Hi there, I'm Sandy Olnock artist here on YouTube and today I'm going to talk about how to mindlessly doodle and I'm going to do a Copic background for a doodle, but you can do that with watercolor or other mediums I'm also going to open and Baptize my new Peter Penn bought myself a new toy and then at the end of the video I will show you how in speed video form that I made this iguana drawing. All right. Let's get started I've been in a doodling mode of late and Partially is because I've did a doodling class These are drawings that you'll get to do if you take the color in line class over on my website link in the doobly-doo down below and I have been working on the ideas for these and then the execution of them for weeks now I have just been in doodle fest mode and I'm I'm excited to tell you that the class is all put together and it's no longer in pre-registration It's in full registration the video from Monday was just those turtles and Lesson one is all the other things that are in that picture And it's called color in line marker and pens. It uses magic markers of various kinds alcohol or water-based and pens On social media yesterday. I showed some before and after like what these pictures looked like before with the Copic ink on them and then the after these are the afters So I'll put a link in the doobly-doo if you want to go see the before's but I added ink to them Whether it's copic marker ink or pen and ink various kinds of things But the way I applied the color was with a cotton ball. So that's what I'll show you in a minute This was another drawing that I did For a specific reason I had a video that I posted a reel over on Instagram on my fine art account and it was an iguana And it was not this iguana. I did this iguana that I'm going to be posting today on that Channel and that's of course the iguana you're going to see in a minute, but the iguana that Went viral over there was one that was just in pencil It was just a pencil drawing that I did some erasing on with electric eraser eight and a half million views At the time that I am recording this. I mean it's crazy So I thought okay. Well, I'll do another iguana. I want to see if it's the iguana content that went crazy or if it's The eraser or I don't know what it is. Anyway That that's just why I did an iguana because I'm not an iguana person necessarily But let's first do the mindless doodle because there's lots less work that goes into this And it's very easy to make backgrounds out of anything This happens to be with copic refills And copic refills are just the alcohol ink that goes in a pen And I used colors that I don't reink very often. I mean, I just don't use those markers particularly often So if you've got some reinkers that you just aren't going to use This is a great way to get some kind of use out of them because otherwise it's going to sit on your shelf And you've bought it for nothing So, you know use up some colors Like that or you can do something with watercolor just splashing watercolor and covering the page Doing stuff with stamping inks and just squish ink all over the page and do something fun So that you have some patterns and shapes on here And that's what's going to make it easy for you to do the mindless doodling that i'm talking about And it was kind of funny that youtube sent me a link and said Hey, here's some things that people who watch your videos Are googling so you should go do a video on these things because that's what they're looking for and how to doodle Was one of the top things So that's why i'm doing this video called how to doodle So i'm layering in some colors I was in a fall mode because it is really very much like fall at my house right now I know some of you are sweltering still, but it's 62 degrees inside my house and i'm wrapped up in blankets So there's that So i picked some fall colors to do this with put a line around the outside and I'm going to show you the back of the page because Much people are going to want to know did it go through and it only went through a little bit To the next catch page. I have this little thing that i've been slipping inside the sketchbook But for some reason this paper doesn't leak very much to the other page Don't know why but i'll take it So next up is opening that peter pen I bought this for myself a while ago, but I said until I got that class done and launched and finished I could not open my new toy So I decided to do it. I bought this pen to support peter Peter degladesh. He's an artist here on YouTube who has inspired me to start doodling I was not a doodler until I started getting obsessed with watching his videos I just put them on and I just kind of like to listen to him talk He just rambles which is kind of fun And he was working with narwhal and he made a pen And it has his name on it and I know he's going to make some income from it So I thought you know, I don't need another pen But I'm going to buy it because it supports another artist. So why not? So this pen has beautiful swirls on the outside of it And as with most fountain pens screw off the top And then there's a plunger inside they call it a piston And what we'll end up doing is lowering the piston and then raising it up. It's like a turkey baster Think about it that way Sometimes it's a little hard to get it started, but then the piston lowers down you dip it The nib of the ink or the nib of the pen into the ink And then squish it back up and it sucks ink into the pen So that's what I'm doing here is pulling ink into the body of the pen And I like clear pens so I can see how much is in there and how soon I'm going to have to re-ink So if I go somewhere with my pen, I'll know how long my pen will last I just did a little test scribble on that catch sheet that I had And it felt really good that the weight of the pen itself is quite nice I like a good heavy pen and that's kind of what this one is So it was a matter of then putting it to the test and doing some drawing with it Now mindlessly doodling for me Is literally I'm just not thinking I'm tracing I'm looking for shapes that I see in the colors And I look at where there's a color shift or where there's a Value shift sometimes, you know, there's a light spot or a dark spot And I just draw lines around stuff and then start decorating different sections With textures or patterns There's just some that are my go-tos and One of the things that this will do for you if you want to do more Kind of fun doodling that's not like this guided kind of doodling You just want to take a white piece of paper and do some beautiful line work This is going to help you to develop what your style is Because the way that you trace shapes the way that you see the shapes Is going to help to figure out what your hand likes to do and what you do well And for me, I've started to discover in the past couple months That I like things that have a flow to them some kind of movement And that tends to be where my brain goes when I'm doing my mindless doodling Is filling something with a series of lines that indicate movement and depth because You curve things in a certain way and they'll end up looking like there's some motion and depth in them What you do is going to be different than what I do But giving yourself a background like this Is a great way to have something to start with you don't have the fear of that white paper Which a lot of people are just terrified of the white piece of paper But it also gives you in some instances some shapes that you might never have thought to draw So here when I've got all these little tiny bits I can trace around them And I get different kinds of lines And that's going to help me to practice different kinds of lines And I might discover something in one of these that I suddenly think well, that's really cool It would be nice to do a whole drawing with just that Or you know a section of a drawing with that kind of thing And having it all in a sketchbook like this So if you fill a sketchbook with backgrounds and then just doodle on them It's a great resource So if you're ever trying to do a drawing and you have a section that needs to have some doodling Filling in an area you've got a whole sketchbook full of ideas The class that that I was talking about earlier in this video the color in line class would serve as a great resource for that as well And I'll be using some of the techniques from the class and from this doodle In the background on the iguana So kind of all comes together And I wanted to add some darker black lines in there something stronger with more contrast So I used a Copic marker so that I could get that brush nib feel I could get that Thick thin kind of a line And just add some of it in there And trying to be strategic about it So I get some motion across the page rather than just a big little blob of something There's sometimes when I block out just like giant swaths of the drawing and it adds such drama to it But I decided on this one. I didn't really feel like doing that So just adding some smaller portions here and there to divide it into sections that are more visible So let's talk a little bit about the peter pen and the one that I got is the fine nib. It's the middle size It's not the the medium and it's not the extra fine. It's the one in the middle And I would have to say I like the weight of it. As I said, it was the first impression I got And it refills just fine and all that kind of thing It does have a few moments every once in a while and it might be the paper It might be that I don't really know. This is the only test I've done with it But it does skip periodically Like it'll do 20 lines just great and then it'll have one line where it just The first eighth of an inch it takes a second for it to get going And I don't know what that is. Sometimes it's just, you know, a pen needing to get warmed up and that might be the case here But I'm going to try it on some better paper This sketchbook is good for lots of things. It's good for lots of mediums Which is the reason I got it so that I could use different mediums in the same sketchbook for that class But there are definitely better papers for pen and ink than this one So I'm going to try it on some of that And in a couple weeks, I'll be back with another video using this pen and give you a more full verdict If you're someone like me who supports peter and you want always wanted to know whether his pens were any good This one is a decent one. I mean I I could definitely say Certainly get it in support of him But it's like 60 something bucks If you're looking for a starter pen and I do have lots of people who Leave me questions asking about You know their their first fountain pen what what I recommend I still will recommend getting a twizby eco and probably the fine nib the one in the middle Of theirs is a really good starter and it's you know, like 30 something bucks instead of 60 something bucks So this one would be more for people who just want to you know, they want to try a narwhal pen Or do it to support peter because that's why I bought the pen because you know New toy plus supporting an artist whose work I enjoy and am inspired by So there's my finished doodle And it's something very doable by anybody just trace a bunch of color Just put a bunch on the page And if you want to get the peter pen, I'll have a link for that in the supplies list Now let's take a look at that iguana And I'll tell you some funny stories while I do the drawing of this thing I have no idea why I decided to draw an iguana in that Stone paper sketchbook and pencil I just did it was my pencil week And it was just a picture that inspired me because I thought I could do something interesting with the electric eraser So maybe it was electric eraser that got it but you know eight and a half million views On any