J.D. Stanley, Chief Technology Officer, Planetary Skin Institute / Public Sector Chief Technology Officer, IBSG, Cisco.
We wouldn’t know that there was a European financial crisis because we got a very clear, optimistic view of the future from our European friends. We’re going to go from the European scale to the planetary scale. We decided that before lunch time, we would offer you two viewpoints. J.D. Stanley is here. He is the chief technology officer for the public sector group inside the internet business solutions group. He is also the driving force behind the technology innovations that you’re about to hear in the Planetary Skin Institute. When we ask ourselves how do we take the technology innovations and apply them in ways that would substantially, and maybe dramatically, change our behavior, our policies and our practices, Planetary Skin is right there at the cutting edge. So J.D. Stanley…
J.D. Stanley, Planetary Skin Institute
Thank you, Gordon. Gordon wanted us to rock the house so let’s what we can do in ten minutes. What I’d like to do is talk to you about a couple of things.
One is that from our perspective at the Planetary Skin Institute, we are a nonprofit research and development organization that was spawned by NASA and Cisco to go do hard, intractable, complex system problems. So when you think about space, you think about sensors, you think about the trillion nodes that we have someday, there are some really hard problems. Cities face large problems. What you have to also realize is cities face larger problems if all the scarce resources that we have – from forest to water to energy – all those actually run into hiccups and problems; we’ve seen it. So what we’re also focused on is how do we take and do better risk management, better risk mitigation across these scarce resources.
So I’m going to show you two videos of two programs that we’ve developed. We’ve developed these programs in months. We’ve worked with NASA, INPE and University of Minnesota. There are all kinds of data algorithms behind these, all kinds of ecosystem models behind these. There are satellites that we could get direct feeds from. There are ground sensors that we’ve embedded over the years. We’re leveraging simple concepts that we’ve been working on for years inside the internet business solutions group which also relate directly to the urban side. There have been conversations on ecomaps in the past and personal travel assistance. Some of the same philosophies learned right there through data, data exchanges, data aggregation are actually embedded in these. Then we’ve added more of those around collaboration and facilitation.
So there are a couple of things. One, remember the term SMASH and try to figure out what SMASH means by the time I’m done. If I’m done and you don’t understand it, then I didn’t do my job.
Second, I’m going to show you stuff. I’m not going to stand here and present presentations. I want to show you real working applications that are here to solve policy problems, to actually help you make better decisions, to really work through these issues together. It’s not about the tool.
In these 10 minutes, do not walk away from anything I show you as “Hey, that’s a cool tool. I want one of those.” These tools that are built, these what we call cognitive decision spaces – I’m just going to throw it out there – cognitive is what we do, decision spaces is how we do it. The reason for that is because of the human network element, which is the H of SMASH. It is about how we facilitate the conversation between you and you and you, policy-makers, private sector, public sector, NGOs. What we really want is those aha moments. How can I take one dataset that is very disparate, bring it together, mash it with another dataset and have a conversation with you. We’re leveraging something to facilitate that conversation. That’s the gist of it.
Now, why do we do that? One, there is more. We have more data, more video, more social media, more noise in our system. A lot of things work great that are out there today for social media. I changed all the rotors in my brake pads by watching a YouTube video and I did it in three hours. If it wasn’t for that thirty-second part about how to compress the caliber, I wouldn’t have known that. I would have been sitting there for hours thinking “How do I get these parts apart?” But because I watched it, I saved hundreds of dollars.
However, solving policy issues about how much energy should be renewable, how many electric vehicles could be on a car [? 04:14], the implications of those are large. What’s going on with deforestation? How can we look at the past to see what we can learn to go to the future? Those are much more complex. The more data, the more noise and it’s too much noise for experts. When you look at what we’re doing, we’re also trying to figure out how do we take the experts in a room like this? How do we take the experts that are scientists sitting in those back offices? How do we take the experts in all these communities, whether it be urban, rural, cross-sector, cross-discipline, and bring them together to facilitate those aha moments? More is too much.
We also have too many things that are too consumer-driven. The expectation is everything will run on the iPhone. Everything will run on the iPad. That’s not the case when it comes to ecosystem models that have millions of datasets that have to mashed together to model out what the implications on material for electric vehicles will be plus the energy demand of that. Those are hard problem sets. We can’t just say, “I want 20% renewable” without understanding what’s the effect, what’s the impact, what’s the level of effort. Those are the types of systems that we have been focusing on.
There are two of those systems that I want to show you, but the design elements of these, you have to understand the following: we built this based on cognitive behavior. We’ve inherited IT systems that forced us to work the way they want us to work. We have to click a certain way today in most browsers, most desktop applications and most enterprise applications. We have to go to certain websites to do certain things. We are forced not to work the way we as humans actually interact and get those aha moments; we are working more about how IT is for thirty years. IT’s broke; we can’t work that way in the future.
Now we can’t solve that problem at Planetary Skin but what we’re trying to do is look at certain things like closed-loop programming. What is that? It’s something we made up to allow you different ways to actually get to the same dataset versus “You’ve got to click 1, 2, 3, 4 to reach that dataset; otherwise, you won’t find it.” There are simple things. Taking opacity which is a way to look at various datasets and we mend those together so I can look at high-definition video, interactive datasets and do it. Most people also think that the coolest tech is the coolest thing. I would be personally telling you that after five years of doing this, the coolest tech is the most distracting thing for these experts. Do simple, practical things that you can implement.
