>> enemy of the people which is a phrase i spend a little bit of time in the book about the origins. it's a very ugly phrase that has been used by stalin and used by hitler and during the french revolution. basically the justification was the people that were targeted by which they were found guilty it uses that phrase enemy of the people. now on booktv, a lorien

pratt. >> why is it good intentioned people who try to solve great problems often make decisions that have terrible outcomes? >> we said we were going to change the world. did we? probably a bit and probably for the better. but i think at the same time some physicians have led us to better consequences as georgia said, we live in a fragile world today and it is a class of complex difficult situations that we as a society have yet to figure out. on a personal level i make decisions every day. i bought this car got this car. it's a hybrid that but i tell i have no idea if the co2 i get for my 70 miles per -- 70 miles

per gallon and the commercial impact of the battery after it gets stored and maybe we would have explored new technologies that have less of an impact. this is my mom. i bought a scarf couple of years ago at christmas. if i bought that scarf 100 or so ago, fine, no problem. today, i don't know if it was made in a sweatshop using cost-benefit pesticides grown and there are huge amounts of co2 as it traveled from a long way away to the place where i bought it. my choices have actions and i have no ideas what those impacts will be. >> you have a new superpower.

the choices you make have a ripple effect through the world. i like to think of it as a fish in a pond and everything is going okay for a few years. connecting it to the ocean and mike is, they swim upstream and downstream into the environment is fundamentally different. i think we are in that situation today as a society. things are changing radically. that is what this is about. it's about the actions that we take and the decisions we make as we consider those actions and the path through which those actions become reality.

how can we be responsible with outcomes if they are visible, if we can't see the outcomes and if the fish are no longer in a small pond we simply didn't evolve the situation. >> i'm lucky i went through the computer resolution and when i was a kid, these were big things nobody could accuse hi and my mm didn't know the difference between software and hardware. i have to explain it to her. we have gone through a democratization of computing technology. we are in the same thing today with ai and data and new technology. ai is done to us, bu that we dot have control over it. the data is overwhelming at the distance and at best we get the data visualization, but i have had the honor of interviewing

hundreds of people as a technology and asking them what are you frustrated about if it solves one problem for you, what would it be and over and over again i heard a similar answer and it looked sort of like this. this is why i'm pretty sure i know what i'm doing. just a background, i've been building machine learning applied systems a really long time, over 30 years. i built 100 million over government budgets and machine learning novels mostly supervised learning. who would have known it would still be with us so many years later. there is something missing all of these years into something

that is missing is we have been coming up from the technology instead of putting humans at the center of the equation i am honored to be here. i like that she called it intelligence. it's putting humans at the center of the equation again. i found a position archetype to. it's an action and thought process that in a complex way moves through some stuff and i don't know what buying that scarf or that car is going to do to the world. it's going to have an impact but i don't feel very motivated because i can't see the impact.

it doesn't grab my brain in a way that makes me think i really need to buy a hybrid car. i can't see it and the data today isn't getting back to me. i'm training him to be a service dog and i've had him pretty much his whole life, he's about 11 months old and i had this thing happened to me a trainer teaching me who taught me about antecedent behavior and i think my head exploded. that is exactly what i heard from the humans-interviewing. they are always talking about the antecedent. he sits down and the consequence, he gets a cookie so this is a universal archetypes. it isn't just one way to think about how we might use the data

and i will tell you about how that comes in at the moment. i'm pretty certain this is the way to think about it because it has the lowest friction to how humans think. busy people live in complex environments. they don't have much brain power to learn the optimization or inference. it's created a giant cultural barrier between people that have governments at the head of businesses and even the. they have to decide what to plant down the road they don't know if it will make them productive or what's going to happen because they have fewer

