Persistent Forecasting of Disruptive Technologies—Report 2 (2010) / Chapter Skim
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Transcript of Breakout Sessions for Appendix E
Transcript of Breakout Sessions for Appendix E
Pages 157-327
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From page 157... ...
Appendix E Transcript of Breakout Sessions GROUP 1 Group 1 Participants: Moderator: Derek Bothereau Richard Genik Fred Lybrand Jim O'Connor Peter Schwartz Paul Twohey Norman Winarsky Philip Wong Michael Zyda NRC Staff Member: Shannon Thomas Team Activity: Designing a Scanning System ZYDA: The big (loud crash) on the Internet was the Webcast and the fact that somebody had defined HTML.
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SCHWARTZ: One of the places that this diagram begins is decision-makers defining needs. Is a place that this system begins – Recognize we have to have a real client, namely the Department of Defense, Defense Intelligence and so on, do we – It seems to me that one of the important things we have to understand is what is this going to be used for.
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Appendix E CD E-3 SCHWARTZ: And what they want from it may be very different. WONG: The systems may even be different.
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Or does the system need to be designed so that if somebody asks a question, which is probably more likely in terms of "Oh, this has popped up on the radar as a potential threat to the country, go ask the smart guys down – the analysts – SCHWARTZ: So it's probably both/and. [Simultaneous comments]
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Maybe this whole process ought to be simply – Michael Nacht says, "I got to know does somebody have cheap launchers," and it feeds it into the system. Maybe it's purely question driven.
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CD E-6 Persistent Forecasting of Disruptive Technologies – Report 2 ZYDA: So what I envision is something like Facebook where – because if you put that as a post on Facebook then all of the relevant friends that you have could comment and say, "I've got a whole study over here, here's the link." Bang. UNKNOWN: [chuckle]
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TWOHEY: The whole point we're trying to solve is like not what the popular kids want. So if you go after – [chuckle]
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CD E-8 Persistent Forecasting of Disruptive Technologies – Report 2 WINARSKY: That's really true. This kid from Afghanistan that's in a tribe is not going to necessarily be popular but he's going to come up with something particularly valuable.
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So we're actually building an entertainment game, putting it up onto the Internet, putting big servers up supporting several thousand people and we're putting various behavioral models that watch play and also interact with the people. But one of the things we're doing in that project is taking in extremes of real-time news feeds.
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So there's an elaborate architecture underneath the human interpretation, some of which is purely mechanical, some of which is simply the integration of a whole bunch of demographic data. So it's not an either/or, it's a both/and.
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You're a scientist and you write a technical paper in an area and what you'd like to know is "Did I get all of the prior literature right and is there anybody else working in this area? " You take your PDF and you drop it onto the search engine and it goes out and it reads the whole document and finds people who are working in Transcripts were not edited.
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Whereas I'd love it to tell me - TWOHEY: No, no. Have you ever seen Google Alerts?
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We actually selected our several hundred beta testers from the best responders. TWOHEY: There's a company called Wolfire Games.
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BOTHEREAU: This is starting to get in kind of to the bottom of this graphic which is allocate resources. So once you seem to have a forecast that's emerging, how do you actually allocate either people or computer systems to track it and actually start to figure out the details of what's going on?
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This is the idea that – using the launching – an inexpensive way to launch satellites today would be the idea that somebody is gestating somewhere around the Web. That's what you would want to identify as an output is that.
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Like we're a bunch of white dudes around a table - ZYDA: We might not be able to launch satellites – [Simultaneous comments] WONG: And one Asian guy.
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Now is the anxiousness caused by the person lying or is the anxiousness caused by the person WINARSKY: Situation. GENIK: -- just being – situational anxiousness.
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ZYDA: No, no. But by the time it's gone to the big jump.
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You want to look at papers, you want to be able to pull these things in, run these kind of queries, you want just a richer like set of things across all data that's publicly available and right now like it's not enough. So it sounds like what you want to do is you want to fund a post-Google search engine strategy, right?
