My name is David Benjamin. I'm an assistant professor here at the school and also have a design practice called The Living and I'm going to introduce the panel on computation. Of course, we all know that computation in architecture has a long and rich history. And by way of introduction and to set the stage for this panel, I'd like to briefly highlight a few episodes from this long history. So of course, not meant to be comprehensive, but just to set the stage a little bit and put a few markers in the ground. So first, in 1967, the Department of Architecture at Cambridge University formed the Center for Land Use and Built Form Studies. The center was modeled on scientific labs and it aimed to make architecture more rational and less intuitive, more scientific and less artistic. The leaders of this effort were Leslie Martin, Christopher Alexander and Lionel March. As documented by Sean Keller in an essay in Grey Room, March was a mathematics prodigy who entered Cambridge with a letter of recommendation from Alan Turing and then eventually transferred to architecture actually just like Christopher Alexander. As head of the center, March argued, my case is that the modern movement has disintegrated in part because it failed to make any but the most superficial bridges between itself and developments in mathematics, sciences and other technologies. He went on to advocate that architecture be reconstructed as a serious business with legitimate and a fundable role for advanced research like that carried out in other departments at Cambridge University. And in fact, the center at Cambridge was in fact financed by research contracts and grants primarily from British government public agencies. This work was of course applied to engineering problems in architecture, problems of structure, lighting, acoustics and heating, but it was also applied to program, to the arrangement of space. And computation in this center and with this research was considered a kind of conceptual model in addition to an electronic device. So computational thinking in addition to using computers to solve problems. Although the center and its research quickly faded, many of the ideas and directions from the center are still active today, including an awareness that computation might relate to environment. In one summary of the center's mission, it was written the mission of this work is to encourage objective, communal and socially responsible answers to the quote unquote environmental dilemma of the early 1970s. So maybe we can see some trace of that in the use of computation today. Second, in 1994, as of course many people in this room know, the paperless studio was invented at Columbia GSAP. The name and the proposal came from then Dean Bernard Schumme and several young faculty members, including Greg Lynn, Hany Rashid, Scott Marble who's here, Stan Allen, Keller Easterling and Lee San Couture who's also here began explorations of how the computer might lead to new ways of designing architecture. As Bernard explains these days and looking back on it, the aim of the experiment was to explore how one could think with computers. As Bernard argued, there was already a new language and a new sensibility among this young generation of architects and the use of computation had the effect of accelerating qualities that were already in the work and in the thinking. In the initial four paperless studios, Stan Allen explored patterns and field conditions, Hany Rashid explored collage and layers, Greg Lynn explored computer models coming from other fields such as fluid mechanics and Scott Marble explored animation and fly-throughs. So a bunch of different ways of thinking about and using computation. This project was funded by the University, by Columbia University with the recognition that something important was happening in this domain. In contrast to the work at Cambridge, this was as Bernard described, testing without a plan but similar to the work of Cambridge, this work had an impact on the field. As Bernard describes, the use of computers in architecture schools created a kind of inversion in the education practice model. Up until this time, what happened in professional offices guided what was taught in architecture schools but in part because of the pioneering paperless studios at GSAP, what happened at architecture schools started to influence what was happening in the professional office. Third and lastly, in 2016, about a year ago, a computer called DeepMind invented by Google defeated a human champion at the game Go, which was once considered a game for uniquely human intelligence. It was thought that Go was impossible for a machine to win, unlike chess, due to the nearly infinite number of outcomes and the difficulty of calculating which player was leading at any given moment. Google's computer used a new version of artificial intelligence called machine learning and this new victory may signal what some people believe is a new age, an age of computers able to resolve specifically humanistic problems. And this is as opposed to solving mathematical problems and as opposed to solving chess. Machine learning is now being applied for financial trading, advertising, language translation, malware detection, computer vision and countless other applications. And I believe that in the near future it will increasingly be applied to architecture and planning and increasingly explored in architectural education. As with all of these technologies and really all uses of computation and architecture, machine learning involves assumptions and biases. But the biases of machine learning may be even more troubling than other biases because they are hidden sometimes even from their own inventors. This concept has been articulated by recent writing including Kathy O'Neill's book called Weapons of Math Destruction and Kate Crawford's essay entitled Artificial Intelligence's White Guy Problem. And in fact, this same concept or related concept was brilliantly articulated by Kevin Slavin from the previous panel in this exact room several years ago at an event that I organized here called Postparametric, the idea that algorithms have this life of our own and are impacting us in ways beyond our typical knowledge of them. So Kevin Slavin and O'Neill and Crawford have shown how the biases of these algorithms can lead to things like racial profiling and policing, sexism and job listings and uneven distribution of resources in urban neighborhoods. And their arguments imply that understanding algorithms requires understanding the humans who create them, the humans who are displaced by them and the humans who are affected by their conclusions. So it's kind of in this context that I believe this kind of complexity, these sets of inter-relations can set our stage for the current version of computation in architecture and education. And in this context, I'd like to introduce our speakers for this panel and both of them, I think Jenny Saban is not able to be here today but we're gonna have two speakers. And both of them I think are thinking about computation in a new hybrid and synthetic way with an emphasis on both qualitative and quantitative aspects of design with deep work in research, including scientific research and deep work in applied built examples. And I think we could also say with the focus on environment and multiple scales and interdisciplinary collaboration. Anna Dyson teaches design, technology and theory at the School of Architecture at Rensselaer Polytechnic Institute and she's founding director of CASE, the Center for Architecture, Science and Ecology which develops new building systems integrating science, technology, and advances from diverse research fields. Kasper Gudlager Jensen is an innovator and developer at architectural firm 3XN and founder of the firm's innovation company GXN which combines digital processes, ecological design and new materials. And with that, I think, are you speaking first Kasper or did we have Anna speaking first? Okay, so with that, Anna Dyson will speak first. Thanks Anna. Thank you. I'm gonna stay for the panel, I'm just freezing today. So one of our obsessions at CASE is personalized thermal comfort and some day I hope we'll all be able to be completely comfortable here. I'm sure there are some people who are worn. But anyway, I could not have thought of a better panel to introduce this day. It was really profound, the discussion at the end particularly. So I did insert a few slides in response to that discussion and I hope it'll be worth it if the panelists are able to stay for this. In particular, I think I don't need to, after Kevin's talk, I think make the point that we are firmly within what we might call a bioinformatic revolution. Every era has its technological sort of revolutionary moment or a series of moments that characterize it and whether we know it or not, I think I would agree completely also with the point that David has just made, which is that really all of this information, the machine learning algorithmic work and relationships and understanding the world from a lot of those principles are being recombinated and designed in a sense in spite of ourselves. And one of the reasons is because we're really, in addition to being in the middle of this bioinformatic revolution where metagenomics and all these other aspects can be sort of tangibly and viably brought to our fingertips in terms of the methods and the information that we could have access to, there's also a concomitant issue that we've inherited a highly specialized world. The last 300, 400 years of the academy have really been marching towards these kinds of vertical silos of specialization, which are in a sense antithetical to what the panel was describing, which is the architectural activity of in a sense expert integration across fields rather than going up this vertical silo. So if you have, for instance, incredible