For Democrats, they're very reticent to give the Trump administration credit for anything. Just do the traditional partisan politics. And then for Republicans, obviously, everyone knows vaccines are highly controversial and divisive within the Republican Party. And so we're seeing presidential candidates in the next election kind of running away from this huge success. If you flash back to Trump introducing this concept, we've pulled a clip from 2020 when he first was rolling out Operation Warp Speed, which will play in just a second. The reaction to that was quite interesting and telling from both sides. And things have kind of flipped back and forth multiple times, but let's have Adam, our producer here, play that clip and then talk about that issue for just a second. It appears to be employing the promise of a vaccine as part of his reelection strategy. Today, he took three opportunities to suggest that we could even see a vaccine by election day. So what's the earliest we could see that a vaccine? Sooner than the end of the year. Could be much sooner. Sooner than November 3rd? Oh, I think in some cases, yes, possible before, but right around that time. I believe we'll have the vaccine before the end of the year, certainly, but around that date, yes, I think so. It wouldn't hurt, it wouldn't hurt, but I'm not doing that. I'm doing it not for the election. I want it fast because I want to save a lot of lives. Under Operation Warp Speed, two vaccines are already in the final stage of clinical trials and we'll have a vaccine very soon, I hope, long before the end of the year. Dr. Anthony Fauci weighed in on that with his own reality check. We're trying very hard to ultimately get a vaccine that might be available by the end of the year or the beginning of 2021, but that's many months away. So what strikes me about that, first of all, is Fauci seemed to be pretty much cautiously agreeing with Trump on the timeline, despite how Brian Williams was characterizing at the time. And secondly, it's a reminder that the hesitancy back then was coming often from the political left. We've got a tweet here from Eric Topol, a well-known Twitter MD who at the time was stumping and here in this tweet, he's actually kind of bragging about the fact that his efforts delayed the authorization of the vaccines until after the presidential election. This was right in the midst of a major COVID wave. So who knows how many lives that potentially cost, but I am curious from your perspective, as someone who's trying to put together this kind of transpartisan coalition to look at issues of technological progress or medical progress, what are some of the strategies you're using to try to overcome that kind of partisanship? Yeah, I like that you use that phrase transpartisan coalition because that's a term we like to use a lot here internally within our organization. I think for some of your audience, that might be a new term, they've obviously heard of bipartisan or nonpartisan. The way we think of transpartisan by contrast is that if bipartisanship is a messy compromise where one side gets half of what they want, other side gets half of what they want, then everyone kind of walks away dissatisfied with the bipartisan compromise. A transpartisan deal would be if Republicans and Democrats support the same policy but for different reasons. And so I think Operation Warp Speed is a great example of in theory, and not practice is a different, but in theory it has a lot to offer each party. So Democrats like to say they're pro-science, pro-public health, they're pro-masking. I know all the things that go along in that bucket. And Operation Warp Speed delivered these amazing biomedical innovations very, very quickly. And then for Republicans, there's often a concern every year around wasting taxpayer money. So whenever the government has a multi-billion dollar big public spending program, there's skepticism that like the government will get a return on its investment or it's worthwhile. And I think some of the tools they use in Operation Warp Speed could be used to get in part of their programs. And the key is that you're paying for performance. So the government said, we're gonna do pre-purchase contracts with all these companies that participated in the program. And if they deliver, invent a safe and effective vaccine approved by the FDA, then we'll purchase hundreds of millions of doses from the company. But if the company can't deliver on its end, then the government does not spend the money. And so I think that kind of public-private partnership that protects taxpayer money should be highly appealing to Republicans. And then also talking about the feasible alternative at that point in time if you don't get the vaccines quickly is a continued quasi-lockdown in much of the country. And so what's the more freedom-oriented, liberty-oriented thing to do? It's to get the vaccines out quicker as opposed to more draconian measures that would need to happen in a counterfactual scenario. But why did government need to guarantee demand in that manner? Yeah, so again, I think this is where like there's a hindsight situation where we know at this point in time that there were lots of variants that the pandemic ended up dragging on for two to three years, even post when the vaccines were available. We can go on more into like promises around transmission and things like that with the vaccines. But I think it's useful to look at previous epidemics around the world. And this is what obviously vaccine manufacturers and pharmaceutical companies do. There are previous SARS outbreaks in East Asia that seemed like they might become global pandemics but ultimately didn't. And so if you were a vaccine manufacturer, in particular, building capacity of like, are we gonna actually start winding up manufacturing capacity to deliver hundreds of millions and ultimately billions of doses of vaccine? You're in a really wait and see cautious scenario. You wouldn't wanna sink too much money into fixed investments that ultimately there might be close to zero demand for the vaccine. And so the government is de-risking that. They're guaranteeing demand for the product so long as you meet the performance metrics in terms of it's safe, it's effective as a term by the FDA. That could have been a world we lived in where the pandemic did peter out for other reasons and they still invented safe and effective vaccines. And then we bought a lot of doses we couldn't use. But that's the kind of, I think, preparation you need to have for the worst case scenario. And we were in a bad scenario where a pandemic just kept going. We wanna go through each individual components of operation warp speed just to understand exactly how it works and exactly what lessons we wanna learn. But I think it's kind of important to address like the, you know, what the vaccine did and didn't deliver, you know, what we all kind of think about that because that is partially how you judge whether it was a success, whether it, you know, what level of protection did it deliver? What level of risk did it bring with it? And, you know, just from our previous conversation, I think where Liz and I are both kind of coming from and you can chime in here, Liz, if you want to is that, you know, vaccines were on net, imperfect and certainly far from perfectly introduced but overall a lifesaver, a net lifesaver. And we're both keenly aware on the heels of some recent work that we put out that a certain segment of our audience strongly disagrees with that. But I do take it as extremely likely at this point that the vaccines on that saved a lot of lives which you can see just by observing the gap between vaccinated and unvaccinated deaths per capita. Is there anything else you wanna say on that point, Alec or Liz, before we move forward? Yeah, I'll just add there that, yeah, I think that the data we have is extremely strong that the vaccines save lives on net. But yeah, it's not to say that the operation warp speed or the messaging from public health officials following the invention of the vaccines was perfect. And I think the key mistake they made in terms of communication was, and again, it's well intentioned they wanted to encourage people to get the vaccine because they wanted to save lives and hopefully end the pandemic was they made claims about the vaccines effect on transmission of the virus from person to person that I think ultimately did not hold up in real world settings. That's because the clinical trials weren't designed to test the effect on transmissibility. And but the vaccine, the clinical trials were designed to precisely measure effects on severe outcomes in terms of hospitalization and death. And those outcomes mostly did hold up in real world effects. And so talking about like 90% reductions in severe disease and death. And that's a huge benefit, but I think it's understandable if people were promised like, hey, get this vaccine you're gonna protect your friends and family you're not gonna infect them.  That transmissibility part I think they didn't deliver on and they shouldn't have made that claim in the first place because they didn't have the evidence. So that was the kind of messaging error from the CDC and the public health establishment more so than some sort of failure of operation warp speed. Exactly, yeah. But I think they're also not so separable just because operation warp speed was an interagency effort. All those officials were also being asked all the time to comment on these particular things. And so we held an event last fall with the original operation warp speed team. That's one of the things that Secretary Azar said he wishes they would done differently is they would have handled communications differently. He didn't say anything particular about operations or how the contracts were written but it was around how they were communicating with the public. I struggle with this a lot because it seems like so many pandemic era health authorities are very sort of like late with their apologies and then very imprecise in it. And it's like, well, I would actually have preferred not to have been nobly lied to and a little bit more of a robust atoning for such sins and learning from those mistakes. And ideally some of those people being out of jobs would probably leave a better taste in my mouth. The other thing that I think about here because I'm very grateful for vaccines being administered to the 70 and 80 year olds in my life. But the other thing that I keep coming back to is like was there really a reason for like New York City where I live to force children to be vaccinated in order to like go dine inside in restaurants in the winter with their families just given the what we know about transmission and then also given the mortality risk to six year olds and seven year olds that just seemed totally unjustifiable to me. But of course this was this patchwork of federal government policy and media pressure and then lots and lots of localities and states making their own decisions on these things.  I do want to go into a few more specifics though. So like what were the specific components of Operation Warp Speed? You guys identify like a few different prongs. Basically the thing that I'm really curious about in general, which you mind describing for our audience solving market failures, using market-shaped mechanisms what exactly are those? What do those actually mean? What did they look like in this case? For sure. And just one quick comment on what you said about the, I think this patchwork thing is so important. This is where again, I would just separate what IFP works on and kind of our mission from other really controversial, tricky public policy questions that are gonna be handled in different states and cities across the country. Which is we think there should at least be agreement that it's really good to quickly invent a safe and effective vaccine. And then that's outside of our wheelhouse in terms of what's the best policy in New York City? What are the values and decisions that go into regulations around access to public spaces? Those are really important questions other people can kind of decide. But I think what we shouldn't do is let that latter conversation affect whether we design government programs to work with private industry that invented vaccine in the first place. Hey, thanks for watching that excerpt from our conversation with Alex. Stop of the Institute for Progress about Operation Warp Speed, the Trump administration plan to accelerate development of COVID-19 vaccines. If you liked it, you can watch the full interview right here or another clip right here.


