tv The Fuzzy and the Techie CSPAN June 11, 2017 6:09pm-7:19pm EDT
i am a fellow at the. while liberal arts rule of the divisional world. they had known each other for a little while now. i immediately thought of my freshman year in college. i decided on what i should major in. i love history and political science. no one cares to read about. i decided to meet the professional world halfway and major in business. when i read this book and i talked to scott about it made sense because my career like many of yours as well was taken different pivots into turns and i realized that now
what i didn't know then which was in many ways the liberal arts in business has applicability to the technology world even innovation in general. with that i want to turn it over to scott before scott tells you more about his book i know scott from our time in new york. he like myself has spent time in the venture capital world also worked for google and facebook. and has the dream resume. he has spent some time as a presidential information fellow. have the fortune or misfortune of getting some good experiments in the government and driving innovation. maybe just tells a little bit about yourself and where you have the idea that writing the book.
thank you to the new america for having me here today. for all of you guys for spending your lunch with us here. my emphasis for writing the book really came out of observation i spent my time and i grew up in the bay area in the boom and bust of silicon valley. it just carried this interest in public policy and the fuzzier side of things. i found my way into google and facebook where i was working at a venture capital firm. in the process your job is to meet with entrepreneurs on a day-to-day basis and track where you may be going and work with other partners in the firm. it was at odds with the norm that was coming out of the media and what i saw on a day-to-day basis. silicon valley was this group
of techies creating innovation and there is no other contributor to that world. if you go back to the 1990s for the love and technology that we have today. you may have been more of a true statement. they had been propagated to the media. i hope to flip that and say they are feeding the world. it has become the application. it is no longer the case that you have to be a techie in order to participate doing five minutes a day with different entrepreneurs. at least half of those they were coming out of all these different likes.
they were coming out of academic backgrounds. and they were partnering with it to kind of put the new tools. with the thesis of the book. as it has been more commoditized the comparative advantage and to how we apply also comes from the people that are coming from these other backgrounds and experiences and has the passion into the interest to apply the technology to what they know. the term actually come back on the stanford campus and it was a lighthearted association and monitor of our you more of a techie. and really, in the set of terms. they referred to people that studied the arts.
they were more self-explanatory. the book also is not about the opposition of these two and it's not that i'm a fuzzy and you are a techie or one or the other. if you look within any of these programs you look within social sciences you have statistical software they have to match. in working with big data sets. you are engaging with independent and dependent variables and if you are doing those things. they are not the funny subjects are not uniformly fuzzy. you have the design thinking which is basically user psychology there has been a lot about user experiences and the interviews which are kind of sociological and how they work.
you kind of compare these terms and you realize we are all a bit of both. the secondary part of the book which refers to how the liberal arts were there. they had been to some degree thrown under the bus. they have said those are the soft skills. i have nothing against shoe stores. i don't think that they will be baristas in the founder has said. basically the liberal arts has no value in the future economy. and first of all if we look at this. they incorporate mathematics. if you look at some of the most emergent fields and gene
sequencing. in the things that come out of that. it's a study of biology without direct application. and when they are tugging on their mind. those are the premises of the liberal arts that i mean when i say these are the things that will rule the digital world. that is sort of the rationale behind why i wrote the book in the over arching thesis. if you listen to podcasts or check websites or watch any of the major networks you would feel like we're in a world it is consumed by ai and automation. where people including administration are talking about talk about the role of automation and taking jobs away.
they are piggybacking off of the comments you just made. how or why should a world that is consumed in artificial intelligence should even care about liberal arts and things related to anthropology or history. looking across that. when i see silicon valley and i mean the geographic location i mean at-large the technological layer. as we are seeing in places like lexington kentucky. in chattanooga and a number of those places. they had access to information with a lot of these tools. in the rate of the technology has meant that we have a much more bargaining aware technology exists.