work going on in genomics, omics research, let's say, in different areas of knowledge production, biomedical engineering, other things. At the same time, they're not necessarily speaking to each other and there isn't always, there aren't always very good interfaces through which we can really understand what David was mentioning as this kind of multi-scalar cascade of both effects and consequences. At the same time, in architecture, we have always had to, in a sense, think in a multi-scalar fashion and we've always had to consider all of those skills, whether or not we've had the bandwidth, the information, the time, the budget to be able to do that. We know, in a sense, that we are creating built ecologies. We've always known that, as the previous panel said, the world has not gotten more complex. Just perhaps our access to information has gotten more complex. So what does that mean for us? We found a case, actually, in response to a request from our former dean, Alan Balfour. And I think one of the most important things that he recognized before a lot of us was that we would not be taken seriously as research or as partners, in a sense, in technological innovation, unless it were funded by the major engines of technological research, which are basically the government's industry, et cetera. And we had started to get a lot of funding for our collaborations with engineers and scientists. So he said, why don't you form a PhD program around this work? We didn't even have PhDs ourselves, and we were, you know, honestly, really just very much coming from the world of architecture and wanting more information into the architectural process. Usually, for what we have characterized some of our primary concerns, which is how can we build with better energy flows, certain performance parameters, the intersection between energy, material life cycle, water metabolism, et cetera, were of great interest to us. And being the recipient specifiers, as we all know, of the solar panel that was developed at NASA for an extraterrestrial application or something else, is really suboptimal for what we would call built ecologies, whereby we don't do anything for one reason or another. Always there are multiple criteria, very complex criteria that are culturally driven, that are historically driven, that are technologically or performance driven. And there's a trade-off. We could never talk about optimization in our field the way you might optimize an engineering problem or you might optimize the design of a scientific experiment, for example, or reduce the variables in order to understand a certain phenomena. Reducing the variables in architecture usually always leads to catastrophe. We want more information, more variables, and more complexity in the process. So what I wanna actually talk about today is how can we grasp all this information and what does architecture have to give, actually, to this problem? Let's say the meta-problems, the big global challenges that Craig started off the day with, which is climate change. If we look at climate change in the context also of the bioinformatic revolution that we saw presented so eloquently by Kevin just recently in terms of what we can know about our environment, the question is who will be the glue figures to start to look across those scales? Who will look at all of those different, both cultural and technological issues simultaneously? And I would say that architecture is one of the rare disciplines that really has a history of that and has had to do that. So the fact that we've kept generalists is very rare. Maybe the downside of that is we've never really had PhD programs that do what we do until recently. Now we have programs and we have people here from some of those programs, MIT, GSD, et cetera, that are literally design-led research where we can have more time and more, let's say resources and collaborations in the process to be able to do that deep dive that people have been talking about all morning. And that's essentially what we are trying to do with our program. I just threw this slide up. This was actually an earlier slide that we created a few years ago, just what are all of our areas that have emerged? Ultimately, the computational scientists that we work with would say these are kind of all the same system. They could actually change equations and then we go from the plant-based hydroponics system to the display or the concentrating solar facade. We were dumbfounded and it was hard for us to believe this, but increasingly we are seeing that they can do this if they have the resources and if they have the means to do that. And ultimately what we've come to see as well is that really what we're doing is shaping energy flows, right? There's energy and basically if we were talking about living systems this morning and that living systems are all around us and they're largely unseen, ultimately what's driving those living systems is energy flows. First we start with solar, obviously, but everything cascades from there. Whether or not we're in a very, very large timeframe such as fossil fuel combustion or whether or not we're on the very, very short timeframe and very small length scales such as the microbes. But I don't need to tell you after Kevin's presentation that really that microbial life underneath everything is something that is really changing the way that we're able to look at the world and able to look at what we're doing. So this is, I had this before Kevin's presentation and I was like, oh, I've gotta throw a few more slides in there because we'll be able to connect to what he's saying. What we would actually say as architects when we're looking at this, at case we would say this is actually an emergence of yes, the mother as Kevin had said, nutrition or what we ingest in our genes, but it's also very, very much a function of our environmental influences. So right now in this room, we could look at this, for example, just as something external that we're gonna study or we could say we're actually sharing microbiome right now. We've been sharing it. Largely what we have in here is a human microbiome and that's a problem for us. We've excluded a lot of other things. This is just to show that these are all images from a solar facade system that goes across all of those scales. And what we're doing in that system at each scale is we're manipulating materials and we're using computation increasingly, obviously, to understand the relationship across those materials. But historically, we've been manipulating those morphologies, those materials at one scale, a single scale. And what happens is, if we look at it in the context, for example, of microbes, microbes, the way that we shape our environment at the large scale all the way down to the crevices or the surface of this material that we have here will basically either, will basically preempt or will promulgate the production of certain microbial type activities or life activities. So certain species grow in certain environments, certain solid state environments. Certain microbes grow in certain. And so the same microbes will not be on this jacket, not just because I'm sort of spitting here and spitting there. It's a function of what that surface condition is and what that material condition is. And we now have that feedback loop computationally. So at the top, you see that this is part of 10 aims. Everybody knows that mid-century, 20th century moment, which was so amazing when we could visualize all these scales. The moment we're living now is increasingly we can design at those scales simultaneously and we can have the feedback loop of what the consequences of those designs are. Now, in the shaping, let's say, of this world, let's say that historically in our, I'm sorry this is so blurry, but this is a zoom in from the powers of 10 from the last slide, the aims powers of 10. Historically, we've sort of been asked to zoom all the way out, let's say, to district scale. Let's say we instinctively had to train as architects to understand the behavior of materials, certainly the behavior of materials in association with bio-climatic flows, so-called environmental design, et cetera.  And what we're increasingly now, what we have in our system is we have a system where we're going to have this interdependency that is where there's more and more information known about that. And that's very, very consequential for us as architects because if we think, for example, we already know it's really, really hard to get all of the information, let's say even for a Leeds Gold Building or all of the sort of more prescriptive type of programs that we've had so far to how are we gonna think about performance of the technology of the building. Just getting that amount of information flow, and this is also an older diagram, looking at how this is for a Department of Energy grant where we were looking at the relationship, I'm sorry, it's so blurry to see the relationship across these different areas, but if you just think about the major areas, fundamental research, enabling technologies, integrated systems, and economics, behavior, and policy, ultimately, if we're gonna shift something in the built environment, there's a back and forth nexus. If we're gonna have really move the needle in a fundamental way, not just incremental improvements on pre-existing systems, we're gonna have to have that interface between all of those different areas. And so what we immediately start also doing is building computational models to understand what would be the information flows, and I can show you after this how we interactively start to look at the information flows between these different sectors in terms of setting up projects or executing something. But, and before I get to my actual case study, I just wanted to throw this one system up that also very much speaks to the first panel's discussion, which is that if we can know