migrant workers and the situation has changed. this is newsnight ask what product to launch and as they talk back through you will hear much of what my dog hears. for the launching of consequence that is limited and we need computer help. so again, the decision is an imaginative process in our head as we think through the actions and in some contexts that would lead to some result. if you remember nothing else, remember this template and what is clear about the decision intelligence which is what the book is about, it's because they start with humans, i can teach you this today, you can take him immediately so that is my promise if you stick with the

talk. how do we make the decisions today? i am sorry to say i had a sense that this was happening and especially in this complex world going back in the media from human evolution, we don't really think through the consequences of our decisions very deeply. we are much more likely not to think through these rationally, and instead to use social signaling and look at someone that looks successful and is dominant or prestigious and simply copy the decision-making. it turns out that it's very effective and it has been hugely successful and that is what separates us from many other species that we develop behaviors and patterns any individual cannot understand that society this is what we are

programmed for some prestigious person to do what they do as opposed to thinking through the consequences and that was great for a few but the situation has changed. if there is a bad actor and they tell us things they can subvert our behaviors. it is rapidly changing. we need to be developing new ways of coping with an ocean that is admittedly different because it keeps changing. the water flowing back and forth and the old ways of thinking through problems at the societal level are no longer working. these are complex dynamics we see when they call -- winner

take all. they get the benefit and the massive inequality. we talked about the intangibles are important imported, too. anybody that worked with data we tend to focus on the things we can measure easily. money, size, price. we tend to overlook their reputations, happiness, or how and if i had a decision that didn't have at least one feedback loop that involved something intangible, a soft factor. we must start talking to the sociologist, the cultural revolution is and all the other disciplines to understand. the decision intelligence creates a roadmap of how to do that. the future is no longer like the path.

they are based on the past and we don't realize the situations changed, some used to be black with white. i believe it can solve this problem. >> we are going to collaborate. we have a dream and i don't think we have realized that dream. we will talk about it a little practically right now. how do we do this?

we start with people. we don't say where is the data. we don't say we can't do this without the data. i'm sorry, the data is great, but there's a huge amount of human knowledge that is in no data set whatsoever. we are good about coming out the actions lead to outcomes. your homework is to go home and ask a friend who didn't come to talk to david about a complex decision they will talk about action and those will lead to intermediate effects and some outcomes and then we will talk about the context. so, what i do is i sit down with a diverse group of experts, young, old, gender and i say what are the outcomes you are trying to achieve? so many have never sat down and stormed through the outcomes. icons faulted senior levels with

organizations and the outcomes are different and each person. let me tell you you don't need the technology to get better. you just need a brainstorm process where you think through the outcomes we are trying to achieve as a team. is it higher revenue, net revenue after two years, is it some kind of a military advantage? a military advantage that doesn't create a backlash that will hit us ten years later in terms of the psychological reputation of the country. what are the outcomes we are trying to achieve. make sure to ask that question. second, brainstorm through the action. many folks don't take the time to have an open brainstorm session where they allow bad ideas for the actions we could take that might abuse those outcomes. do that to the creative side because when you block this site

of your brain which i guess is over here, you don't have room for the creative side. so, separate them. spend some time on the creative and sometime on the analytical. these triangles here are where a guy gets a if most decision models i believe as a democratized ai, this is the pattern. this is how we will do it. let me talk about a decision that i am facing today. she was so compelling and said we have a climate crisis. it's simple, stop working about an hour this and at the very least pay for some trees. there's organizations all over the world. that will sequester some carbon.

she said it would make a big difference. i haven't sent any money to h. e. organization yet. i can't visualize how the money i might send leads to a chain of events. if i'm going to use ai to benefit me, i want a visceral interactive fun experience and so this is what i think is the future is going to look like a videogame and i hope that we can do some of this in the basement because we can do this. we expanded them to the actions they might take and we are letting the computer help us understand the chain of events that can lead to the consequences. it's also highly valuable at an organizational level. let's see if this works.