the reason i think it still matters if you look in 2014. they came out with a study that said 47 percent of u.s. jobs were at high risk of machine operation. this was the rise of the robots. and thinking about the reality that there was some e jobs that were at risk. in january of this year they came out with a follow-on study where they looked a little bit more granule level. wait a minute. let's look at a hundred occupations. let's look at what comprises of the occupations. if we divvy up occupations by task and then we attempt to match that with what they can currently do and what we reject them to do down the road. we actually find that based on the 5% of jobs which is still a nontrivial number they have massive implications for all
sources. and the question of basic income. and they are commonly there. it's not 47 percent. what they also said that for 60% of jobs 30% of the tasks were things that would change. generally over an eight to 20 year timeframe. the reality that we are living in is much less about this wave of full automation and i'm taking them taking over jobs. it's much more if you lip the letters from artificial intelligence to the argumentation. that something to really think about. we look at self driving cars and we think over what time will they just be humming around the roads by themselves. we've been undergoing this process for a long time. all the way back to the
intellect breaks. we are starting to see the benefits of lean guidance. and being on the freeway with no potholes in good visibility. will start seeing a ton of vehicles more and more. it's sort of all or none. when they have them one of the interesting things in the book is if you actually unpack this on idea and say where are the tasks within our jobs that can be taken away. the best practice that we have could be a machine practice. if you have a best practice
you can generally see that we have done that before. it can be scripted. if they can be programmed obviously there is a machine that can do that. with you look within your job. what are the best practices those are generally the simple things. they are highly routine and they can be moved away from machines. if you focus on that. there up at the harvard graduate school. basically the social skills and soft skills as being the dark matter in the educational world and something that we can't really quantify. we know it's important. but how do we put our finger on it. we know that it's out there. we can't quite put our finger on what it is.
when he talks about is in this world where all of that simple tasks are scripted and eroded by machines we actually specialize. we actually encounter friction. in the transaction. what reduces the transaction cost. it's actually soft skills and social skills. and things you like through that. and i think it is a really interesting second deck to this whole wave of automation is to say if these things do start in the legal space they were in a study that i talked about. in the legal profession where this could be a task and working take it away.
with the legal tasks. it doesn't mean that 13% of lawyers disappear there is sort of a small subset of tax. we can obviously outsource that to machines. i think it gives some of the new skill advantages. they have empowered smaller startups. i think the same way with automation. they may be able to compete with the big law firm. those are some of the ideas around the way to change the soft skills.
in the machine like world. >> i can definitely tell you i would personally pay a lot of money for whatever dr. -- dark matter helped with that. that would be a valuable skill. it's interesting you mentioned the automation because i think it could be more towards the international security. i've often thought about the man for instance. with the terminated like tanks. they will go out and wipe everyone out. i have a conversation with someone recently these unmanned planes in the noncombat missions that we see.
up to 80 people are required to keep the things in the air. maybe by having a predator in the air certain individuals are no longer able to have the job that they did. there is an whole new set of job skills. and they were massively undermanned. to keep the systems and may be over ran. i think really the international security in washington dc. whether the individuals or agencies get this paradigm. with the presidential innovation. have you run into people here in washington that seem to understand this.
we were talking before and i think it's an interesting concept so what he was working with president obama it was a second cto he brought this program to for mission. in the attempt was to bring technology from outside of washington into the beltway. in the innovation and things to different agencies and really come in and kind of like the white house and the tact along with cto within a particular agency and to make that agency a little bit more efficient. focus on data visualization and making these physical records in the different archives that was an example i think of importing techies in some ways and we were chatting
about it earlier this idea of whether it was exporting i think it's more exporting problems and understanding the depth of understanding of problems from places like washington where we have a finger on the pulse for maybe coming with legislation and the data that is walled up in government agencies they can be made open and accessible through application programming interfaces. where they can then pipe that data into new tools those are ways that i think we can start exporting the fuzzy and import the techie. i think a really great example of this was the prior secretary of defense bringing that defense industry out to silicon valley.
we've always try to bring technology into washington but i thought his attempt to bring that out to silicon valley was interesting and through the process of that it's the defense innovation experimental incubator that is out they have started to create all sorts of programs and really exporting problem sets. they have those things. and one of the outgrowths of that is that partnership between the entrepreneurship. the lean startup. that professor who wrote the book lean startup. it is the pioneer of this build measure mentality and working with the two former army colonels.