how we are shaping microbial conditions, the question is how would we do that and in what way and for what reasons? And this is an experimental system that I actually wanted Kevin and others from the first panel to see as well as everybody because this is looking at, in a sense what this system does, and Grosso Motor we call this fresh air building systems or amplified modular phytoremediation system, it's basically a system that is investigating the possibility that we could produce fresh air from within a building, that's the first hypothesis. Can we do that at scale and what would that mean for different zones within the building? So we got into this building, this is public safety answering center in Bronx because it was a security issue, it's one of the programmatic requirements that you have to have in high security buildings and really the mechanicals can only last for seven to 10 days. But more importantly for us, we were interested in that relationship between living systems. Ultimately, if we just said that Jessica Green and Jack Gilbert and others that were presented in the first panel have shown us through their tremendous work that we live in a very, very low biodiverse microbiome, it's a human microbiome in this room for example, what does that mean for our health and wellbeing? Well, emerging evidence is starting to show that even just the presence of rare flowering plants or their absence, let's say even in suburban or farming communities could lead to a decline in human immunity. So at the same time, there's this bioinformatic revolution at the microbial, there's an information explosion across the board of living and non-living systems and the relationship between these. And so our question was, you can see actually all of these are from the single genome, but our question is, if we start to complexify with multiple species, maybe indigenous species, if we start to give different geometric conditions at multiple scales to these things, because all the way down to the small scale, a microbial ecologies will flourish according to even the shape of the crevice, as we just said, within let's say the root rhizosphere, et cetera. But most importantly, it also goes back to, I would say an entire sort of social way of thinking. If, as Kevin is saying, we're actually poisoning ourselves by, through antibiotics, we're actually creating surfaces that are antimicrobial because we're trying to fight the proliferation of microorganisms that are pathogenic. In reality, what we're talking about is basically looking at it from a very different standpoint, a probiotic standpoint. That is, if we diversify, for example, we have Legionellis all over forests, but they don't become pathogenic in the way that they do in buildings. What happens if we start to complexify those microbial ecologies and work with living systems rather than against them? This has tremendous implications also for the reforestation of cities and really the remediation of a lot of the molecules, like VOCs and other toxics that we produce from basically our sort of inherited technologies. But very, very quickly, I know I've taken up my time with what wasn't supposed to be my example. That was more speaking to the last panel. I just wanna quickly go into one case study, and I'm gonna talk about the display technologies that are in the pink there. And the reason why I'd like to talk about these is because I'd like to talk about how, in essence, computation is human, even chess is a human construct, right? As even the basic parameters or impetus intentions for the game. And ultimately what we're investigating with a lot of our computational technologies are what is the interface once again between energy, then material manipulation, and society, social goals, individual desires, collective desires, and how did those tensions start to arise? So in this display technology, for example, we're looking at many, any different, normally within the development of material, you might have a few criteria. We're looking at architectural criteria, so by definition it's very, very broad. But what happens, for example, if you, so this is, I don't have time to get into exactly how this is working technologically, but if you imagine a movable frit, or a frit that could disappear and reappear on a screen according to different criteria. That's what you're looking at, this system here that we're developing, literally a subatomic scale. So it's a solid state system, but it's looking at doing a lot of different things that living systems do with energy in terms of concentration, artificial photos, and this is functionalizing, and it's a graphene-based system. But ultimately, it is about that interface between our environment and different programmatic criteria. And so this image is showing what is historically our trajectory in the last 50 years. As architects, we've become recipient specifiers of technologies that were in development for many years, often without our criteria embedded within them. And so they become very much single function, i.e. non-architectural. They do one thing, an electrochromic glazing might go dark or light depending on what you need, but it's very limited in this function. And our questions are always, what if we embed this material with a lot of different criteria, and that that criteria is embedded within the process, within the lab, within the development. And what would happen if we started to really complexify that through computational protocols and really understand what we would need? And ultimately, in this context, within, say, the social condition, we might put it on a kind of optimization protocol for sort of diurnal swings, occupancy, solar resources, privacy concerns, whatever, and the pattern could become whatever is desired, let's say, at any time in the process. But at that very, very small scale, simultaneously, we need to embed that criteria within the lab. So what you're looking at here is just basically a graphene layers that are wrinkling and basically taking an assumed geometry according to whatever the criteria is, do we want visible transmission to run a completely clear glazing, but at the same time, we wanna control the solar heat gain coefficient, for example. Now, at the subatomic layer, if we're controlling those energy flows, we can do both, right? And so when we say that it's a bioinformatic revolution, it's also an information revolution in terms of how we can manipulate energy and material flows simultaneously. And that's super important because if we can go down all the way and embed our architectural criteria at that very small atomic scale, we have more handles on the materials and we can do much more with them. We're not just taking wood and working with wood in terms of how it, you know, what it can do or taking a photovoltaic in terms of what it can do. And so just lastly, a quick comment about how we can work sometimes, you know, and this is a big international group that came to RPI during the Smart Geometry Conference and we're just so indebted to that conference for bringing people under one roof because if you have experts from all of the world from biomedical engineering to, you know, math sciences, et cetera in one room to work, there's nothing that replaces that type of thing. We can be working with very large teams but at the end of the day, the human interface and the sort of genius of having people's ideas in the room and their voices in the room, I just don't think anything can replace that. One of the amazing things that came out of that experiment that I just showed is the different computer scientists and other folks put their protocols and their preferences and optimization protocols into the algorithms and we started to get this kind of emerging biomorphism coming out. I mean, we weren't sure what would happen once you start to try to optimize for multiple things simultaneously. There was really interesting effects that that started to give. And, you know, it's an open-ended question. It's not something that we have answers for. It's just a question of like, if we have the opportunity for these massive information flows, how would we and for what reasons make decisions according to so many different complex criteria for evaluation, et cetera. This is also just a diagram of how one of our PhD students was imagining herself because always one wonders when you have big machine learning processes and others in the process, where is the person and how do you start to, in a sense, identify with these very large teams and how do you get the popcorn trail. The very same team was visualized in this way by another PhD student who was on the other side of the team. So I think it's really, really interesting also, I think what's gonna start to become really important is how do we identify these tensions between the individual and the collective within these very, very large teams with so much criteria? Because at the end of the day, if we want more information into the process, we are really talking about more people. We're talking about more participation. And in some cases, as was pointed out by David, we're talking about sometimes losing the thread, right? Not knowing where stuff is happening. The big mechanism stuff is super scary to us, but we don't know how we could work without it. At this point, we're starting to become very dependent on big mechanism things because no one has time to read all the papers. And even with causal modeling and setting up all these relationships, if you do that, we definitely are creating, in a sense, a bigger and bigger black box around the process. Flip side is, can we really take on these very difficult ecological criteria of global warming and bioresponsive buildings and really working with natural systems and living systems, energy flows, as opposed to combustion energy, fossil fuel-driven systems, which is our legacy. If, you