Hi everyone, Joe for Jaspy's casebreaks.com, coming at you with 2022 Panini Immaculate Baseball 8 box, pick your team number 9. All cart chip in this. We've got another case loaded up too if you want to run this back. Big thanks to, big thanks to a lot of people here. Big thanks to the people who bought spots straight up here in pick your team 9. David you ended up with that last spot mojo with the diamond backs before we pulled teams for those two random number block randomizers which we did in separate videos. Got to change the toner there. But thank you, alright. There's one number block right there. We did that a little bit earlier today but there are the results for that. That's for that set of teams. And then this is for a bigger set of teams right here. That's the one we just did recently. Keep that one here, let's see if we can keep this one over here. But if it or not, I don't really have a lot of space here. Let's move that over there, let's put that on the cart over here. Alright. And it's a full case break. All eight boxes. Here we go, box number one. We'll use this blank card with a little Jaspy sticker right here to slow play the hits. Big fan of the Immaculate Baseball. Thanks everyone for getting in. Alright, we're going to lead off with Byron Buxton, nine out of ninety-nine, dual relic for the twins.  Tony Santolin, one out of ninety-nine, two color patch and autograph again for D.Y. The Red Legs, Ron Cincinnati. Redemption, shadow box signatures, Max Scherzer taking it to the max. Now is this Metz edition of Max Scherzer? Still Dodger's edition of Max Scherzer? Let's go to Immaculate Baseball, let's go to the checklist on CardboardConnection.com. Max Scherzer, shadow box signatures, card number twenty-four, he's a Met. Max are a number block team, part of that big group of teams there, so that goes to Metz zero. That goes to Jeremy, Metz zero. Yeah, tons Tommy, check the site, jaskyscasebreaks.com, a lot. Four out of twenty-five, we've got Tim McCarver, player edition of Tim McCarver. That goes to, also goes to the big number block group. St. Louis six, St. Louis four, I mean, can't read. That goes to the X-Line and the Cardinals, last spot Mojo strikes again. And then we've got Kyle Muller, four out of forty-nine. That goes to the Braves, number block group, that's the other number block group. And Braves four, number four? Braves number four, Martin. Alright, nice, yeah, that's for the X-Line and the PCs, the Cardinals guy. And out of ten, Brian DeLaCruz, that'll be for the Marlins. Part of the big number block group. Jeremy with Marlins zero. Go by that digit right there. Got a train whistle, that's Yas, Carl Yastremski. Old piece of his jersey, going to Jonathan and the Red Sox. Jonathan Kent getting the Red Sox straight up. Game-worn news material right there. And out of fives and under. All aboard, the big hit express. Got Curtis Terry, sixty-four out of eighty-three. Two-color patch and autograph, Rangers, Walter with the Rangers straight up. Brennan Davis, nineteen out of forty-nine. That's for the Cubs, Kevin M and the Cubs, got the Cubs straight up. And we got Maury Wills, recently passed away, RIP, twenty-three out of forty-nine. Who's got my Dodgers?  That'll be for Martin. And then we've got Alec Minoa, forty-six out of forty-nine. That is for the Bluebirds. Again, part of the big Baltimore, Dodgers, Angels, Cardinals, etc. group. So that will go to Toronto Six. Toronto Six, Patrick with Toronto Six, nice. Eight out of ten, Nick Madrigal for the Cubbies. That's also for Kevin. We've got Camilo Duvall, sixty out of ninety-nine for the Giants. So San Francisco Zero, that'll be for Jeremy. Corbin Burns, eighteen out of twenty-five. Brewers, also part of that big number block group. Brewers, eight, Martin. Buffalo! Scott Van Pelt talking Buffalo. Ooh, look at this. David Peterson, piece of his jersey and autograph, Metz. Is that six out of six or nine out of nine? That's a number block team. Let's see if I could find a similar one. Jonathan saying it's nine out of nine. All right, I think it goes this way. Because if you look at this, I'm going to switch screens for a second. That one of one is printed that way. So that's a six. We met six. That'll be for Patrick. You know what? Look at this. It wouldn't have mattered anyway. Patrick has six and nine. Nice. Sometimes it works out nicely like that. All right. Next one is a one of one, Andre Jackson. Triple Relic and Autograph had a really nice appearance for the Dodgers the other night. It's a great factor in the playoffs. Dodgers won. That's for Martin. Martin all aboard the Big Hit Express. I'm sorry, Miss Jackson. And we got a Cal Raleigh. 34 out of 36. Some nice colors popping in that window. Seattle part of that New York Yankees group. And that is Seattle number four. And it's also for Martin. There you go, Martin. I would zero in front of them, but then what if I think it's 60 out of 60 or 90 out of 90? They really should just do it this way. 59 out of 99. Matt Duffy, Triple Relic to avoid the confusion. That'll be for the Angels. Angels nine. That will be Patrick. Angels nine. One out of two. Brand logo hats. Connor Wong. That's for you, Jonathan, the Red Sox. Train whistle time. Get ready. All aboard the Big Hit Express. 23 out of 25. Eli Morgan. Wasn't Eli the Paul Dano's character in There Will Be Blood? Cleveland. Walter. Got the Cleveland Guardian straight up. Cleveland. This is for you. Got Dominic Smith for the Mets. 15 out of 49. Mets five. Will be for Martin. There's Curtis Mead, 16 out of 49 for Tampa Bay. Tampa Bay's part of the Yankees number block group. And that will be for Martin at number six. Then we got Immaculate Marks. Fernando Datis Jr. Not that Immaculate. A little smudge there. 