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GENIK: Next thing you know it's real money. SCHWARTZ: Well, it's twelve billion a year.
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But then I asked a different question, I said, "Can you tell me about disruptive – "I asked people "Can you tell me about the things that are going to be disruptive technology? " And they all kind of looked at me like I was on Mars, right.
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[Simultaneous comments] ZYDA: And the grad students that did the work are from China.
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The things that we're looking for are the 1% or one tenth of 1% events which most great universities would look at somebody who might try and want to do some – who was really excited by some cool idea like cold fusion or something like that and say, "What are you thinking of writing a dissertation on? " O'CONNOR: But I think one thing to take notice of is that if you have these grad students who are these experts, self-declared experts, in one place that is kind of a honey pot for those fringe people.
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WONG: Just before we go though, we talked about the technology – well, we talked about the sort of idea of PhDs and graduate students and thinking about the cutting edge technology, right, and I just wanted to sort of bring us back a little bit to, well, actually, use cases, right, and to the guy again in sub Transcripts were not edited.
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I mean there were booby traps before and re-labeling them TWOHEY: But what I mean is after the first month of IED attacks in Iraq like this whole question of disruptive technology was moot from like our perspective here because at that point in time it's a disruption that the military's already noticed it happening and … SCHWARTZ: I'm sorry. I have to disagree.
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[resume conversation at table] TWOHEY: Like if you look at disruptive technology in the Silicon Valley area it's – VC funds have a ten-year time horizon, right, and they have a 70% failure rate, like 10% like massive success rate.
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GENIK: Like DSSC. TWOHEY: Well, there's some prediction market, right, for terrorist things and… WINARSKY: Right.
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LYBRAND: Well, I would either come up and say, "Where's my regular report that I want every X." I don't know what time period, you tell me. Or I would say, "Here's my question.
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"Customer, you didn't ask and we're not at a regular report interval but a thing has come up you need to know about." So analyst-driven output. And then regular systematic output because the truth is that bureaucratic systems need that.
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SCHWARTZ: That's analyst driven. WINARSKY: Can we follow that a little bit?
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[Simultaneous comments] SCHWARTZ: We've got output that is query driven, output that's regular system driven, and output that is analyst driven.
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I mean it's the supply chain that generates the - WONG: It isn't intermediary because to that person it's an end output.
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[laughter] LYBRAND: Fred Lybrand.
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CD E-34 Persistent Forecasting of Disruptive Technologies – Report 2 SCHWARTZ: Why don't we go to the other end and what are the inputs?
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Feel free to pull out of this. WINARSKY: Right.
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UNKNOWN: Exactly. ZYDA: The interesting thing about tech papers on the Internet … The stuff before that is still not there… [two conversations carried on simultaneously]
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As an ex-PCV I'm on the blogs and the intelligence that you get – it's actually fascinating. When I went in the Peace Corps before we got on the airplane they told us, "Don't talk to anybody from the embassy, they will be CIA trying to subvert you." [chuckles]
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WINARSKY: And then there's going to be narrative outputs. [Simultaneous comments]
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So we have some guy out there - ZYDA: No, a Special Forces guys. SCHWARTZ: Okay, we add them, Special Forces guys.
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GENIK: Yeah. TWOHEY: But you'd have the data that you could actually start looking at.
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I mean this you have to have people talk to them. BOTHEREAU: Before lunch there was some talk about incentive systems for getting people to participate.
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So where does it go? Is it going onto a Website, onto a computer system, are there – SCHWARTZ: Twelve people sitting in a room reading it every week?
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So they're going to give us some amount of – whatever this thing they're going to design, right, they're going to get some amount of money, they have a long time horizon and they just want to make sure that like the world didn't blow up on their watch, right, and like we kind of kept the train on the tracks. LYBRAND: I think the model of this is similar to a VC in that it's a prospecting engine and it's always turning over every rock and it's very good – SCHWARTZ: That's not a bad analogy, actually.