know, really taking those on at the scale of global populations and cities is really, really not going to be possible without, we believe, getting more information. So I think just talking about that trade-off, you know, between getting more information into the process and then losing the thread or losing the sort of intentionality and clarity and maybe transparency is something that I think will be really interesting to discuss. Thanks. So thank you, Anna. Thank you, Craig, for the invitation. I'm Kaspar from 3XN and GXN and I'm super thrilled to be here today and get in awe over all the other speakers. I mean, I see myself as the kind of a practicent of research and architecture. We are a Copenhagen-based company, around 100 people and we do a lot of projects. Beside that, we have parallel started up GXN, which is our innovation company, we are a small group of 10 people working on how we can take in knowledge from other sciences, other industries and apply that to architecture. So I think kind of also what I'm taking to the table today is very much kind of what is on kind of an edge that is ready for scaling and ready for getting applied. So first question is like, what if tomorrow's waste can become tomorrow's buildings? I mean, I've been talking about sustainable architecture for 10 years now. And it's in the past kind of a few years that it's gotten to momentum because of taking the whole kind of holistic approach on how environmental benefits really is a combination of having a social focus and an economical focus. So I think the talk today is very much going to be about economy. And that's something that you often tend to forget talking about sustainability, but I think that's the way to approach things for the industry and for clients and developers, for politicians. And I think the biggest problem we have is that we are using more resources than we actually are in hands of in the near future. So my question is, I mean, can we be as cool in construction when we build? I mean, this is not my photo. I just like this image because it talks about how precise we are and everything gets really like precisely carried out. But if you forward to how we deconstruct, how we demolish, I mean, this is really un-precise. There's a huge value loss. And I think this kind of in this mix, combining this kind of really wasteful way of looking at building resources and combining that with computation. And what if computation can make our materials talk? How can we use the knowledge we have from sensor technology, from the virtual design construction, the kind of the whole digital platform that we are communicating on as people in the building industry? How can we vision our buildings to become a material Google? And how can we take what's happening in other industry and transfer this into building industry? I guess that's kind of my core focus today. So in numbers, we are around a hundred. This is our office in Copenhagen. We have two small offices beside that, one in Sydney, one in Stockholm, Sweden. And we do kind of a shared focus on 50% home market and 50% international. In GXN, we have been going for it for 10 years. This is my part of the company. We are four owners. And my responsibility is in to how can we look at applied research? And we've carried out 84 projects for now and are trying to integrate that into the kind of the mindset and the execution at the kind of traditional projects. So under the same roof, we have the research with project design, with competitions. I'm bringing three of our research projects forward today. One is a collaboration we had with William McDonough and Michael Browncott on Cradle to Cradle for the built environment, not just for like material certification, but how do we actually take this kind of circular mindset into construction? I did a project called Material World on how we can actually design for the invisible, how we can design materials. I think that is a very fascinating discipline and kind of makes design kind of a broader agenda. And finally, the title of the input today as well, like building a circular future. It's a collaboration across industry on how can we actually reconstruct the way we build and collaborate. So we can see our buildings as a permanent kind of resource and a value that we can recapitulate ongoingly. So in a way, I mean our office, I mean just the 10 people in GXN, we have a mix of professions and of course architects and designers, engineers, parametric designers, one psychologist and one anthropologist. So it's actually kind of a mixed group of people, but it's more importantly like who we work with. So in all our projects, we are like taking as a kind of interdisciplinary stand. So it's very much about inviting biologists and chemists and do this kind of interwoven approach. And I think it's needed in kind of a practice that you establish this kind of parallel focus because I mean all offices will kind of argue that all projects are unique and are experimental and are giving answers to kind of new solutions. But I mean you're always also under time pressure and kind of financial promise and under guarantee. And all that actually enables kind of the more kind of experimental research to carry out in practice. Unless you have of course kind of an unlimited budget and unlimited time, then we rarely have that. So it's to have this kind of a parallel space where we can take in these kind of collaboration and mature knowledge that we then can apply to practice. So I mean the big question is what if we by design can eliminate the concept of waste? Because kind of waste is, doesn't exist in nature. I mean in nature we have ecosystems. I am kind of seeing a building industry as a kind of a manmade ecosystem ideally. How we can actually interact and use each other's potentials in new ways so we can see materials as something that has a value in all stages of a building life. I mean in Denmark we are super proud to say that we recycle 87% of building waste. I mean but that's more like a statement of percentage of weight, not value. Because we kind of use a lot of our concrete as road fill but it's really a very low value compared to that of a standard concrete element. And a good parallel observation is in Denmark we have this kind of recycling bottle system where you get some money every time you go and recycle a bottle. So instead of just like crushing a bottle down and using a lot of energy and effort to remelt it and make new bottles, we are having like bottles recycling cycle is somewhere between 14 and 16 times. So I think that's kind of the economical incentive that you are doing this transaction. Recycling elements in a high value and this is more or less what we also want to see in construction. So the project I'm gonna talk about as an example of how we are upcycling high rises is Key Quarter in Australia. And I mean of course there's a very renowned Danish architectural example in the opera that we are so lucky to be giving a commission on doing the high rise in the second row in the Bay Area. And to us it's about approaching the site. The high rise in front is the first high rise in Australia so it's a listed building. So we need to like find a way to maximize the view and kind of ensure that we get the best quality of the building. So we're pushing back this envelope and doing what they call a kind of a vertical village and also doing what we do best. I mean all of our projects is about human scale and I think it's important in a high rise as well that establishes kind of human space and scale in a vertical village where all of these elements of visual transparency, giving a space for organization to work better to get inspired to interact to meet. That's very much what we do. What we also do here is we take an existing superstructure and the sits in high rise and reusing two thirds of the materials and 98% of all the structural elements in the kind of the vertical loads. We're doubling the square meters, we're cutting back so we're getting these kind of great moments of a lot of daylight, a lot of interactivity. And that's very much what we try to do in our project having people to meet having to collaborate in new ways to exchange. All our projects have like these kind of various central staircases. So actually the elevators only stop at every second level forcing people to move a little. We have the food courts that have this kind of coffee machine effect so people kind of tend to meet during the day and having these spacious atriums through up the high rises. It's kind of the winning argument for the project. But another kind of winning argument is it's very much that we are reusing everything in the center so we are taking away the red and adding the green. And that leads me to the concept of building a circular future which is our latest publication. And it's kind of again a collaborative effort where we are working together with contractors and demolitioning companies, engineers, people that are doing the business models for us. So all of this is built around like these three premises that we need to establish an awareness about our materials through material passports. We're designing for disassembly, not just for construction, but very much also deconstruction. And we need to have these kind of circular economy aspects on the way we negotiate in our buildings. So why is this important? I mean, for the next 50 years, we'll build as much as we've done up to this day. And another kind of image to that is if you see the Pudong River in Shanghai. I mean, with a 15 years time difference, it's crazy what's actually being built in China in the past three years. That's been erected as much concrete as US have been built for 150 years. So I mean, it's a matter of looking at resources in a new way because we don't have enough simply. The conclusion and intention of the book was very much can we establish a positive business