 Mark gave a great presentation on the university approaches for innovation. What I wanted to do was do the same speaking from a utility perspective. As Tom mentioned that I was the innovations chief at DC Water for 16 years and I wanted to really give a snapshot of what I would call the right of my life. So right now I'm actually the CEO of New Hub, a clean tech, but before that I was the innovation chief and I was responsible for developing, implementing, adopting, commercializing technologies for a large facility that's called the Blue Plains Advanced Waste Water Plan and it serves Washington DC and the metro region. And we implemented nearly a billion dollars worth of technologies in that 16 years, in fact a little bit over a billion. So one thing that I wanted to do was before touching upon the whole innovation aspect was to describe how Sudhir Murthy thinks about innovation. So when you start working at a utility, you have infrastructure, huge amounts of infrastructure that last for 100 years. The facility at Blue Plains was built in 1938 and that same investment is still there. And so the concrete infrastructure that we have at Blue Plains or in what a utility I call them the elephants. They are long lasting technologies. They don't go obsolete and their gestation periods are hugely long. So it takes long time for them to evolve and so their innovation cycles are quite different. And then the machines, the mechanical equipment are what I call the horses. They have shorter gestation periods and shorter lives. They're about 20 years, the life of a horse and they grow to adulthood faster and their obsolescence rates are about in that range. And then of course, more recently in what we are now calling the post-industrial revolution, we have the hairs, the smart water technologies and they go obsolete much faster, like your iPhone, you don't keep it for more than five years perhaps. But the gestation periods are faster, the obsolescence rates are faster. And in a utility sometimes we confuse it. We sometimes think of a hare as an elephant and an elephant is also sometimes confused into how quickly we can implement it. And our procurement methods are quite not evolved to manage all of these three different animals that we have in our utility. And sometimes the incentives, for example, the patent laws are created around horses. You know, 20 years is an average patent life and they're not made for elephants and they're not made for hares. And so how do we manage the incentives associated with all of these different things that are out there? And so you could have a smart sensor, you could have a mechanical equipment and you have these concrete structures. And from an innovation perspective, an innovation chief has to really think through how you actually are someone who manages an elephant versus putting in smart technologies in a water utility. The other thing that was important for a utility employee was you have infrastructure and sometimes you're building new infrastructure. And often you want to really put a lot of flow or, you know, wastewater or whether it's drinking water in that infrastructure and you want to maximize that and we call that intensification where you do more in a less volume. But usually when you start compressing and condensing things like a mock talk about MBRs, you need more energy. And so intensification usually needs more energy. And so a lot of what we were developing was how do we actually get convergences between doing things in less space but also using less energy. And really the introduction of the sensors and the smart systems really allowed us to actually bring in huge intensification but also using less energy, less chemicals and so on. So to really do intensification, you have to improve the physical factors that limit process performance. People, it's really simple, right? In the end it's the physics that limits intensification. It's the physics that limits getting out of this auditorium in case of an emergency. So if you can manage the physics, then you can address what limits process performance. And it could be in this case it's hydrocyclones where you're managing gravity, gravitational forces, it could be flocculation, it could be anything that really addresses the physical factor that limits process performance. And every single adoption of that billion dollars that we invested was a physics related implementation. And then the sensors and process controls usually come in to help increase efficiencies and then drive those convergences. And then if you can address, in wastewater treatment we use biology. We use bacteria to do the treatment usually. And so if you can manage the physics to create biological selection, then you can actually get huge, huge intensification because now I'm retaining a lot of my desired biology and I'm getting rid of my undesired biology. So it's a combination of those three that were what I felt were the essence of a lot of what we did at DC Water. And so all over the plant we were able to drive those convergences, whether it was the use of thermal hydrolysis right here. And really the physics was viscosity. And here it was dewatering where these are the flocculation tanks, gravitational forces, compressibility and so on. So it was really understanding the physics and then driving huge. And when I say huge, it's 100%, 200% increase in throughput rates and getting more out of your process. And once you get