 Go Bob. Then I think I can take it from here for a brief intro to the award session. Welcome back everyone. Okay, so we are at the Wikimedia Foundation Research Award of the year session. We're going to spend the next 15 minutes just celebrating the work of the researchers who have spent substantial work during 2021 to research and develop tools, datasets for the Wikimedia projects. My name is Leila Zia. For those of you who are joining just now, I'm the head of research at the Wikimedia Foundation. I had the pleasure of working with Benjamin Macohill from University of Washington to review the work of 2021 and choose the the winners. Jimmy Ways is also with us and Jimmy is going to present the award to the recipients. I'll pass it to Macohill. Macohill, please take it from here. Okay. You guys can you hear me, Leila? Yes. Okay. So let's, all right, so you're driving the slide. So I am thrilled to be able to introduce the this year's, this is the second annual sort of Wikimedia Foundation Research Award of the Year or WMF RAY. The goal of the awards is really to recognize and celebrate research that has been done recently, which has the potential to have to really sort of like this both great research and which also has the potential to really have impact to change and positively impact Wikimedia projects and other Wikimedia research. The committee this year was myself at the University of Washington in Leila Zia who you guys heard plenty about, but who's the head of Research at Wikimedia Foundation. And the eligibility for receiving the award are sort of several things. First, the research must be about or using data from or sort of on in some sense, Wikimedia projects or have the ability to impact those projects indirectly. So if it's not about Wikimedia directly or uses data from other source, but is sort of framed in a way that has sort of clear sort of impact on the goals of the Wikimedia movement and the projects, then that's sort of what we're looking for. The papers have to be sort of recent. So for this year, the requirement was that the paper had to be published in the calendar year 2021. There's lots of great stuff that we looked at that was published that has been published this year or that will be published in the future. And we look forward to coming back and visiting those things later. Because the committee is the two of us in English is I believe the only language we have in common. There needs to be a copy of the manuscript in English, although it is often the case that work is published in more than one language or with the translation. And critically, there can't be a conflict of interest between the award committee, that is to say myself and Leila and the and the authors of the paper. So there was a lot of really great work that that, you know, I'm like, wow, this paper is really good. It was like, actually, that person's on my team. I'm like, oh, that makes sense. This this work is really good. Oh, yeah, they're in a student in my lab. So for all the I did see a couple. There's at least one student in my lab out there. So know that the reason you're not being recognized is I'm sure only this but in fact, there was tons and tons of really, really great work that we had to evaluate we we we had a spreadsheet with more than 230 papers, which we identified in a series of different ways. We had a public call for nomination and a number of people submitted some really excellent work, including some self nominations as well, which are welcome and really encouraged. There we went through the list of all research that was sort of tweeted or retweeted by the wiki research account. If you don't follow it, it's a really fantastic way to stay up to date on new research that's happening in the sort of wiki world and within the wiki media sort of research universe. So we looked at every tweet that was there. And then we also after doing this did an extensive Google scholar search where, you know, Layla and I were looking how many pages that we're going to go until we're going to keep going. So we we went we went deep to identify things that we may have missed and we've seen most of them, but we did identify a few other great candidates that way as well. This year we're giving to restructuring things a little bit differently. We're giving decided to give two awards. The first is the WMF Ray 2022 award, which is an award that we're using to recognize sort of the best paper according to our evaluation that sort of uses wiki media data to understand and improve wiki media projects or the broader web ecosystem, which opens sort of critical questions, you know, all the stuff that I sort of mentioned in terms of the goal. And then we decided this year to actually recognize a best student paper, which is a paper that does all those things, but which also was written by a first author who was a student at the time the paper was published. So this way we can sort of highlight some of the really great work that students are doing in the space. So that was sort of the goal here. All right, let's go to the next slide. So there is a physical award and a certificate, which will be given out this year. So a number of you will know that wiki medians, people within the sort of like broader and actually on other wikis as well, tend to give these virtual awards called barn stars to each other. And their barn stars are real things in the world. There are often pieces of metal that are sort of like attached to the sides of barns, but there are these virtual barn stars. These are these sort of pictures of words that anyone can give to anyone else for work that is really special. So if you notice someone on wikipedia, if you're a wikipedia, and you notice someone on wikipedia who's really just going above and beyond, you can go and give them a virtual barn star, this little award and say, I award you this barn star for doing something really great and important. We will be sending physical barn stars to each of the winning groups, which means we'll be in contact if you're one of the winners, about a physical mailing address. And I should say because I noticed Kai, who was one