 Hi, my name is Misha Benjamin, I'm legal counsel at Element AI. For those of you who don't know, Element AI is a Montreal based, we're still a startup but a rapidly growing startup in the AI field. We were started with strong collaboration from certain people at University of Montreal and we still have that strong collaboration. We have researchers that we share with several universities here in Montreal and that relationship is very important to us. It allows us to want to innovate, to have access to the latest innovations that are happening within universities and it allows us to keep that talent somewhat in the university so that they keep evolving, driving research that we can then come in and apply into services. And I think because of that link with the university, we have an interesting take on how open science initiatives can be translated into industry and obviously one of those elements is how we use and then re-contribute open source software to the community at large. So I definitely agree with Richard and I think everyone else here that open science is something that's very important. It drives innovation in a way that little else can. It also allows a certain democratization of innovation where I think we all know that there are certain companies that are either in-house or buying up a lot of the innovation that's happening and this open atmosphere helps us compete on a larger level with them. So speaking of some of those companies, I think we also have to be aware that although many larger companies have a lot of rhetoric around supporting and contributing to open science and open innovation, if we look behind the scenes, what we see can sometimes be a little more concerning. So for example, Facebook had a large PR push around how they were putting tons of things under open source and allowing licenses and allowing people to play with their data. But if you looked at their custom open source license language that they had crafted and deployed, basically what it said was that there was an underlying patent license to all the open source data that they put out. But if you sued Facebook for any reason, that license was pulled. So the effect of the license that they were putting out for PyTorch, which was a pretty fundamental tool that a lot of people were using, made it so that no one could sue Facebook in the future. Luckily, and it was great to see that there was a large outcry against that type of language and that type of control of open science that they were trying or open innovation that they were trying to exercise. And they backtracked on that, but that won't always be the case. And it especially won't be the case if people aren't aware of the different rights and aren't a little more critical of a certain rhetoric that there is around open innovation. So what does that mean for people who actually work and either contribute or use or exist in open innovation areas? So I think, as I said, one of the biggest effects of open innovation is an explosion in open source software. So for those of you who aren't in the AI field, you'll know that a lot of the innovation is actually available for anyone to use and play with. Most people contribute at least something to open source software. A lot of companies who are applying AI to real-world problems are actually pulling their original models from open source repos as well. But the way people think about open source software today doesn't necessarily reflect the reality. So a lot of people see open sources binary where something is either open source and can be used in any way or it's not. The reality is that just because something is accessible and open source doesn't mean that it can be reused in any way you want. And the flip side of that is that it's possible to contribute to open source communities meaningfully and really drive innovation that way, but also retain certain rights or certain advantages that you have as competitive advantages in order to build a business around it. So there's a whole toolbox of rights that exist within the software and any systems that you might develop. Unfortunately, unpacking them takes quite a bit more than 10 minutes. There's everything from patents to trade secrets. You can open source the architecture of a solution you build without open sourcing the actual code. That code remains protected through trade secrets. That's your competitive advantage. But at the same time you allow other people to innovate around that architecture. One example among many. And it also allows the choice of open source license or how you use open source software allows you to keep certain rights to yourself. What that means is you can retain your competitive advantages while contributing to open source software. And what it means to is that as a community if we make certain choices, we can keep certain tools in order to keep everyone honest within the open science community. So there are a lot of people, there's a lot of money involved in the fields that are the most open right now. And unfortunately that gives motivations to certain people to take advantage of the communities. But a good example is let's say if more people contribute under the MIT licenses, they retain the patent rights to their solutions. Most people who contribute under the MIT licenses don't enforce those patent rights but if there are bad actors and if as a community we decide that certain people are taking advantage of the way we're acting now, we retain those rights and have the possibility of keeping people in line. And there's a bunch of different ways to do this and obviously we can't go through all of them today but I think the most important thing is to remain aware of, to think more strategically about what licenses you're using, what you're inbounding and be a little more critical of what some people are saying around open initiatives and whether or not they're being honest about it. The other area that I want to touch on quickly, I know we're short on time, is open databases. So at the moment there are a lot of great databases that are publicly available. When you're using them, you have to be aware that just because they're publicly available doesn't mean you could use them for any purposes. There's a big, the licensing use around databases at the moment is quite unclear. We hope that in the future there will be a lot more clarity about what you can and can't do with a publicly available database. For instance, the rights could go from using it to benchmark a solution so you just run a model on it, see the results and delete that train model. Everything, you know, ranging to being able to embed representations of that data in your model to have it constantly accessible. And those are two very different things and they affect the rights, you know, for talking if there's privacy rights involved with that data, they affect the people whose data it is in very different ways. And it's a conversation that we're not really having at the moment. I think elements in other academic labs are going to be publishing more and more around this soon. And we're going to make sure that there is a sort of common vocabulary at least used to describe these different rights. But one thing that the academic community can push is a standardization around license rights, around bias in data, things like that. There's a lot of papers that have been published recently about the quality of the data, trying to standardize that. But I think that has to extend a little further. And that standardization of both rights and quality will allow more privacy preserving tools that will help us share our data more broadly while being respectful of people's rights because those tools can apply to a bunch of different data sets that have now been standardized and are more easily shareable. So obviously there's a lot to impact on all those things. I would say that the most important things that I've seen from my time sort of at the forefront of open innovation with large companies involved as well as universities is that you have to take a more pragmatic approach to what's actually happening on the ground. We have to make sure to protect our SMEs and startups, make sure that they're aware of the rights they're giving up when they use or contribute to open source, and also push towards standardization and make sure that we're acting as a community towards a more open system. Thanks.