24 out of 49. But that'll go to the Padres. That's going to go to Padres. That is a number block team. So that'll go to Padres four. That's the X line. All right. Four boxes to go. Jaspies, we have the bricks. Bum bum bum bum. Is that Bing Reims? I think who does the Arby's voiceovers? Maybe he can do a commercial for Jaspies. Jaspies, we have the bricks. Bum bum bum bum. Willie Ademis, 81 out of 99. Shouldn't it be Jaspies when we have the heat? Sure. We have that, too. Brewers, Brewers one is Martin. We got Juan Gon Gonzalez and Pudge. Ivan Rodriguez. Two-color dual relic. Rangers. Walter with the Rangers. Got him straight up. Legend dual relic. Diego is gone. Diego, sorry. We got Thomas and a redemption back there. Thomas and Boogie, 87 out of 99. That's Mets seven. That'll be for Jeremy. The redemption, Joe Ryan. Rookie triple memorabilia signatures. That's for Minnesota and that's Jarrett with the twins straight up. Wait, Joe Ryan is twins, right? Yeah. That's for Minnesota. There you go, Jarrett. Thanks for grabbing the twins straight up. Two out of ten, Jake McCarthy. Silver ink on the eye black. David with the diamond back. Last spot mojo strikes again. 70% of the time. Last spot mojo hits 100% of the time. And Lars Newt Bar. 22 out of 26. Three-color patch. Silver ink autograph on the eye black. Hopefully Lars Newt Bar will have his own line of nutritious Newt bars. We put the Newt in nutritious. This is the tagline that I made up. Cardinals two, Rick with Cardinals two. Hi, I'm Hall of Famer Lars Newt Bar. We put the Newt, we put the Newt in nutrition. With my high protein, no sugar Newt bars. Perfect for any Hall of Fame athlete like me. Lars Newt Bar. It does sound nudy. Hi, I'm Hall of Famer Lars Newt Bar with all my friends and family that I was going to create my own line of nutrition bars. People said I was Newt's. There's Brandon Lau. 25 out of 99. Raise five on the other list. That's for Michael. We got Kershaw. We got Verlander. Dual relics. Is that a randomizer? The Dodgers are a number block team. And the Rockies were bought straight up by Nathan. So that is a randomizer. 17 out of 25. So Jeremy, you're part of the randomizer. Nathan, you're on the other side of the randomizer. There's Taylor Tramiel. 24 out of 99. Mariners. Part of the Yankees number block group. So Mariners number four. It's for Martin. There is Immaculate Signatures. Taking it to the Max. Max Muncie. And that'll be for Jeremy. And number zero. Redemption zero. Right. Did I say Rockies? No, obviously it says Houston on there. Nathan has Houston. Did I say Rockies? It's losing my mind. TJ Friedel. 44 out of 49. That goes to D.Y. and the Reds. And there's Jared Kelleneig. That's a nice looking patch there. 10 out of 25. That's for the Mariners. Mariners zero. Michael. Two boxes to go. Two out of 10. Dylan Carlson. Cardinals two. Rick. Sean Minaya. 33 out of 55. His jersey number. A's.  Chas McCormick. Three color patch on autograph. 66 out of 99. Nathan with Houston. Trevor Larnock. Train whistle. Two out of five. Minnesota. Jared with the twins. Got the twins straight up. Nice little shadow box autograph. Man. All aboard the Big Hit Express. Woo-hoo. Got another case of this in the store right now. JaspeysCaseBrakes.com. Go and get it. Burger. Jake Burger. Three color patch and autograph. 35 out of 99. Nice RPA for White Sox number five. Martin with five. And Peter Lanzo. 14 out of 15. Metz are also part of that number block group. That's the X line with Peter Lanzo. Metz number four. All right. Final box. Good luck. Three out of six. Brock Holt for the Braves. That goes to the Yankees number block group. Braves are part of that. Martin with number three. Last spot. Mojo. Nice looking triple. Got Bobby Mercer. 73 out of 99. Bobby Mercer is that. Yankees. And that will be for Yankees three. Another one for Martin. G-Man Choi. Eight out of 10. Reyes. Reyes number eight on the other list. Goes to Michael. Was Brock Holt a Ranger at what time? I think so. He might have also been a Red Sox at one time. He might have been on the Rangers too. There's Lucy's Fox. Nine out of 10. I want to say, did we look this up? I might be making this up in my head. I want to say that he is named after the same Lucy as Fox in the Batman series. Nine out of 10. Nationals nine. That'll be for Patrick. Got Brian Dela Cruz. 35 out of 77. Marlins. Marlins five. That's going to be for Martin. The last one here is Hunter Green. Nine out of 10. Local kid I think from the LA area. A flamethrower as well. That goes to DY with the red legs. There you go gang. That's the break. We got another fresh case in the store. Jaspy'scasebreaks.com. Let's do a little randomizer. Let's make one person happy and one person sad. Got Kersha. We got Verlander. We got One Dice Roll. And the team on top after nine. Five into four nine times. One, two, three, four, five, six, seven, eight. And ninth and final time after nine. Goes to Houston. And that will be for Nathan who got the Astros. Straight up. There you go. Two color dual relic. 17 out of 25 that is going your way. And there you go, Nathan. There you go everybody. I'm Joe for Jaspy'scasebreaks.com. That was Pick Your Team Nine. We got more baseball in the store. Check it out. And I'll see you next time for the next one.