platform is just crazy huge Out of that, I you know, don't ever think that just because a video goes viral like that that you're going to get a million followers Out of eight million people a million would follow no I got like three thousand followers out of eight million So it was not enough to get them all to come follow and learn more about my work or anything, but you know, whatever so I am really curious about what causes one thing or another to go viral So that is why I decided to try the The other lizard the gecko and that didn't pop at all and i'm going to try this one On instagram and see if that takes off or gets any attention. I don't know but one of the comments that I saw A bunch of people make and a lot of people were commenting in other languages and I didn't understand stuff So I was doing a lot of google translate to see what people were saying about it But one of the words that was used regularly and instagram didn't know how to translate it was the word jogras j-a-g-r-a-s So I had to google it. I had to see what the heck is this And there is apparently something called monster hunter. I think it's a game And it says that jogras are fanged wyverns first introduced in monster hunter world These monsters are members of the great jogras pack and will flee upon seeing one of their own parish So I identify with them somebody of my own that parishes. I will run away, too They're also known for ambushing larger monsters and a moment's notice They have been seen inhabiting the ancient forest and guiding lands They're a smaller monster that travels and hunts in packs It's hide blends in with the forest environment has characteristic spikes on its back And if you chase a few away, the rest will usually follow suit They tend to keep their distance when large monsters enter their territory But sometimes the entire group will attack if they feel particularly threatened So maybe it's a gaming thing I hope all these people don't think that I'm going to have a lot of gaming content on my page If that's why they followed because they'll be sorely disappointed because that's not my thing but Coloring something like this is kind of an interesting challenge anyway And I enjoyed the process of putting all this color down and just mixing the colors to make something that looks like this crazy crazy beast So now it's time to start doing the pen work The process that you're seeing for this is the same for creating everything else in this sketchbook that I did It's all you know drawings that are done in markers And then we put the pen and ink work on top of it And if you're you know wondering can I draw these things? They're drawn as loose and rough as this is and in some cases looser and rougher It's the epitome of what a lot of people will say about my work and what I've said a number of times It's a hot mess until it's not So if you think you can handle some minimal drawing like we're not drawing anything crazy like this iguana But we're doing leaves like if you think you can draw leaves Then you might be able to handle this class And that sort of thing you'll need to be able to pick your own colors And make your own decisions about that because i'm not going to hand hold that part It's a level four class But it's just giving you some ideas On how to create some pictures that you can then add pen and ink to And in the class i'll show you the still photo of what it looked like before So you can see what yours might end up trying to look like in color and then what it looks like With the ink on it and I show you in a couple mediums each class each lesson has two videos There's like crazy three hours of content in this class. So it's a lot of fun I don't know if I need to talk about it any more than that But i'm trying to vamp while i'm waiting for myself to finish this gizzard This uh crazy lizard i've been laughing with a friend of mine On instagram about it because when it first started getting bigger And like at first I had like 55 000 views on it And nobody was commenting And nobody was following I even lost followers in that first 50 000 views So like a whole bunch of people left. I have no idea why but you know a bunch of people left And then once it hit like 200 000 300 000 A little bit more commenting started coming And then I started getting direct messages from people often it was young men And they were young men who were artists like they were saying can you come and give a critique of my work? And of course so I would because you know they asked and it was very nice to you know have them request my feedback And then they would tell me like well gee whiz you're like the only artist who's ever answered me when I've asked that I thought well, that's not very nice We're not very nice to each other if we're not giving each other help and feedback now Granted if a million people did that I wouldn't have any time to do it, but nonetheless it's been kind of interesting to meet people from other countries and see their work and what they're doing and Be able to give them some encouragement So it's been a lot of fun. I have no idea if I'll ever see any of them in a class or something, but I do hope so They tend to as I said be younger men who are just looking for some help and feedback and And growth and I hope that I was able to provide that for them The background here is done of course with just scribbles of the marker instead of using the cotton ball But it's the same idea just mushing some colors in here and then dividing it into sections to doodle into And I made it a little more structured simply because I wanted it to feel like a forest or Tree bark or something behind him since it's an actual picture instead of just a doodle But I'm using a lot of the doodle elements that I used in The doodle that you saw so you can combine all those things to create a nice finished picture And get inspired by your own doodling in order to do it So that is my two projects for today. I hope you enjoyed this if you did click the like button subscribe if you have not yet already and Links for everything are in that doobly-doo and I will see you again next week with some more inspiration to get you rolling And I hope you have a great weekend. See ya. Bye


OK, Mike Graham, I kind of want to retitle mine. It's more about, I'm not a UX expert at all, but I do have some experience in the last few years in changing what we're doing based on student feedback. So I guess the more accurate times it would be based on student feedback, improving user experience for students when using videos in Moodle, but I guess in higher education in general, actually. I work for King's College London, and in the background I started there in 2015 and I was brought in to work on the redesign of the medical curriculum, which started in the 2016-2017 year. And one of the major things that they changed was they moved students from starting their placement within an NHS hospital from Year 3 to Year 2, which meant that all of a sudden they had to deliver a lot of content within Year 1 before students started placement in Year 2. So that was a huge push to have content online. Limitations, probably a lot of you aren't new to these, but we had time and scale were a problem, so we had a very short period of time to create a lot of content. Digital literacy of the staff, many had no experience with online learning, and there were limitations on the technology, so we were moving to Moodle for the first time and we had no video streaming solution at all. So the solution that a lot of the staff went with were 45-minute narrative PowerPoints. And this was because all of these online lessons were timetabled and students were given an hour to complete them. So staff were told they had an hour of content they had to create. So this is what they created. So we then tried to infuse as much online pedagogy in them as possible. So we chunked them, we added titles to create signposting. We put knowledge checks in between them to create some interaction, and we presented them in an online way. So this is using the lesson activity within Moodle. So you've got the chapter titles on the left and then we were using iFrames to embed the videos. And then in January 2017, after the first year of being going for about five months, I had a baby. So I then took 12 months off in maternity leave and left my maternity cover to deal with most of the online lessons and all of the issues that cropped up in the first year. So while I was on leave, this happened. The videos went back to being uninterrupted 45-minute presentations. And this was in response to very strong student feedback. And they didn't like the display because it didn't show the lesson duration so they weren't able to pace themselves during that one hour that they were timetabled. So students were saying it was taking them two, three hours to get through something that should take them an hour. And they wanted the videos to have the same interface features that they were having at lecture capture. So students loved lecture capture. Absolutely, medical students loved lecture capture. But they didn't like these online lessons. Two of the features in particular that kept cropping up that they wanted was to control the speed and to view them full screen, which is interesting because I did a survey on user satisfaction pipes recently last year and asked them how dissatisfied they were in controlling the speed of their lesson. This is for online lessons, not for lecture capture. And only a third of them were dissatisfied or very dissatisfied with controlling the speed. So actually not that many. I would say not that many. In the same survey I asked them what was their usual viewing speed of lecture capture and almost two thirds of them watched it fast or really fast. So I don't know if anyone's watched a lecture capture at double speed, but it's doable. So you can understand why students would prefer to control the speed, particularly when they've given an hour to do something that will take them 45 minutes. The other thing that I wanted to look at was how often they pause. And actually over 60% of students reported that they paused frequently. So when they start something that's 45 minutes long within an hour long time slot, it does take them two hours. And medical students seem to be a unique breed of student that likes to transcribe everything. So they will take three hours to transcribe everything in the lesson. So I don't think we took that into account when we timetabled them for only an hour. Another thing that happened while the maternity leave was Kaltura was adopted. So we got an email asking who wants to be a Kaltura super user. So even while it's on maternity leave, I said, yes! I was very excited to have a video streaming server. So this is how the lessons look now. So Kaltura has a load of wonderful features. Chapters, which is brilliant, so where we can put the pedagogy back into it, we can put signposting back into it. But the simple things like being able to control speed, being able to view the duration is really important. Close captions, too. They really want close captions. So we can include them in Kaltura. So in conclusion, I want to say that there's just a need for consistency across the different video displays. And it's also important to check back in on students. So, you know, we're not perfect at it, but we're trying to improve it. So checking the viewing settings, I'm curious to see how many are viewing them with closed captions on. How many of them are viewing them with full screen? How they're revising? And also how they're taking notes. So the more videos that we put online, if they're using their computers to take notes, how are they taking notes while viewing those videos? So, yeah, that's it. I think I'm ahead of time as well.