case around demolitioning business case? And today it actually cost money to take a building down. The whole kind of publication, everything we do is based on open source knowledge sharing and it puts forward like 15 principles on addressing these three keystones. And the conclusion is it is good business and it's gonna be increasingly good business as the kind of the market of resource prices are increasing for the future. So like three quick chapters on design for disassembly. I mean, as an architect, I don't think that everything should be standard and be kind of looking like Lego. It's very much not what's going on in the work we do. This is our headquarter for the Olympic Committee that we're building at the moment. And again, I mean, this is bespoke solutions that are giving these kind of dynamic spaces. Again, these kind of signature staircases and kind of advocating what we believe in as an office doing architecture that are shaping behavior, stimulating interaction and collaboration. But if you look at this was kind of some kind of a building industry x-ray, you can see that even though this building is maybe one of our most expressful buildings in terms of geometry, I mean, there are more than 60% standards. And in other of our projects, you can see up to like more than 80% even though there are pretty iconic compared to kind of all the rack architectural buildings. So what we're doing is that we're working with these contractors, demolitioning partners, with industry partners and seeing like how can we make these different building layers? And I think we need to like look at building in different layers and different speeds and different potentials. And eventually, I mean, what's happening today is that this kind of concrete element will get lowered in and you will fill everything with concrete and it'll be impossible to reuse. I mean, we need to like look at this and rethink the way we construct. So we can deconstruct, not just for the sake of deconstruction after a kind of a building's life, very much also to create a faster construction, a more flexible layout. And we're doing this with the industry partners on how we actually can do these new kind of conceptual and kind of architectural interlocking ideas, taking the learnings from Stuart Brand, obviously, and looking at buildings in different speeds. And we're doing that when you look not just at the kind of the superstructure and the reuse in this case of the existing high rise, also the new layers, of course, we're mapping out where are the different qualities, how can we make better maintenance, flexible floor layouts and trying to just look at the kind of the long perspective but very much about what's the value now and how can it also enrich the building use and maintenance. And we are looking at this material passport, learning from other industries, the automotive industry very much and also the super ships from MERSC, how they're actually mapping the resources like the different qualities of steels so they know what the kind of intrinsic value is and how they can build new ships when this ship is done sailing. And we do that in construction today, working with six dimension with BIM and VDC and we take advantage of like all elements have a lot of information attached to it and this is just a matter of kind of adding a seventh dimension of how we reuse this and in terms of seeing the financial potential, I mean, we can look at buildings as building banks, building owners can actually see what's my building worth, what's the value and as said, I mean, it's not just about kind of a long perspective but also like a midterm and a short perspective of how can we argue for this value creation and as Craig said in the beginning, I think it's also very important to look at the qualitatively value, looking at better materials, better quality, better indoor climate. And this, I'm getting close to the final slides. I mean, we're doing it today and just by taking this simple move of reusing the superstructure before actually considering all the benefits and the economical gains on the next layers. I mean, we're saving the client for 130 million Australian dollars before we even break ground in material and time saved. So I think, I mean, this is my kind of conclusion. We need to think about sustainability within economical approach and I think we need to work in new ways in the industry and we need to like redefine revenue and value creation. And everything we do, as said, is open source and this publication is out on building a circularfuture.com and please connect to me up here to learn and discuss and meet people. So thank you very much. Okay, I just, why don't you introduce her and then? I just wanna say we're actually going to make a quick change to the program and add an additional speaker now in Billy Faircloth. I'll let the introduction come after but just wanna say because of some travel plans coming out, we're just making a quick change. So bear with us as we load up. Okay, well, I think in some ways this speaks to the connectedness of the panels that we can swap in a speaker and it can seem just as relevant in this. So I'll just make a brief introduction but Billy Faircloth is partner at Kiran Timberlake and she teaches at Penn, Harvard and the Royal Danish Academy of Fine Arts. She leads a transdisciplinary team with a focus on architectural research and innovative buildings. So please join me in welcoming Billy Faircloth. All right, so I just sort of walked off Amsterdam Avenue and ran in here and gave a presentation to someone and so that's where I am. Okay, so I wanna tell you a little bit about where this presentation comes from. So I was just at Columbia recently in the summer talking about making, talking about making with students and talking about students in their agency. And I've also been recently teaching at the Graduate School of Design and having many conversations with students about construction systems, energy and agency and sharing a discourse with Saul Craig and Kyle Moe on first principle definitions of matter, energy, information, information as a stand-in for construction systems. And what's come to the fore many times is a question of agency and an assumption of where agency lies when we deliver pedagogy. In other words, when we teach, when we stand to teach these types of subjects, what are we saying about our agency or what are we communicating about the agency of the architect or of the designer and of everyone else because there seems to be a way that we split people into groups. And so from a series of challenges from students and my overuse, admitted overuse of the word agency, I started thinking about a way to come clean on where my own pedagogy presumes agency to lie. And I started thinking about a way to challenge our assumptions about where we start to have these types of conversations. And you're going to have to excuse me and indulge me a little bit. I've never presented this before, but it's a framework for mapping agency and it's meant to challenge the group that I was in, which was assembly. It works for computation too. Great. But it's meant to challenge those things. So first I want to just start out with a simple axis, a practice axis. Now I can't see my notes. First time I presented, can't see my notes. It's going great. Okay, so a practice axis. So what do we mean by practices? What we mean by practices is simply rituals and habits. Now let's admit it, we all have them, right? We all have rituals and habits. And I'm going to talk about this from my point of view. So I have my own rituals and my own habits. And I'm talking about this in the context of the methods that I choose. You're going to be able to fix that. The methods that I choose to take up. So I have mine and then you have your worse, right? And I understand that you have your worse. You all have your own rituals and habits. You have very different backgrounds perhaps than I do. And you have very different knowledge sets. But then there's somewhere in the middle, there's ours. And so mine or my methods, my rituals and habits, yours. And then ours begins to get at a kind of integrative approach or some knowledge or let's just say some acknowledgement that we have to share some things. Okay, but then there's this other axis, entrenchment. Entrenchment, unable to change or unlikely to change. Unlikely to change. So we can say that in the habit of using methods, we have to admit that there's some methods that are mutable, likely to change, likely to change. And change seems kind of good when it comes to the things, the kind of things that we do. And that there are some practices that are immutable, unlikely to change, unlikely to change. Now of course, if I put robotic fabrication down here with a slight little bit of a wink and I say that it's yours, well what do I mean? From my perspective, from my perspective I would say, well, you're the one who knows the robot, I don't. Or perhaps some of you in here, how many of you are engaged in robotic fabrication? Anyone? So you guys would put that down here and mine. This is my practice, this is my ritual and habit. I understand thoroughly robotic fabrication. If you and I, Craig or Casper, were to work together, we'd have a discussion about overlaps of our knowledge sets, right? But where do we put that? Where does everyone put this, right? Where do you wanna put it? Now let's talk about that, right? Let's talk about change. Like this is yours. Well, I'd certainly like to know something about robots. So maybe I wanna move it down towards mine. How many others in here would want to slide robotic fabrication down towards, this is my method, this is my ritual, this is my habit? I'm sure there's lots of those that we could come up with, right? But it gets really interesting when we think about these types of technologies in the context of mutability versus immutability, right? So