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CD E-44 Persistent Forecasting of Disruptive Technologies – Report 2 SCHWARTZ: We debate it and discuss it. That's the committee I'm talking about, okay.
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This is analysis. TWOHEY: Like you need a little bit of soft power kids.
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WINARSKY: The beauty of what you're saying is automated processing might only work 20% of the time but actually the truth is these signals keep coming. These signals don't go away, especially if they're going to become a disruption.
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Appendix E CD E-47 SCHWARTZ: Hundreds. BOTHEREAU: Hundreds maybe.
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SCHWARTZ: Yeah, so back here the loop is I have a question. I'm Assistant Secretary of Defense and I say, "I want to know about whether in the next five years somebody's going to develop a cheap launch." GENIK: Where's our question.
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We've got our committee and Michael Mack, the Assistant Secretary, has called the head of the committee and said, "I want to know about cheap launch." WINARSKY: So that committee is an analyst committee, is that right?
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Do we think there's a Third World country that's copied something? Blah-blah-blah.
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SCHWARTZ: Break down the question into sub-questions and then feed it through the various input sources. WONG: But then I think you have to develop – you have to develop a - GENIK: These are the inputs too.
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CD E-52 Persistent Forecasting of Disruptive Technologies – Report 2 [Simultaneous comments] TWOHEY: They make equipment for mining like fuel, oil.
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WINARSKY: Okay. So automated processing brings us to these - UNKNOWN: Yeah.
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WONG: Well, I think it's still a single process but you're saying that it's more than one, right? TWOHEY: The way I think of it, I think that there's like three separate systems that you have kind of running in the little Olympic-style rings.
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that the right twelve people – that all the people we would like to have participating wanted to be heard by. It's Steve Jobs, it's Steven Spielberg.
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WINARSKY: So there's another thing about analysis that I'm puzzled by – not puzzled but torn by. We crowd source a lot of the inputs.
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And what comes out – so the crowd is – kind of all these inputs are feeding. WINARSKY: All this stuff can eventually be inputs – SCHWARTZ: And through the Disruptipedia they get to participate in crowd source analysis.
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CD E-58 Persistent Forecasting of Disruptive Technologies – Report 2 ZYDA: It's an agile process. [chuckles]
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BOTHEREAU: So we've got the inputs, we've got crowd source, incentives. WINARSKY: Right.
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[Simultaneous comments] LYBRAND: World domination.
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If I can answer, I'll talk to you, right. WONG: I guess I still don't see how – I can see the system being useful for gathering data, I still don't see it being as useful for the analysis part.
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ZYDA: Don't move the page. Team Activity: Identifying the Human and Technical Requirements UNKNOWN: Plus you analyze what's going on.
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We don't necessarily have to tell anybody about it. SCHWARTZ: One idea I'd like to add to this is the hypothesis engine from the first guys.
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SCHWARTZ: And how do we want to define that parallel structure? WINARSKY: The parallel structure is the intelligence community side that isn't the open crowd sourced side.
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So there's a potential to have a little bit of mayhem caused by this thing called client site certificates. And so for certain people, especially on the high side that rely on this, like this is a little bit disconcerting.
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O'CONNOR mentions the insurance industry. TWOHEY talks about the X Prize and Netflix' automated processing model.
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Feedback to workshop participants in Appendix D GROUP 2 Group 2 Participants: Moderator: Carolyn Mansfield Steve Drew Jennie Hwang Gilman Louie Philip Koh Bill Mark Mark McCormick Phil Nolan Ben Reed NRC Staff Member: Kamara Brown Team Activity: Designing a Scanning System HWANG: Yeah, I just want to follow up and say, you know, you use one word, "rely", you know.
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CD E-68 Persistent Forecasting of Disruptive Technologies – Report 2 UNKNOWN: So -- Gilman saying there are two different pillars and that may be one of the things that we're going to have. McCORMICK: The only thing that concerns me a little bit about those conversations, I think a lot of the stuff we're talking about works really well for something that's going to be sort of passive architecture.