Good morning Singapore, hello and good day wherever you are in the world joining us online. I'm really excited to be here with this amazing panel talking about one of the things that I've been occupying all of us recently, which is generative AI and specifically how it connects to what we all do on the wiki world. So I've gathered a few friends to kind of have a good chat and the idea for this panel is we've just heard in the morning panel from the wiki media foundation and what it's been developing and I think this panel is more about community focus but I still wanted to have in the room some representatives from other stakeholders. So we have Leila here from the wiki media foundation and Jimmy that no one needs to introduce kind of representing the board but the rest are community folks and this is what I'm hoping for this panel. I'm hoping that this sparks a discussion that is way way beyond this panel essentially because I believe that as a community that is global and dispersed we're all you know into doing our own things sometimes but what is happening now is massive, it's disruptive, it's changing the world, it's changing the way that we're consuming information and producing it and I really honestly think that we as a community need to have a global discussion about it and be more strategic. So what I have in plan for us today is to start a small collaborative effort to first of all map out what are the current uses that we're already being seen in the community happening naturally, organically, what are some of the challenges that we've seen but mostly what should be the next steps that we take as a global community together and hopefully this is the part where you can join us and share your thoughts and share your questions online and here in the room. So without further ado I want to begin with a really short introduction of our panelists and I'll start right from the right, at least my right, Andrew Lee. Please explain, like share who you are, although... Yes, hi, how are you doing? Hi, I'm Andrew Lee, I'm user Fos Edo on the projects in the community. Some of you may know I've written a book about Wikipedia and for the last few years I work as the Wikimedian in residence but also the Wikimedian at large at the Smithsonian Institution, which is the kind of our National Museum of the United States and with the Metropolitan Museum of Art. So I've been working a lot with those institutions on big data projects and most recently on AI style projects. So happy to talk about this topic with you all. Thanks so much Andrew, Leila. Hi everyone, my name is Leila Zia, I'm the head of research at the Wikimedia Foundation. If you're not familiar with the research team, it's the research is the team that develops models and insights using scientific methods and helps strengthen the Wikimedia research community. I'm here because as you can imagine the research team is very much involved in thinking about and also developing models using generative AI and large language models. And also we are part of the conversations and collaborations within the foundation with other teams. So I'm going to be sharing some of the perspectives of our team as well as other teams as we are going through this conversation together. Looking forward to it. Thanks Leila. Calder. Hello, I'm Calder González, I'm a member of the BAS Wikimedians user group and also a staff member there. I work mostly in education programs but also other things. And I think I'm here because I'm interested in this topic and I have been talking with this with Sani. So let's see if we can introduce any topic or something related to education. Thanks Calder. Jimbo. Hello, I'm Jimmy Wales. I have a very keen interest in all of this area. I'm not an expert in anything but I'm constantly thinking and testing and trying things with chat GBT and the other large language models. And I see a lot of opportunities for us in this and obviously some risks as well. Personally though, I'm really looking forward to the day when my role can be completely replaced by an AI that would be fantastic. Thanks Jimmy, Netha. My name is Netha Hussain. I'm a volunteer on English and Malayalam language Wikipedia since 2009. I also upload photographs to commons and I'm also active on Wikidata. I'm basically using chat GBT and mid journey, these two generative AI models to make my volunteering work efficient. And of course, I believe that you have, if you are using these models, you have to be quite, you have to use your discretion before you put your content up there online. So I will be speaking more about how to center yourself, how to have human centric AI to be able to use your discretion and put up and understand your content and then place it up on the internet on Wikimedia projects.  Oh yeah, my name is Tillman Bayer, username HAB on English, German, Wikipedia and some others. So I'm the editor of the Wikimedia research newsletter where we like summarize academic research about Wikipedia, a lot of which is about large language models or natural language processing and even before chat GBT came along. Also I'm involved in design posts with the English Wikipedia newsletter where we report a lot of going on, including on such concerns. Thanks so much for being here, all of you. You really do come from different portions or parts or corners of our movement. And I think it would be really interesting to begin with simply trying to map. What have you, what are some of the uses that you've already are either experimenting yourself with with Jenny, Jenny, I in our work in the wiki world or have seen witnessed with the caveats, right? There's one caveat. We're not going to map everything like I'm sure there are things that are going to be missing. And we're counting on you, the audience here in the room and also online to help us map out all the other uses that are currently used happening that we're not going to cover here in this panel. So let's begin with Netta. I'm actually very interested to know what your experience has been so far. Well, in my case, I experimented with mid journey. It's any generative AI technique. It's a generative AI software that will help you to create images. So I tried to create historical figures, animals, you know, I just played around with things and figured out that it would it produce, it produces pretty decent images, but not a photographic quality. So I would get portraits of, you know, Alan Turing or Adelaoules or, or any of these, you know, famous personalities. But they are not exactly as is in the photograph. So the small blemishes on their faces or, you know, the small features, they are not always adequately represented in the images that mid journey is creating. And I uploaded this content to Wikimedia Commons. And in the end, I found that they are widely reused externally, not it's reused within Commons to an extent, but externally, people are using it on postcards. They are using it on mugs, t-shirts, wherever. And I even found a website which is selling this content for money. So that's something I've been seeing with mid journey generated content. I also tried experimented with shaggy pity to try and see if it writes a decent Wikipedia article. And it really wrote me a convincing article about a medical condition, but it was completely wrong. And the references that I got, they were completely made up. So it, it, the articulation, the way the article was written was so convincing that it even lured me as a medical doctor to believing that it was true, but it was almost 90% false. So I find a use case where you can actually use shaggy pity to rephrase sentences to, you know, summarize some content, but not in the context of creating an entire Wikipedia article. I also tried shaggy pity with writing small pieces of code. So I wanted to find out I had a list of articles related to, you know, COVID-19 and how it affects different countries. So there are articles about COVID-19 in Argentina, COVID-19 in India, COVID-19 in USA and so forth. And I wanted to find how they compare with each other when it comes to bite count and article quality. So I wrote a simple code using Python. It was completely done by shaggy pity. Chaggy pity wrote a beautiful code for me to go through all these articles, find their bite counts. And I found that the article about COVID-19 in Yemen is 10 times less in terms of bite count compared to the article about COVID-19 in the USA. So these kinds of smaller research that you can undertake on your own, it could be offloaded to shaggy pity. But as I always say, it has all these, you know, generated via technology has to be used with discretion.  Thanks so much, Neta. These are quite different uses. Some of them with pictures, some of them with text. In a sense, demonstrating that this is a quite a vast world that we can be using in various different ways. And I think another maybe different aspect may come from some of the uses that Andrew has been experimenting with. Andrew, do you want to share a bit more? Yeah, hi, thanks. In the telegram group, I put in a link to some of my documentation of playing with chat GPT in the early days. So I'm not alone. As many of you probably tried, I think a lot of us are people who like direct experience with these types of tools. So I think my first experiments with it are similar to what you probably saw, which is write a Wikipedia article about X. And hopefully that X was something we don't have an article about. So you would actually be testing chat GPT's ability to scrape and collate rather than just regurgitate in existing Wikipedia article.  I absolutely positively do not want you to make up any sources. They must be true.  