of our winners from last year here in the room, we actually failed to send physical awards to the winners last year. So one, we apologize, and two, we're including you in the process as well. So we're starting the process now and we've actually made a lot of progress and identified some. We're ready to get these sent and we apologize for being late for the people that won last year. So before I turn it over to Jimmy Wills, we'll be announcing the winners. The process is going to be that Jimmy's going to introduce each of the winners of the awards and tell you a little bit about the papers. Everyone will be invited to unmute and clap, and then we have invited the authors of the papers to say something for a few minutes. And with that, I am going to turn it over to Jimmy. Great. Thank you. Great. So the WMF Ray 2022 award goes to a paper that describes a new dataset. The dataset builds on data from the Wikimedia projects and has the potential to enable and significantly accelerate research and development for adding captions to images across more than 100 Wikipedia languages. Today, more than half of Wikipedia articles are un-illustrated and more than half of the images available to us don't have captions. Wikipedia needs more images that have captions in local languages that support different learning and accessibility needs on the project. Captions are also very important in proving the search experience of users on Wikipedia. Manually adding captions to large numbers of images, particularly in under-resourced language communities, is an enormous task. In recent years, models have been developed to automatically generate captions for images. However, these models are generally biased towards English and Western content due to a variety of reasons, including biased training sets. The researchers and engineers behind this paper take the Wikipedia data as already publicly shared, process it, and ultimately make it immediately usable by many members of the scientific and developer communities. All signs indicate significant impacts by this paper. Less than a year after publication, the publication already received more than 30 citations. Wikimedia Foundation organized a public image caption matching competition based on the dataset, which in turn has resulted in at least four open source solutions for automatically retrieving text closest to an image on Wikipedia. A new community has come to life with a focus on multimodal and multilingual machine learning research on Wikipedia, with the first event of this community, WikiML3 Workshop, to take place on April 29th as part of the ICLR conference. For all these reasons, we award the WMF-RAY 2022 to WIT, Wikipedia-based image text dataset for multilingual machine learners, a mouthful machine learning. So now, I think Mako is going to get you to unmute, and that's great. Okay, so we'll move on then to the student award. Wikidata, the free and open knowledge base, is enormously important to the Wikipedia community, as well as the broader ecosystem that Wikimedia operates in and serves. Across the world, people and businesses rely on the statement stored in Wikidata for a range of activities, such as making new content available in languages that we don't have content in, building smart systems, training AI systems, and more. Many of the statements in Wikidata come with references. According to Wikidata's community policies, these references are to meet three criteria, reference, authoritativeness, and ease of access. Wikidata's quality and reliability and its impact depends upon the fact that its references are generally perceived to be high quality in all of these three senses. The WMF-RAY 2022 best student paper award goes to a paper that seeks to understand how high quality the references in Wikidata really are. This research evaluates the state of references in Wikidata. It describes an enormous amount of work that involves a creative set of methods that combine multiple rounds of automatic and manual assessment into a complex and multi-stage research project. It conducts its evaluation using Wikidata community's own guidelines, making its result directly usable by the community. Its analysis is conducted in six different languages. It provides a detailed report card for the Wikimedia community about the state of Wikidata references. It makes the full data encode for the project available so that others can reproduce this analysis as the community improves and matures. And it was conducted by a team led by Gabriel Amaral, a PhD student and research associate at King's College London, whose dissertation is on this topic. For all these reasons, we award the WMF-RAY 2022 best student paper award to assessing the quality of sources in Wikidata across languages, a hybrid approach. There we go. All right. If I could just add just one more little thought, sort of a personal thought about these. For me, one of the most important things, one of the most fundamental and important things about the world of Wikipedia and Wikimedia is the global scope of everything that we do. And by global, that means not just languages, but also accessibility in all of its forms. And so I think both of these papers really are incredibly helpful for that. I love the concept of giving machine-assisted tools to the communities to help them do their work in a more efficient way. Let computers do what computers do best and let the humans do the creative and fun, clever stuff. So thank you very much. This is fantastic. Wonderful. Thank you, Jimmy. All right. So I think that we're going to try to do, one thing we wanted to try to do was do a group photo with the winner. So maybe let's do the first piece, the WIT team. So I think that the process, what's the best process here? We want people that are the winners to maybe turn their video on and the people that are not the winners to turn their video off. Is that how this is going to work? That is correct. Okay. All right. So if you did not win the award, better luck next year. And if you turn your video off, if you did win the award, if you're from the WIT team, first, maybe turn your video on. Is this...