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Hey there welcome to today's Protocol Labs research seminar. Today we are joined again by Joel Chan who is an assistant professor at the University of Maryland's College of Information Studies or iSchool and Human-Computer Interaction Lab. Previously he was a postdoctoral research fellow and project scientist at the Human Interaction Institute at Carnegie Mellon University and received his PhD at the University of Pittsburgh. You can find a link to Joel's previous talk with us below and today he will be talking about accelerating scientific discovery by lowering barriers to user-generated synthesis of scientific literature, which will include discussing how scholarly practices could be transformed. So Joel, I will let you take it from here. Good morning, good afternoon, good evening. I'm excited to share an update on what I talked about in the previous talk which was linked in the chat. And so let's jump right in. The focus of this talk is on synthesis. And so I thought we could start by talking about what I mean by that. My goal is to remove various defective synthesis so any scientist can ask better questions faster. So what I mean by synthesis is probably an intuitive concept by some examples include theories, models, design spaces and very good systematic or literature reviews. And the key intuition is that you create a new innovative conceptual whole that's created in some of the parts of things that you're integrating. You wouldn't just be surveying and saying here's a bunch of literature, but you'd be sort of like giving insight into here are some of the gaps here are where we should be going next, driving progress forward. So one concrete example of this led to a Nobel Prize. This is Astrid Dufflo, a recent Nobel Prize winner, who credited some of her key inspirations to a masterful survey of the literature in a handbook chapter of kind of developmental economics that really laid out some key problems in the field that she was able to sort of connect with her expertise in experimental methods. So synthesis is super important. If we ignore it, we risk wasting our time on questions that trivial, impossible, misframed. One of my favorite phrases is you can't play 20 questions with nature and win. In fact, there's a delightful paper that recently talked about how to play 20 questions with nature and news in the kind of area of brain training research where they lack this kind of synthesis theory of what's actually going on and just had a bunch of experiments. One of the key things that I'm interested in is this problem that synthesis is actually really hard. Not sure how much this resonates to your own experience, but this definitely resonates with mine and lots of people that I've talked to. One concrete example of systematic reviews, which are really important. And they're really, really difficult to do. They take a long time to pull off. And they're often not updated because it's difficult. And so often they're out of date pretty quickly after they're published. I think of this as a lower bound on how difficult it is to do synthesis, in part because systematic reviews are very singular focus on a single question, usually a single version. Whereas often we need to synthesize across multiple disciplines across different kinds of evidence at least and so cleanly focus on just a single dimension. However, right, so just to give another like metaphor here, studying an ethnography of medical system reviews. This bucket paper talked about this metaphor feeding its slave to the trap data. You have this sense that of fighting against the infrastructure. It's going to be easier. The growing burden of knowledge is making this harder. And especially as we face problems that perhaps will always into this pretty required as to converge and integrate ideas across multiple fields of knowledge. This is pretty pressing. So pretty, pretty neat. The core conjecture of this line of work is that it's not just about the tools. It's not just about our motivation. It's about the infrastructure. It's about the unit analysis. All right, why, why does Google scholar work that way. Why is it indexing papers. Why is it, what is the data structure that's operating on that's that's my focus. You know, when you go search for papers, you're looking for papers, but really what you care about is what's in them. The ideas that claims the arguments, the theories, findings, and the discourse relations between the right support opposition repetition lines of evidence lines of contradiction. Right. These kinds of things are what you actually care about. Instead, you get documents, metadata and article types. So a concrete example of this, if you're interested in whether bands are an effective intervention for hate speech, you key that into Google scholar. And what you get back is a bunch of papers, many of whom will, some will be relevant. They're all pretty much with me in the same topic area, but I'm going to answer your question about what's the evidence for this particular class of intervention in the setting that I care about. There is evidence problem solutions. They're not first class students. Same thing in other tools like semantic scholar. I should say it's not their fault. Okay, that these indexing systems will work with what they have. The data structure is what's at issue. Similarly, we have entire industry of system review tools and processes that are essentially dedicated to working against the underlying data structure. They're sort of like we've got these papers and we need to work our way around it and so we all these processes for screening for data extraction to get to the thing we actually care about. And then we don't share anybody else and we have to start from scratch next time around. This is a slightly cheeky mean about how this kind of system reviews are sort of a band aid on top of a, to me, fundamentally broken scholarly. That doesn't allow us to ask the questions that we want. So I think it's pretty important. I see connections between this terms of, I think it's an underexplored underappreciated potential mechanism for what people are perceiving as a slowdown in scientific productivity and progress. It's getting harder and harder to win at a well price. You got to sort of spend a longer time to kind of rock the field. We're doing more of our high impact science and teams, which brings its own challenges and overhead. And I'll give this somewhat controversial but to me pretty concrete observations of some slowdowns in the impact on research. So the core question is how to accelerate some of the things where we bite lowering barriers to synthesis. Today I want to talk to you about these cross graphs. What are they? Why do we think that they're interesting? Why is it hard? Why don't we have them? This is core idea about authorship bottleneck and I want to spend most of our time talking about this solution path of scholar powered contributions as a sustainable authorship model. Okay, so first this cross graphs, what are they? So comparing to what we had before where we're looking for bands and hate speech literature. Something like this is actually what we're trying to construct or look for, right? We have a question, are bands an effective way to mitigate anti-social behavior in online forums? We have claims, which are answers to that question, right? To be banning as a strategy is effective. Banning strategy cannot scale and then we have underneath that evidence, right? Particular results that are supporting or opposing these claims. Okay, this is a pretty intuitive model. We think about how we'll talk about it. Sometimes complain, this paper makes claims that are not supported by its data, right? Or we talk about theorizing from data. This is kind of core distinction between claims and evidence. This is not an invention of mine. This has been talked about for a long time. You know, decades, right? We've got a kind of decades of literature on standards for how to be actually formally represent a discourse graph. We have the Scolanto ontology was based on claims. We have micro publications. We have CPO. We have a bunch of these very mature, well thought out, clever standards for essentially how do we represent a discourse graph? Give you a little bit more intuition about why discourse graphs are a good idea beyond just the match on the surface with the kinds of questions we're trying to ask. I like to think about it in terms of three Cs, compression, contextualizability and composability. So compression is the first one that we were talking about where we actually want to find and manipulate compressed units like claims, not just whole papers, right? Papers contain lots of different things in them, but we actually care about manipulating thinking about the underlying ideas, right? So we've got these models of scholarly argumentation, but also in creativity with literature. If you break things down, that enables you to more creatively recombine them into new theories and models and concepts. Conductualizability is super important, right? This distinction between claims and evidence turns out to be pretty important, right? To really understand the scope of the claim, be able to question it, be able to repurpose it. It's really important to begin to, right? If you have a claim that most private annotations are useful to other people, you want to know how is most measured, what kind of annotations in what setting to really understand what this means for, say, some question you're interested in. Same thing on the band side, right? It really matters, for example, to distinguish sometimes between different subreddits that have different subcultures or different platforms or different kinds of behaviors and interventions and so on. The devil or diamond is in the details, right? We have lots of recent examples of the importance of context where we really care about unbundling, for example, children into different age bands. Turns out infants' risks are quite different from young kids who are different from teenagers and many studies that don't distinguish those. We lose resolution and ability to synthesize. There's a pretty fun Twitter account called JustSaysAndMice that kind of riffs on this where a lot of medical studies are reported on and talked about without the context that this was a mouse model, right? And I'll say, like, this implies X form for people, but actually the context is lost. This really impairs our ability to synthesize. So we need access to that easily. Okay. We see this actually in user behavior as well. Scholars will constantly reread during literature review. They'll return to papers. This is repeated also in studies of general sensemaking where you've got to keep the data information around. And in studies of computer-supported corporate work where we have this kind of knowledge to management infrastructures, it's really important to retain the context of how a thing was produced, how a piece of information was produced, by whom, and so on. One fun desire path that inspires this work is, you know, people repurposing tools for different functions. So qualitative data analysis software is meant for things like demanding analysis, interview analysis. But we actually see scholars a good amount of them actually repurposing it to do literature reviews because it supports the ability to do this dialogue between claims and evidence, between the theory and the data. Which is really interesting. This core distinction between claims and evidence seems really important. Lastly, composability. We get some of this from formality. If you have these units, but you also have connections between them, you can construct more interesting representations that help you reason about things like tables or causal graphs or arguments in