they were now have the hacking for diplomacy. it's around 13 different colleges and you mentioned texas a and m jm you here in virginia. they have this program as well. but basically it takes problem sets from the particular agencies or teams within the military they need to have better information about biometric data. it appears that problem with a team that is mixed between computer scientists and people from techie fields with international relations. these work together for a ten week quarter on whatever problem they are assigned to. and that innovations had been really amazing. in this short sprints by exporting the problem in getting crowdsourcing if you will a different perspective
on how we can fix them. i think that is one idea that really kind of gets to the heart of the book not just about bringing the techies into washington but taking some of the things that we really understand here and exporting them as well. there is another example of joining with regulation and if you think about as an innovator and someone sitting and trying to build a company. if you have information about where the world is changing. so much of that is not just the problem and the solution that you have but the timing. it's by now. why is important today. if you are you're right at the wrong time you are wrong. i think one of the big things that washington could help with is helping people understand the timing of particular things. i know later this year there is a mandatory electronic locking advice -- device that
becomes mandated. suddenly you have to have that information that's not just paper notes when you're sleeping and when you're driving. it's regulation for safety and when you can drive and how many hours a day. now there is a mandate. the logging device. it is a pakistani american from texas. his family knew of the trucking industry and he said eminently use my cushy job there. case in point here is a liberal arts guy. then he went and started the company that's doing very well. he found it keep trucking. they have created an coyote device and it provides that real-time information about when the truck is running and
went the rpms are. they're starting to cultivate all of this data around which lanes for real-time shipping information across the u.s. which of them are highly optimized. with noah's shipment. those are the kind of things if you have information on changing legislation and regulation they can be big drivers of innovation. it's interesting because i've have a number of friends here in dc to have gone out to work for a technology firm or a venture capital firm and inevitably the gun into government relation positions. the in-house lobby into person for that firm.
with places like this. it's kind of bothered me only because to your point i thought there has to be a lot more value that someone who has spent time here in dc not just the tea leaves on capitol hill but really understanding how it works. there has to be real business value not just being a congressional advisor. there has to be real value on the business side that these men and women can bring. not just the subject matter. if you look at the people. one of the truths that i had thought was there. was against the grain of the theater.
if you look at those are history and literature major. you look at alex kart who runs the data company. you kind of go down the run. there is a lot more people that i would expect. ben silverman was a political science major at yale. stewart butterfield is the founder. is a new corporate medications platform that is trying to become the alternative to e-mail. more efficient or you can take
peoples names, like twitter. it was a photo sharing app back in the day. before that he was a philosopher and he did his undergrad and grad studies in canada. in the process of creating that. we look at these companies and we say if i only had that five years ago. the startup methodology. they don't have the foresight. it have to start doing something and entering their way towards what becomes a chore and tour version of what works. it started as a gaming company. in the process of building this. they used the communication tool. they started integrating towards that. and as what came what came
they attribute that process to the methodology in philosophy. if you think about it. you sit at a roundtable and you to be ideas. they get towards the idea of truth. how do you get closer and closer to what product market is. there is so many messages. google and all these companies. it reminds me of a conversation i had one time and i met with a very senior executive household name technology firm.