i've been having a lot of fun on this. there's an institute for the future that has no purpose whatsoever except to show you there is a physics engine running. what's going on, we are trying to make a decision how much money i am going to pay each year to sequester carbon. as i changed the decision, the data is telling me about the future that puts in motion. i spent some money, i change my decision, it changecommand chane number of trees i will purchase, here is the biomass into the carbon sequestered years the total atmospheric carbon so i can change the events. remember the expert in the background done some research and by the way each expert has an opportunity to say how they connect to the outcomes. i can not only change my decisions but also who i can

trust because i can see different people and experts claim different things. ultimately i can also click on their name and see the case for how it can help us and that is going to be like the site that s curated that we can use to understand people, sorry, to understand the situation. this looks like a business intelligence dashboard and like stuff we've been building a long time. let me tell you, it's not. we are not looking at the data set. we are looking at the future. in the background depending on the choices we have an engine generating the implications. there is a lot going on and as i change my investment i can see

how my decisions interact in order to infect the outcomes they care about. this is a universal pattern. this example is an example of something you do in your head 500 times a day into the organizations struggle with understanding the impact of today's decision tomorrow. survival give you a couple of examples. these are the machine learning models here and we might have built a machine learning model that detects whether it has a permanent happening right now. it is pretty widespread. whether it is an attack or

something else. and this picture is typical of many decision model situations. it's got to this, but that is not laughing ho stopping how pee naturally think. the common view is how you are thinking about things. we are using the mechanisms today to communicate the decisions and it ends up being an architecture diagram for the machine learning system that shows how it fits in and then we have some choices. i can share it with the police did investigate the intrusion and impact the outcomes. if i called the police every time it might be costly. we need to call only when it is necessary. this is a decision of all i will put a bunch of experts as a part of that big project i talked about that has 23 phd's and

institutions finishing up the proposal would be the national center for excellence and agriculture. what's important about this as i diis ididn't have to explain the decision intelligence to anybody. i simply sat down with my team of experts and said what is a typical farmer trying to achieve and if they want to be profitable at the end of the year and since then, i said is that all and they said they don't want to take any action that is going to put them out of business in a few years so that is the second goal that they need to balance against that and then we talked about the actions and they make choices about how they sprayed their crops and the schedules. this gives us a map to understand how the technology fits together because there's a

couple that tell us how the positions will impact the yield and there's anothe there is anot says that type are contaminants. but from the cost and also long-term viability and the climate and pollution point of view. i promise you having this on paper is a lot better than what's going on right now, which is this invisible in people's head. it is an artifact and we talk about the design thinking. we design a decision. a decision is something we can design. do fine. that is pretty radical. but when we do it, we realize we

can bring all of those engineering best practices to the decisions. we can continuously improve it. it acts as a blueprint to connect. this is their mental model but it connects them down so they know where they fit in. we've been working together before this diagram and i don't think anybody knew how the whole thing fits together. you don't have to explain it in words anymore. we've got a map. we know how it fits together and where it will go in. >> what's going on in this intelligence those of us that have known me a few years know it's been quite the flying around the world trying to talk sometimes three people in the audience. i'm really happy to the extent this matters there is intelligence on the cycle now

and i consider that a big accomplishment. also there's 20,000 people and she is the other big evangelist in the space. and then there's a bunch of companies. a and then in the decision model surrounding them into it connects actions to outcomes. so it's really cool. she said is the discipline of turning information into bitter

action. we have a slightly different way of saying it but it's essentially the same thing. it answers the question if i make this decision today which leads to this action, what will be the outcome tomorrow? a lot of people have different approaches to this. my causal decision diagram is mine. other people have other approaches. some are sociologists, economists and more. what is common to all of us as we've taken seriously the action to outcomes. i recommend you take a look at

this he i piece basically discod that by the end of the series. who knows who this is, that's janet. here he is trying to understand why there are all these negative unintended consequences and here is janet who is helping him. i don't know if they read my book. i hope they call me. it would be fun to keep this going because it is important to the future. with great power comes great responsibility. you have the ability to take action as an individual and as an organization that has a giant impact. i am an optimist. i believe in the solutions renaissance where it isn't just