We've already mentioned Jeth, so you've worked on FDH for Stranger Things as part of a team. What did this cooperation look like? Did you have a chance to discuss and negotiate particular sound identifiers? Is it common in your experience to work in a team on such projects involving FDH or cross-sectioning? I think it's becoming more common. And I think the opportunity to communicate is where we're going to create the best outcome. I think where that communication isn't there, conflict and opinions could change the way that the direction that you're going in. But I think especially now when I'm working on creative content and entertainment content like that, I find it extremely important to have a collaboration and a discussion. Because, for example, one person might view it in a completely different way, and just a two-second discussion could put you both on the same page. And so I really advocate for it now. What's the best way for this cooperation to be organised? Speaking from an LSP perspective, would you want to get an email of this other person working with a title or have some sort of a chat where you can talk what would be your preference? My preference would be the chat. I think that emails often there is a slower way to do things and having that instant chat where you can go back and forward over things, I think that's the most beneficial way to do that.


Hi, I'm Donovan Brown, a Senior Program Manager for DevOps on the Visual Studio Cloud Services team. In this video, I'm going to show you the improvements we've made to release management. We're going to begin with a quick overview of some of the notable changes in the system and follow that up with a full demo and finally share some additional resources. Release management is completely new. We have replaced the old WPF client with a new rich web interface where you can author, queue and monitor your releases. We have also made the system highly extensible and even open sourced our task library. Release management allows your team to fully automate the deployment and testing of your application to every environment. No longer will you have to manually copy and edit files to deploy your application. Release management can handle any binary and deploy it to any platform. Release management is now fully multi-platform. You can now run your release natively on a Mac, Linux or Windows. You can release from Team Build or third-party systems like Jenkins. Deploy .NET, Java and Node.js applications in the cloud or on-prem. You can even test your application during your release with UI tests like Selenium, CodedUI, Web and load test. Now let's see release management in action. For the purpose of this demo, we're going to assume we have an application in version control for which we have enabled continuous integration. The application happens to be an ASP.NET MVC application with an accompanying UI test we would also like to execute during our release. We're going to implement continuous delivery by having release management deploy our application into Azure. Currently I'm on my Azure portal and as you can see I don't have any web applications. If I attempt to visit the page in which we're going to deploy to, you can see that it's a 404. I'm going to show you just how easy it is to deploy to Azure using release management. To begin, I simply need to create a new release definition. Release management comes with templates that know how to deploy different types of applications into Azure. We want to use the Azure website deployment. When I do this, it's going to create all the tasks that I need to to deploy and test my application into Azure. I simply need to select what subscription I would like to deploy to. The name of the application and where in the world I would like that website to exist. I'm also going to go and set the test category attribute of my test test to make sure it only runs my UI test. Now I can go and change the name of this environment to something more appropriate like dev. And if I know I'm going to do the same thing in dev that I'm going to do in QA, I can simply clone this to create my other environment. I will call this one QA. And then simply update the task here in QA to make sure it points at my QA environment. Finally, I will duplicate it a third time so that I can have a production environment. Before I enter and leave my QA environment, I would like to have an approver assigned. So I can simply assign an approver so that I get notified before the tool attempts to deploy into my environment and also before it releases my code to the next environment. Finally, I need to link my release to a build definition. So I happen to have CI already set up, simply link these together and now I'll give your release a name. We'll just call this connect. Now I can save and simply start a new release. I get to choose exactly which environment I would like to deploy to and it will take me through dev, QA and production and halting at the points where I asked for there to be an approval. Simply click on create and from here I can start to go see and monitor my deployment. As you can see, we get real-time logs from our agent letting us know exactly what it's doing on the environment. While this release is running, let me go talk to you about some other features that we have. One of which, if we go back to our connect definition here, is we have configuration variables that allow you to set information that's specific to your release and we can even encrypt this information if we need to. These variables will apply to your entire release definition. You can also set up triggers. These triggers will allow you to have continuous deployment to where as soon as the developer checks in code, this release will be triggered and it will try to run all the way into production. If I wanted to stop at a earlier, I can simply choose that stage instead. I can also define additional artifacts should I need to and one other thing I want to point out is when we're defining our task, we have an enormous task library that we can use to deploy our applications. As you can see, we have integrations with Chef, we have integrations with Docker, you can deploy to Tomcat, your DacPak files. Anything that you can do from a command line, you can do from our release system and we can do that using a batch script, command line, PowerShell, or even a shell script because release management is multi-platform. You can run your releases on a Mac, you can run them on Linux, or you can run them on Windows as well. Not only can you set up variables for your entire release, but you can actually come to each environment and set up variables there as well. As you can see, those variables can be encrypted. At this point, we should be able to go back and see what our release is running like. Let's go back to releases. As we can see, we have a release who is waiting for an approval. If I were to go back now to this website and press F5, we should be able to see our release. I can go back to our portal here while that's loading and verify that I now have a website actually out in Azure. Just that simply, you can deploy an application directly to Azure using release management. Now that we've successfully deployed to our Dev environment, let's go and audit our release. By double-clicking on it, it brings me to the Summary page. The Summary page allows me to see all the environments, which ones we've successfully deployed to, gives me even access to our test results. So I can go back here and quickly see if my UI test were successful or not. I can also see any build artifacts that were associated with it, who caused it, and also go back in and see the approval that's pending. By coming in here and saying everything looks good, I am giving release management permission to leave the Dev environment and enter the QA environment. Release management will rinse and repeat this process until we've made it all the way into production. I hope you enjoyed that quick overview of release management. Here are some additional resources that you can use to learn more about release management. I would also recommend that you follow me on Twitter and my blog for the latest information. Until next time, I'm Donovan Brown.