that intensification, then you can drive biology. In the top part of this chart you see we use hydrocyclones to retain the heavier. And because biological phosphorus removal requires the retention of phosphorus in bacteria, these are heavier and denser bacteria. So we use hydrocyclones to retain these heavier material in the bottom of the hydrocyclone. And of course you have to develop the equipment and build the entire process together. And then in the case of a concept called anamox, which is used for nitrogen removal, we retained the anamox organisms, these are the red bacteria that have the heme protein, in the retain them using screens while we let and wasted the material that we didn't want. So again, if you understand the physics, you can create a huge intensification and then also you can drive selection of morphology. But once you select morphology, then you can drive function. And one other thing about physics-based inventions is that when you have a physics-based invention, it's a horizontal invention, it's not a vertical invention. And you can go and drive those inventions across multiple verticals. So if I have a technology which uses a hydrocyclone manifold, then I can use it across multiple verticals, whether it's a sequencing batch reactor, an IFAS process, an A2O, which is really a conventional BNR process and so on. And then you can drive it across different scales. You can do it at a very large scale or even in a compact facility in basically prefab metal tanks. I concur with Mark. Every innovation and every commercialization we did at DC Water was in collaboration with academia, with manufacturing and really it was doing it together rather than the linear model that most people talk about. Linear models do not work. At DC Water over the 16 years, we had about 80 masters and PhD students working at DC Water to do their masters and PhDs. And these students actually saw right in front of them their creations being implemented at a very large scale. And when you go back, when you go beyond the invention, which is discovery, as we partner in the invention process, we also partner in the demonstration process and in the commercialization process. And we were agnostic. It was not, it needed to be in the United States. It was really, we were driving these approaches all over the world. I wanted to close with a couple of slides. You know, the title of this talk was Diffusion, Improving Diffusion of Innovation. And maybe this is a controversial thought. Diffusion is too slow a process. It's a molecular process. What we need to drive is dispersion of innovation. And what I mean by that, we need these external drivers that motivate humans. Today, almost anything that drives world GDP is driven by human capital. And we need external motivators that create dispersion of innovation rather than diffusion of innovation. If you look at the world GDP, this is an estimate made from 180 to today. The change occurred in the 1700s, late 1600s, 1700s. And some of it was really associated with the patent law that was developed in the United Kingdom in 1624, which really drove the human potential. It drove the creation of intangible assets. And it really drove innovation in two or three orders of magnitude faster than the first 1700 years. And that was dispersion of innovation. And now we are really, and that industrial revolution is, I would call a misnomer of sorts because we depersonalize things. It's really a scientist and engineer's revolution. It's not an industrial revolution, but really it was driven by scientists and engineers. In the post-industrial revolution, the need for scientists and engineers is going to be even greater because intangible assets, this is the latest economist, or at least the last week, where the percentage of the S&P 500 market valuation of intangible assets is now 80%. And so in this post-industrial revolution, our ability to manage human capital is really going to be important. At a water utility, we usually go through an approach of finding a problem and solving a problem. And utilities all over the world are littered with one-off cases where we've developed a nice new approach and we have not done anything about it. You know, it's there and it's just there in that one utility. We need to actually disperse that, you know, create a dispersion of innovation and we have to go through a whole process of discovery, generating that intangible asset, developing, you know, the equipment and methods associated with it and then driving it forward. One final graph slide is the scientists of engineers of today and tomorrow are really living in a complex society. We have, you know, the same graph I showed of industrial revolution is this graph in space, which was developed by McKinsey a few years ago and around 81, the center of gravity of world's economic GDP was somewhere between China, India and Asia and then Europe on this side. And with the spread of industrial revolution, it moved right out here with the United States and Canada also pulling it in this direction. It's now moving backwards and the pace, the most rapid shift and it's really because of the post-industrial revolution that is moving in a really rapid way. It's creating huge disruption, social disruptions. You know, there's a sense of tribalism versus globalism and so we really need to start looking at how we as scientists and engineers drive that post-industrial revolution. And with that...