ai under a blueprint of the cultural revolution, all of these fields many of them traditionally soft fields. they are deemed specialists. i think we are entering the age of synthesis and those of you that are generalists know what i mean by this. we are the experts that focus on the outside. it doesn't matter we know what goes into it and what comes out of it and we start to know how to glue that together to crystallize solutions so we understand the solution to water impacts poverty and they hit all

of us, democracy, government, companies. we call these the sustainable development goals and i think they are all the same problem i can't fault them separately and the only way we can solve them is to have a new approach to understanding how actions bounce around to the whole world and ultimately lead to the best outcome. >> thank you. [applause] do we have a chair transporter backs to the >> i'm shocked you finished under the time. the question i have is do you have a case study or story to tell that shows how this works in practice?

>> the example i showed is a good on one if we build an initl model for the government decision regarding reducing complex sub-saharan african countries and it was a preliminary study that showed we need to take actions in two places at once. i would say the causal changes four to five weeks long. but it was just an initial preliminary study. >> how long did it take? >> a few months to get it done because i spent a lot of time interviewing experts.

>> i wonder how much you feel this could be because they think in metaphors and in terms of how these things are like other things and whether you can actually imagine it being defended that way. >> to understand they have been in one place and bought another one of them is the pattern from the action in the short-term and long-term in the horizon that would throw us back. i see that everywhere. if they put vending machines in kids schools, he think it makess happy but down the road it isn't such a good thing.

>> [inaudible] >> we have the ceo of empire. >> [inaudible] i have a background in climate science right now and it is an interesting time when everybody is kind of weighing in on climate change. i find it interesting because you have a lot of people want to speak to these links. they don't realize they are naïvely putting in the links.

what he'll be able to not have to teach the science that demonstrate the complexity as a way for them to start to appreciate? if you like they are not seeing the complexity, and to just be able to show it and make this tool to influence people's thinking without them having to painfully go through it. >> i will use a metaphor to answer the question. i can drive a ferrari without seeing what is under the hood that certain people are impressed and you open th you od see all the complexities. some of us don't want to hear about all that. we just want to hear someone who trust tell us that it will lead to this other outcome. some people we want the ability to open up the hood so we have

to have a multilayered approach to this and beautiful video games with very much attention but to be able to click on the expert and see the thought process and mechanism for that model. >> i think we need both. we are obsessed with the under the hood stuff and haven't paid enough attention to the user interface and the augmented intelligence. >> thank you for a wonderful talk and a great book and

inside. i loved when you said that the vision was just getting started to bring the humanity together you could see people around the world ca understand, empathy, connection. the reality of the last few years has been much more divisiveness and tribalism and breaking it into pieces and i've been trying to imagine a world in which it is everywhere where everybody has tools to make decisions and i wonder if you think that will help people understand one another. >> the first thing that it will do the executives i talked to say this in particular i don't want to talk to those guys i don't understand a word they say if they have interfaces like this, they will engage with the evidence and all this.

i think it is individual. the biggest initial impact will be the sense of agency that we will balance out the inequities that come when they make us do things and click on things and we start to use it for our own personal needs, so i think that i am an optimist. we democratized the computer. let's democratize a ai index complex stack. >> speaketh >> let's make this do that for people. >> that is the culture of the mind. >> the technology empowers

people and enables a kind of democratization. >> let me repeat myself. if you do nothing else. and we are not doing it. they are so lost in our specialties. >> [inaudible] it became an argument you are actually designing a tricycle. he wanted t it to be much more robust but i think the good news from what he's talking about most changes take 30 to 50 years

to be an overnight success so you may be on the verge of that. was it 60, 70 years ago? >> 1956. ai was about 2012. >> the data at the time of the various debates whether to call it artificial intelligence or augmented intelligence. >> that was 1956, yes. >> to me that slowed evolution in the field but the good news is now i think it is possible that it's coming together and can actually work. >> what fits well into that she's a brilliant technologist in the southern speaking on it you might designate with the view you need to be more sophisticated and complex but we don't have to be either or if we

