Okay, thank you very much for the delay and I will start quickly, I only have 10 minutes
so that's really tough.
My name is Richard Haigle.
I'm one of the founders of Halloweld and Halloweld is the company behind the Blue Spice Media
Week.
Open Source Enterprise Week as XWiki, which are good colleagues here and fighters for Open Source
in Europe.
So, what I want to introduce you is our some thoughts.
We have, we have in the evening, if we sit in Riggsburg and have some beers and it's about
the relationship between Wiki, okay, we are Wiki manufacturer, right?
Well, so everybody is talking about that.
To bring you on the same page, I want you to bring you quickly back 20 years ago.
So, 20 years ago we have had something, what I would call a Wikipedia shock.
The shock needs, it means okay, we have something like collective intelligence,
wisdom of the crowd, is a new paradigm in the way how you deal with knowledge.
So, even the youngest people here in the room, maybe don't know what that means.
So, people are really impressed that we have an easy to use Wiki as a central source of truth
in the web and it works because people are working together, they collaborate, they generate user,
they use a generate content and structures, by the way, not only content structures,
as important as the content I think, and it's easy to use because you can work with natural language
and with free text.
It's made a difference in the way how we search for knowledge, okay, you're using Google
but the second click mostly is Wikipedia.
And you have a fast and centralized access to initial information.
And this paradigm was transferred to the private sector, of course,
and the companies also become used to have a wiki internally for internal information
and sharing processes stuff like this is of the interest.
We have seen a few challenges from the beginning.
It was about how reliable is the information we see there.
And where does the information come from?
What are the sources or the references to that?
Can we categorize everything and what is a good category?
What is a good structure beyond a single page?
And last but not least, is that what I see and that what we have as information?
Is this relevant somehow?
I give you, in my opinion, that has to be properly solved.
All these problems have to be properly solved by the wiki media community.
And now we come to something where is it not solved?
Today, we see something like a jet GPT shock.
Sorry, it's for me, it is a shock.
Because you have now the way that you, like with a human,
can take conversations with artificial agents, with a large language model,
and you get impressive in a passive way, you get answers back in a quality,
which you haven't seen so far.
And in the background, there is a lot of mass data.
And so people become used to look things up in jet GPT or in others,
or deep seek of whatever, and get the information.
If you know how those engines are working,
it's incredible that people think they get a reliable information from them.
That's the reason how jet GPT or the LLM's work,
they do a lot of good stuff in talking with you,
but they have no clue what they're doing.
And you have no clue where the sources are, or what is,
where does the content come from?
But people believe this is how they get proper information,
because it's a convenient way to deal with.
And so that will not go away,
so maybe we should talk where are the strengths of these tools,
when there are oh, five minutes left,
and where can we do something different?
You know all the problems,
Hellenization, you know,
deterministic behaviors.
So if you have a prompt and you put the prompt again and again,
and again, you get different answers.
Give it a try, ask that GPT,
what is 2 plus 2, 10 times.
And you will see the 9th results will surprise you.
So, what we see now,
because people say, oh, oh, oh, oh, no,
Google is gone.
Wikipedia is dead, so it's a replacement,
not true at all.
So what we see and I have learned over the last two years,
there is a symbiotic relationship,
so between those two technologies.
Because, okay, first of all, everybody knows that,
that engines where the models are trained with Wikipedia content,
okay, that's given.
But what we see also is that Wikis,
as knowledge-based,
can be a very reliable and valuable source for controlled internal information,
and we can give this to the chatbot.
So we can, or the ideas,
we can enhance the reliability of the answers of a chatbot
by using Wikis.
How is that done?
Okay, use the chatbot.
Look, I have a pretty nice source for you in a Wikis,
for instance, just get the information from there.
And maybe I also use a search and enterprise search
and say, make a pre-selection,
what content should be part of the answer.
So with that, it's easy now,
you get from a controlled source,
the information back,
and with the search, you can also say,
okay, that other references, that very sources,
we have looked up.
So now it became more and more interesting.
You can add references,
maybe you also give,
for the answering machine,
you give an ontology,
you say, there are some relationships,
so you work with guidelines or checklists,
stuff like that.
And in the end, of course,
you can validate if that answer was good,
and you can improve the source where it came from.
One and a half years,
nobody had an idea that will work.
That is one practical example.
That is,
and sorry, is it's manual.
From the Federal Ministry for Education and Research,
in Germany, they have an internal source here for projects,
a funding of projects,
and what we have done is exactly here.
You see there is a chat where they can ask
question how they get the funding,
and some more details,
and you see here where it comes from,
and you can look up the sources.
Another example, a bakery.
So there is a huge bakery,
not a single one-man,
one woman bakery,
but a huge one with hundreds of people
working there on large machines,
and if that machine is breaking,
maybe you need to know what is,
how can I,
what should I do?
I have an error message,
what does this mean?
And what we do here,
together with the Fraunhofer Institute,
is,
okay, you get a chatbot,
but now,
we try to teach the LLM,
that that is a specific machine,
and an ancient,
and there are some relations between,
okay, this is a,
yeah, how this machine works,
and make it easier to get exactly the information
that's needed,
because if that information is not reliable,
you play with the Herz of the people there,
and Herz of other people.
So what we are doing in this research,
project,
is how can we improve the reliability here,
and make something that is not only for a bakery,
but also for other industries useful.
To come to terms,
there we are.
I would say that Ricky's special platforms for using AI,
so everybody comes around,
I haven't seen her,
okay, we are doing something with AI.
We have a ticket system doing something with AI.
Everybody is doing something with AI, right?
But what makes Ricky's unique?
First of all, they complement each other.
They are very, very, very good in working with processing free text.
And then we have a layer for,
for chatting,
or something that is a perfectly communicator,
with the chatbot,
and we have the Wiki,
or the other hand,
that is a perfect tool for control,
content together with other people,
and make it accessible,
but not also only for humans,
but also for machines.
So I have to quit now,
that,
if you want to talk with me about this person,
learn how this works in detail,
just contact me or Robert after this talk.
So what I think is,
there is a lot to do,
because that's not that easy,
and you don't throw an L&M on a Wiki,
and you get good results.
You have to do a lot,
that the information is valuable,
and that's exactly a good process,
a good project for open source communities,
researchers and companies,
who can now,
with open source software,
experiment,
innovate,
and refine to gaps,
reliable,
non-menu,
my new pull-at-heath,
knowledge,
that forms,
for the 21st century,
and I promise,
I will practice this word,
until the next four statements you there.
Thank you very much for your time.