time. Something like this. So as I mentioned, these are new ideas. I'm not the first by far to talk about this. There's a pretty large literature body of work building out these technical standards and infrastructures. We basically have most of the warehouses, they're built, but they're still mostly empty. In the bottom right, I'm not sure you get the reference to the field of dreams. We have some of these people talking about the, you know, we want to have an ocean of these micro publications. These describes crowds at the moment is no more than a puddle. And this is this is repeated across a lot of a lot of different platforms is a key bottleneck. I call this the authorship bottleneck. We have a different models for authorship that don't seem to be quite enough on their own. One of the most popular ones is a specialized curator model. We basically pay people or ask people to volunteer to do this extra work on top of literature. You can think of systematic use as one instance of that where they take in the dirty data, right, and then they kind of add structure to it. This turned out to be very difficult to sustain. This is an extra piece of work that people don't want to do. One very sad recent example is marked to cure effort that was curating literature for NGLY1 deficiency. And they had to shudder because they ran out of funding essentially, they couldn't sustain. Some examples of platforms like Research Objects Hub or Nanopublications, the number of active users is actually very, very small. So we need something more. Some people think that we can do this in a completely automated fashion with text mining. This happens to be partially in my field. It's very cheap, but it's significant accuracy and transparency challenges. Extractive summaries of research papers is very hard, still a very open problem. And, you know, some of the promising approaches that people are exploring today with language models. We have some pretty significant challenges in terms of accuracy and transparency. So I'm not optimistic that either of these are going to solve the problem of their own. So what I want to do is extend the space of possible contributions to look at this thing that we haven't explored yet, which is what I call scholar product contributions. Intuition. She had inspiration for the design patterns and some previous work that I've done where we should think about a collective brainstorming effort, right? Where we have lots of lots of different people producing ideas. Sometimes it's useful to really understand like how the ideas relate to each other, how they cluster. In the field of crowdsourcing, we have these judgments that they'll have people do. They'll look at these like three things to the same which ones are related. Again, similar to what we're seeing in scholarly domain, it's very tedious. People don't like to do it. It's annoying. However, one insight that we saw is that people naturally cluster ideas when you give them a digital whiteboard. And this means something. The ideas cluster together and there's some meaningful relationship between them. And we can exploit this. We can integrate the usually tedious semantic judgment work into this intrinsically morning activity. And then construct things like an idea map that helps coordinate efforts deliver more interesting inspirations with basically no extra work. Right here and integrating to the work that people are already doing. That's one of the things that we're not exploring enough yet. I want to explore more. Okay. So specifically, I'm interested in exploring how we can integrate scholar power contributions and discourse graphs into individual and collaborative synthesis practices. Right. If you think about it, people read lots of papers. Lots and lots of papers. Right. Just some, some rough estimates, but on the envelope, you know, multiply the number of faculty by the number of self reported number of papers read per year. We're in the ballpark of about, you know, 100 million papers read per year. Compare that to the size of literature is pretty big. If you look at it by itself, but it's around the same order of magnitude. Right. So this is, I think an interesting source of untapped creative exhaust. People are already doing all this work. We're wasting it. Whatever we could, you know, integrate into that work. It's also a little bit more feasible to me. There's lots of really strong efforts and very smart people working on this problem of changing the way that people publish work to begin with. Which I think is super valuable, but extremely hard. It's really entrenched in incentives. So I'm a little bit nervous about going into that area. I'll leave it to people smarter than me and exploring complementary paths. Okay. So that's all. You know, we talked about what this process are. You see an intuition for why it might be important why you don't have them yet. So now I want to talk to you about how we could have. Right. This, what does this mean the scholar power contributions. Right. The basic idea is we give people tools to build personal discourse wraps for themselves, or for their labs. We give them the means to share and federate this discourse graph with others. And then over time, we can layer protocols on top of this to start to aggregate these into these centralized comments of discourse wraps. That's the roadmap. So there's two questions here. The first is we started patch on this, right? Is this even a thing that people are doing that we could integrate into, right? Are there integration points for often this first class? And second question is so, so thankful one, right? Is it actually possible to build tools that can help people build discourse graphs that are shareable. I'm going to focus mostly on the second one. Well, move a little bit quickly through the first one. Just give a proof of concept, right? So the first question is about integration points for offering discourse wraps. Right. We've done a fair amount of user research participant observation need finding to really understand there's actually a lot of opportunity here. We've put go pros and people's heads while they do reviews. We've done interviews with people with participant observation in communities of hackers and user tools of thought. And where we've been kind of looking for where our scholars already creating artifacts that have properties of compression contextualize maybe and our composability, right? The things that discourse wraps provide. What we find is that there are integration points in the range of gears tools all the way from people using really specialized tools to just people using every day tools like pen and beaver, which is really interesting. So on the lower end of spectrum in terms of like technical sophistication or I guess not technical. New fangirless. We have traditional tools that are used really well by what we call virtual right.  And in green will be like a piece of context, for example, you know, writing things in structured ways in Word documents and Google Docs, right? They'll talk about the templates they'll have like here's the overview here the arguments here's the evidence right here's the key takeaway. There's these structured things that people already do. There are also other tools that are slightly more specialized that are mainstream like econ explorers using tools like liquid text that allows you to pull out these excerpts and then create structured links between items so we have this compression pulling out these pieces and then Composability having these edges between them and then contextualize ability can jump back to annotations. I told you about repurposing qualitatively and also software as an example, right. And also adoption of like tools like new tools for thought like room research or obsidian of oxygen so on. We also have a lot of hackers who create homes fun system enhancement systems to enable these features. So the Orgmo Emacs community has created a open source part of room research this idea of backlinks into Emacs, for example. We have, you know, it looks like this you can sort of integrate annotation features also into essentially a terminal. This is a simple building on top of tools like so terror to enable you to enhance contextualize ability of your notes, right, you can extract annotations in a way that allows you to jump back to the source of the PDF. Just very briefly, right. We see across all these settings and scholars in the everyday practice without anybody telling them do it already create artifacts with key properties of compression contextualize building composable. Okay, it's not enough though, right, this problem of private public alignment personal notes are contextual the idiosyncratic, they're informal they lack structure. What we want is something that's general or shareable that has some level of standardization and reliable capture. How do you bridge this is it possible. Is it even possible to have some of this creative exhaust. Right. Is it so should technically possible to integrate authoring of shareable discourse graph. So at this point, I'm going to pause while I pull up the demo is a box of a discourse graph tool in the software room research. So you're not familiar with it. What you basically need to know is it's kind of like a document.  And what's useful for us is that you see these like bullet structures and links are actually in the data structure underneath the documents they became parts. So, but you don't need to know that right so just looking at this right now, you can imagine in the context of a research project you might have a single document with a bunch of sources right that you're interested in. So for instance, if you're interested in the susceptibility of children to COVID infection. You might have a bunch of studies that you want to read right so this looks very similar to an annotated bibliography or list of papers to read. And each of these things here right is a is a paper. So if you look here, for example, and see, we've got metadata. This is a paper. The key thing that the discourse graph extension allows us to do is to integrate into people are already doing in terms of say taking structured notes on papers. You can see here this is a paper. It's a meta analysis of previous household studies. And you can see here looks pretty similar to how you might take these structured notes as we saw in our document right you have thinking about the aims of the paper methods what they do you can drop in screenshots of figures to keep the context. Right. You have definition index cases. And then we have like, you know, the results and contributions right what are the key takeaways. So for instance, you can say here one main result is if you met an out doing my analysis of these 11 studies then you get an estimate of lower susceptibility for children versus adults. Right. And specifically you can see the relative risk ratios here. This is be this not too different from what you would see. What we have here is the ability to mark this result as a discourse node of type evidence, just by the manner that looks like annotation. Right, so you can highlight this, hit a key. And you say, this is now a piece of evidence. And then it shows up in the sidebar. This tells us that this is not a document itself that we can reference and search for elsewhere. Okay. So that's the that's the UX right so you don't have to do it from the start we can write informally. And then as we think, okay, this is the key takeaway I want to remember this but later I'm marking. This is not a piece of evidence. If I want to. I'll often do this in my own work as needed. If I want to keep the details handy. I could for instance, just drag this and put this in here so it's just right there. Right. I could also copy over in the Rome sense. The key contextual details of the study. It's all