we take up practices and habits, we learn them, they become part of us, ingrained in our design workflow, and then they become not near future practices, but normalized practices, and they become something that they don't change too much. We become to depend on them, and that's okay. But we all know that sometimes when it's your practices and I'm working with you, maybe I need you to change a little bit, or likewise when you're working with me and it's my practices, maybe I need to change a little bit, or maybe my knowledge set isn't broad enough, and I need to begin to expand my knowledge set. But what gets interesting as well is when we start to put this into a two-axis graph, right? We start to put this into a two-axis graph, and then we turn it into a series of quadrants. It tells us something about the actors and their perception of their knowledge when it comes to making a thing. You see, my supposition is that the future of assembly has nothing to do with tools or robots or computation or simulation. I love all of those things, I love engaging them. Rather, it has a lot to do with people, and what they know and how they orient themselves to practices and their immutability or immutability. So in this instance, we see a very simple series of quadrants, a four-square, and now we can begin to ascribe roles to this four-square. We can begin to say, well, if it's your practice and you're willing to allow things to change, I'm gonna call you a nimble advisor. Now, I'm being nicer, right? Nimble is something that's seen as a good attribute, a good attribute to have that you're quick, you're able to change things very quickly, as opposed to mercurial, right? I mean, we wouldn't wanna be mercurial. We'd want to be able to be nimble, flexible, and willing to change, right? Now, if they're my practices and they're immutable and I hold onto them and I'm constantly changing them and I am flexible and nimble, then I am a nimble individual. Yeah? They're my practices. But let's talk about what happens when they're not mine. And again, this is from my point of view. People that I wanna work with, do I want a nimble advisor or a resolute advisor, right? Now, I'm being nice again. I could have said entrenched, but I'm gonna say resolute. Someone who's very certain about the things that they're about to do, about the knowledge sets that they have, and about the extent to which they know something. Oh, my God, it's right here. Oh, that's interesting. I don't know if I can do that. Okay, this is going okay. So I think, I mean, I feel like I'm being clear. We'll see. All right, so then, if we go to the next project, we have a resolute individual. These are, again, my practices. I'm certain of them. They are immutable. They're unlikely to change, right? So what does all of this mean? What does all of this actually mean? And especially when we begin to look at that axis, when we start talking about what is collective? What is shared? Does a group have a series of resolute practices that are unlikely to change? Or does a group have a series of nimble practices that are likely, in fact, likely to change? So what is your agency? Now, this is actually all about agency. And the reason why the word agency comes up is it's because it's the power to change something. It's the power to make a change. Who in those different quadrants has the power to make a change? In other words, the reason why it's so interesting is to put robotic fabrication up on that first axis is because, well, we all know that we'd like to, or often we'd like to, appropriate new tools. And it's going to be me, the designer who appropriates those tools. But the reality is far different than just me, the designer being able to appropriate new tools. My agency is confounded by the actual process of assembling a building where tools are shared and are very different across an entire supply chain. So all of a sudden, the pragmatics, and I'll even say the aspirational pragmatics for delivering and assembling a building, and my desire for using those tools actually come up against each other as a kind of tension. Now we all'd like to say that it's a creative type of tension, but sometimes it's not. Sometimes it's a frustration. How do our pedagogies begin to teach agency where the seat of power lies in a pragmatic way but also an aspirational way, in a real way? How do we begin to address the seat of power? So when we look at this quadrant, the series of quadrants in this biaxial graph, what we can begin to do, or one could begin to do, is play through a series of variables and try to understand what it means to put them where they are and to understand what it means in terms of what we are attempting to do. So let's just go through this a little bit. Now construction, architecture, well let's just say this, this is a moment in time. This is a moment in time. Buildings cannot be simply defined as the time that happens just after someone occupies it. I think that we can safely share this definition that in order to expand and operate with a real definition of time, as we've just talked about in Casper's presentation, we need to begin to understand extraction processes, processing, manufacturing. We need to understand something about all of the embodied environmental impacts that happen along the way when we engage in the specification of materials. We need to understand something about what happens at end of life. Time becomes incredibly tricky thing to talk about when you start to define construction that way, right? So we can define assembly and I would define assembly not as the point of assembly, but as the action of assembling before my time and after my time, right? So when we talk about that as the ultimate act that we're engaging through the wonderful process of design, when we talk about it that way, we do begin to understand that design is a multivariate endeavor, a multivariate endeavor. Now, our pedagogies have a tendency and traditionally to want to focus on the interrelationship between theory, material and technique, which comes from James, Mars, and Fitch, theory, material and technique, right? Especially our construction systems courses and I have taught those types of courses, but when we practice, and I can only say this, given my own personal trajectory and having gone from academia to practice and having engaged in practice at Kieran Timberlake in a way that begins to legitimize definitions for logistics, new definitions for time, new definitions for ecology and equity and structure, new definitions for all of those things inside the process of design, what I can say is that we have to begin to expand, begin expanding the variables that we engage. In other words, we might be tempted to draw a boundary around theory, material and technique. As part of our agency, we have the power to draw that boundary and say that's what we're going to teach and therefore that's what we're going to learn and therefore we're shaping the perception of agency with pedagogy by drawing the boundary around those three things, but what happens when we start to draw a boundary much more broadly and then try to define those things much more literally. How do we define energy? What is energy? How do we define a construction system? What is a construction system? How do we define materials? What are materials? There's a first principles definition operating in each one of these and here I am before you, arguing for the agency to revisit those first principles definitions. That's what I'm pleading for. The agency to revisit those first principles definitions, but why? Why? Do I do that from the point of view of trying to give students a much more sobering and aspirational point of view of what construction systems are and what design actually can accomplish or am I trying to do something else? I would say as the former, there's no ulterior motives. The goal here is to convey both the complexity and the opportunity of design and architecture. So when we look at the multivariate problem and we look at this quadrant and we begin to ponder our own perception of agency, we have to start with the things that seem the most tricky to us. So I'm just gonna plot a few things up here and confess that some of these, begin to suggest a discourse. Now, the discourse is a little biased, I admit. There's implicit bias here, but let's just go ahead and say that I'm working with someone and they're using the method and this is a methods issue. They're using the method of energy modeling. Now from my point of view, I'm gonna say, hey, that's your method. I don't wanna know anything about energy modeling. It's actually not true. But we might hold that belief that when we engage design, that we work with people and we say, that's your method, it's immutable, it is the way that it is. There's no way of opening it up, cracking it or hacking it. It is what it is, right? We might say that. Or we might have this instinct that we have to move that method somewhere else in this quadrant. We actually have to take responsibility and take up some of the agency to redefine what energy modeling is, right? So do we want to move it over to someone else who has done that, who has engaged that intellectual exercise, someone else who knows how to work through energy modeling in a new first principles kind of way? Do we ourselves as individuals need to take that up? What's the argument to make? Now, we also might say, well, of course, as architects, we're very good at descriptive modeling and what I mean by descriptive modeling is simply that we're very good at modeling geometry. That's ours, that's our domain. We're really good at it and so when we start to put a pair of these things together and we know there's an interrelationship between say to descriptive modeling and energy modeling, we start to pair these things together, we start to see why, what some of the motivations might