take seriously if been building for 50 years but the visa for the car industry in general has a different set of controls. we need a common set of controls easy for everybody. it's classic information. it's computer science principl principles, to use another metaphor. >> thank you. that is a great question. >> thinking about the possibility of all of this is implicit in understanding this and wondering about the buy-in when there is an entry about one of these people, there are rules set up for how people can edit. how would it be possible or

should it be possible to have multiple editions of these characters rather than only one set of perceptions of their outcomes. >> said this is a challenge and i think that this will happen over the beginning of the next year or so which there will be a new kind of wikipedia that house roles that maximizes how to get reasonably accurate information from the crowd sourced critiquing. it when you do this that causes this. when you give money to this charity, they will buy ten trees and they will have someone that owns the link and will curate that and say here's all the reasons i think when you spend $10 you will get one into someone else will have an opinion we spend $10 on this

charity if someone else does the next link. i think what is critical is we create what she would call the warm version that talks about the link as opposed to it is great but it doesn't say when you do this that causes this, and that connection between things that caused other things is what we have been symptomatically missing for years. >> so that is what it helps. one of the things happening as we are getting these unintended consequences because we are not modeling the context in which that happens. so when i start a new project for the first thing they want to say is we have been closing the data and i said put the data aside. tell me what the decision is that it's going to be used for

because unless we understand the context of the decision that will lead to some outcomes, i might develop the wrong system and they say you mean you can do this without data? >> this is back to software engineering. if you don't understand the requirements you will sit there and build things that break and essentially it is nine out of ten ai projects fail. this is part of why the. >> i liked your point about the car i think that is an interesting point. one topic that came up was having access to birth control.

so they talk around it what are we going to do about overpopulation, we need to educate women and make it more viable, now we talk of secondary. i'm curious what technology can do. i would love to see the technology be able to resolve when we have these multiple interpretations. do you see a way to build a system that could somehow make people see that link? >> i don't think we have the lack of research that demonstrates these kind of things. in fact i think science has been getting randomized trials. we have got a lot of results. i think what is missing is taking those individual links and connecting them to actions. let's say that i care about the status of women globally, what

can i do about that? show me evidence, a depiction, not in the boring sense, but video games, virtual reality, and make imake it fun, immersivn see &-and-sign move and i pick my expert, maybe joe would curate this particular con and others with other planes. and i move and i can see women's happiness. .. i'm interested in.

[inaudible] was predictable, now that's a different sort of model than having brain causing a model you can sort of see it a bit like culture or religion other things which people don't know what the decision is going to do in the big scheme of things it's quite a powerful adaptive model. i'm wondering. [inaudible] [laughter] >> guest: i think we have been through 2000 years. ai was an extreme example of this we can only be intelligent if we can verbalize it and talk about his inference and logic. there are massive amounts of

subconscious rationality that is very smart and this emergent intelligence were individuals in a group, and none of them can explain it they are doing, none of them even know what they're doing on their own. instead, somehow, the signals they are giving each other emerges intelligent at turns up cultural revolution know that too and humans have been doing that. i think we are in a new fishpond, i think we're in a situation where natural instincts for how we behave like birds doesn't work anymore. i think it surfaces assumptions. edit creates a challenge to the cultural evolutionist to really study the emergent behavior of humans as they make decisions. and it is an invitation to that part of the world to work together with the ai in the data people of the world in a coherent way. it's a great point is that we do have these emergent behaviors and we have to model complex systems and my friend,