What I want to do is show you the first video on Planetary Skin. It shows you a little bit about how we’ve taken these concepts and built a worldwide program that actually has 6 million change-detection points that looks at every change at 90% high confidence rates that come from satellites. All information has been validated with 7 different algorithms behind it that allow you to look at the trends and patterns of flooding, deforestation, cropping patterns, other types of things like infestation, even the beetles out here in Colorado. You can see it. You can visualize it. We’ve tracked it in a pattern and we can recognize it. Play the Planetary Skin video, please. In the middle of it, I will pause it slightly.
Planetary Skin Video
These are all the 6 million points you’re seeing here throughout the world. There is 3D here but what you’re going to find is that we can do 3D; we’ve done it in the lab. But you’ll find that I’ve done other things not to do that. Our engineering teams are focused on 2D. We took 3D technology and actually moved it to 2D because of the practical nature of 2D. The global access that people have throughout the world, especially in emerging markets and impoverished nations, can access 2D; they can’t access 3D. It consumes too much power in the desktop. We here in the US, Canada or in Europe, yes, we have it. Majority of the people in the world do not have access to high definition, 3D GPUs and all those things that are out there. So we moved many of the things in 2D to make it simple and accessible, to cause that catalyst of a conversation between farmers, between deforestations experts, between field workers.
Pause for one moment. This is taking multiple different layers. The Planetary Skin has over 200 spatial and temporal layers mashed together. If I cached all this, it is a petabyte of data. Because it is a beta environment, I’m not caching. But if I wanted to have access in seconds, this isn’t too bad – in 10 to 20 seconds sometimes. This is a petabyte of data in months that we put together from around the world. Keep playing the video, please.
What you’re finding here is change detection, the patterns of change. What it means to you is all about risk. These are risks we face. It doesn’t matter if you believe in climate change or you don’t believe in climate change. Whether you believe about large mega-cities or not mega-cities, whether it’s small communities or large communities, change and risk are the realities of the world and we need better tools to manage that, to monitor that and to facilitate that. This is just going through a couple of those.
The design principles – as I said, SMASH – some of these are simple. Make it simple; don’t distract through technology. Another part of this is make it the moments. What you’re finding here are all about decision flows – the single moments of when I have to make a decision to interact with you or to do an analysis. Those are all built into this. Another program that we have besides this lab here, which is all about 140 sensors that’s one of the most instrumented force in the world – I can’t tell you what that is because it’s still in R&D lab – but we’re getting live data feeds. We’ve done everything that people talk about. We find that sometimes it’s useful, sometimes it’s not. We can do those things but was it useful in the 2D world? No. Most of those things we didn’t show.
The next video we’re going to show is about energy. We worked with Otherlab (Saul Griffith and Sam Calisch). We worked with The Climate Group Molly Webb and so forth, and the Planetary Skin. We built an energy flow model based on a lot of work that Otherlab has been doing with the DoE for years. When you’re in the mathematical environment with algorithm- based models, you will see flows. They use Sankey diagrams all the time. Sankey diagrams are a good visualization but Sankey diagrams on their own – unless you’re an expert – you may not know what they mean. We’ve taken the spatial models that Planetary Skin has done, we’ve taken the climate expertise of The Climate Group, and we now merged that with the Sankey diagrams which are mathematical models and put them all in a web-based application. The reason for that is when some politician says “I want 20% renewables,” we can go back and say, “OK, do you know what that effort is to get there by 2050? Did you know you need to install so many square feet of solar to do that? Did you know what the implication is to do that? What are you going to reduce? Is it coal, peat, nuclear? What are the tradeoffs?” Those are hard questions. We did this in the surface level question first. Let’s get to the first-level question first. First is “What’s the level of effort and adoption rate?” The second part to that is “What’s the impact? Does it reduce my cost or reduce greenhouse gas?” So I want to show you FRED. FRED is an energy flow, demand-and-supply model that merges many of these concepts together. Please play FRED.
How is the world doing quick stats? As you know, data is extremely hard to get. We have experiences from ecomaps and Harry knows that; he’s worked with us. Data is hard to get in common formats, in comparative ways, in ways that need conversions. We’re building mathematical conversions in the backend of this to, at least, bring energy together in a common way throughout the world; it doesn’t matter where it comes from. What you’re finding is the combination of these algorithms, these models, these spatial data is where it comes together in a decision flow. The Sankey diagram is important but the what-if scenarios are even more important. As I mentioned, it is very important to understand that the tools that are out there are cool but it’s not about the tools; it’s about facilitating the conversation. The Planetary Skin has been dedicated to that. The lessons learned we have, we’re doing rapid prototypes, we don’t do operational programs, we’re getting those out there. Using things like Webex and telepresence help using tools like these help facilitate even more human connection.
SMASH is about simple. It’s about moments. It’s about analysis. Those are really important things. It’s also about spatial media and immersive environments. Most of all, it’s about humans and taking advantage of all that tacit knowledge. The only way we get tacit knowledge – because it’s not in Google, it’s not in the systems, it’s barely in videos – is when we have that conversation, especially across sectors, across disciplines and across experts. This was meant to show you a little bit about what the art of the possible is, about getting experts together, select trusted groups in society and social networks, taking advantage of the crowdsourcing of the trusted groups and to facilitate the conversation and the change.
If you’re ever interested in helping us in this, datasets are hard. We need data stewards and data shepherds. Educational institutions are excellent for that because it’s really hard for public sector and private sector to have data stewards and data shepherds. Help us ignite all those neuro-circuits that we need to solve these hard problems. Thank you for your time.