be. We cannot get feedback loops between our iterations and how we say something is performing, what be it a steady state condition or something even more pervasive than steady state. We cannot get information on how something's performing simply because we don't know, simply because we've let someone else define what energy is for us and we don't have that knowledge, right? But moving it down to descriptive modeling begins to suggest that we need to know or at least that we have this instinct that we need to know something more, right? And that we have a pairing. Now let's look at robotic fabrication. So if we say, and I'm gonna admit that that used to say tools, but I decided to be a little bit more specific, right? So if we had tools up there, now there's been a huge sort of discourse and I've appreciated that discourse, I learned through that discourse on appropriation of computational tools and digital fabrication tools for architecture. I participated in it, I taught it, I've been there, I taught those seminars. I have championed that every architecture student should appreciate and enjoy the type of agency that engaging in fabrication gives them. No doubt engaging in fabrication and making. But I also have had a reality check when I go to have a conversation with other people who use their tools and have their ways of knowing and I began to understand that it has nothing to do with the tools. It has everything to do with a series of conversations about techniques, right? But nonetheless, when we want to have this conversation, there's been a broad conversation in pedagogy about what we move down there in that quadrant, what we shape in terms of individual students' perception of agency when we give them these tools and why. So through pavilion after pavilion that's created, through every sort of project that we engage that is at scale with students, we are giving them, we are shaping a perception of agency, what kind of agency, how are we defining their agency by doing that, right? So we can begin to see that we're building up a series of relationships. Now, materials have often remained in that category. I don't characterize materials. I am not a materials engineer. And yet, there has been incredibly robust discourse on materials and the microscale of materials and what matters about materials over the past 20 to 30 years. There's been a resurgence of that discourse again and again in the history of architecture. And am I tempted to take up the agency to characterize materials? You bet, bet. Do I need to depend on other people to do it? Right now I do, but where does materials characterization go? Now, there's also another thing here which we've ignored. We've ignored this quadrant that is about immutable in mind. I'd like to say that I'm always flexible, always nimble, but the reality is that even our own practices, we adopt them and then they transform into a type of practice set that we've come to rely on, we've come to rely on. So perhaps what that suggests is that we have the agency to continue to change that practice set, yes, but we also have the agency to begin to practice a little differently and understand the questions we're asking, often by understanding the questions we're asking we're then put right in the position of not knowing and trying to assemble a unique agency map versus one that's rather traditional. Oops. Oh yeah, okay. So at Cure and Timberlake, we have gone after many different methods. We operate through a transdisciplinary group and through that transdisciplinary group, we have begun to expand or connect to the practice of architecture and the process of design, many, many different types of methods. We've expanded our agency and we've experienced what that's like to be able to go after those types of methods. But we've done that not as a way of owning or grabbing agency for agency to be ours or the power of these methods to be our own. We've done it more because we've begun to recognize that we have to actually operate somewhere in the center. Now I don't mean to propose that the center is some idyllic utopian place for practice. I don't mean that at all. But what I do mean is that we often right now have a series of let's say disagreements or misalignments about where the power of agency lies and we often want to do a power grab. When in reality, we know that some of the best projects that you've all engaged come from places where collective intelligence thrives and your ability to define energy is forever changed by someone who operates with the first principles of thermodynamics who's trained in chemical physics, who you can work with as part of the design process or your ability to define sight is forever changed by someone who truly understands the interrelationship between vegetation and materials and building. So our practice has persisted in operating through collective intelligence and by questioning where the power of agency lies. In other words, not trying to grab the agency or pretend it's our own but trying to admit that we don't know, that we don't have sufficient, we don't have the practices that sufficiently see and comprehend the true nature of design. So the question for us is one of agency and pedagogy. And what I've tried to suggest by doing this is that when it comes to assembly, there's not one source of agencies. There's competing agency and there's alignment and realignment of agency. And by mapping my perception of agency, I've also mapped the bias of my pedagogy which I wanted to remind you of. And I've also am trying to recognize other people's practices and my own expectation of them. And I'm trying to recognize the entrenchment of practices, yours and others, or the degree to which you can depend on the mutability or immutability in order to take action. I think we can agree that when it comes to assembly, action matters, but we can agree that the perception we are shaping through pedagogy is something that requires us to have a very, very clear definition for a much broader set of variables than the ones that we normally talk about. The other thing that's clear is that the future of assembly does not depend on a tool or a technology, and I said this earlier. I believe it depends upon people and their willingness to take up a sobering discourse on agency, add the conceit for new types of agency, a mission of epistemological preconditions, a mission of epistemological preconditions, the importance of collective intelligence, and the efficacy of transdisciplinary design practices. So the question that I would ask us to discuss is how then should our pedagogies respond to this future of assembly? Thank you. So we're gonna have a short panel discussion in about 10 minutes and then a lunch break. We should be roughly on target for a still two o'clock start-up for the presentations in the afternoon. So I wanna just start by speculating on whether it makes sense to return to the concept of computation, the frame for this panel, and if so how. And I think one thing we could say by seeing the three presentations and also maybe just thinking about the set of speakers today is that maybe we're at a moment of thinking about computation where it almost doesn't make sense to talk about it alone. It's so integrated into everything else we do. It's hybridized. It's hard to separate it from issues of material or construction or even culture in some ways. And certainly environment could be a theme of all the presentations. In this panel. But nevertheless, I wanna ask one question about computation and see if it gets us anywhere. And that is, I really love the way you posed it, Anna, that you said there's maybe this tension or a trade off between getting more information and losing the thread. And there are these kind of dueling things that I think in general we're doing right now as a moment of technology and practice. And so I'll push it a little bit further and say on the one hand, there could be an interesting argument that computation can enable transparency and participation and exposing our decisions and giving us a platform for discussion about first principles or values. And actually that was an argument being made back in the late 60s about computation saying like let's be clear about what we're doing and rational and then have this discussion. And I think there's still a sense in which that could be true, that computation could enable a more inclusive discussion about what's important to us as opposed to just being the realm of optimization and efficiency. But on the other hand, I think we could say that there's this other thread or trend that computation can enable like a massive processing of data in our big data, big computation machine learning age and that it can enable things that it can enable things like correlation without causation that maybe it's, we're no longer able to say why things are happening but we can note that they are happening, note similarities and that's in some ways at the heart of machine learning. And it can lead to again like what you said, and I'd like bigger and bigger black boxes. And I think that potential trend is pretty interesting as well that maybe one of the things this means is that we could have to get more familiar with designing with uncertainty and with black boxes and without complete control. And I think that probably touches a little bit on the kind of biology angle that if we recognize that this is happening and this is important, then I think the next thing we have to do is recognize that we can't completely control it. So then what do we do? So I wanted to see if either of you, any of you have thoughts about those two possibilities or those two potential trends of computation now as opposed to 20 years ago or 50 years ago? Yeah. Do you mind if I take this? Because I'd love to relate that to this amazing framework also that Billy just offered us because I think if we're talking about