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There's 64 different maps and what's really cool about it is it maps out how you think, how you process information, but more importantly, are you the kind of person in the kind of society that makes decisions first and then thinks or are you the kind of society where you go through and -- you start to really get very predictive on how people operate on a daily basis, you know. I mean, it's just one of the ones -- And the nice thing about it is you can use a lot of software tools these days for looking for different types of key words to figure out all right, there's an 80% probability rate for this and things like that.
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REED: I can tell you. Why don't we just alter the corpus of science fiction, analyze it and then figure out which ones are going to come across.
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Appendix E CD E-71 technique that we use to evaluate, okay, and we're almost always evaluating relevance. So we're doing a relative evaluation usually on a five-point scale.
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LOUIE: Yeah. NOLAN: For example, what Ben was talking about as sifting through the corpus of science fiction to spit out ideas, it's going to be creating ideas faster than – STRONG: That's also automated.
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I hear the idea -- when we think of idea creation, we come up with some high speed ways of doing it. STRONG: Harvesting will certainly do more.
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I want to make a humongous big impact but I don't have any explosives because he discovered pilots who could be recruited and that led to, you know, that was a good innovation. There must have been a thousand lead up innovations that didn't make it in the big time.
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I'm still not getting a good impression of evaluating with respect to what. So there have been some good ideas about idea generation, throwing things out there and getting reaction to them, okay?
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Powered flight was not a surprise to them at all, okay? So the, if other people -- There's that famous thing in the New York Times about saying that, you know, the hundreds of years before there was powered flight and it was like the same day or two days before - NOLAN: Oh, I didn't know that.
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Appendix E CD E-77 LOUIE: Look for the natural communities. That's what's interesting about communities.
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And mapping's really important because you want to be able to touch each one of those lives and whatever your impulse is, so whether you're following experimentation, you're following science fiction writing and possibly literature or you're following, you know, funded experiments, whatever it is. It shouldn't be addressing all this.
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I think the bigger issue from an experimentation perspective is back in the what you don't know you don't know and that's kind of the science fiction slash experimentation of hey, here's a theory we have.
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So what we are talking about on the one extreme, you know, just to put all the science fictions together and then to see why they come up, the other, of course, to, you know, all the hard data, people feeds and all those kind of things together. So regardless which extreme on the spectrum we are looking at, who are the ones going to evaluate it and how they going to be evaluated, and what are the criteria to be evaluated in order to give some meaningful things to come out.
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Only today Gilman said it has to have some sort of human and Dan was talking about science fiction, and help drive a successful science fiction writer to find his or her abilities to find human relevance. UNKNOWN: Right.
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CD E-82 Persistent Forecasting of Disruptive Technologies – Report 2 NOLAN: So this is games for idea generation, right? And the high level word there is "games".
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Appendix E CD E-83 MARK: We were doing, yeah, we were doing the high levels up here. Spelled it out said okay, goes out there somewhere.
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MARK: Pick communities? Pick communities.
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NOLAN: Interesting 'cause it's both evaluative and idea generation. You can make a new area if you like.
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STRONG: Brainstorming and harvesting and this is deep dive into harvesting. This is harvest social nets and science fiction with probably dot, dot, dot.
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There's a lot of love down here we haven't seen. So everybody can hear, you want to slide down a little bit and we can get… Can't we do both?
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There's always sort of the inverse as well because there's the fastest, there's the slowest, there's the most expensive and the least expensive. It's like Rolls Royce actually plays in this.
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McCORMICK: It's actually called a peer team. UNKNOWN: Yeah, kind of ride on the backs of this– McCORMICK: And I've got a bunch of that stuff for us.
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This is the analysis, all right, so 64 different categories, you know, you can start to figure out how people think and how they react. And the last one I had was sort of profit motivation and scenario analysis, so what exactly is motivating people to do what they do at the end of the day and getting down to - actually, this goes with this last one.