they are proclaiming the wonders of their company they sent we only hire people who know how to code. i said i wanted to code in the '90s. does that count. it has the current language. you know how to code. i have to scratch my had saying you're a key driver of value. for the major technology firm in your fuzzy. what's interesting the tools have become more democratized. if you look back. to the '90s and well before
that the syntax that you have to master it to be a techie was really as close to the metal. it was highly complex. have you gotten for there and further and further away. more objections away is moving towards natural language processing. i think it is the ultimate level. the things actually worked well. they were able to command access they are the ability to ask the question. not the ability to have the data. i think they will go all the way back in with that. if we want an answer we will ask a machine. if we want to question well had to ask a smart human. those are some of the things that i think as a become more
democratized even when they are building these tools. they have 25 million plus people learning to code through these online dashboards. you actually follow directions and you put code into this. it is a company. the political science major with a couple of shout outs. if they're looking at hiring the top people. and people come out of the programs. here it is a language i need to build. and none of them have the requisite. they have a great grounding
and c++ and things that taught them the building blocks but they still have to go to the general assembly. the concept that we can graduate with any slip of paper whether it's a stem slip of paper and head that be the relevance in the future economy. i think those days are numbered. it's much more about keeping our education in data a work in progress. it is myth busting in the book. if you are techie and you study you still have this relevance in the future world. this is a changing target. so really about the ability to be a smart questioner not just had the answers.
that reminds me it teaches you i like what i said with education and data i'm not sure i'd be interested in getting your thoughts. nearly technology-related trade cap classes or schools. or code academy. relatively easily tell people who want to learn and help them learn how to go with specifics. without having to go and spend $200,000 for a masters plus
level class. and i think that has become a personal interest. with the international security work. a lot of times my fear is that people don't ask the hard questions and because the answers are going to be really ugly. i reverted to my true form. one of the first things i taught is that it is changing the least bad option. in order to get to those options you have to ask very hard questions that you know your boss or your peers aren't necessarily going to know how to deal with or have a great response to that. in that meeting. answering these questions you might be going back to the method.
it goes back to 1959 and well before that. it was dubbed the two cultures. he talked about the opposition. the science in the community. he basically said if you people learning the laws they should also be reading shakespeare. with big data and all of this. that we see. they have done the session. the answers are going to appear in actuality going back to those. with information not being the
same as knowledge between information and knowledge requires human input. and to go back to that. in the world a big data if you go up to newport rhode island why are we still having that. if we have all the data in and all of the intelligence. when the heck do we do more gaming. they had force on force. see what happens and what doesn't happen. and thinking about all these different ideas. it is the reason why even in the big data world we still do that. you have all of those they moved to different waters.
is this something bigger. it is the context in addition to the code. it is a human perspective. that is a sort of reality behind those buzzwords. with the machine learning and so those are some of the examples last quick question. it's interesting you mention that. that's where i got my start. the one wargame i missed because i was already at another one. was infamous millennial challenge. they have a marine general they have a scenario in the
persian gulf and they were the commander of the red team. they were going against the various routines that were commanded. they have their own staff. what if we did these certain operations what would the enemy do. the big data scenario is there. they crash the force ten times out of ten. when i get a whole bunch of those in swarm this battle group sitting in the persian
gulf. they are not thinking the entire fleet. and the only reason we know about is because the u.s. navy said we can't have that outcome. they said this is completely ridiculous. they walked out of the wargame. and the whole scenario that linked to the wall street journal. leave it to the media to ask the hard questions for us. the final question and i will turn it over to you. what i would imagine when i sit down to write an article. the question what was the
initial question you did ask yourself? is at different than what ended up in the final product? >> of course. what they talked about. the same way it is true with all of the productions of the book. so the original thesis was if i think back to one of my earliest drafts which was a few years back at this point. you have to be technical to succeed. it was some of the original. i knew the title from the moment i thought about it. it was white you could be be
non-technical in the tech world. and still succeed. and then as we got into that what embodies that. it's really more like the liberal arts. and as other great books have come out. with the weapons of mass destruction. it is fantastic. it is about being a realist if you look at that. if you think about the predictive policing. technology can probably help. but if you look at what is the source of the data that is informed. is that data based on crime data. is all crime data in there.
is there a bias in learning where and how certain types of crimes are reported. if you start running all of these other ones. in the propagation of that you can get to the outcomes. it is about asking the questions a bias. if you code something into zeros. it doesn't become any more objective. they are creating these things. with the real diocese. and just recognizing those truths behind the buzzwords. i'm not sure if we have a microphone.