There's a quite a few things we need to do here to help our scholars be successful with this process And the first thing is to help our teachers to be successful with it So one was teaching the rubric so there was a rubric developed to help us score the Written portion of the project so it was a matter of calibration and professional development to help our teachers get up To speed with what the rubric was push back get some understanding make some tweaks to it so that it fits us at Auckland high We've also had to create some to use professional development time particularly in the last month or so near the end of the spring semester for reading of the paper so after the English teacher Scores it then we have two teachers read it So we have all of our staff all of our teachers and one large room with stacks and stacks of multiple copies of their senior project Papers and everyone's scoring them based on the rubric And that's a intense process every paper gets read twice if there's a significant degree of difference between the Two scoring then they have to go back at it again and reread the paper and get to a common Score for their papers And each individual pathway goes about it just a slightly different so you have to Give the Directors of those pathways and the teachers in those pathways Opportunities to look at how they want to tweak the the senior project process the capstone So that it meets the the needs and interests of each individual pathway


Welcome everybody. I'm excited to be joined by these great panelists today. My name is Justin Colonino. I'm a lawyer. I've been an open source lawyer for almost 15 years now, which is a lot to take in for me. And I've been at Microsoft for the last six, which is kind of still a surprise for me given the history of Microsoft. And I've been on the board of the OSI for the last two years and just renewed for another two-year term at our last board meeting. And before I introduce the panelists or let them introduce themselves, I want to set the stage for what an exciting time it is for open source and then in the AI hype cycle as well. I'm really happy to stand here and say open source is one. Right? It's ubiquitous across software development. The world has really realized that transparency, collaborative improvement, autonomy, and the freedom to use code for any purpose drives innovation and allows everybody to learn from and build upon what's come before us. Right? That's fantastic. The open source definition has guided this for the last 25 years and it's been realized. But we heard from Mirko that with great power comes great responsibility. Like the regulators are here. There's the CRA focused on security. There's a PLD focused on product liability. There's all sorts of other things coming down around AI as well. And there's a lot of work to do in those. But the fact that we're talking about this and we're saying and we've been out there talking to regulators when out there talking to policy makers means in some way we've already won. Right? Like people are listening. They're hearing us and they're hearing that we need to think about open source and open source innovation cycle as we think about how we build software that's critical parts of our ecosystem. But then now comes AI. That's what we want to talk about today. Two other building blocks on kind of how open source and open source development is one. Right? The first is that even as there have been some closed open AI things that have been put out there we've been seeing developers put open source pre-trained models out on the internet. Right now I just checked. There's about 335,000 models available to download pre-trained models available to download on Huggingface. And if you're like me an open source lawyer thinking what are my clients internally going to be doing? How do we think about ingesting those and putting those into product? What's the responsible way to do that? And at the same time much like the CRA and PLD we're seeing that policy makers are accounting for AI in the way they're thinking about open source AI without definition in their innovations, in the way that they're regulating AI in particular. So it's an amazing moment kind of the fact that they're even thinking about this, the fact that developers are defaulting to open at the beginning of this cycle is kind of like a dream from 10 years ago, from 15 years ago when I started in this space. So that brings us to what the panel is going to be about which is why does getting the right definition of open source AI matter? What are the challenges in that? And how do we both protect the transparency, collaborative improvement, autonomy present in open source with that definition and also get that innovation cycle going, that open innovation cycle against this regulatory backdrop and focus on AI safety. And so that's what we're going to be talking about for about the next 25 minutes. With that I'll turn it over to Steph, Shashiko and Aster to introduce themselves, tell you a little bit about their organizations and how they've been looking at this space. I'll start since to your right. I'm Stefano Maffulli, I'm the Executive Director of the Open Source Initiative. The organization has been maintaining the open source definition with the community for the community for 25 years and we come, we build the open source definition on top of the shoulder of the giants, the free software movement that started 40 years ago. And I think this weekend they're celebrating the 40 years. So part of that continuum from the GNU manifesto to the open source definition and that matters on AI too. Yeah, I was going to say that Aster stole my perspective but now I realize that I go before you. So hi everyone, I'm Shashiko, I'm the Chair of Open Form Europe and I'm also a Senior Researcher at the Swedish National Research Institute, RICE. And I think for this panel I was going to let Aster talk about sort of the policy perspective and sort of what Open Form Europe does in that space and the added perspective I could bring is even though I've been involved in open source policy for 15 years or so I was going to take an outsider's perspective since in the day-to-day now I work at the national level with public sector organizations trying to go through digital transformation. They're feeling the pressure to innovate for the good of their citizens and I can tell you that I find your introduction a bit provocative because I would like to say that open source is one and to some extent we have but it's just that the world doesn't actually know that and that causes problems. I think unfortunately a large part of the people that we are talking to every day are kind of unaware of the fact that our societies depend on open source and that there's all of this opportunity as well around open source collaboration so we still have something to do. Crawl walk run maybe. I turned it on or turned it off just so I wasn't like hotmiking when I was out there. Yeah this is a lot to do so the doing part is what Open Form Europe tries to do. My name is Aster I'm the executive director of Open Form Europe and we've been around