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You need the measures of interest in general but there are ways to get signposts without measures of interest. The signpost is a recognizable potential future that also has a recommended action.
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MARK: Like pick communities has to go in front of this. MARK: Harvesting's a little bit further down.
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NOLAN: The automated idea generation things are the easiest ones for, the first one you can do. You can do that before you have communities, right?
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Actually one of the ones I think is probably going to be the most critical in this whole thing is going to be the scenario analysis. [Simultaneous comments]
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Team Activity: Identifying the Human and Technical Requirements GOLDHAMMER: Okay, so you're supposed to be back with your teams, trying to figure out these additional requirements… [General Conversation] MARK: So okay, cool.
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I don't know why they went for 2.0 LOUIE: Hey, it's very much crowd sourcey DREW: The contests look a lot more like the brand intelligence process. UNKNOWN: It's because they have some web 2.0.
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It's like they might be important but not urgent whereas other people are trying to create importance… LOUIE: But there's an organization implied here that's not really in here, which is there's an organization implied that lives on top of this, who is querying, asking, poking, provoking, yeah, and concepts because this is kind of what I call driven focused, vision driven focused. In other words it starts with what does the future look like and then finding a way backwards into the technology versus other approaches which starts with here's this technology.
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CD E-98 Persistent Forecasting of Disruptive Technologies – Report 2 see if there's any ideas related to asteroids because I saw a movie and I was just wondering about asteroids." UNKNOWN: Yeah, so what that – you would change it - LOUIE: Well the would've been put in the input so [General laughter] NOLAN: That one's actually easier because that one would say demonic possession.
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Appendix E CD E-99 [Simultaneous comments] McCORMICK: You could basically create from that the set of hypotheses and the signposts and then feed it back in there– [Simultaneous comments]
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LOUIE: Yeah, yeah. UNKNOWN: I would love to see that comparison because there'd be two teams and you can have one on the harvesting – LOUIE: I'd be very interested in how some of these are going to - McCORMICK: And actually, the competitive component's actually pretty good because if you have multiple teams going through each one of these and you're pitting one against the other, you get lots of different perspectives, it kills the group think issues, etc.
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NOLAN: It's actually -- You know what, I think where it plays in is it's sort of another element of the scenario analysis in other respects. STRONG: Scenario is not just the narrative.
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I mean, which one of these scenarios have tags showing? LOUIE: What new key words are showing?
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LOUIE: Feedback loops. UNKNOWN What I usually find for adoption for technology, it's always you've got this great idea but there's one little thing that's missing.
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There's no way - GOLDHAMMER: Why don't we come back. GROUP 3 Group 3 Participants: Moderator: Jesse Goldhammer Harry Blount Stewart Brand Mark Culpepper Danny Gray Darrell Long Ken Payne Paul Saffo Al Velosa Stan Vonog NRC Staff Member: Sarah Lovell Team Activity: Designing a Scanning System GOLDHAMMER: All right, so why don't we sit here and let's talk a little bit about what we're going to try to do here.
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And so how do you thoughtfully construct the right or incent the right – people to participate including diversity GOLDHAMMER: Yeah. Well I think diversity, I mean, I think based on the conversation I heard earlier, making sure that we have mechanisms for incorporating lots of different opinions from around the world is pretty critical.
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GOLDHAMMER: Yep. Okay, so definitely a lot around diversity, a lot around persistence, we need feedback loops, we need young.
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VELOSA: -- to hear a question. I would expand on that a little more 'cause I mean, like that doesn't really seem - SAFFO: Well rather than a grand challenge, a whole bunch of little mini challenges, depending on the question asked, you create a different fictitious organization to attract ideas.
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SAFFO: And one way to – [Simultaneous comments] BRAND: We really need another anomaly detection.
