what should I do?

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I have an error message,

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what does this mean?

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And what we do here,

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together with the Fraunhofer Institute,

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is,

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okay, you get a chatbot,

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but now,

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we try to teach the LLM,

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that that is a specific machine,

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and an ancient,

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and there are some relations between,

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okay, this is a,

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yeah, how this machine works,

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and make it easier to get exactly the information

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that's needed,

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because if that information is not reliable,

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you play with the Herz of the people there,

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and Herz of other people.

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So what we are doing in this research,

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project,

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is how can we improve the reliability here,

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and make something that is not only for a bakery,

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but also for other industries useful.

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To come to terms,

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there we are.

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I would say that Ricky's special platforms for using AI,

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so everybody comes around,

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I haven't seen her,

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okay, we are doing something with AI.

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We have a ticket system doing something with AI.

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Everybody is doing something with AI, right?

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But what makes Ricky's unique?

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First of all, they complement each other.

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They are very, very, very good in working with processing free text.

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And then we have a layer for,

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for chatting,

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or something that is a perfectly communicator,

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with the chatbot,

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and we have the Wiki,

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or the other hand,

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that is a perfect tool for control,

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content together with other people,

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and make it accessible,

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but not also only for humans,

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but also for machines.

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So I have to quit now,

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that,

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if you want to talk with me about this person,

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learn how this works in detail,

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just contact me or Robert after this talk.

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So what I think is,

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there is a lot to do,

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because that's not that easy,

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and you don't throw an L&M on a Wiki,

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and you get good results.

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You have to do a lot,

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that the information is valuable,

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and that's exactly a good process,

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a good project for open source communities,

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researchers and companies,

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who can now,

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with open source software,

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experiment,

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innovate,

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and refine to gaps,

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reliable,

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non-menu,

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non-menu,

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my new pull-at-heath,

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knowledge,

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that forms,

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for the 21st century,

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and I promise,

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I will practice this word,

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until the next four statements you there.

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Thank you very much for your time.