in Brussels for I think it's 21 years now trying as well as we can to explain the merits and benefits of openness and technology to different policy makers at different levels so both the member state levels sometimes municipalities and sometimes EU commissioners and now we are like everybody else also wrestling with okay but what does open mean for AI so we're often looking to OSI for guidance there so I think it will be an interesting discussion. Okay at the outset there's a kind of question kind of like open source of AI but like AI is what right and you know in software there's kind of the traditional you know source code object code or you know source code and then you know package source code if you're in a scripting language but AI there's it might be helpful to kind of start out just to level set for the audience some people might be technical some people may be less so like me and want to know kind of what makes up an AI system and Steph you recently with OSI been doing a deep dive into both the technology and kind of policy around this and maybe you could set the stage for us a little bit on the technical bit. It's been quite a journey for myself because I'm not a developer so diving into AI was for me interesting to notice immediately how there are new components new artifacts in there and I traced it back a little bit it helped me understand the history by today history by looking backwards and I noticed that for the AI systems the machine learning systems that we hear about so much on the press we need data we need data there are there are components that software systems that train that data and speed out models and weights and other parameters and and after that the the application can be applications can be built using these these systems there are various new components that are being built by by machine inside the machine learning systems and all these new components there they share a little bit of similarity from when software started to appear on the horizon in computer science when legal systems started to become kind of confused by source code and binary code and there was no legal framework that immediately could be used and adopted there was a policy decision made to apply copyright to source code at the time today we're facing a similar a similar moment where data being built into datasets that goes into training a model and weights these new artifacts build bring new challenges and they don't necessarily fit into the legal frameworks that we already have so when I was looking at the open source definition I was looking at the 300 000 models on on hugging face the immediate challenge to me was how do these models inside hugging face why do they use the Apache software license for example the Apache software license has a lot of terminology that refers to copyright talks about source code and what is the source code of a model when when it's made of training datasets it's made of model weights parameters other source code software software tools all these confusion I think made me realize that we the open source definition doesn't fit squarely into the space anymore and that's why we need to we need to look into this right and and just to play on the Apache license for a second that that defines source code is the preferred form of making modifications to the work do you have any thoughts about what that might be in a AI system right it's source what is the source code is the preferred format to make modifications which brings us to do we need data to be able so data goes into training the model some intuitively think that data is therefore source code to the model you need to have that in order to modify the model but technically not every model I mean some models many models can be modified by themselves you don't need the the data you can give it new data and retrain it fine tune it there is a very it's very new and as a as a system so the old paradigm work up to a level not fully got it so so what I'm hearing is that there's you know if you're going to be building an AI system you need software for for running the model the software to train the model that produces weights which are are kind of part of the network and then you need the underlying data we might want to it might be helpful to get a perspective staff and Ibrahim who's not here about how should we be thinking about these categories how they intersect and how we should build projects around them should they be built as kind of a monolith of you know the data the weights and the code is it just enough to have the data just the data just the weights what what are the what's a good way of building projects in this space if you have if you have a thought if I have a thought so it's mainly it's what I'm seeing happening that there is there are many projects that are being developed and and and distributed with with or without mostly without data at the so if you look at the very latest like llama to from from from meta or falcon from other you know there are from a lot of AI is an exception but most of these large language models and large models they get released with some sort of transparency about how they've been built but not all of them have the the full set of training data training software model architecture maybe a scientific paper attached to it also that describes the inner working of the system and and and and we're seeing a variety of approaches and all of them call themselves open source AI at one level or not so there is a lot of confusion inside the these these environments and that doesn't really help anyone it doesn't help with the policy making it doesn't help us with understanding inside you know explaining to to policymakers in in Europe and United States what they should be doing what they should be paying attention so we're helping the community driving together a large conversation to start putting ourselves a little bit of clarity okay with that kind of groundwork of of you know what the parts of an AI system are and and how they might you know interact in a project let's turn a little bit to policy so I was excited to see you know some discussion in the EU AI act about open source and I'm also excited to see the amount of development you know like I said hundreds of thousands of projects developed in the open kind of embracing you know openness on some level maybe not all the way just embracing that and and living it so but Aster what are you seeing at OFE from a policy perspective now as we are considering the regulation in the space yeah I don't know I think most of you probably were here for America's presentation where he went through some of the impacts of the kind of current state pre-tri-logs negotiations of the AI act as it is right now and that's I would say kind of where we were and what we kept on seeing for the last year or so when it came to open source in this space with the relation to the AI act that's of course what we focused on at open forum Europe and Brussels but and there the discussions were you know often very much stuck in the okay should everyone be able to access and use these things isn't that incredibly dangerous