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What I do when I look for – things that are weird just instantly get my attention and they keep rolling around. I actually think anomaly detection might be team based, a small team, choosing a network, pulling people in and then nose out, start asking questions.
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VELOSA: Okay. Well one thing actually, maybe it's a precursor to that or as a response to that, it sounds like the things we've been talking about are a little bit on the presence mode.
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And it's amazing how quickly I got calls who said, "You're full of garbage, basically, on this and here's the reasons why." So essentially what you want is to essentially have something that stimulates that type of reaction so you get the feedback loop that's immediate and fast. So the question is, is it almost speaks to needing some kind of broadcast of the narrative to a group that somehow or other is incented to respond and it doesn't have to be monetary incentive and maybe it goes to Paul's comment about this grand challenge of being provocative in a heretic is a very creative way of getting a rapid, strong response that's broad based.
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It's when you say something that says that I'm wrong, oh, boy, then that's when I react. PAYNE: But it's not a bad idea to have the things that you actually believe that may be institutional knowledge because you'll get the outliers coming there too.
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Once. CULPEPPER: You know, as I listen to this I think about marketing and go-to-market type structures and a lot of times if you put out a press release in a business you do it, you're doing it because it's bait.
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I've for some instinctual reason feel like that pinging the system, it becomes very difficult to know whether you're observing effects that are sort of native that you didn't have an impact on or whether you actually caused the thing -- You know, the Heisenberg's principle: Are you causing the thing that you're actually looking at. BRAND: Especially if you ping it in paranoid mode.
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They have directed innovation and then they have kind of open ended innovation. VELOSA: And I really like actually what you guys have been saying so far because in terms of going back to the weak signals, it's one of those where, you know, it'd be really interesting to do that kind of active/passive sonar.
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GRAY: Well I mean, there's a -- The Learning Channel actually has something for grade schoolers that I've been interested in 'cause my daughter's five and a half. VONOG: And maybe you have a scout, like in the Imagine Cup they have demos, worldwide finals and all.
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And this is a biological waste stream of stuff that's not 25 years old, it's five years old or less. SAFFO: And you can take it a step further.
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CD E-118 Persistent Forecasting of Disruptive Technologies – Report 2 GRAY: Right. CULPEPPER: It's very scalable.
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Appendix E CD E-119 ways in which it gets collected. I think we can kind of capture some of that stuff on the table.
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LONG: You guys just said something that actually worried me a little bit and that is we'll have these competitions and we'll get people to try stuff. A lot of the stuff I'd rather that we didn't give them the idea.
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That was a weak signal and all of a sudden I had to make a bunch of phone calls. And it quickly became apparent that -- and so one of the key criteria here is I knew who my customer base was and what was important in terms of impact.
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VELOSA: You have to have folks that are, can take a weak signal and then get listened to as they come out - GOLDHAMMER: Well here's a totally radical proposition, just to follow on that point, which is what if V.2 of the systems is not about identifying disruptive technologies? What's the low, I mean, is there the so-called low-hanging fruit in the disruptive technologies space and is the first version of a system just simply optimized for identifying the disruptions in X so that you can establish that credibility and you can actually start building out from disruptions in hard drives, disruptions in telecommunication devices, disruptions in something actually quite specific, and then build out from there?
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And, you know, if you have, say, five or six functional areas that are, you know, we came up with – we identified some signals, we identified some populations and got some big questions and we brought it down and this group of five or six areas worked on it and then you turn around and you feed it forward to somebody who is the presenter. And that's, and to some degree you might want them – I know this sounds funny, but you might want them blinded to the process of how they got that information.
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People need to be coming up with -- well, you know, you're talking about technologies, right? What happens if a disruptive technology happens in hard drives or communication or whatever?
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I think he said there'll be a million people that die from an instance of bio-terror or bio error by 2030 or something like that. VONOG: I had a small point about the anomaly detection.
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You know, we're asking a really big question. Hey, solve all these things, right, where we can say, "Hey, how would we find out if somebody is developing X?