in one place right this compact micro publication has everything that we need to make sense of it using instances. It's got the summary. It's got the methods that produced it. If I want to dig into that later, which we'll talk about in a second. So that's no creation annotation. Let's talk about how do we create edges between a discuss right for this work out have notes and edges. The edges are part of the, it's one of the more annoying things to do without formality. So let's go and see inside here what it might look like. So inside this document resources, we also have a space to write in our life. So what I want you to notice here is right here. Let me zoom in a little bit. Let's talk about this first claim that you're not equally susceptible. This is a reference to a claim. Right. I've marked this as a thing that I want to remember. This is a type claim. And then I've indented underneath your some results. Right. So yeah, this one is the evidence. This is an evidence. This is an evidence. Yeah. And I have written down here following is the case for this thing. This is a support relationship with this. Right. If we go inside here. Right. We can see. Right. As you saw before, it's got all this data in here. But additionally, we also have discourse context. Right. We have this relationship now that it supports this claim. Why? How do we know that? Because we said so. We said this here. And so we have this information now. And if we jump into this claim, the reverse is also true. Right. We know all the papers that support all the pieces of evidence that support this claim. Right. This is what we just saw. That is part of what makes it formal in the sense that, you know, you might specify this informally in an outline, but you can't query it. Right. We need the reverse. Right. We need to know that evidence supports a claim and also the claim is supported by evidence inquiry from either direction. Right. And it's horrible. So this essentially makes the relationship real. Right. So what the plugin is doing is it's recognizing these relationships and essentially making them real. Right. We now have this evidence supports claim. Evidence supports claim. Same thing here. We have another claim here. We have these supports and we have actually surprising amount of evidence. Which I'm still digesting. Okay. That's edge creations outlining. Right. So creating the edges between the discourse graph. No, it is not an extra task, but it's integrated into my desire to structure my thinking here. So as I outlined it, I created that structure. What is this binding? Right. So now that the nodes are real, the edges are real. Let me give you a couple of intrinsic benefits. Right. So beyond, I just wanted to do this anyway to structure my thinking. Right. So if we have the nodes and edges and we know what the type of relationships are, we can do some computations. For example, we can see the number of discos relations that claim participates in. Right. So here we can see it's supported by these guys. Right. You can get a feel for roughly how much we thought through a particular idea. Right. So if I reference this in an outline, this claim has four discos relations. This claim has 15 discos relations. I might want to, you know, look more into this one. For example, if there's, you know, not, not evidence. I don't want to kind of really weigh those. Same thing about my hypothesis here. I can keep track of the fact that it's the hypothesis that I've got one piece of evidence, maybe that's the supporting this. Actually, I've not. Right. There's no evidence behind this. So I might want to sort of look into this more. That I can do with structured query. Right. So if we know what the nodes are, we know what the edges are, we can also query over the nodes and we can maybe create attributes for them if we want to. Right. So for instance, if you wanted to look over the evidence base and say, okay, well, you know, maybe we have this evidence for lowest susceptibility because there's something going on with the testing the under testing the kids. Right. We can now more focus and focus me. We can, you know, more focus we go over the pieces of evidence to say pull up, you know, which of these results were from what kind of testing. Right. Are they all, you know, symptomatic testing or they some of them, you know, exhaustive filter for the ones that are exhaustive and see what the result is, for example. That's just a quick overview. Right. Of the prototype. Right. This is what we can do. We can, we can incrementally formalize integrate the work of creating nodes and edges into our normal note taking workflow and writing workflows.  We can also export to this cross graph. So let me stop. Let me now sit back. There's a few things that make it alright so we talked about this idea of incremental formalization where this targets a key long standing bottleneck and sort of integrating structure into this early stage creative knowledge work like literature reviewing and thinking. Right. Now this is a pretty old problem. Since it's early as the 80s, people have identified this right. The work of this system of issue based information systems is pretty, pretty famous back in the 80s for structure thinking over design. And even there we have this observation that, you know, the early phase of consideration of writing is needs to proceed big country forms and if you force people to only think in terms of structured nodes and edges, for example, it sort of kills the thinking process. Right. And so they talked about providing tools in the structuring of raw materials where we don't have to organize them in the beginning. We sort of graduated that and that's kind of what we're going for here to enable people to write informally at first and unstructured ways and then add structure as it's useful. So this is accomplished with like we said, no creation is annotation. Right. We create nodes as we need them as we're ready for them. And we create edges not by typing out properties necessarily but by integrating them to our writing and aligning work where we have this immediately useful notes with implicit discourse structure that the plugin parses into a usable shareable explicit discourse valve, which I didn't show you but you can export actually a CSV as Jason as Mark down. We have this structure that's preserved. Right. And then we also provide media and choosing benefits we have structure querying we have top back in terms of the level of development of particular ideas and exploring your notes in a more systematic way. This is a screenshot of what I wanted to show you which is your export. This is in the open standards compliant with your 4j version of the nodes and edges. Okay, so technically speaking, all you need is three ingredients need a convention for those writing which people are actually pretty open to adopting and actually most people already do something like this. We need something like a hypertext notebook that allows understanding of links between things of which there are many. This is Roman research. We have obsidian, you have notion, you have tinderbox, we have Emax, we have Athens, loxie, chrome, remnote, lots and lots of different tools can implement this kind of protocol. And then a simple plugin that parses notes into a discourse valve. I want to show you this because this turned out to be pretty important in our field study. The way that the system knows how to translate the writing patterns into edges is through a grammar that is user customizable that essentially says when I write something like this, which is on the left. I want you to save it and recognize that this is a particular relationship, right? If the evidence is indented under a claim and has a word supported by there, then there's a relationship supports between them. On the right here is essentially a data lock query pattern that creates the mapping. And users can actually extend this and create patterns of their own, create nodes in their own. This turned out to be really useful. So some brief snapshots from the field study that will close and have a conversation. So right now we're about 100-ish or so alpha testers. We are sort of deploying in these different contexts in the discourse community of academic Rome users, there are no taking courses, experimental journal club. Just to give you some snapshots, right? So obviously I'm actively using this pretty actively in my lab finding very helpful for my own thinking, my students are finding helpful. It's useful for our discussions, right? And this kind of gives you a flavor also be, you know, this is an outline for a paper that I'm writing that integrates these claims and evidence that helps you structure my thinking. And the right here you can see the context being easily available. There's another library information science research team is doing a systematic review that's using this to structure the thinking over evidence to understand impacts of, you know, a culture of stress on mental health or physical health, for example. We've done an experimental journal club working through a bunch of papers and structuring out the claims and questions and evidence that are in that knowledge base. A microbiology lab is using it to kind of structure a project in the review. I'm not going to go through the details here because I don't understand all of them, but essentially this is kind of cell biology for a particular team. And what we see here is that, you know, structuring a project in terms of questions, and also enabling you to then tie those questions to what are we thinking was the claims that we have and play what evidence we have so far. So what we can see at the bottom, we have what looks like results and hypotheses that are also added to the project. Right. And so this this user actually extended the discourse graph grammar to also enable thinking through conclusions and results that are primary, primarily produced by the lab as opposed to from the literature, and they can all be part of the same project so that whenever new new member joins the project they also have a full context of past results but also understand the chain of thinking. An international lawyer use the discourse graph to write two books is my understanding that really think carefully through a bunch of evidence. It was really helpful for structuring thinking. And he also extended the grammar to say distinguishing claims and conclusions, for example, and adding relationships like substantiates for him was useful to distinguish between those that flavor of support. And so we have a couple from is using this for road napping that are trying to tie discourse graph evidence to a discourse graph of outcomes of constraints and solutions to understand opportunity areas. And so here again you have to extend the grammar, and we can. Right, you have constraints relationships as opposed to support or oppose for example. Because it's out in the wild and open source, people are starting to part this of the other tools. So tinderbox is another tool that's quite one of the original hypertext books and you have a user forum post describing an implementation of discourse graph protocol into the box. Nothing to do with me. It's like sort of by chance. You know, you can actually tinderbox has pretty powerful affordances for exploring a discourse graph that relax and implementing this and that is pretty interesting. We have a grassroots funded bounty to part this protocol to lock seek open source hypertext notebook. And we have efforts to part this discourse graph to HTML annotation standard for broader web published news. So, excited about what's been going on. I want to talk about some really key initial insights from this field study right. The first is that I was mostly surprised that we didn't have to do any thing that's super flashy to