 So I think we have some sort of critical mass in here now so we're going to start with our next session. This is a new thing that we're doing this year so we would like to hear your feedback in some form during a social later or poster session or send us an email if you think that this was a useful session. We have a number of speakers here who are going to talk about potential market growth areas and synergies as well as bottlenecks or unceased opportunities in neuroinformatics, AI, etc. Our moderator is Richard Gold from McGill. He is professor at the Faculty of Law and also the founding director of the Center for Intellectual Property Policy. Our speakers include Christian Dancero from Perceive AI, he is the CEO and co-founder. Graham Moffat, Chief Scientist and VP of Regulatory Affairs at Interaxon. Misha Benjamin, Legal Counsel at Element AI. Randy McIntosh, one of the co-founders of The Virtual Brain. And Sonia Israel, co-founder of AI Fred Hope. And last but not least, we have a late addition in the form of John Collins, the director of Healthcare and Life Science at Dell. Richard is going to moderate the session so I'm going to hand it over to him. Hello, everyone. So they asked a lawyer to only speak for 10 minutes. We'll see how that goes. Actually time myself, see if that helps. So we'll come to questions at the end where I hope you'll join in. I'm the odd duck in this group because I'm not coming from an industry perspective but giving you more an overview of some of the issues and a particular model that we're studying that hopefully will both accelerate research and innovation. So because I don't know if I have enough time, I'm going to tell you the conclusion and then we'll work through the rest of it and we'll see whether I can fill in. So the first few slides have text because I'm a lawyer and then the rest are all pretty pictures but not as pretty as scientists do because I'm just a lawyer. So the story is that while we live in an era where we think innovation is all around us, in fact there are storm clouds that are happening out there when you actually look at the statistics. If you look at the last 100 years or so, there are a couple of trends that are worrisome. One is we've seen exponentially increasing costs in research and innovation. And the line is pretty straight, you'll see it. At the same time we have declining researcher productivity. So we're spending more and each person is producing less and this has worked to expand overall innovation because the pace of investment in research and development has been higher than the decline in individual productivity. So we basically have been buying ourselves out of this declining rate and I can go into why we're declining. That's not so worrisome because we were keeping up. But the last 50 years, since about 1950 or 1960, this has become more serious. We're starting to see that our research spending, especially in the last 20 years, has just keeping pace with declining productivity and we're soon likely to see a decline. We've also seen that the economic effects of innovation are declining. That is, the rate of increase in our growth due to innovation is going down. And as I said, the last 20 years, research funding has plateaued in the sense of we're just keeping up. Also the patent system, which has long been one of the mainstream ways we transfer knowledge, is becoming increasingly disengaged from the innovation system. It's not about innovation per se, but it's about other things. Again, I can go into that if we have more time. So one possibility is we're hitting what physicist Jeffrey West calls a singularity and not the singularity in the AI sense that we're going to have a thinking machine because I know that's what you were thinking about. But the singularity in terms of the big bang type of singularity, that is we're going to reach a point where we just can't keep up with this exponential growth and what will happen afterwards, he predicts is declining innovation and growth. So we'll have less innovation and the economic growth will stall. Unless we find a way to innovate ourselves out of this. So it's not that we need innovation in the technical sense, but we need innovation in the way we innovate and we do research. We need to restructure ourselves. So some of the things we need to focus on is we have to look at increasing the efficiency of research and development. We just have to get more bang for the buck because we can't put more bucks in. We have to accelerate the speed with which we get value. If you look at most technologies, the first entrant into the domain is not the one that actually makes the money. It's usually further down for Xerox machines and other took 20 years. We can't afford that long a time to wait, so we need to accelerate that. And as the possibilities of ideas, because we got lots of ideas, searching that space for ones that work requires better heuristics. We need to better find more quickly the possible solutions in which we should invest and get rid of the rest more quickly. So there are three areas I'm going to mention and focus on the first one. The first is really about how we do science and innovation, and this is open science. And this is not open science for open science, say make everything available, blah, blah, blah. It is to