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GOLDHAMMER: It's a big massive thorny systems problem. So if there are no alternate suggestions, let me break pieces off and feel free to start doing some designing.
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We'll scrap that. CULPEPPER: So media tracking is basically, there are a number, not to be too technology centric here, but there are a number of systems out there that allow you to track qualitative and quantitative data and that's what that's Transcripts were not edited.
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CULPEPPER: So you include in that, Harry, kind of P&L, just any sort of money flows, capital flows, or down to a P&L at a company level? BLOUNT: I think it's actually maybe even more basic than that, is following, like research grant flows, how -- You know, the first time you see a new flow of money into an area, that's an interesting data point.
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Say more about some of these, story generation, collaboration tools. VONOG: So story generation was this direction of thought that when you start with a story and then develop data around it and think about the stakeholders first and how you sell it.
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GRAY: All of these, like data mining and different things like that. So what you're trying to do is you're trying to identify the big question and then on the next part, which is the active part, I think that feeds the active part, which says, you know, you're trying to come out and say, you know, are we right, are we wrong, you know, and try to get the input from - GOLDHAMMER: We're trying to elicit a response.
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BLOUNT: I think that one thing is missing, a step ahead of that, which is you've got to generate a hypothesis before you start - GRAY: Right, right, hypothesis generation. [Simultaneous comments]
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GOLDHAMMER: Yes. LONG: And with feedback loops.
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[Simultaneous comments] GOLDHAMMER: Story generation is definitely active, right?
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GOLDHAMMER: Team steward system design? You want that – UNKNOWN: Yeah, that would be hypothesis engine, yeah.
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Before you can actually generate big questions you have to understand who the customer is and what drives their world. GRAY: Well, and that actually, it's kind of interesting 'cause that's going to be that big, that's going to be that big loop down here.
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What are we really worried about, okay, on a large granularity and then what hypotheses can we make about what kind of technologies or uses are going to get popped out and we use the data to inform that and then it gets evaluated, whether this is nonsense or not, my favorite being the Chinese time machine. [Simultaneous comments]
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GRAY: Right. So I think – BLOUNT: So it's the big question to hospital is different than to a city, than to the globe.
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We wanted to vet the big questions, okay, or get buy-in. I mean, you know, because I think the first thing you have to do is, is there – what is the big question and if you're identifying the big question, what's the big question for whom?
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But the biologist, you know, monkeying around just manufacturing, maybe you need to worry about that one a little bit.
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LONG: Maybe there's a little bit more happening here. CUKPEPPER: Yeah, maybe there's something on the tail end there.
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So this is the eval- and I don't like -- I mean, again, I go back to it's not technical. Hypothesis evaluation is all of these things, scitech, gaming and crowd sourcing, social - Transcripts were not edited.
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GRAY: It's going to cost a billion dollars or for that, like the guy on his own in Montana who has no connections to anything to bring his technology to light, it's not going to happen.
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LONG: Let's do my Chinese time machine. Oh, crap, these guys are dreamers.
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They make up some, "Oh, my gosh, we see all of these things" – maybe the Chinese time machine, right, and these guys are going to come down here and the gamers are going to say, "Oh, yeah, time machines. Those are cool." Okay, and they're going to want to do this and these guys are going to -- the only people who are going to say no to the time machine are here.
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Seriously, I love that idea. GOLDHAMMER: What is – other than the movie proposal to Dreamworks, what other outputs are there?
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LONG: Unblinking eye. VELOSA: Oh, so that's your "always data coming in?
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GRAY: This goes back to - LONG: Yeah, yes. BRAND: Leading the world peace.
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GRAY: Yes, the hypothesis engine, these guys get the raw outputs – LONG: Yes, this goes here. GOLDHAMMER: These are raw outputs?
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BRAND: No, the happy large liver. GOLDHAMMER: I'm going to kind of, I'm going to convene this meeting back together and I think what we're going to have to do is it's going to be like a little bit of a movable feast.