It says yes. And it spits out an article with fake sources, right? What we call hallucinations. So yeah, in certain situations, if the content you're asking to write is very much based on press releases, very factual information, it doesn't okay job. But anytime it goes outside those boundaries, the hallucination problems and those types of things take root. And I was talking about this with some tech folks, it's like, it's almost useless at that point, right? Because to try to fix a problematic article by checking every source, you might as well just write from scratch yourself, right? So that's, that was one of the early experiments we saw using that. But that doesn't mean I'm down on the technology. It means you need to, you need to ask it the right things to get good results, right? So in terms of being creative and being advisory, I think chat GPT is huge potential. Then we went to chat GPT and said, here's an existing draft of Wikipedia article given our policies around PR speak about advertising copyright. And we said, like, I don't want to see these type of words, I don't want to see something sounds like a press release, do you understand? It actually did a pretty good job of saying, you know, is this draft good enough to be in Wikipedia? And we actually were playing around with folks in the community like saying, maybe we could use chat GPT for new editors to say, how adequate is my draft for Wikipedia? So I think that's a better use case than just saying mimic Wikipedia editor, which is not a very satisfactory thing. And the last thing I'll add, I'd love to hear other situations here about how these are used is when used in the area of wiki data, I was very impressed with what chat GPT could do out of the box because in the museum and library world, we often have what we call kind of fuzzy metadata, right? You have an art historian saying, this artwork was created in the first half of the 17th century. And you're like, how do I put that in wiki data? Because wiki data doesn't like first half of the 17th century. It wants to say start date, end date, and then what we call sourcing circumstances like circa, maybe probably those types of things. And if you feed chat GPT, like 50 examples, saying that when you see first half of 17th century, you should put this into wiki data, what I did using quick statements, if you know what that is. And you fed a lot of things like, here's the exact date, here's the probably date, here's the first half date, here's the latter half of the 20th century. And you gave it a lot of trained data. When we fed chat GPT, new dates had never seen before, it did an extremely good job at spitting out wiki data, quick statements that satisfied what we wanted to do. So that's my, I guess my bright spot story in that for transforming data, huge potential, especially for saving our folks in our community that don't really know how to program or don't want to learn how to write Python scripts to say, hey, chat GPT, here's 50 examples. If I give you a new thing, can you do the right thing? And it has done the right thing in many ways. And that's really exciting, at least for me, to see that this is a bridge for people who want to get the advantages of coding without needing to learn how to code. And that's exciting to me. Thank you. So we've heard so far from only two panelists, but you can already appreciate the array of different things that we've been kind of doing. And I'm wondering before we move on to the next topic, if any of the other panelists have an additional use that we haven't mentioned that you want to mention, Jimmy? Yeah, I just wanted to talk a little bit about a use case outside of the Wikipedia, Wikimedia contact. So at Fandom, which most of you know is a for profit wiki company with pop culture and so on. For a long time, the community has been very eager to have quizzes, the sort of thing we wouldn't really consider having a Wikipedia, but the quizzes are fun. The community enjoys taking quizzes. They enjoy competing with each other and so on. And so, but they're very, very, you know, even though the content at Fandom is more casual in a way than Wikipedia, they still like at Wikipedia, they don't want you to get anything wrong about Star Wars. So the community enjoys taking quizzes and they have said they would like to make quizzes, but actually not many quizzes get made. It's not that fun to make a quiz. It's a lot of work. And so we've been experimenting with using ChatGBT to generate quizzes where we feed it an entry on Darth Vader to be simple and say, give us a 10 question multiple choice quiz with answers in JSON format so that we can quickly import it into a quiz tool. And again, it hallucinates. It gets things wrong. In the earliest attempts, it would get sort of three out of 10 questions would be just completely crazy and wrong. But with some refinement with some yelling at it, as Andrew sort of described, the team is getting it up to like 98, 99% accuracy, which is actually good enough that then the community can go through the quiz and just delete the ones that don't make sense. And so suddenly there's this ability to generate huge amounts of quite interesting and entertaining content that's reasonably accurate and so forth. So it's a different kind of use case from what we're using or what we're thinking about. But I thought it was very interesting. And obviously in our context, maybe there are some uses in educational settings and things like that for that that kind of sort of repurposing Wikipedia content in a new way. Absolutely. And I think what I've been hearing from, you know, just the beginning of this panel of the current uses to me says, you know, we live in a really exciting times in that sense. It's the rise of the machine people. And we get to see it live and experiment with it. There are some benefits. There are some challenges. For sure, there is a learning curve, right? And I think some of what we are seeing here is that we as a community, first of all, need to really experiment with these tools and then try to understand how we can apply them to what it is that we are doing. And we see all sorts of innovation, including quizzes and just things we wouldn't have thought about originally. And I know Gaulder hasn't spoken, but your Wikipedia has been creating mythological pictures using me journey to illustrate articles. And you've been experimenting with summarizing some of the text, etc. But I don't want to ask you about that. I want to ask you about some of the challenges because I want to move us to some of the issues that we've been seeing using these different platforms. And certainly it's not just chat, GPT, right? There's such a huge array of tools out there from create. Yes, the chat, GPTs and Claude and Bard and all of these, but there are also ones that create PowerPoints and quizzes and pictures, etc. And it's really endless and voice. They're they're really automated animations and really things that we haven't really considered properly for for our world. So I want to ask you, as someone who has been experimenting quite early on with these types of tools and doing quite innovative work in your community, what have been some of the challenges that you've been seeing? Okay, so I had prepared my first experiences, but let's go to the challenge. And one of the challenge I wanted to mention here will be let me introduce a little story. My elder daughter, she's six years old, and she came home this year like they had four first time computers in the school because we six years old is the first time they had it. And she came very excited, saying, do you know that there is a page in internet where you can know everything? And say, oh, wow, which is the page? No, it's Google. So she said, oh, you can ask anything at Google and it will give you the answer. So I would. Okay, let's ask. So she asked it where this is her actual first question. Where do panteras sleep? That was her first question. And it was a really strange question. And the funny thing is that if you write it in English, you will get an answer. But if you write it in Basque, you won't get an answer. If you read the Wikipedia article about panteras, you will have a really huge taxonomy article. But not an answer.  There is no information about where panteras sleep. But chat GPD says it correctly that they sleep sometimes in dens and sometimes in trees. So you can't get the answer from Wikipedia and you can't get the answer from Wikidata and you can't get the answer from Google if you don't type in English. You actually can't get the answer from Commons because we have a lot of images of panteras sleeping. That will be one goal how to also find these kind of answers. The second one is related to this is languages. You can have really good answers working in English. You can have fairly good answers. I have tested working in Spanish, French, and I assume that there will be also good answers in German, Russian, Chinese, Italian, maybe. I mean, the most spoken 10 languages, but you won't have really good answers in all the other 7000 languages in the world. So whenever we are talking about let's do something we have to be aware that there is a highest speed road for English. There is like a middle road for other 10 languages and there are no roads for like most of the languages in the world. And the other problem that we are facing is that as we are using automatically created text everywhere like automatic translations and so on and so on. The next models will be trained in the things we have created and the things we have created has been created by a machine. So the next language model will think that the machine created test is the correct one because it's the one which you are training with. I don't know if you understand this, but every iteration will be the language poorer than before because it assumes that that is the correct way to say things. So if we