convince people to use it. Fostering more careful and creative thinking patterns was most of the time enough for people to adopt this. They want to think in this way, and the tool helps them to think in this way and that's that's good. And using it to find an access to more ideas like later on they can use the more advanced features but just simply having the discourse graph as a way to structure. Right that model as a way to structure thinking what's really useful. Also extending a personalizing grammar is crucial right many extensions were finer distinctions right we have different flavors of claims, or different flavors of evidence. But you can also be collapsed together if you wanted to sort of translate to a different graph. And this connects to this kind of concept of boundary objects from information science and CCW that enables coordination between different social worlds. We have this like weak structure and common use very minimal ontology of questions came to evidence, and you can extend it in local use, but enables you to sort of translate between those. This is I think a promising design pattern. Okay, so summarize in versus question to I think we have a cool concept it's possible to write close to pros and create shareable discourse graph as a high product. So I'm going to, you know, now talk about some conclusions and we'll address some more questions right so where we want to go next is discuss graphs from or to everywhere. Right, I think we've established sort of the utility of the model. We've established that there are integration points. And I'm excited about the idea of this being a synthesis protocol. There are tools as clients on an open protocol, integrating this close graph extensions in the mural Twitter slack hypothesis and so on. Right. Why would the spectrum like, how do we enable portability transferability property in particular to avoid the one standard problem. We don't necessarily force everybody to use a single standard. And so this idea of translating between user extendable grammars, but also having a similar underlying idea seems like one promising way to enable this peer to peer. People will be concerned about formality machine readability. Right now we can see there's minimal formality in terms of like we don't have any ontologies in here, right there's no like connections to wiki data or all or anything like that. But it could be in principle if you wanted to, you could integrate that into the document itself, right you can sort of have an all representation of the particular relationships that are stacked out in a piece of evidence. For example, there's no technical reason you can't do that. So I'm excited about the sort of middle layer between the sort of more structured knowledge graph that's more granular and the more course documents this kind of middle layer of the discourse graph to enable sort of communication across the systems. I don't have much to say about this, maybe an LTO useful for stitching things together, because at some point we need some way to understand which pieces of evidence are the same or similar, which pieces of claims are similar or not. So ontologies probably won't be enough and so that's something down the line. I think it's doesn't seem I'm not working on it yet but that's like something something for the future. Okay, so revisiting a large vision you have this building block for a new infrastructure young items for papers. And yeah, we start by just facilitating collaboration system and you can scale up by prioritizing decentralization and federation. And then you can publish to say databases like ceramic or the graph or so on, and you can subscribe to graph queries you can start there as opposed to every publishing to a single streaming you can start to have people talk to each other. I like this metaphor, the way that we're working as opposed to starting from the top and saying everybody's going to do this, we start from the bottom and build tools for people to adopt and then you grow the infrastructure. This is the kind of general strategy that we're excited about. So close to the call to action for my friends, the tool builders HCI people. There's a lot of space for two building and science reform, it's not just about the institutions or incentives of funding. It's also about what we do in our day to day, the tools that we use. And so I'm hopeful that more of us work on this problem. And, you know, not just make it require, but also make it easy to be able to bridge from the bottom to the top. Okay, so we just light up and we'll have a chat. You know, so this is hard, our infrastructure pillars are wrong in your analysis, discuss grass can help, but we do lack sustainable means of offering. But I think we've got some promising directions with tools for a scholar power contributions. So, thanks for having me excited to chat. Thanks to all for as always, very interesting and thought provoking discussion. Join us again at our next research seminar I believe it is nine August, take a little bit of a break for the summer.


We continue on with Chapter 3, Miracles as True Perception. I have stated that the basic concepts referred to in this course are not matters of degree. Certain fundamental concepts cannot be understood in terms of opposites. It is impossible to conceive of light and darkness or everything and nothing as joint possibilities. They are all true or all false. It is essential that you realize your thinking will be erratic until a firm commitment to one or the other side is made. A firm commitment to darkness or nothingness, however, is impossible. No one has ever lived who has not experienced some light and some thing. No one, therefore, is able to deny truth totally, even if he thinks again. Innocence is not a partial attribute. It is not real until it is total. The partly innocent are apt to be quite foolish at times. It is not until their innocence becomes a viewpoint with universal application that it becomes wisdom. Innocent or true perception means that you never misperceive and always see truly. More simply, it means that you never see what does not exist and always see what does. When you lack confidence in what someone will do, you are testing to your belief that he is not in his right mind. This is hardly a miracle-based frame of reference. It also has the disastrous effect of denying the power of the miracle. The miracle perceives everything as it is. If nothing but truth exists, right-minded seeing cannot see anything but perfection. I have said that only what God creates or what you create with the same will has any real existence. This, then, is all the innocent can see. They do not suffer from distorted perception. You are afraid of God's will because you have used your own mind, which he created in the likeness of his own, to miscreate. The mind can miscreate only when it believes it is not free. An imprisoned mind is not free because it is possessed or held back by itself. It is therefore limited, and the will is not free to assert itself. To be one is to be of one mind or will. When the will of the sonship and the father are one, their perfect accord is heaven. Nothing can prevail against the son of God who commends his spirit into the hands of his father. By doing this, the mind awakens from its sleep and remembers its creator. All sense of separation disappears. The son of God is part of the Holy Trinity, but the Trinity itself is one. There is no confusion within its levels because they are of one mind and of one will. This single purpose creates perfect integration and establishes the peace of God. Yet this vision can be perceived only by the truly innocent because their hearts are pure. The innocent defend true perception instead of defending themselves against it. Understanding the lesson of the Atonement, they are without the wish to attack and therefore they see truly. This is what the Bible means when it says, When he shall appear or be perceived, we shall be like him, for we shall see him as he is. The way to correct distortions is to withdraw your faith in them and vest it only in what is true. You cannot make untruth true. If you are willing to accept what is true in everything you perceive, you let it be true for you. Truth overcomes all error and those who live in error and emptiness can never find lasting solace. If you perceive truly, you are cancelling out misperceptions in yourself and in others simultaneously. Because you see them as they are, you offer them your acceptance of their truth so they can accept it for themselves. This is the healing that the miracle induces. And from the workbook, lesson number 17, I see no neutral things. This idea is another step in the direction of identifying cause and effect as it really operates in the world. You see no neutral things because you have no neutral thoughts. It is always the thought that comes first, despite the temptation to believe that it is the other way around. This is not the way the world thinks, but you must learn that it is the way you think. If it were not so, perception would have no cause and would itself be the cause of reality. In view of its highly variable nature, this is hardly likely. In applying today's idea, say to yourself with open eyes, I see no neutral things because I have no neutral thoughts. Then look about you, resting your glance on each thing you note long enough to say, I do not see a neutral blank because my thoughts about blank are not neutral. For example, you might say, I do not see a neutral wall because my thoughts about walls are not neutral. I do not see a neutral body because my thoughts about bodies are not neutral. As usual, it is essential to make no distinctions between what you believe to be animate or inanimate, pleasant or unpleasant. Regardless of what you may believe, you do not see anything that is really alive or really joyous. That is because you are unaware as yet of any thought that is really true and therefore really happy. Three or four specific practice periods are recommended and no less than three are required for maximum benefit, even if you experience resistance. However, if you do, the length of the practice period may be reduced to less than the minute or so that is otherwise recommended. I see no neutral things. So as we gaze around, we are allowing the idea that we do not see anything as completely neutral. We are praying and calling upon the Holy Spirit to bring us a different way of seeing with the admission that everything that we are perceiving is being given a meaning, a false meaning. And this false meaning is one of judgment. If I have judgment in my mind, I must see a judgment world. I must see infragmentation. And so this idea is another way of opening to the meaninglessness of everything that is perceived. I cannot see anything, perceive anything clearly. If I continue to make distinctions, if I continue to make categories, levels, degrees, gravations, it is important that I use the lesson of today to admit that I see no neutral things. If the thoughts that I think I think and the images I think I see are the same and if my thoughts that I think I think are not neutral, not neutral at all, then the images I see are not neutral at all. This idea today follows from the previous idea that I have no neutral thoughts. I have no idle thoughts. They are merely true or false. And these images are merely true or false. But these thoughts and these images are not neutral. They either extend the truth or they multiply illusions. And so I can't begin to talk about neutrality in perceiving. If the thoughts that are underneath my perceptions are not real, I have to be open to be shown a new way of perceiving first and then beyond that vision, the vision of Christ, pure light. And so, in faith, I practice the lesson of today. I see no neutral things.