alex lam knows this better than i do and he built simulation models that help us understand how we get that emergent behavior. hopefully you can help. >> a book i've been starting to read this certain sense i hear you articulating much more clearly something that i have been studying most of my life. as very familiar. i guess i am curious, what have you found effective in terms of when you go into an organization, and you provide consulting maybe teaching a craft, how do you get these ideas across more effectively? and also, as of particular tools you find useful. >> guest: i go in i would say naked, but that's not it, i go

with a not a lot of studying because i don't want to bring my biases into the picture. i go with the methodology i think it's chapter six thrill simply brainstorm the outcomes you brainstorm the actions in the illicit peoples and 8 cents of the change to get it from one to the other. then, only after we do that i usually will go back and be this really ugly whiteboard picture be really gross. i'll go back and clean it up and we look at it together. i haven't used any special technology, only after we have an agreement to that picture what i bring in one of these immersion visualization environments at that point they can associate their intuition going for how the action lease of outcome. and that can be in process so we can have these decision desk boards that don't show data from past and present missing given the decisions were making and the data we are in, climate and gdp that's life, here's where our decisions are going to go. is all new, i don't use a lot of tools a lot yet.

this a lot of machine learning more than anything. so think it's how you present some of the material to people, especially the visualization week grow the information in the bits of graph she went to use. i think it's not very clearly done yet. should partner say people like more to make that sort of an arts and craft positive people can be shown. i do think you can get people's understanding by different types of graphics. they get put off in some ways it's not very clear sometimes what to do. >> guest: i just want to double underline what you just said. i am not an user explains

person and i don't have a huge career you have. i would like to make an invitation to you as well as anybody who is in the user experience base, i'm not claiming the spaghetti diagrams or amuses game environments of the place go. that is untested. okay, i am not the person to do this. those of you who know this space, and i hope we do get to work together, they need to be invited into this picture and so far that hasn't happened. >> i have settlement things crowding my brain right now i could use one of your. [inaudible] i need to figure out how to get serious. i have been working on responsible innovations in the last talk i gave i said i'm going to stop using responsible ai innovation because what he proposing?

are you making irresponsible and that's the only rise it exists. i think a lot of things that are talked about it really came together for pieces and portions of your working on. i have it technology saying consider using responsible ai 's is really good engineering practices and good decision-making. and the question is how do you decide? i'm very interested in your talk because i've been thinking about how to. [inaudible] make the comment of technology is kind of taken over. i actually find the business mindset the way we have designed our economy and economic. how do we make a dent in that structure? for example if you hand me tools these, to the same

people who have the same desire for the same outcome. you just go without common actions the outcomes don't change. in those people don't get that place at the table to change the outcome. you're just given amazing tools to the people to create the same outcomes in a much more delicious even maybe way, maybe a larger sort of way. anyway this is what i've been everyday thinking about. how do we take this amazing but change. >> guest: there's a lot there and you're doing amazing work i went to respond to one point which is the reason i believe in this is because it's making me entrant the invisible visible. soda matter what it does, it invites and insist they draw a map. i love before we made any big government decisions, they would insist on someone drawing a decision model and

you'd have a collaborative diverse team with multiple genders races their degree to that model so we would surface. what's happening right now i don't if you've seen it, the executives i talked to is like the loudest guy in the room gets his way or who can tell the best story. i think we have to combat that. the way we do that is by taking the invisible make it visible. >> mention something i think what you are saying, the challenges i really want to work with you on a figure this out. the challenge is your talk at one of the approaches we took we talked to five you put it in comes in and overload you take bad decision in intelligence all of these variables in different perspectives. how do you kind of synthesize this synthesis now. to a point where again, the

loudest, richest person say oh we gave you the platform you got to say your piece, and now this is what were going to do. >> guest: again a really good point let me speak just one aspect of it which is a principal. the main thing is read the book and that will answer your question that's a methodology is there. but one of the key best practices in this space is the outside of the box sources inside of the box. talk about this thing, complex system model, people talk about the math and cited that will fill your brain a get overloaded separation of concern to new computer architecture principal the interface definition the role here is to go to glue these technologies together in effective ways. we don't need anymore insight or answers, when he more science but we have an awesome a lot of scientific results they're sitting around unused because nobody has connected