only train, if we create a whole Wikipedia with chatGPD, the next iteration will be based in what chatGPD thinks is the correct way of writing. That will be very good in English, it's great, but it's very poor in other languages. So I think these are some of the things we have to be aware. Absolutely. And I think in that sense, you know, talking about some of the challenges that go really beyond just different languages, right? Maybe the most essential issue for all of us Wikimedians is provenance, the fact that there are no references, right? Like, to me, as an educator, as a researcher, and as a Wikimedian, the first thing that I look at in any text is show me the reference. Where does that come from? How can I verify that this is true? And in essence, what all these GNI tools are doing are creating another barrier between the consumers of knowledge and the information itself or rather the source of that information. And that has been a huge, huge deal for our community. We're now trying to tackle it, at least experimenting with it slightly. And maybe Leila, you can say something about the plug-in for Wikipedia and other efforts. Sure. So the Wikimedia Foundation, as some of you may know, is experimenting in the space of LLM, large language models and generative AI. The foundation recently launched the ChatGPT plug-in for Wikipedia. And if you now go to the ChatGPT tab corresponding to plug-ins, you can get the Wikimedia plug-in. All the Wikimedia plug-in does is basically it creates a prism that you can basically use to focus on Wikimedia content. So as a user, you can ask a question of ChatGPT and then you can also ask the question of ChatGPT using the Wikimedia plug-in and then get a sense of what would happen if ChatGPT would focus on the Wikimedia content. In other words, what is Wikimedia's perspective on the answer to the question that the user is asking? So if you have not experimented with it, I encourage you to experiment. This is definitely an experimental plug-in. The purpose is to try to understand how people engage with this kind of technology and what are some of the affordances that we can have. As Shani mentioned, the issue of provenance and verifiability is core, of course, to the work that we all do in the Wikimedia movement and particularly around Wikipedia. So on that note, I will say that you're looking at applications of large language models and generative AI that can help us innovate basically in a shorter-term, medium-term and also think about the longer-term problem of provenance as we move forward. In the short-term, we think that text summarization is an area that we should invest in and we actually are investing in right now. Text summarization can have many different applications. You can think about, I mentioned this in the previous session, the perennial sources discussion on English Wikipedia, for example, has been going on for more than two decades. When we were last in Stockholm for Wikimedia, one of the requests from the community, parts of the community on English Wikipedia, was can we receive like summaries of these discussions? Because if you're a new person going to that page, you have to read basically two decades of discussions to understand where we are, unless the editors have done the manual work of creating summaries in the perennial sources. So this is one of the places that large language models can be utilized using the existing content that is on Wikipedia, in this case in the discussion pages or in the policy pages, and then surface the summaries of what has been discussed and also say where the information is coming from, because in this case it is very clear. They are basically the technologies processing the pages that are on Wikipedia and it can actually point back to where the information is coming from. So we're looking at, you know, text summarization as one piece of technology. Of course it has many applications. It can be related to vandalism detection or other, you know, article description, summary of edit descriptions or other applications. Where are the places that we can use this technology to reduce some of the load that is on the existing editors on top of thinking about future audiences? Thanks, Leila. And I think, Tillman, I know you've been experimenting quite early, right? As early, if I remember correctly, as July 2022, even before Charge.GPT was deployed with all sorts of Gen.AI platforms. And I think you can tell us a bit more from your perspective about the different challenges that you've encountered. Yeah, I mean, I was referring to an experiment that the signpost did by myself, but which direct us to Leila's point about text summarization. So the signpost has these arbitration reports, which is about reporting what the arbitration committee decided. And it's always very formalistic, like they sanctioned this user because of this evidence, et cetera. And actually went with GPT to those before the release of ChatGPT and in work pretty well with corrections, of course, not unsubmarised. So I think that was an early example of the value of summarizing our huge amounts of discussions we have on Wikipedia, right? I think that's kind of what Leila is referring to. I do want to mention, I think that ChatGPT plugin, yes, you should try it all out. I think it's generally only available to paid subscribers still, right? We pay $200, but I think the foundation has this offer that if you email that can send you an email with a signup link for free, is that correct? So just keep that in mind if you try it out. So you don't necessarily have to pay the $20 to try out the Wikipedia plugin. And lastly, I want to mention a general remark about these limitations and challenges. And we really need to be aware that this is a field that's really moving very quickly. And for example, yes, it's right that ChatGPT doesn't cite sources and it's outdated. But we've had the Bing AI, which Bing is actually citing very current sources, right? So if you want to try out a writing Wikipedia article, you should actually not use ChatGPT, you should try Bing, which at least tries to give you current references, which may still be hallucinated, but you have a better chance of getting this to work. And also looking forward, it's again, things are moving very quickly and the people who are involved in this field, even the full-time AI researchers are kind of overwhelmed, right? There's several artists who are like, oh, God, I don't know what's going on. And every day, there's new research results and new products. And we all need to keep this in mind, what's not working today may work tomorrow or what's not a problem for us tomorrow. Today may become a problem tomorrow, so I just want to advise some general caution and open-mindedness. Thanks, Thielman. And I guess this is again, just the beginning, the tip of the icebergs in terms of the challenges that we're facing as a global community. There have been additional ones mentioned in today's session, including attribution, including the whole issue of copyrights. There's a whole issue of the general public simply not understanding that they need to critically look at the content. So there's a digital literacy or rather, generative AI literacy that we need to kind of attend on a global level to new audiences communicating with these tools and how it's impacting us because more and more they will go to different tools and less to our platform. So it raises all sorts of questions. And I guess that brings me to maybe the core reason why I wanted to actually facilitate this session and have this global discussion and for you to start helping us mapping more systematically both what is already happening but also the challenges is that we can also then take the discussion one step further and kind of talk about what are the needs. Leila kind of prompted us this morning to ask to request to and to request we need to kind of agree on what it is that we need. And I can start us off by saying, you know, Andrew mentioned wiki data and I see Danny here in the room. Right now, generative AI, large language models are not using wiki data at all. They're just using wikipedia, tiny portions of wiki source and tiny portions of one of our other projects. I'm not sure which comments maybe but they're not including a huge amount of what is it now over 105 million items on wiki data last time I checked and now wiki functions launching. So there's the whole discussion of how this whole world of linked open data is connected to generative AI that to me is an unsolved issue that we still need to kind of grapple with. So that's another issue down there down the road that we kind of need to explore. But I guess to me it has to begin with simply mapping out what's happening and maybe it's a good time to say we've been doing again stuff in different corners of the movement. There are some wiki pages. There's a telegram group. You're all welcome to join. There's a Facebook group. If you don't like telegram or vice versa, there are various corners but we don't have one space where we all come together to talk about these and I think we should. I really do. So this is about you, all of you including the people at home and we want to open the floor in a sense to hearing what do you think should be in the next steps. I'm going to start us off actually with the panelists. So I want to ask each of you to say one thing that you think is like tomorrow morning, what do we need to do as a movement to start strategically thinking about us? What do we need? What is missing right now for us to tackle it in a more strategic way? I'm just going to do it by order. So Andrew. Oh. Not to put you on the spot or anything. Yeah, I think I guess this is looking beyond the chat GPT horizon which I guess I don't know lately you can tell us