you've heard the discussion I bet um but the thing that has I would say changed as of end of June or we'll see how much it actually changes things it was a statement by President Macron of France at a large tech conference in France where he yeah he said en croix don't know open source we believe in open source and this was a change of position things that had happened the company mistral AI got a large valuation in France this did not go unnoticed and there seemed to have been maybe a penny dropped a certain connection was made in the LSE Palace that open source is actually very much in the interest of the economies that are challengers in the digital space open source is a way of scaling fast and innovating and it is fascinating like what Justin says that the open source model have the level of sophistication that they have at this point in the kind of the market development um and this has been appreciated by the French now there's whispers in Brussels of a 180 turn of France on the AI act to make sure that open source AI which they now believe in and love so much needs to be protected I haven't seen exactly what this will mean in the negotiations the French position etc but it is definitely a very interesting change of rhetoric around open source away from the kind of standard talking points of how incredibly dangerous it is if anyone can use large language models or AI models for XYZ to this also has some benefits this is actually very interesting in terms of innovation and competitiveness for for European economies so that is something that we're following very closely and if you also look at a statement from Ambassador Verdi the digital ambassador of France he essentially you can make this connection to the infamous Google memo that you might have seen a few months ago about there are no modes for the large language models he essentially made a statement where he said well we don't want to build a regulatory mode for the ones who are currently the market leaders we don't necessarily want to pull the ladder up you know some of these companies that are just ahead now might want to pull the ladder up behind them and these things are changing the discussions around open source AI but here and to really linked is then to the question of definitions what do they mean when they say open source AI because we don't have a definition and if it's now starting to get a lot more attention we need to get this definition right in one way to make sure that they preserve the benefits that come with open source as it been in a definition is extended to to open source AI but also if we look at policy making we talked about the PLD we talked about AI Act and CRA making sure that we get to a good definition and then stick to it is going to be very important because if I sit and communicate to a policy maker that we should protect open source exempt open source we can't have any questions about what it is that we're exempting if companies stakeholders start to be loose with these definitions and start to not follow these expectations and principles then the policy maker will over time turn around as but what the heck is this like we can't so these definitional questions suddenly very glad that I was I started this work already last year because we need to get there very soon things are being put in print in loss no pressure yeah we're we're supportive but this comes to the question because I think here also there's no question we can't talk about no regulation for AI or large language models that's off the table that's not what we're we're talking about it's I think we need to get away from conversations about over regulation no regulation versus regulations really start looking into the right regulation guided by the principles of the open source definition principles of what open competitive markets can do for you know the AI offerings that LLMs that are out there for the users for consumers by extension for citizens that kind of focus and that kind of quote-unquote good regulation I think there's a lot of opportunity there to for once get kind of an exciting new market where open wins elastic um Sachiko putting your public sector hat on for a moment how might it consistent in definition of open source AI benefit the public sector so I was gonna first almost inter interrupt you asked her because you know as you kept talking you stole more and more my point so I knew already that this was a bad setup so given that we actually work together so but coming last but not least I think a definition does matter a lot I think it matters for us because we've been talking about the importance of having an educational you know campaign with policymakers and public sector officials and we cannot communicate effectively if we don't you know have clear definitions that you know that we don't know what we're talking about so and I think it's going to be needed because I think you say open source is one to some extent it has but you know new technologies come around there are new forms there are new opportunities for lock in okay every time there's a new technology you know there's going to be you know there's going to be actors thinking how can I lock in my customer into a proprietary system and I think a lot of the logic that we have sort of been trying to explain around open source for the last 15 years or or more you know a fee it's it applies here as well and I think and the opportunity is maybe bigger than ever because we used to want to say that okay technology should move into the boardroom decision should move from the back room to the boardroom I think with AI we might finally be there because we're starting to more and more audiences they don't just say oh it's just technology you know it's going to get to higher levels of policymakers that also business executives that are going to say okay this is not just technology this is important for like you know has important implications for for for citizens and their rights etc so so what's at stake is is you know it's just more at stake and the calls for transparency are coming from from more and more places so the opportunity therefore open source to come in and and as a way to sort of mitigate some of the concerns around AI it's a big opportunity at the same time and I mentioned just this just during the break what I see is that you know there is a lot of open source there's a lot about open source as counterintuitive to non-expert audiences and this is going to be gonna not necessarily play in favor open source because you know non-expert actors will say AI has a lot of opportunity and a lot of risk the the way that we can engage with this is going to be safer to go with you know one supplier and the term open even though they like the transparency aspects it's going to sound dangerous so we will need to you know create good narratives and and analogies to illustrate to sort of the benefits of open source and you know notwithstanding the fact that the ball you know the penny has dropped you know with micro and and France I don't think that it's it's not as widespread as that right so and so just like being able to communicate