be more practical. It's to reduce the cost of doing science, of bringing partners together. So it's about reducing duplication of effort. It's about reusing our investments in data and materials more wisely and making them more available so that we can achieve the value sooner. And if, and I'll get into this a little bit later, developing better ways of finding the solutions that work. But there are other technologies, and most of the firms here are about this. So one would be deep learning, machine learning, which hopefully will help us also identify the potential things to investigate more quickly and quantum computing, which helps us search through space better. But quantum computing is a long way off. Okay, so that was the conclusion. Let me now spend some time demonstrating this. I'm going to go through these charts relatively quickly. This is a chart of patents versus populations. So in the 1870s, we suddenly had a rise, number of patents per person, and then it flattens out and then starts declining. We can look at the yellow line. We start to see the patents per technical person goes down. This continues on through the 1940s and 1950s. So here we're seeing evidence that per worker we're getting less out of it, but at the same time our costs are going up. The red line here is the cost per scientist is going up. So we're spending more on each person and we're deriving less from that person, more evidence of the same. So this happens until about the mid 1960s. This translates into, in the marketplace, higher costs. So if we look at the cost of a single drug brought to market, that's the trend raising exponentially. So that's the cost to the drug company. If we look at the cost to the consumer in the same time period, the same thing happens naturally enough. The market passes on this cost to the consumer. So research is becoming more expensive, even per researcher, and we're getting less and less out of it per individual while costs are increasing higher. We can look outside the medical field. This is aggregate research and innovation and its impact on the economy. It's been declining the blue line while the number of researchers going up. So we're spending more resources, hiring more people to produce less economic growth with time. This is true in ag, it's true in pharma. It's true in, if you look at cancer, heart disease, and this is by increases in lifespan. So we're getting less out of that investment. Since 1980s, mid 1980s, we see a little blip of increases. This is the patent system, disengaging from the innovation system, where firms start to acquire patents for strategic reasons. They don't really care about the quality of those patents, so the number of patents goes up. But the overall trend in terms of costs are maintained. That is, we're going up and we're getting less out of it. So this is the patent applications. You can see they suddenly go up in 85. They plateau in 2013 when the US Supreme Court tightens up patent law. But this is the big trend in the end. We're doing this to create value. And that value peaked in 1915 has been declining. This is a measure of the economic effect of innovation. So we're going down, there's a bigger decrease in 1980. But if we continue that general curve, it is not encouraging. What firms are doing, and researchers are doing in response to this, is narrowing their focus. Rather than going for breakthroughs, the tendency is to make smaller improvements in known fields, taking less risk. So the black line is the potential ideas, but they're only doing some, the red. And from in structural genomics, there's a set number of kinases that one could look at. And if you look at the blue line, those were the kinases being investigated till 2002. And lo and behold, they were still the same kinases being investigated in 2009, leaving at the white spaces. So what this is telling us is researchers and firms are investigating the same thing over and over again, not moving into new areas. This is another mapping of the same thing, but looking at patent data. This is looking at drug similarity over time. So all of these trends are showing increasing costs, decreasing productivity, and narrowness of innovation, leading to ever lower rates of economic growth. So the model that we think is one of the solutions, only one, is open science. Open science is a set of practices and the way we talk about it, it's a partnership. It's not openness for openness sake, where we have open data, open publications, and no restrictive IP rights to get in the way of the use of that knowledge. And we hypothesize, and we're trying to test this out, that it reduces costs by having more people look at it. It increases speed by having diversity of point of view, hopefully leading to better predictions. And I'm going to end, because I'm 24 minutes seconds over. We're doing this through something called Open SciNet, which is an NCE application. And we're trying to actually build business models to take open science and transform it into products and services that could be sold. Using either traditional patent system or alternative ways, like tacit knowledge protection, data secrecy. And one of our specific foci is building those business models, and ensuring freedom to operate for companies. So I'm happy to talk about it later on. Thanks.