more about it but when I was at the Hackathon in Athens earlier this year, I was very encouraged to hear that there's going to be a adoption of a framework to experiment more with machine learning, right? This is called lift wing. I believe it's the first time I ever heard it was in Athens earlier this year. How many people here have heard of lift wing? Okay, not that many folks, right? So I thought it was pretty cool. So you may know that we already have some AI being used in the movement at scale like ORS, the Objective Revision Scoring System we have is its own machine learning system and there's other machine learning systems. So I think lift wing is a way to have a common platform to plug in and have these places in one space and potentially that'll be a place where community members can write things that plug in as well. So I think that's what I'm most excited about is providing the same way that we have things like pause is like a Python platform for our community to easily write Python scripts. If we have a framework like lift wing that allow people to very easily not have to buy a subscription somewhere or define computing power on Google Cloud and we actually have a framework for people to start experimenting I'm excited about that potential. Thanks, Andrew. So I'm hearing a platform to experiment with as a first step, Layla. Yeah, and maybe just to follow on what you shared, Andrew. So if you haven't checked out lift wing, you can. It's online and there is, I think, I believe there's also a session on the topic. I'll come back to you with that. However, what I wanted to say about lift wing which is a technology developed by the machine learning team in the Wikimedia Foundation is that the team is really centering their thinking around how to enable the community to govern the machine learning models that the foundation or AI models that the foundation or the community develops. This really is anchoring right now around the one piece of way of thinking which is called model cards. These are some of the latest recommendations by experts in this space that it is important for every machine learning model or AI model that goes to production to have a model card where actually it is clear where the data for this model is coming from, what is the technology that is being used for developing, for training the data and developing the model. What are the potential implications of using this model? So these are all documented. So if you look at for model cards, Wikimedia page on meta, you can see which models are right now in production and what are the model cards corresponding for these models. And I really applaud the work of the team on this front because it's really opening this space for everyone to engage with this piece of technology in terms of governing it, by the community members, for the community members and the projects. Maybe if I can go back to your question, Shanee, about what is one thing that we should talk about? It's a hard question. It's in my heart to say it since the previous session so I say it here that I think we haven't really talked about Wiki Source and the importance of Wiki Source in this conversation. As we're talking about languages and the potential of leaving languages behind, we should remind ourselves that a lot of the content from a lot of the knowledge that we're talking about is not actually available anywhere on the web. And Wiki Source as our online digital library is going to play a key role for helping that kind of content to even come to the internet and get connected and be on the web so that we can talk about the kinds of technologies that we're talking about. So eventually we can build if we want to and if it's useful, large language models for other languages. I'm really hopeful that Wiki Functions at some point will help us address that. No pressure, Danny and James. But yeah, I think that in the further future we will have to address the language issue more systematically and Wiki Source is part of it, Wiki Data as well. And by the way, how cool is it that chat GPT can now give us sparkle and like, I love sparkle, but we don't need to know it anymore to actually write queries, just in parenthesis. Galdor, what is your wish? So in Buzzweek Media, we are now with a project of multimedia content like pedagogical videos, say, added to articles and which are especially expensive. So we have been testing an idea about what about automatic podcasts?  So you can hear it like every morning in Spotify where you are driving because you can't do that. I mean, it's possible to do that. Well, this is possible in English actually. We have this tip and it's great, but only in English. Well, it's actually possible with some other languages, but not for us. So, could we do that? Could we do something like that, like the featured article, for example, in any Wikipedia could be summarize, text-to-speech, add it with a music, you select free music from Commons and then you have it like in other media, like in Spotify or whatever podcast or whatever you need for that. And this is something we are researching, like not heavily researching, but we are trying to do that. But is there a specific need that you see right now? Yes, the problem is that we can't do that in other languages that are not English. And the problem is that summarizing in other languages is very difficult. The problem is that text-to-speech is available in Basque, but it's not available in most of the languages in the world. And you have like 40 different speakers in English, like with different accents, but this is reduced to one and very computer-like robot-like in most of the languages in the world. So, I think that if we want to do something like that, we need a lot of steps that are beyond what simple Wikipedia's, even if you have some money to invest in that can do, because we are talking about very large models, we are talking about technology that is out of the scope of any user group, and it's something that Wikimedia Foundation could invest, but not only the Wikimedia Foundation, it's something that goes beyond Wikimedia Foundation. And I think that, well, I don't know how, but let's dream on the kind of ideas. To me, by the way, that also implies having better tools to monitor the content that is, the flux of content that is being produced by these machines, like in a more efficient way. I mean, we've been doing some of it already, but in a sense, maybe working on that as well to make sure that our volunteers, I think it was mentioned also in the morning, making sure that volunteers do less mundane technical stuff and do more things that require actually a human brain. So, helping our community with tools would be some of the implications, I think, of what you're talking about, but I'm not sure. Partly. So, so. I don't actually know. But what do we need to do that? I mean, it's beyond my... We can imagine things. Actually, we can do it by hand, and we can fit an article, summarize, translate, fit two artists to speech, then add somewhere in commons, and then build a podcast in Spotify. But that's not the idea. The idea will be something like, for example, anyone in the world can open an article, just click at the button and have a good summary of what they are, I don't know, cooking or driving or whatever, or just working in the factory and they can hear knowledge. I mean, that idea will be different, and I don't know which infrastructure we do need for that. It's way beyond my knowledge of what the infrastructure we have, we can have, and they are actually possible for the Wikimedia Foundation. Thank you. And now that Mike is moving to Jimmy, I want to invite the audience to start writing questions if you do have them, so we can see it and address it in a few seconds when we finish the round. So if you do have questions, post them. Jimmy, what is your wish? Well, I mean, I think I want to leave enough time for the audience, so I don't have anything specific to add. Other than to say, I think we all need to be really actively, intellectually engaging with what's going on in this space. It clearly is going to have huge impact on our work, there's huge opportunities and huge risks. And so I'm happy that we're having this session, but I also think in two years, three years time, we're going to be having a load of sessions and a load of different things going on that in some way are impacted by this generative AI. Thanks, Jimmy. Netha. Yeah, I have something to add on to what Galdar and Shani said earlier. This is concerning myths and disinformation. So we kind of know that with generative AI, it's quite easy to turn out a lot of content which looks really convincing. And in future, or already in the present, there are probably people who are generating content from ChargeGPT or any other generative AI platforms and trying to put that content up on Wikipedia. And it's quite hard to detect if that is myths or disinformation because they sound very convincing and it's not exactly blatant misinformation. People are probably putting that there in good faith and they don't really know that this is misinformation. So my wish is to have more tools to be able to detect this kind of subtle misinformation and also to revise our policies and to establish new practices where you can easily detect, more easily detect misinformation and reduce the burden of volunteers to keep on the watch, be vigilant for this kind of vandalism. Thank you, Netha. Tillman, last but not least. Yeah, I want to encourage us all to think a bit more what it means from the reader perspective which we haven't talked a lot about. So we tend to have a good understanding of how people read Wikipedia articles. There's been a bit more progress in recent years. But I've seen several anecdotal reports of people