that trade-off between getting more information into the process and then losing the thread or let's say also not just more information, more data, but more techniques for analyzing the data and moving through the data. I think the example of energy modeling is a great example where the immutability actually gives agency in order to follow the thread along so many different let's say players. And we're moving for example into an era where we're starting to actually lose the multiple energy models that you used to have on these big projects and those multiple energy models were amazing because they were all different. And so it was so great because you had a chance to actually say, okay, what are the varying assumptions? What are the different threads? What is the different information in the process? And now that we've got these jumbo models that can talk to each other and we're losing the diversity of sort of start and process and then finish and product within ways of seeing projects and conditions, we're losing that discourse. So I think it's a really good example of where we've got to figure out not just I think certainly the immutable formats of energy modeling are highly unsatisfactory towards a lot of different ends and they tend to reinforce let's say material systems and preconceptions of what material choices and systems will be. On the other hand, the question is like, how do we actually get to that immutability and that interchangeability while being able to follow the thread? Do you know what I mean? So there's this kind of chicken and egg condition in terms of agency. Yeah, I don't. I mean, I guess there's two things that's interesting in terms of computation. So I wanna pick up on something that you said about uncertainty and then uncertainty in the relationship of energy models. So uncertainty is a literal thing if you're working in any sort of data environment and you're doing any kind of statistical analysis, you know that we're going to have uncertainty factors and everything that we calculate and compute and I would say that's one of the hardest things that it's difficult for architects to wrap their designer strap their heads around the fact that there is a discourse on uncertainty that it's literal, it's literally a thing and it's okay, right? The okay part is hard, but when it comes to energy modeling we haven't even been able to understand first what it is we're exactly modeling and so I have seen the same trends that you're speaking to Anna where models are getting bigger and bigger and closed down even more, which has resulted in our practice resisting that more and more and trying to introduce frameworks where we do shared energy modeling or creating new platform forms where we stop and we start from first principles in order to understand exactly what it is we're modeling. In other words, collecting all of the inputs and then doing all of the processing and the data interpretation with the entire team, but it's taken us a long time to figure out the causation of our frustration, right? And to get at the fact that it actually has to do with some sort of stopping and discussing those inputs and getting to a point where modeling and it is a computation and that kind of computation requires a deeper discussion about the data inputs and the interpretation of them and the uncertainty that goes along with them. And in my mind, this also brings us back to the question of the conference which is pedagogy because Kieran Timberleg is obviously out on a limb and a phenomenal example of being able to build that much research into practice, but it's very, very difficult as it meant that the budgetary and time constraints make it really difficult to follow that thread and I had wanted to bring up the example of the other professions, other disciplines where there is a coupling between research within the academy and practice. Medicine is probably the most obvious example where you really do have a much tighter coupling than we have and it's well understood what the sort of cost benefit is of funding that research. I talked a little bit about funding, but I think what's critical in this case is understanding how we can parallel track those kinds of projects that you're talking about where there's this frustration, right? Where you can have others within a different food chain, i.e. the academy, start to do the deep dive, you know what I mean? And start to really, you know, and I just have that question maybe for you Billy, like how do you see that emerging and can you see that emerging and growing in our field where we finally admit, yes, uncertainty quantification, statistical analysis, you know, semantic ontologies onto computational models are a thing and they're a thing across all of the disciplines and we need them in our discourse as well and it's not all about better, faster, cheaper that sometimes we have to take more time and we have to take the time that other disciplines allow for these kinds of activities to emerge and grow and develop. So in your practice, how do you interface or would you see maybe a kind of ideal interface in that regard? I think that, you know, an ideal interface, no, no, I haven't given a lot of thought to this, I'll be frank, an ideal interface only because what we find in our practice is that regardless of where someone originates from, that the goal is always to do projects. You have to do projects. You have to get out there and do projects. So if we had waited for someone to create an energy modeling framework for us, we're not gonna wait, right? And so what we'd rather do is engage whoever it is from wherever they are in a series of questions that allow us to do proof of concept projects that allows us to move something forward. I definitely see that in your work as well. I think that's a really good question. And I'm very happy to sit right here between Anna and Billy because I mean, I'm sitting between state of art kind of academic research and state of art practice research. And I think it's, and we're talking about computation. And I think very much it's then inspired by, it's also a matter of dialogue. And I think that is what computation can do. It can actually create, establish some information that should lead to a dialogue and to have this kind of informed approach to how you drive and develop your projects. I mean, but it's more than a project because behind a project is some clients, some users, some financial structures. And there's like a big collaboration team. And I think it's how to establish, I mean, because we are getting information to engage in new conversations. But how can we then ensure that we have the time? Because I mean, ideally I guess we all can agree that we want to like spend more time in the beginning to kind of raise the level of quality and learning from how antibacterial surfaces in the gypsum wall is a bad thing and why we're doing something else. And I think that's a real trigger to like find out how we can establish this kind of dialogue within, I mean, for my sake, a practice framework. And we try to do that and find like, where's the value and how can we engage early with the client to talk about that we are like value-driven and we want to have like another process than the typical defined face-by-face process. And I think that is maybe like a super outcome of kind of applied computation or like getting all this information that Anna is working with and weaving it into practice as Billy is. So I think that's a real question about how can we in a kind of practice format establish the space for this dialogue? Yeah, I mean, I think that's, we'll take a question or two from the audience in a second but I just want to build on that that I think that's maybe a really interesting term to be kind of navigating computation is dialogue. But I think it's totally fitting and I just wanted to kind of relate one example that's been on my mind recently. I read an essay by a neuroscientist recently who was talking about the inevitable rise of artificial intelligence and getting used to that and what it would take. And he was basically arguing that artificial intelligence is a fundamentally different species of intelligence than human intelligence. And if you kind of believe that and follow that thread, the next thing he said was the most fascinating to me which is that if we acknowledge that it's a different species of intelligence then what we should do in education, he was actually saying, is the exact reverse of what we're doing now. What we're doing now is we're investing more and more in expertise about computation and in training people to be computer scientists and getting deeper and deeper about data science and algorithms. And he said exactly what we should be doing if you follow the first part of his argument is reinvesting in the humanities and in the literary novel because that's the part that we're in danger of losing with this inevitable rise of artificial intelligence. And so just to return to the deep the deep versus broad question. I mean, it seems a little oversimplified but I have always been fascinated by the kind of T diagram for that that you have to be deep in one thing but then be able to talk across all things and have this kind of dialogue. That's interesting. We're gonna have to wrap it up. We can't do one question. I wanna say thank you to the panel. We obviously have lunch available because it's already starting to eat in a very biological way.  We're planning to start back at two o'clock. We have another panel on assembly after lunch and then pedagogy and fauna and a round table and a box. So we have a lot to get through in the afternoon. I won't be back at two. If any of the speakers here could you come see me just after this afternoon break? I need to see you just quickly, okay? Thank you very much, everybody. We'll see you at two. Appreciate it. That's a great side of me. Thank you.