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If it's low probability, it has to have high impact if it's going to be important to us. CULPEPPER: Right.
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GRAY: Yeah, but I think it definitely is a cost/benefit analysis. This is a weak signal but I'm [..?
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Is there any energy to sort of, to flesh this out a little bit back here, like what the actual outputs are, the reports or something like that? SAFFO: I think Stewart's suggestion is great but I think it's done - BRAND: Grab their four things, plop them on there, we're done.
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I mean, that has to be the most disruptive technology – VONOG: And maybe you tell like a little bit of the story– [Simultaneous comments] LONG: It's also technology from, you know, for fighting in the 1970's when people were slogging around Vietnam and we didn't solve it then either.
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LONG: Okay? And then somebody observes that we can launch satellites, okay?
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Those are just things that get the signals from atomic clocks. Okay, so we have atomic clocks that are now miniaturized.
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There's no reason you couldn't do it now. LONG: Yeah, and you could actually start a competition if you wanted to see what you wanted to, you know, you wanted - SAFFO: Well you can summarize active competition with Arthur Clark's famous quote, "The best way to predict the future is to invent it." That's the most extreme case.
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GRAY: The hypothesis engine of the time would be I read this book about this competition. I know that there's this technology now that's a very, very accurate clock.
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Intelligence Agency have gutted this stuff so there's a – BLOUNT: Complete inverse of China then. BLOUNT: Complete inverse of China.
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Can we put this together and develop this system before somebody else? GRAY: But what the venture capitalists are going to help decide here is - LONG: This is a "can," this is a "will." GRAY: -- is this a privately funded venture or is this a government funded venture?
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Appendix E CD E-161 PAYNE: That's assuming somebody else is doing it. If we're ahead of the problem or we think we're ahead of the problem, we're looking at can we do it and how do we do it and finance [..?
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LONG: You wouldn't go and get X congressman and put him in your system, right? They're external actors.
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GOLDHAMMER: Okay? So this whole thing, I mean, the principle here is you actually have people inside and outside, across all the evaluation who are doing it.
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A stakeholder comes to the Academy with a hypothesis, they bring together - VELOSA: Oh, like a TIGER committee for this? BLOUNT: Well or this committee or any other committee.
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And even, they even control the DARPA money, you know, and so it's, what happens is, and part of the hypothesis evaluation, the hypothesis engine is they gather – and we're talking DOD – is they gather requirements, they gather, from the combat and command, which is like SOUTHCOM and CENTCOM, all that stuff, and they gather all the things from the intelligence community within the Department of Defense as far as, you know, concerns. And you need to have, where that comes together with science and technology has a role in it and right now it doesn't, it really doesn't.
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I think one of the -- when I look at this I think of Hollywood and the whole market of Hollywood, right? And these things happen all the time in Hollywood and there's an entire ecosystem that's built up around it and if there's a way that you could replicate that ecosystem, you know, be it DOD, be it Department of Transportation, be it the private sector, finance, it doesn't matter, where you can basically have the -- because the enablers are the active and passive data feeds, right, and then a vested set of interested bodies who are generating the hypotheses, right?
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But you then have to have the hypothesis generation and you have to have folks with an ability to understand the technical issues as well as the objectives from the questions. And to me, I'm not sure I understand your logic about why you wouldn't want them to then be involved later.
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CD E-168 Persistent Forecasting of Disruptive Technologies – Report 2 hypotheses and say do these make any sense. You know, your job is to be a visionary and think of things that are possible.
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I would go search engine big time. CULPEPPER: Search engine on this side.
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You play in that world a little bit. LONG: It depends on who this is, right?
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Appendix E CD E-171 LONG: If this is just going out to the Internet and reading blogs and, you know, whatever, then this is completely unclass. If this came through, you know, interesting ways then – GOLDHAMMER: Any other final comments and we can otherwise head back to the table?
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Key Terms
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