saying, yeah, actually I find myself only going to chat with you instead of Wikipedia now. And yes, I know it's hallucinating, but you know, Wikipedia has vandalism too. And also, yeah, we're not the only website on the web where people go to for information. And if you look at, for example, Khan Academy, which is a bit, which is like our competitor, but if you want to learn about the topic right, you can go to Wikipedia or you can go to Khan Academy, watch their videos. And they jumped head into this, why they built an AI assistant based on GPT last year. So there's a big contrast to our movement, why we like kind of set back and let it happen. And we'll look at Stack Overflow where we have been very concerned about where their readers go and there's a discussion which is happening. So yeah, my wish is to get a bit of better understanding if the readers are leaving us for chat to what happened. I mean, maybe the foundation has looked at this. I mean, it wasn't mentioned as far as I'm in this morning session, but if there's data on this, what if you're losing readers traffic and then secondly, just experiment more with reader tools. And there's some things like I cash people to check out, for example, Santos question answering bot experiment, maybe I don't know if you'll mention in the talk, but there's some experimentation going on, how we could integrate generative AI with our articles for better reader experience. Thanks, Thielman. Thanks to the whole panel. I guess I wanna throw my own ask and mine is quite a humble one. I think we're at the very initial stage of like this global discussion and we should be much more proactive. I just want to have a space like one designated place where we can curate all the work and make sure that we engage everyone for a starter. So that's my ask and I wanna include a comment that we got from our online audience and this is someone saying, thank you first of all for engaging. Should the movement play an influencing role highlighting responsible tech or AI for tech organizations of all kinds? I think that's a great question. I think we've been already doing it and will continue to be doing it, but maybe Jimmy, you wanna say something about that then Leila? Yeah, I mean, I think that the question, I mean, to me that's an obvious yes. I think we have a strong voice about technology and ethics and being responsible and being thoughtful and I think we are already are. I mean, when I had a call with Sam Altman and my very first question to him was, how is the work going in terms of not hallucinating references because that's for me one of the most problematic aspects of the current thing. The answer is it's a really hard problem, but yeah, I think we definitely should play a positive role in talking about positive uses for technology and education and so forth and responsible uses. Yes, and in that sense, responsible to me expands to free also because I think one of the highlights of what we do is making sure that every single human being can access knowledge and I think what we've been seeing growingly because these tech companies, all the chat GPTs and et cetera are managed by for-profit companies. The reality is that some of it is free but many, many of the attributes, the possibilities are locked or require further payment. So to me, that creates an even wider divide in the world between those who can pay to get access to accurate knowledge and those who can't. So that's another thing for us to kind of consider as well as advocates for freedom of knowledge as a basic human right. Leila and then Galdur. I think my answer is also yes. I think the question is like how much more and in which ways some of the work that is already happening as some of you know, I mean, we as a movement and Wikimedia Foundation as a foundation, we take our values or guiding principles seriously and we put those into work in the context of AI and large language models. For example, the legal team in the foundation is working with the research team and machine learning teams on developing AI human rights checklist. This is a checklist that is going to help us understand what type of technologies are we going to basically productionize and put on lift link or other platforms that we offer in the future. And this is going to guide us and this is the kind of thing that at least by doing it we can help influence others and lead other companies or organizations. Communications plays a key role in this space. We can do so many amazing things but if we don't communicate and we don't communicate proactively and when I say we, I mean all of us with all the different affordances that we have where we are in different parts of the movement. So I think communication is also a key aspect for influencing but we should definitely do that, yes. Thanks, Kaldar. Okay, this leaves us roughly 10 minutes to hear from the audience. So any questions, any comments? Yes, you, can you bring a mic to him? There are mics, okay. Hello. Hi. Jonathan, Director of Engineering at Wikimedia Deutschland. I work in AI as well and with AI ethics sector. I was wondering about the future of not just Wikimedia but our effect on society in general. Two factors, positive and negative was curious to your input on this. One is not just all large language models ability to proliferate disinformation and when I started at Wikimedia I found it to be a balancing act for disinformation. I know that some can be passed through Wikimedia or Wikipedia but I was wondering if there's a way I think the AI checklist would go along with this as well but just the idea of how we could be in elements against disinformation now that the large language models are hugely like can emphasize it dramatically and in a slightly different direction and pick your choice but on what's like larger population Wikipedia articles if you want to retrain a large language model you only need a small amount of data and so you can take what the English large language model is doing and then convert it to many many other languages depending on that scale and I agree. I was wondering if we've looked into how much of our data can be used to do that. How many Wikipedia articles do we need to retrain an existing large language model on BASC since you're here. Whichever direction you wanna take. Who wants to take the first one? There is a small problem, not only about languages it's also about what each language or culture will highlight on a topic and I mean we have been doing that for example summarizing science articles for children in Chiquipedia and our children's encyclopedia where there is not a large bias like I mean the article about Jupiter will be slightly similar in every language in the world but not for other topics and maybe introducing a cultural bias will be also a problem there but that's something we have to figure also with Wikipedia and with many other things. Yeah I guess in a sense to your question to me it goes beyond misinformation, to me there's an unholy trinity of misinformation, disinformation and actually missing information and I think our community has been trying to do slowly, slowly, by and by in various languages consistent work to close different gaps. We have tons of work ahead of us but I think it only highlights what's happening with Gen AI is only highlighting the importance of what we do and why it's so crucial. Netta is sitting here, she's systematically self-handedly curating medical content related to women and consistently working to close gaps in that space right and that's just one tiny example. There's so many other gaps, so much missing information also and that gets perpetuated and internal bias that we all know from society, from our history as a human race gets perpetuated by these machines that don't know any better so in a sense our role has never been more important than it is now and to go back to the first comment of do we as a movement have a say? Absolutely, who else if not us really? Like who else is able to tackle it on a global scale than all of you here in the room? I think it's our job and I would argue actually that we are probably better equipped to do this than other folks just because we have the understanding of hey, what is references? How do we construct knowledge properly? So any other comments from the audience? Yeah, Doug. Thank you, I've got a comment and a question. The first comment is you mentioned sort of the list of platforms which the community has to discuss artificial intelligence. I just wanted to add one more. Since 2018 we've had a artificial intelligence group on Meadowiki and we've got a plushie, the dodo. The question I have is could anyone just summarize what is the current resembling consensus on how to use AI to edit Wikipedia at the moment? Is there even a consensus? I know there's a lot of discussion on this issue. Okay, I can try to tackle it because I've actually been beginning or attempting to research the topic in various studies that I'm conducting at the moment. To my knowledge, which is limited and here all of you are to help me if I'm incorrect but to my knowledge there is no policy. There are individuals working and experimenting but we don't have policies. We don't have case studies like bits and pieces of it, right? Which is why I wanted a panel. Like we need to be so much more coordinated in this work and so much more strategic. So it's in a sense just the beginning. We're in an experimental mode and different people have been experimenting and sharing their experiences in various corners but I haven't seen just yet like full policies around changes of policies or tools at the beginning. We see the beginning of experimenting with tools but nothing so massive coming from the community just yet that I'm really hoping will spark that, yeah. I mean, if