that so just coming back to the definition it is important because we need to be able to communicate effectively right and that in that transparency but danger is something that that people are going to be hearing about yeah um well so kind of going back to to building maybe the right definitions maybe picking up a little bit on that transparency piece right there's this concept we were hearing earlier about how you know the code and weights are what people are sharing and then are able to kind of make further modifications to an AI system but what's the right way to balance that you know versus the transparency in the data and particularly with data what's what's kind of tricky as many if anyone here has been working in the open data realm for some time you know some some data it's no problem but other data like medical data which there's a lot of applications for AI around that's something that's not very easy to share and so how do we balance that transparency of how the system might operate the biases underlying the data versus in that definition if you're asking me that everybody yeah i mean if i i can start with that but that's probably one that's the crucial point that we have to address and it's quite clear from the early conversations within the group that is drafting the the definition now is exactly that what's the how do we assemble a large amount of data what kind of policies do we need to write what kind of approach do we want to simulate and and accept for data to be shared to be shareable without limiting the risks from the people who share it from the groups who share it because it's not just a privacy issue there is also copyright issues that are being now challenging the the openness or the advantages of being open because the paradox of open future talks about this quite a bit it's a group in europe the the fact that by being open groups that have been developing large language models that are available for everyone to share to use and and without limitations to study how to work and modify etc are a disadvantage because they have exposed the source of their data and the source of that data contains copyrighted material so paradoxically open ai is that the company is at an advantage by being secretive yeah yeah i think it's going to be very interesting for regulation and for us as i don't know software policy people to really properly at open forum europe deal with for example the revision of the gdpr which is coming up very early by the way for for regulation but also there just imagine how many questions we will have to be able to answer in exactly this okay well yes private data mixed data sets how do we deal with these things it's going to be you know very very important to get to now when open source has won we're also going to be expected to be able to answer these questions and as an ecosystem part of it is everybody in this room have to solve the kind of collection a collective action problem of looking into this funding this research providing the answers finding the spokespeople this is another element of collaboration that the open source ecosystem now stands you know i was going to say that you know and some of these things can't be solved at a high level either you know you talk to policymakers and things like that they're talking in principles what you when you're working for example with public sector officials they need clarity but you know that and some guidelines that can help them with their specific you know situation i think you know this is a bit of a challenge i think for us because it's not just it's not just highly sensitive data it's it's also about sort of i'm part of some collaborations like energy communities and things like that and they there's just a question of you know who do i even have you know is this even my data you know if this is is the kind of result of a project where we have collaborated where there are many different sort of actors there's the you know there's the consumer there's the there's the energy supply you know company there's like some kind of middle people there there is it's not just sensitive data it's like who owns this data do i even have the right to you know is it even up to me to make a decision to give this this data so i think we have to you know some of the work needs to be going into actually some more painstaking work about looking at specific situations maybe looking at for example municipalities or or in certain sectors like you know just try to sort of i'm not an open data expert but we will have to work with these people right and that's you know that comes back to like this this notion about different communities coming together you know if we're gonna have this kind of definition about open ai we're gonna have to also work with the open open data community it's not the same as the open source community how do we do you know are we speaking the same language um you know oh yeah maybe in the last two minutes um one big picture one minute big picture thoughts from from from y'all how what we you know we've seen how open source have shaped the industry over the last 40 years um what's your hope or expectation for the way open source ai might shape the industry my hope is that we we really gather these thoughts very quickly and and we we we empower society we empower companies communities in in very wide terms to to to stimulate the progress of the discipline the same way that open source the open source definition of free software definition have empowered the progress of computer science and software about evolution for 40 years that's my hope maybe i'm hoping that you know um ai will sort of bring open source the mainstream that you know next dinner party i can talk about open source and people won't just say you know let's mingle you know so perhaps these concerns will become more um the concern of of the everyday man or a man on the street yeah now you were the one like taking the thing i was gonna say but uh i think i mean then i really hope that open source ai and i really think there's a good chance of this actually happening because of the sophistication of these uh where the the open source models are now we will see a an ai lm market that is competitive is good for users not overly concentrated and all the things that have let's say haunted the many other digital markets in the last 10 years or so and i really hope that we're not going to see that here all right well thanks everybody could give a random applause to our panelists here and and osi is busy crafting this uh open source ai definition um you should check out and it's at open source dot org yes come come see me uh mond tuesday and wednesday have a office hours type of type of thing at the at the osi booth at the linux summit at the open source summit yeah we we need help uh this is a complicated problem and it's important that we get it right so thanks everybody for your attention


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My name is Cheryl Levine. I'm a second lieutenant in the United States Army. I'm a tank platoon leader in the 4th Infantry Division. I have 4 tanks and 16 soldiers.