if you have a question please raise your hand. or introduce yourself. >> you talk to education and jobs and the leader across the street over here keeps on talking about jobs and bring jobs back. and also economic growth as we have into a financial crisis if you didn't get the economy moving. they are prepared to educate and what is the next integration. will they applied to the rest
of the world. the jobs are very difficult to find. if they are in china i know. if you don't see it. and in france. can you speak to that a little bit. >> thank you for your question. the status quo obviously we have to recognize the changing landscape and world around us. there is a way to go back to this that they are not hugely exclusive things we can have them both. how can we teaches in a way that engages in new new technology but doesn't remove the old framing. if we take old subjects and apply them through the lenses. in the case of ethics or philosophy.
we can read that. what if we can read it and apply it to this modern context of self driving cars. if you are building on the machine learning to go left or right in the trolley problem that people talk about if you have a trolley on the track and had to choose the left or right and there's imminent death on both how to make that choice. these are unanswerable philosophy questions that could be paired with the different ethical paradigms. they could be thinking through the classical tests in a modern way. and using the method. looking at k through 12 i
think one of the interesting studies that i try to bring into the book was grappling with messy problems and they were in the era of google were we can google anything and find the answer in moments. what is the point of learning if you can just google anything. of course you can't google everything. if you have the messy questions which one of these it was in kentucky where there is a teacher that ask what if we had that. they do use ipads and google. but there was no right answer. they have to learn about acoustics. with the sources and say i trust that source. they have to grapple with the same challenges that we have.
when we have to grapple with sources and things like that. things that you can engage in those tools. if they're not relative. of course it is. if to engage these things meaningfully. and how can we teach through some of that. it reminds me one of those that you learn is what question you should even be asking. i think they start with that premise. you actually know the question that you're supposed to be asking. i have a 20 month old at three -week-old at home right now. with both of them you see the personalities come out early on. one thing out hope for them is that they would always keep
asking those questions and does not at 8:00 when i try to put them down for bed. alexander with the tribal institute. it's an area i study. there is this zone right in the middle i was wondering if you talk a little bit about it. you people majoring in sociology or science technology. do you see those majors also plane kind of an important role in how all of this plays out in the workplace. i talk a little bit about the stem education. mix in there. i love these interdisciplinary
exploring mac. >> my question is two elements. one, what is your finding with respect to the larger families and siblings. was there a competitive nature with either technology or the second thing with the type of experiences towards peace paths? >> i think that we obviously live in an increasingly globalized world where there is this notion where you've got this carte blanche. if you are highly created in the
infrastructure that jobs will always exist but there is this notion that that's going to be your bread and butter forever. it's between new york and laos and there's great universities in nigeria giving them the skills further develop and with the whole team that they outsource from google and microsoft. so you look at the skills that we had with the consulting services and business profits outsourcing to india in the '90s and in 2000. i think we'll see some of that in the same way.
my website for example in the book had quoted for about a thousand dollars in ukraine for a week, so this is all over the place. it is hugely important. not knowing too much and to stay humble, the problem writing this book with a tech background i knew nothing about writing books but it stumbled my way into the same time my sister's french and creative literature measure was working on manuscripts and in the process of writing the book, she helped launch a startup in new york so in terms of not
knowing too much in the sibling rivalry i don't know if that answers your question or not. >> dot gets to how many studies have been done that show that people can have a very severe opinions about things when they are exposed to the subject of their opinion whatever it may be. i don't about sibling rivalry that every other case i'm sure it does. >> thank you very much. fascinating conversation. i wonder if you have reached any conclusions in the book about the comparative advantage of the firms that combined weather the society places monetary value
above others. one of the things that is a poignant example is a company called stitch fix. they effectively are netflix for the fashion and they implore a bunch of stylists and classify that piece of fabric according to 100 or 150 ^-caret mystics and they run it through a machine algorithm based on sort of you connect to your pinch risk board or set of preferences and they send you those items and they get better and better. so they've raised about $50 million they are doing hundreds of billions on this new
hybrid model. what they do is pass the machine to a set of humans and there's each algorithm and the interesting thing is they've got about 70 date of scientists and about 4,000 in each algorithm and what they do is contextualize so they have a subset but then they know a little bit about the demography and conversation and where you are in the country is that different than if you are fashion forward amid town manhattan they've done the incredible job of bridging in the sense that sort of business
and social commerce background and she partnered with the metal netflix algorithm. to me, that is on the job front with 60 or 80 and the 4,000 humans to power the stylist engine. then also the magic of bringing the two together. and so, a huge proponent of surveys. the second question was more about the pushback? >> the larger point of the question is whether or not you think it is a natural thing that will happen at liberal arts and technical skills will melt because the market favors that or whether the market favors at the moment because the structure
of the market and because the valuation of the profit over other values whether the technical game is an advantage over the less monetized skills. what was the reason why snap chat when this sort of gen x demographics. why didn't these people go to integrate more facebook? the major epiphany that they have had is for people that grew up with digital abundance. they didn't know the digital scarcity.