from the Hard Rock Hotel in Las Vegas. It's theCUBE, covering Hoshokon 2018. Brought to you by Hoshok. Hello everyone, welcome back to our live coverage here in Las Vegas for Hoshokon. I'm John Furrier, host of theCUBE. It's the first inaugural conference on security in the blockchain. Security is obviously not new to the blockchain, it's the number one concern. Crypto is crypto, decentralized networks is what people want.  This is theCUBE coverage here in Las Vegas for Hoshokon, I'm John Furrier with. Steven Sprague, the CEO of Rivets, who's a security and an entrepreneur I've known for almost 20 years now. He has been at this through multiple ways of innovation, multiple security paradigm stacks, not new to the problem. Great time for you, welcome to theCUBE. Oh, thank you for having me. So I've known you and I've known your father as well for almost 25 plus years. You have been at this in one form or another with security and the waves are different. I mean, there's different, there's the web waves, there's different architectures.  They're just different evolutionary steps. Now is the killer time because we're seeing the most action. You got web, internet, mobile, global, new economics, new money. The stakes are higher. It's not just like some isolated box. You got cloud. This is the time to harvest the work you've been doing. Give us an help for you. Absolutely. I've been at this my whole career. I started down this path in 1990 doing digital rights management micro transactions and video games and was part of the formation of the trusted computing group in the 2000s and helped ship 1.4 billion PCs with hardware security on the motherboard of the PC that's still out there today. So started Rivets in 2013 to really go after how do we enable the hardware security and mobile devices and just about instantaneously ran into the blockchain. And at my first Bitcoin conference, which was the Miami Bitcoin conference, about a half an hour into it, it dawned on me two things. One, we were talking a lot about crypto but nobody was talking about cybersecurity. And there's a gap between those. Just because we talk crypto all the time doesn't mean that we know what we're doing in cyber. And the other one that was true was, oh my God, I've been looking for this for the last 10 years, which is how do we enable the user to own their own keys? And I don't mean like single keys on each device. I mean the root key that controls all the other keys on all their devices. And so this is a super interesting space. We're just the very beginning of it. In some ways, the Bitcoin side, the sort of value or money side is the demo. The real opportunity is this is the infrastructure that's going to replace how we do normal enterprise compute. We're the end of PC computing. We're about to have a new paradigm. Blockchain's coming. I agree with you, there's an infrastructure shift over because the efficiencies that are gained and the disruption around what's not efficient, whether it's venture capital or infrastructure, IoT, whatever, the supply chain or the decentralized ways is the way to make it efficient. So it's an opportunity. Every entrepreneur that I know is looking at their chops going, wow, I can come in here and create value. The mainstream adoption is around this complexity, around use to your point. And then the fear of being hacked, the cybersecurity piece, whether it's for money or a hostile actor. But think of it in a different way. Security, nobody cares about security. Nobody buys security, nobody wants security. Security is UI. So if I ask you what your favorite multi-factor authentication experience do you think? Like fingerprints and all this kind of stuff? It's not true. The send button is your favorite one. Dial the number and you push send and it just works. It works everywhere in the world. It works every time. You've taught mom how to use it and the kids how to use it. It's simple. So we would never use like dial the number and we're going to use AI and big data to determine whether your phone is in the right condition to complete the call. And then a message is going to come up and say, would you please breathe deeply and calm down because you're clearly agitated.  Like you'd never use that phone. So why are we going to use that for the rest of our enterprise experience? I just sent you a pin number on your phone that you can't use before you can make the call. Again, I agree. It should be under the wire. It should be transparent. Security should be native, always on. That's right. And that's what you're getting at. Okay, so in your opinion, where are we in the progress? Because again, I think this connects the dots for your career, what you've worked on, the itch you've been scratching in security because you have the perfect storm. You have full mobility penetration. That's right. You have commerce on top of it and you have full global connectedness. Those three things alone make it. And we have decentralization. So the thing that's important in blockchain is it's important to remember while the data on a chain is immutable, we know we can seal inside a little envelope, a message and sign it. And we write it to a chain and it never changes. What we don't know is whether the data written to the chain was intended. So all the information on all the blockchains is fake news. It's important to understand that. If we take a blockchain to court and try and prove something, all we can prove was the data hasn't changed. I've absolutely no idea whether your private key was written on the bathroom wall or stored in Fort Knox.  Or if I'm trying to defend that you didn't do it on chain, and somebody stole his private key. So actually the data on the chain is fake. It's real. It was signed by a private key, but we have no knowledge to the quality of the private key. And if you told the blockchain community that we got to go to your Windows log files to see whether or not your key was compromised at the time, and that Windows log files are the way we secure all blockchains, we're not going to get there. So the problem is, That's a roadblock for sure, no doubt. Yeah, yeah. So the problem is that blockchains are decentralized, therefore they're censorship proof. All of network security is censorship, therefore blockchain is network security proof. Oops. So everything we've spent in the last trillion dollars in cybersecurity doesn't work on blockchain unless I run private chains. All a private chain is running inside the enterprise security wall using all Juniper firewalls to secure your chain. That's not what we're talking about. We're talking about a decentralized solution. So match the security pro posture for the architecture that you're working on. So we're going to have to do for the first time something that's crazy, we're going to have to do secure e-commerce, which is when we form an instruction because blockchains aren't authentication either, this isn't about logging into a node, getting a webpage and filling out a form. No, this is about sending an instruction. So a blockchain instruction, a nuclear launch code, an e-commerce transaction, an IOT instruction like turn the lights on to 50% are all the same thing. It's an instruction based paradigm. So it's not only about protecting the key, but also the protection of the instruction that tells the system what to do. And so in order to do that, the device that creates the instruction has to be a known device.  When you turn this machine on, you didn't check to see it wasn't run by the North Koreans and you can't tell. Yeah, they could be in there. They probably are. Absolutely, more so than you would want to know. So what, where is the answer on this? So get to the, cut to the chase here. In your opinion, as the people figure out, okay, we have all this great hardware that was built for a certain generation. Now I'm using it as mission critical in my life. It's integrated to my lifestyle, where it's my watch, my computer, my phone, now my in-house Siri, portal, Facebook thing. So we need to get away from Apple's embracing of the CompuServe model, where you have a mobile phone that is a terminal equipment, you log into apps and your identity is based on your login to your phone. We don't actually check to see if the phone is really your phone. And we need to move to the concept of mobile, where it's a device identity network, where services are delivered not based on the username and password, but based on the identity of the device. And really, ultimately, we need to get to what looks like an IoT network, which is a device identity network, with messaging as the primary protocol. So secure message is sent. So fundamentally, we need to demote the importance of user authentication and promote the importance of device identity. So that I have a known device in a known condition with known controls that is producing the instructions that are sent to the chain. Ideally, you'd like in every chain a second hash, and that second hash represents a manifest of controls that were in place. So I checked to see, I was in the building, I checked to see, he's still an employee, I checked to see my device is working properly, I checked to see the trust infrastructure in the hardware of my device is working properly. And that gives me a hash. I can write that to chain with the same immutable transaction. Now I can prove that John's device in this condition with these controls wrote this instructions. So authentication powered the last architecture. Blockchain to your point about being, you don't know what's on the data, needs to have an identity model for the signatures. For the robot. For the robot. For the robot. So people are like, oh my God, but what if I lose my phone? And the most important thing is you notice. If I steal your private keys, you don't notice. I steal your phone. Like I just touch your phone, it makes you feel nervous. Yeah. It's a very, but that's 100,000 years. I know when I leave my phone at home, I turn around, as soon as I'm three feet out of the driveway, I'm like, okay, go back to the phone. And so that's cybersecurity training. It starts when you're 18 months old. When somebody gives you an important object, you're not supposed to forget places. Like heaven forbid, you remove the fuzzy rabbit from the three year old. You can lose an arm, right? So that model buying device. The good news is, the trusted computing standards of the world have given us embedded hardware security in the chip sets as a standard capability in every ARM processor. Now in every Intel processor, we can turn these capabilities that have been deployed in these devices. We can turn them on to provide an effective hardware-based wallet for all of crypto. How does the hardware wallet work in your vision? Because I think most people, generally, and me included, would say, look, I love crypto, but I'm busy, got my four kids, two are in college, two are in high school, I'm running around, you're running around. Bottom line is, I got my key, my cold storage, I got keys everywhere.  