I could just say a lot of our existing policies and practices already helped quite a lot. In other words, things like when you make an edit to a Wikipedia entry it should be well sourced. It should be accurate. It should be true and you're responsible for what you've done there. That remains true and that's actually very helpful. So it's a reason why we don't necessarily need a rule that says don't just ask chat to BT to write an entry and then just copy paste into Wikipedia with made up sources. That's already against the rules because it's gonna have made up sources. That doesn't mean that there won't be any sort of tweaks. I mean, I think certainly we have bot policies that vary across languages. Can you use a bot to do X? Well, can you use a bot to spell check? Well, yeah, but there's parameters around that because spell checkers are sometimes wrong and so forth. Can you use a bot to whatever? And I think that those will begin to emerge. The kinds of things I'm interested in is using chat GPT and AI to suggest things to editors. I think there's a real opportunity there and I can imagine someone making a bot that only writes on talk pages that says, hi, I'm the bias bot and I found these statements in this article that don't seem to be supported by the sources. Maybe a human might want to look at that. I think people might welcome a bot like that as long as it's generally not a waste of time and so forth and we don't yet have a policy or need a policy about things like that but it's the sort of thing that I think is going to emerge over time and that fortunately nobody thinks, great, we'll just use bots now because that would be a disaster. Thanks, Jimmy. Yeah, I would just add that. Just a second, we have two and a half more minutes and I promise, Thomas. If you go to the English reviewer, W, colon, LLM, that's a policy job which is pretty far along as further reading to cover some of these use cases. Thomas. So there's been some mention about how LLMs are not amazing for generating new content from scratch but they're all rights currently reformulating existing content into different formats. One of the things that Wikipedia in particular traditionally is terrible at is video content and there have been some experiments in the past but they've tended to be quite manually intensive. Have you seen any experimentation using generative AI in any sense to reformat Wikipedia articles specifically into video? We are doing it at Buzz Wikipedia. I can't talk to you later because we have one minute left. Talk to you later. Just a quick note, I admire Bass, Catalan and those Wikipedia's can move quickly and try to experiment with a lot of stuff. I don't think you have a hope in English Wikipedia that they have a very big bias against visual content. We need to fix that on another occasion not during this panel but I think Thomas to your question we're at the very beginning of experimenting with let's say generated avatars or generated videos even from text which now there are specific tools that can do it to illustrate certain articles. I'm sure the community will discuss and continue to do that but that's a good question, thank you. Last remarks, we are less than a minute from finishing the panel so I want to thank everyone who participated. I think we've just scratched the beginning of this and I want to first of all say a huge thank you to Andrew and Leila and Galdor and Jimmy and Netta and Tillman for joining today and sharing from their experiences. I'm hoping you're all going to help us curate some more information online and that we can continue as a movement to have this discussion of what it is that we actually need to have as next steps. Please stay in the room if you're interested in the topic because we're going to have another session connected to AI just now. Enjoy the rest of the day and thank you. Shani, one last thing. There was interest in people wanting to continue the conversation so if you want to do lunch back there in the roundtables you can grab a bento box and people want to talk about AI you can lunch back there, I'll be there and some other folks will be there too. Perfect, thanks.


So, today guys comes a hotfix for Borderlands 3 adjusting Vault Hunters, Weapons and a few other things. Has it gone guys, my name's DPJ and today I bring you another BR3 video. If you do enjoy leaving a like really helps out and subscribe if you do want to see more. Also guys for all your gaming needs no matter what platform you play on at amazing prices check out G2A linked within that video description. Okay so today comes a hotfix for Borderlands 3 and I've literally just dropped a post on it so we'll go through the entire thing. Today we will release a hotfix for BR3 which will be live on all platforms by 12pm PST. This hotfix begins two new events. Slut machine mania and trials take all and includes some buffs to the gunner, weapon balance and addressing reported community concerns. The doorbosses mini event ends at 9am PST today, this was the event where legendos were seen in vending machines across the board. We are also excited to start two new events, Slut machine mania and trials take all, they go live with the hotfix today. Slut machine mania pays out big time, there's an increased chance to win legendos gear from slut machines and for gunheads to spawn because who doesn't like a little risk. There's still a chance for the other loot to drop but the rate has been reduced so you can win legendos and take damage. Trials take all, boost the rewards awarded during the proving grounds trials, buses will have a 100% chance to drop the loot from their pool during the event. Activate Slut machine mania mini event until 9am PST on April 9th. Activate trials take all mini event until 9am PST on April 9th. Adjusted the pearl of the nephroborn knowledge artifact to do plus 90% damage and updated the item description to show the correct value. This is an artifact I did indeed cover where it was well people thought it was glitched, but it was actually I calculated doing 136% roughly boosted damage over what you thought it would be which would be going by the description of it around that 35% damage boost mark. But it says here that they've adjusted it to plus 90% damage so that's a drop over what it was under my testing but they've also updated the item description to show the correct value too. Just a report of concern that the siren stone class mod was sometimes increasing damage taken instead of decreasing damage taken and that does sound like a problem. Adjustments for moles of the gunner. We've been taking a closer look at moles the last few weeks, here at the first set of hotfixes based on those investigations, primarily focused on improving her survivability. We will be further adjusting some areas we identified as trouble spots for her in the future. Reduce the cooldown time for iron bear. Increase the amount of fuel for iron bear. Increase the bonus on behind the iron curtain skill. Increase the bonus on security bear skill. Increase the bonus on experimental munitions skill. So are you moles players out there? I'm sure that will interest you. Weapon balance. The damned. Increase weapon damage. Hyperion weapon shields. Increase the amount of damage weapon shields absorb. Pretty cool. The lob. Increase fight rate but reduce projectiles from 3 to 1. The lob was OP. Let's not lie. It was ridiculously powerful. It was probably the most powerful weapon in the game but they've dropped that damage which is just crazy. What we see moving on that they ain't touched the anarchy which in my opinion is a better shotgun so looking at them nerfing this is probably going to be a nerf coming for a few other weapons too. Well done again. I'm not sure because the lob was ridiculous let's not lie to ourselves it was scandalously powerful. At least with the anarchy you have to charge up that damage by stacking it but we will see people we will see. The tiggs boom. Increase weapon damage. Increase damage from star form meteors. That's pretty cool. The tiggs boom is a weapon I think many people have forgotten about so I will test that and bring you guys my findings. The moon fight. Increase weapon damage. Increase critical damage bonus. So that's pretty cool too that is a pistol which was pretty good. I do remember it being pretty good but again I forgot about it. The good juju. Non-critical hits. Now increase critical damage bonus by 20%. Now as we know the good juju is a reference to a weapon from destiny. The destiny universe called the bad juju. I'm not sure if the bad juju in destiny had a perk called head seeker but this acts a lot like head seeker. Body shots increase headshot damage. This bonus stacks 25 times and is removed upon manual reload so that's pretty cool too. So with the good juju as long as you keep hitting body shot damage you'll get bonus headshot damage. And this bonus stacks up to 25 times as long as you don't manually reload it.  That could be a good weapon to test also so that's what I would do people and bring you guys again my findings. But those guys are the changes that land with a hut fix which comes today April 2nd, 2020. 12pm PST which is later today UK time from this video being posted. So yeah guys tell me what you think about this down below in that comment section. Tell me what you think about changes to it a lot but I'm interested to know your thoughts. But I'm not guys we have come to the end of the video. If you guys enjoyed it leaving a like really helps out. If you're new around here and want to see more boy lands be sure to subscribe and if you never want to miss a video upload can turn notifications on by hitting that bell button. But guys thanks as always for stopping by hopefully you enjoyed the video and hopefully I will see you on that next one.