it was creating scarcity on the platform. he is a phd sociologist that wrote all about the digital tool and things that are off-line can be fake. the real world is real and they said wait a minute when you look at this stage dust and insta graham post that creates an artifice that can be taken very real. so you look at snap chat and it makes them resonate in this demographic from the inside and similarly i think it's this idea
of classes and transparency and the assumption of them on transparency. to me it makes more sense where you expect to have that conversation indoors and those are actually the reason why products succeed or fail. the product decisions that make things work i think often have this soft skill in the room as well. >> that's one of the interesting points i found reading the book from an international security perspective a lot of these questions that scott is talking about are what we refer to as concepts of employment so
whether you have a technology idea is not that much to have the capability to do something. it's more about how you would choose to use that capability. even from an enterprise. if they were to look at my outfit right now they would characterize it. any other questions? i'm going to talk at happy hour
next week to recruit and i'm trying to tell them what you're talking about is the success of telling the story of a wire cutter that was sold to "new york times" about the founder that took wordpress, 50 people. it worked and he loved it and let him not do as much management. and you can imagine how something like this can be used. you can imagine how this would happen. it would be cheap and they had a master word person on board.
so here is the question. it is really slow going with them and the reasons i think you could figure out why it is that is so foreign to these people that think of them as leaders in dc to civics textbook my question to you is again, let me add one more thing. this is something where if we are going to do police departments, there are lots of low tech skills. with these guys being the leaders, right now it is completely beyond what they can see. before i have a beer with these guys, what should i say. >> i will give you an example. you say they understand this
domain and they are looking at applying work. >> what they do is make things that are like consumer tech software and they look for people like the va. so there are things going on. how can you use the software to reduce the complexity and keep track of all of this data. this kind of software you keep building it into this other type that we are talking about which is a next-generation type of software is a different type of software problem. and again, what you are trying to capture is all of these complex human interactions and also, presumably creating symbolic worlds that represent these very complex human
circumstances so you can know what is going on in the seven blocks that you are assigned to. to them, it is an enormous leap, but it's very much a next-generation application and a doable. >> the company that is in this space i don't know if you are familiar with it but they worked for the general in afghanistan and studied the law and it was fascinated by transparency. so to the point of it happening on a block to block basis, you get transparency issues and have the realization like it is normally the case in silicon
valley that we need to focus on transparency here in the states. so, he partnered with joe lonsdale, and they built this platform but now has about 1400 cities across the u.s., all the municipal data for the expenditures and revenues on a block by block basis. they'd try to visualize and they partnered with the city's and built a platform that rather than using somebody else, they tried to build the infrastructure where the people that understand the problems and the date that they can partner and get the dashboard and the display to then provide transparency to that.
i don't know if this is an answer to the question but it's a great example of how somebody was passionate about transparency and about this problem and was able to without making the competitive advantage of creating this company that has raised a billion dollars and employs hundreds of people and now has 1400 cities with better transparency than they've ever had before because it isn't in boxes of printouts but it's those like the analytics where you can click and see where in real time the city is functioning and where it can improve. >> i think that you can see why this book was a finalist and also voted as one of the financial times.