Where's my key anyway? I ended up writing on a post-it, who knows. So we believe your keys are your collection of devices. So we've actually just done a recent relationship with Telefonica we showed two weeks ago, a dual root of trust handset. So half of your key is protected by the SIM architecture in your phone. Half of your key is protected by the manufactured ARM processor in your handset. So I have two separate roots of trust. So I'm not trusting the carrier, I'm not trusting the manufacturer. They have to work in cooperation. The owner owns the keys. Then I want to back up those keys. So why not, now that I have multiple roots of trust in my device, they can talk to my other devices.  So every time I make a new wallet, you're right, you're running around, you didn't think about it, you don't want to write down 12 words, you're out at Starbucks, you shouldn't be writing the 12 words down on the surveillance camera at Starbucks, that would be a bad plan. Instead you want your device to just communicate out to your other devices. So imagine in the future I lose my phone, I can shut it off by calling my carrier, and then I want to make a new phone, maybe I've got to go like push a button in my Tesla, push a button on my smart refrigerator, and my wife has to push a button, or my girlfriend or whatever the complications are like we all have, right? And that's what allows me to recreate not just my blockchain keys, but my Marriott keys, my car keys, my refrigerator keys, my these keys, and we're going to have lots of keys for all this stuff. Hardware is key in your opinion, you've got to have the hardware. Right, the reason why you have hardware is because we can measure that the hardware hasn't changed. So we can have a hardware root of trust, something that we know is anchored in silicon, in iron, and then really in copper, and then from that we can build a stack that says we know this hasn't changed because it's cast in the ground, now we can build up from there each step and know that this measured environment is running appropriately. So people might be concerned, obviously Bloomberg had a story this week about China putting a mod chip on super micro boxes, that's hardware, how do you talk to that? Because I'm now saying, hey, I love the root of trust concept, you guys are awesome, great job, but what about being hacked by someone else? Modifying. Well let's assume hacks continue on in time. I think the ultimate disinfectant in this is identity of the device. So give me a list of where 100% of those computers are, and are they in any critical systems that you have? So you're running DHS, and you've got 1.2 million servers across your network, can you tell me 100% of the machines that have that capability on them now that you know that Model 45 had that? So we have an example for this, VIN numbers in cars have been a great example of how we've improved the quality of cars, not that we aren't stupid humans and we build stuff that breaks or doesn't work and people die, we just want to know that if he dies in his car that I don't want to drive the same car he drove without fixing whatever it is that broke in your car. So you need ID for the car, asset. Yeah, and so tracking that, yep, we have it for lots of things, we don't have it for free seas. If you ask the average organization, please give me a list of the software that runs your corporation, they have no idea. Yeah, yeah, and the same thing with data too, the GDPR thing, all these regulations. Right, because all, so GDPR is a great example of where now I need to prove I had controls in place in order to show that my data is properly made. They didn't even know they had a server out there. Right, well I don't want to audit once a year, I want to check every time I do a transaction was the person an employee, did they have data at rest in their machine, did they, so we can use the concepts of GDPR regulation to press this idea that I have provable controls at a transactional level for every instruction that's done. I want to know that I have known compute. If you had to write policy for the federal government, it's only known computers connected to sensitive networks and data. That doesn't require rocket science to understand, it's like, don't hook anonymous unknown computers you picked up out in the parking lot and tie them to the nuclear launch because that would be a bad plan. Like, let's start with at least machines we know and that are running software we know and that we've tested them so that we know they're running what we expect and they're working correctly, then let's use them for critical systems. So let's talk about the implications I want to just finish up this segment on looking at what you're saying, which is a whole new operating model is coming really fast. The old model as being operated run by huge companies, Apple, Amazon, IT departments all around the world, governments. So there's going to be some resistance there's going to have to be some change and that change is going to be disruptive. How do you see it playing out? You see people waking up going, is it inevitable or you see a trainwreck or collision? No, I think we have to create a transition. I spent a decade trying to create the trainwreck and that didn't work very well. We shipped the technology on every PC. What we've done here is we're making it possible for you measure the integrity of a device in a mobile phone and then you can hold keys in it but I can apply policies or rules to those keys and those policies can talk to all of my old external systems. So I can ask all my network security stack, where is this device? Is this person an employee? Is my organization feeling good today before I let you use the key? You bring programmability and state into the entire operation. So I can drag along the whole network security stack and all their API called controls and their SIEMs and let's hook Watson up and watch the whole network and apply that as a rule to a key. So now I can sit in Starbucks and my device checks to see whether my organization is good and then logs me into Gmail. I didn't have to tell Gmail to ask whether I was an employee. So I can have a mobile phone that says only log on if you're on the nuclear submarine and it'll work and I don't have to tell GitHub that check to see whether he's on a nuclear submarine. They just have to know that this two-factor authentication, this external, what's making that possible is that two-factor authentication and all the services is fundamentally device registration and as we mature that, as the industry matures those standards, it provides the vehicle for all the services to incorporate a device component to the authentication strategy and then we can engage the robot to make that device smarter. Robot being the machine. Our device. Great, great to have you on. Give the quick plug what's going on Rivets. We'll give us the quick. So Rivets is a fun company going after building these tools. We have a great partnership with Telefonica. We're extending it to other carriers as well and our mission here is to bring the next billion people to blockchain by giving them a hardware-based wallet for crypto, for IoT, for cloud in 100% of the mobile devices that are shipped and use the carriers as a mechanism to deliver that to us. You bring value to the carriers. You also help the users make that usability piece secure. If you can pull that off, man, we'll have a parade on Main Street for you. We need that. We desperately need this. We are so ready for our digital life to become simpler and safer for the user. And really, for the services, it allows them to have more valuable data. So it's the combination of those two things. It's a win both for the consumer and for the services. Well, let's hope it can be a seamless transition rather than a train wreck collision. John Furrier, we're here at Talking Security at Hoshokon, the inaugural blockchain security, the first blockchain security conference. I'm here with Steven Sprague, CEO of Rivets, a hot company in the space, with many, many years of experience. Time is ripe right now. The time's perfect for you. Congratulations. Thanks for coming on. We'll be back with more after this short break.