Dr. O'Hare, this is one of my favorite groups of chemical control agents. You have the peroxogens. If you've ever been in a lab with me, you know how I feel about using hydrogen peroxide as a wonderful disinfectant. So how they work is just remember that hopefully you've seen the earlier videos about oxygen requirements for growth, that all living things that are exposed to oxygen have to deal with it. We think of oxygen as a good thing, but oxygen creates all sorts of toxic forms and if you don't have the machinery to deal with toxic forms of oxygen is going to cause lots and lots of problems. Even if you do have the machinery to deal with it like we do, it still leads to problems over time. So you see here how these work. It says oxidation, which would be the peeling off of electrons, and destabilization of cellular macromolecules. Let's put that in English. They work by creating free radicals. So the most common free radicals are called reactive oxygen species. So the proxogens are going to generate a ton of reactive oxygen species, and if you're an anaerobic organism that can't deal with any of them, it's going to wipe you out. If you're an organism like us or many of your obligate aerobic bacteria, then they're going to have the enzymes to neutralize some of these reactive oxygen species, some of these free radicals, but you can overwhelm them. So the key with hydrogen peroxide is to use a high enough concentration. If you dilute the other proxogens too, we'll talk about hydrogen peroxide first, if you dilute hydrogen peroxide too much, then it may not kill all organisms. But I've worked in the lab for 12 years now. My mentor worked in the lab for 27 years, and we both agreed that hydrogen peroxide is a wonderful way to kill the microbes in a microbiology lab. So all right, so that's kind of the proxogens in general. Let's talk a little bit more about hydrogen peroxide. So by far, hydrogen peroxide known as H202, you can see on the image there, is by far the most widely used peroxogens. So it can be used as a disinfectant and an antiseptic. It can even be used as a gas in gases. So a lot of times they'll spray like steams or gases of hydrogen peroxide when they're creating aseptic packaging like for food and these kind of things. So I already told you how it works. It generates a whole bunch of free radicals that some organisms can't deal with any, some can deal with some.  So here we see just in this image, the reason I shared a couple things. You see a bottle of hydrogen peroxide. Notice those bottles are always dark because light can inactivate hydrogen peroxide and on the right, you're looking at a contact lens cleaner. And that bubbling is actually the enzyme catalase that takes hydrogen peroxide and creates water and oxygen out of it. So when you put hydrogen peroxide on a wound and you see a bubbling, it's not so much evidence that it's working. If anything, it might be evidence that it's not, that it's being inactivated. So I'm definitely not big on hydrogen peroxide being used on deep wounds, you know, surface scrapes, those kind of things are probably okay. But this is why you do have to make sure you use the right concentration of hydrogen peroxide because you're dealing with an organism that has enzymes like catalase, they will be able to neutralize a certain amount of it. All right, so that's hydrogen peroxide. Next, we have paracetic acid on the list here. So paracetic acid, how I've seen this being used, it is effective against some of the organisms that maybe hydrogen peroxide can't deal with because they have the enzymes that can break it down. Paracetic acid, it can be used as a liquor, but also as plasma. I'll cover what plasma is later, but it's the, you can actually create disinfectants that are the fourth state of matter, that aren't solid, liquid or gases. The reason paracetic acid is so good is that it really, really effective at killing spores, which are always a big concern. I've seen it used mainly with fruit, like spraying it on fruit and those kind of things at this point. But I will think, I think you're going to see more paracetic acid, or I believe they call it PAA in the future. A couple more here. So benzoil peroxide and then carbamide peroxide. Benzoil peroxide maybe you've heard of as an acne medication. So it's used to kill propionobacterium acne, which is associated with acne. It doesn't cause it in all cases, but certainly is associated with it. And then carbamide peroxide, this is, you're going to see this one used in toothpates. So it helps to kill the pathogens that in the mouth, streptococcus mutans that can lead to cavities. And also it helps to disrupt the biofilms these organisms like to hide behind. And then of course, the carbamide peroxide can also play a role in keeping your teeth from getting discolored. So those would be some examples. The last example is ozone. So I'll do more videos on water treatment separately later. But ozone is O3, unlike atmospheric oxygen, which is O2. So ozone can be used to treat water. I think at this point in the United States, it's used in about 5% of municipalities. But if maybe chlorine's not doing the job or you want to make sure your water is safe, you can use ozone there as a water treatment. All right, so those are your peroxogens, hydrogen peroxide, parasitic acid, benzoil peroxide, carbamide peroxide, and ozone gas. Very, very effective at controlling microbial growth. Okay, have a wonderful day. Be blessed.


International Girls in ICT Day is celebrated on the 4th Thursday in April each year to encourage more female students to study science, technology, engineering, and mathematics and to consider careers in information and communication technology. Since the first Girls in ICT Day five years ago, this important day has now been celebrated in over 150 countries around the world through more than 5,300 events, helping to empower over 177,000 girls and young women. In 2015 alone, more than 66,000 girls and young women took part in over 1,800 celebrations of International Girls in ICT Day in over 130 countries worldwide. This year, we look forward to seeing even more events celebrated around the world. International Girls in ICT Day reminds us that ICTs help to improve the lives of people everywhere through better healthcare, better environment management, better communications, and better educational systems that transform the way children and adults learn. ICT professionals get to work on some of the most exciting projects imaginable on the cutting edge of all kinds of research. With the global technology sector continuing to expand rapidly, there is a predicted global shortfall of 2 million tech sector jobs that need qualified people to fear them. This means girls with ICT skills can expect to earn high salaries and to always enjoy plenty of career opportunities. Let me therefore encourage you not just to celebrate International Girls in ICT Day on 28 April 2016, but also to spread the world as widely as you can. ICTs represent a great future for girls everywhere.