them to the action to outcome chain. so yes, we can get overwhelmed too but if we keep using these best practices that keep people focusing on servicing their mental models, we can start to overcome that overwhelmed. that is the exciting things as rooms i in there talking and 20 different directions and at them go on for day and then i say were going to draw a map and show how you're all thinking together there's a relief because they been invisibly in their head they are now rendered in the picture it's a mapping technology in many ways. so let's talk. as a couple more questions please. >> thank you for your talk. that is a hundred $40 book. thank you.

you have [inaudible question] >> guest: let's take a behavioral economics thing okay? let's look at our cognitive bias we might make. that behavior economists fits into this diagram somewhere. and will help us learn how when we do this step or when this is true, that will cause that to be true. so the way i see these expertise areas fitting in, as they form the leak so the

pictures just in integration for how we pull different understandings of how people work into the picture. so whether we are modeling that theoretically how another company would behave or how a person would behave, that becomes pretty important. i help i often see still make a wrong decision. and then i realize not necessarily inside this approach you are taking. therefore that could help you to understand with executive outcome. >> guest: wasn't that something in chapter two about this woman inspired is a great teacher and it's an ai system they don't know why if they

had built a decision model that said here is the variable that this thing is using and here's how the prediction will be used in this larger context, i think they could've surfer that unintended consequences much faster. right? so i think if winterson met context which are ai are being used, it's not perfect, at least it makes it visible and stretch it to critique qa it in all this great engineering best practices. around the context of the ai's and the technology solutions we use. thanks for the question, that's great. i'm wondering if you talk a little bit about the role that might play in setting outcomes it's who's involved who's at the table making the decisions

to define the outcomes and if it's your experience or if you're developing this methodology if you thought through opposition? i believe in many situations, outcomes that appear to be in conflict actually aren't. and that if we do these kind of maps and i'm not saying this is mature this is preliminary work. the stuff we did in africa the whole purpose was to resolve conflicts. if you've got to people who have different opinions of the actions we could take and the outcomes we are trying to achieve, and you capture those on a common map, then ai and some of the technology in many cases can find a holy grail. it can find a set of actions that help you to achieve everybody's outcome. because we are better at doing this because we understand these links and we are not at the same time arguing with someone we are having to keep

these complex links in her head and we can't but we don't admit we can't keep the complex links or heads we fall back on arrogance and assertiveness and yelling. and what we don't have to do that, but we are offloaded and we have a map, and it's not you against me and our opinions, it's you and me working on a common model. so it's out this conflict at slugs join together to get a shared view. and this kind of map facilitate that process. great question, thank you. >> they do like to be heard right? >> guest: they do it when you draw a picture of their mind they feel really heard. [inaudible] [laughter] makes you more open to the future. thank you for that edition.

any other comments from anybody? no? somatic thank you ball for the decision to be here today. i hope that the positive outcomes exceeded your time with the negative outcomes of your time and effort to drive here again here. >> guest: sally make decisions together we make the decision to be here together. i also think now it's difficult sometimes to get people out of their houses. and to engage with each other. we do have food for you to continue this conversation with each other. and thank you for having us, thank you bill for bringing us together every quarter wherever you are, and also thank you for being a recorder and being very in the background. thank you very much. >> let's give a round of

applause. [applause] >> weeknights this week, we feature book tv programs showcasing what's available every weekend on cspan2. friday, books and reading. first pamela paul editor of the "new york times" book review officer thoughts on how to get children interested in reading books. then marianne wolf talks about how her brains have reading print dirt versus digital mediums after that bookseller in executive offers his thoughts on that 1000 books he says a person should read in their lifetime. watch a book tv this week and every weekend on cspan2. >> next, scientists and entrepreneur gary marcus weighs on the current state of artificial intelligence. in the future of the field in his book rebooting