Welcome to InterGEO TV here at InterGEO 2018 in Frankfurt. Joining me now is Dr. Ilka Mai. She is co-founder and CEO of LockLab, a consulting company. So let's talk about that. What you do is for me, it's just new. What is the topic? We call ourselves the digital twin company. So what we do is data modeling and we create what people call digital twins. That is virtual copies of real existing spaces, assets, facilities. It could be, for example, 50 kilometers of track or road, or it could be an entire city. Or it could be something like a train or a plant or a machine and we create the copy of that. We model that. When we met last time, your main topic was BIM. It's still a little bit, but now it's more about data, about modeling. Why? Well, I would say it's still the same because when we look at BIM and when we look at what we understand, what BIM is, it's all about data. And that is exactly what we do. We create, as I said, digital copies. And what is special about our digital twins is they are fully semantic, which means they are object-based.  And there will be information attached to it or at least there will be the possibility to connect the asset to a data system like SAP or Maximo or whatever it is. So I would say we are still full in the heart of BIM actually. Talking about BIM, just for a moment, what is the perspective from your side? A lot of people are talking about BIM is very important, but it's going slower and slower and slower. It's not as expected so fast. Why? Well, I think, first of all, we have, especially in Germany, we have still a very broad variety of understandings of what BIM means. And a lot of people still see it as just a 3D model. Other people call it a methodology or a way of working or even a mindset. And I think when we look at the construction sector, we see that this sector has a lot of challenges. It is very disconnected. It is a lot of things like incentivation of the supply chain, collaboration, behaviors, all that needs to change. And if you mingle that with new technologies, you will see you have, you've created a beast and therefore it's not surprising that we are really slow in tackling that beast. You presented at the Smart City Solutions Forum today a speech presentation. Give us some facts about it. So what I presented today was, first of all, how we use games technology in our production process to create the digital twins, to make them very efficient, very realistic, but also highly performant. And as I said, we use games technology and I talked a little bit about how we do that, also to make them very cheap and affordable. What I then also talked about is how we can use these twins and the data on a platform. And that is a really interesting project or a new product, let's call it. It's a platform that we can use now for accessing data from different sources. And that is in particular of interest to asset operators like infrastructure operators or asset owners who have lots of construction projects ongoing at the same time and also who have to operate a large portfolio of assets at the same time and who really need to have access to different sources of information at the same time. Everybody's talking about mass data. It's getting faster and faster. The mobile scanning, everything is working on. But handling and managing the data is a very important thing. What do you think about that? Yeah, that's absolutely right. We see that quite a lot. We see that there's a huge overload of data and information. So we need to find better ways and processes of managing that information. And that, as I said, and also to secure that information. Data security is also a very important part. Now we are starting to collect and to centralize our data in one single point, which is also a point of failure, a single point of failure. So if this data, for example, gets lost or gets hacked or something like that, we need to be very careful about that.  Now you have a booth here as well. What do you expect from InterGEO 2018? Well, just judging from this morning, I think we've had maybe one or two minutes where we had a quiet and a little pause and a break. But other than that, we've made lots of new contacts, especially now international contacts. And I think this is fantastic at InterGEO that we have so many international guests here from China, from Russia, from Ukraine. And that's exactly what we are looking for. We want to have the international connections and the network and obviously more clients and more interesting projects. Thanks a lot. Thank you.


Diversity in banks is crucial for effective governance. It leads to greater stability, greater efficiency and even greater returns. Female representation across banks in Europe has gradually improved over the last six years. The share of female managers in the largest banks have increased from 15 to 27 percent. Still, we aren't equal. And further up the ladder, only 8 percent of the chief executive officers of the European banks are women. On top of this, research also shows women are still less paid than their male colleagues while doing the same job. Gender diversity in banks born will be one of the supervisory priorities in the years ahead. We'll look at the suitability of banks born and take measures to enhance gender diversity. Diversity within the banks' boards leads to better discussion, then it leads to better decision making and makes the banks more resilient. That's why we support women in finance, strive for a gender-balanced banking sector and a more inclusive financial system for all of us.


Um, it was, uh, I mean, I wouldn't say it was a struggle. You know, I would definitely would have loved to be out there with my teammates, but this is circumstances, um, you know, COVID just put us under these circumstances and a lot of guys around the league under protocol. So, you know, it's, uh, something we knew coming into the season that may happen. So I wasn't too upset about it. I'm glad I'm back playing though. What do you feel has to change on the defensive side of the ball in order for this team to be successful? We just got to get consistent at it. I felt like we play some great defensive games on this past week when I was out. And, um, um, you know, throughout the season, I just think we've been up and down on that side of the ball. And, you know, it's a matter of time before we start to put it all together, put it all together and, um, become more consistent with it. And that's going to happen over time. Brian Lewis, New York Post. In your mind, when you look at some of these games that you guys have played, I mean, Steve was pretty, uh, irritated, seemed a little bit irritated about, uh, the, the appearance that you guys thought this game was over when you were up 15. I'm curious if some of the teams that you've played, maybe that some of the games you've looked at, not you specifically, but the team has looked past some of these so-called lesser opponents. Uh, I want to say that I think you got to give credit to these other teams that's coming in here and playing with assist urgency to start. Um, guys are making shots. I mean, they hit 15 threes tonight. They started off slow shooting threes, but once they got confident, they started to knock them down and we've dug ourselves a hole there. Um, you know, to start the fourth into the third.  Uh, but, you know, just got to keep plugging away. Brian Logan, New York Post. Uh, Kevin, you sound like you are really dealing with the circumstances of the quarantine and stuff like that. But then there's a lot of other stuff going on in the world with all the political unrest and stuff. Can you just describe the difficulty that people might be having, uh, handling all these issues on a personal basis and how tough it makes it for a team like yours to come together? I can't speak for everybody.  Um, but guys feel, you know, um, excited to come into work and excited to play. Um, so I really don't know for a fact how guys are feeling right now, but as far as the game is concerned, I think everybody is excited to play and we understand we're under some weird circumstances with COVID, but guys are just, you know, coming to work and being professionals. Christian Winfield in New York. Here we go. Hey, Kevin, you guys did have an early 15 point lead. I'm wondering if you can kind of pinpoint maybe one or two plays where things started to go left and then you are able to swing it back into you guys' favor?   It's, you know, great for him. So I just feel like they played with each other and played with a nice rhythm and you gotta give them credit. Malika Andrews, ESPN. Hey, Kevin, uh, welcome back. I'm wondering when you were out was one, was there any, I know, I know you've had it, but there's so much we don't know about COVID. Was there any sense of nervousness as you kept continuing to move towards continuing to test negative of, oh gosh, what if this one's positive? And then what activity of any basketball or cardio that you have access to, were you able to do while you were out? Uh, I didn't feel, I didn't feel like I was gonna get cold again.  And I felt confident I'll have negative tests and I was able to get some workout at the house and, you know, waves, treadmill, stuff like that, keep me in shape. But it's nothing like game shape and it's good to, you know, I'll be able to get into practice tomorrow with the guys. And it's Schiffer, the athletic. Hey Kevin, you guys had a hard time shooting from three all night. Was that a product of the way they were guarding you guys, since you were the ball in the phone in or something else? Yeah, I think it was just a mixture of both. I mean, they played, they got their hands up on a lot of shots, but they also, we also missed some as well. But for the most part, we just gotta keep shooting with confidence and keep believing in ourselves when we let them fly. Time down, BrooklynNets.com. Hey Kevin, you know, we're about two weeks in here, and I think three quarters of the week is where they're, again, a 500, one way or the other. Do you think that's attributable at all to just some of the circumstances we're talking about and that everybody's kind of looking for consistency and it might be that way for a while? Um, I wouldn't, I don't know, I wouldn't blame it on anything, but I wouldn't pinpoint one thing.  Just getting used to each other on both ends, you know, knowing what we're thinking out there on the floor. It takes some time to, you know, understand your teammates, and I think it'll come. Ian Dudley, I send one. And welcome back. Obviously, I'm not going to ask you to speak for Kyrie Irving, but since you're so close with them, you shed any light on, like, how he's doing, his Apple personal reasons of his specific rights? I wouldn't speak on Kyrie. I'll let him do that for himself. You know, so I'm sure you guys will see him soon whenever he comes back and we got, we have, we support him 100% and pray for the best. Last question. Brian Lewis here, close. Hey, Kevin, you mentioned, uh, placing the Apple lineups that work best. I mean, just, rebounding has been an issue, but this is by far the biggest lineup, really, that you guys could put out there on the floor. Do you think that's contributing at all to any of the defensive issues in terms of keeping people, keeping the ball in front of you? No, I think we, uh, I think we could have did a better job at playing one-on-one defense, obviously, but, you know, I think we rebounded the ball pretty well. And, um, it's just a matter of them getting, like you said, getting one-on-one baskets. And sometimes the team is, uh, just better than you doing that. And I think, I think if we focus in or watch a fair tomorrow, guard guys one-on-one, like seeing where we could be better, then we'll, we'll grow and be more consistent in that area. But tonight we got to get credit to the Thunder for going hard at us.