members of congress to raise the minimum wage and it's very, very hard for millions of americans to live on 725 an hour. >> to watch the rest of the interview is our website booktv.org. and click on the "after words" tab near the top of the page. >> and now on the tv lorien pratt discusses the next level of artificial intelligence that involves decision-making. >> i would like you to consider a question. why is it that well-intentioned good people who are trying to solve great problems often make decisions that have terrible outcomes. we said we are going to change the world, did we? probably, a bit and probably a bit for the better. but i think at the same time that some decisions have led us to better consequences as joy

just said we live in a fragile world today. and as a class as complex and difficult situation that we as a society have yet to figure out. at a personal level, i make decisions every day, i bought this car a while back, a hybrid but i tell you i have annoyed you if the co2 i get for my 70 miles per hour gallon as a benefit to all of us outweighs the cost of creating the car and the potential pollution impact of the battery after get stored. and maybe if we had not done the battery we would export new technologies that have less of an impact, was it good for me too buy this car, i do not know. this is my mom, i bought her that scarf a couple years ago at christmas. if i had bought that 100 years ago fine, no problem. today i don't know if that scarf

was made in a sweatshop using cotton that had pesticides growing on it and there were huge amounts of co2 as the scarf traveled a long way away to the place were about it. my choices have actions at a distance and i have annoyed you what those impacts will be. so that is what i will be talking about today. you guys have a new superpower, not all negative, the choices you make have ripple effects through the world. i like to think of it as a fish in a pond, maybe 200 fish of the size of human society, the fisher in thereupon and everything is going okay for a few years, then a storm happens in a river opens up connecting the fishpond to the ocean.

oh my goodness knew fish swim up stream and it's fundamentally different. because there is saltwater. i think we are in that situation today as a society. things are changing radically, our actions have outcomes at a distance. that woulthat is what "link" is, the actions that we take in the decisions that we make as we consider those actions in the past through which those actions become reality. how can we be responsible for the outcome of our actions if they're invisible? if we cannot see the outcome of our actions, if the fish in the larger ocean is no longer in a small pond, we simply did not evolve to the situation. i am lucky i went to the computer resolution, when i was a kid computers were big old

things that nobody could use and my mom didn't know the difference between software and hardware. i had to explain that to her. we went through massive computing technology. were going to the same stuff today with a.i. and data in the new technology stack. a.i. is done to us but we don't have control over it. data is overwhelming at a distance at best we get a data visualization but i had the honor of interviewing hundreds of people as a technology analyst for a few years and asked them what are you frustrated about, if technology could solve one problem, 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. background for me, i have been building machine learning applied systems are really long time, over 30 years.

so i was funded by the human genome project to graduate school, i built hundred million dollar budgets for the government, i have built thousands of machine learning models, mostly supervised learning, who would've known it would still be with us so many years later, my machine learning friends know what i'm talking about. . . . >> it enters the equation. i'm honored to be sitting here. and were closely with them. i like that he called it intelligence augmentation because we can sort of thing is ai. we think upside down.

splitting humans at the center of the equation again. and when i interviewed all of those people, i found the decision archetype. what is the decision. it is an action, thought process leads to an action and that action in a complex world, and we do some stuff. i don't know what buying that scarf or cart will do the world. it's going to have some impact. but honestly, i don't feel very motivated because i can see it. it's all present for me. it doesn't grab my primate brain makes me think gosh i really need to buy that hybrid car. i can't see it. the data today, and the ai status today is not giving it to me. this is my dog, i am training him to be a service dog. and i've had him much as a life, is about 11 months old. i had this awesome thing happened to me. i have a trainer is teaching me

to train the service dog and she taught me about abc. behavior consequence in my head exploded. it is exactly what i heard from the humans that i've been interviewing. the executives i've been talking to. they're always talking about the antecedent which is the context, fornication, it's a set . behavior. reductions. the consequence, he gets a cookie. so this is universal archetype. it's not just one way to think about how we might use ai and data. i will tell you a little bit about that in a moment. i am pretty certain this is the way to think about it because it has the lowest fiction to how humans think. the lowest friction to how humans actually think. maybe people live in complex environments. they don't have much brainpower to learn the optimism station or ophthalmology or any of those

fancy things. and the fact that we are not has created a giant cultural barrier between people at head of government, at the head of businesses and even we as we try to make decisions and try to use the evidence and data of ai, to help make sure that those decisions have a ripple effect that is good read the farmers i am working with, they have to decide what crops to plant. down the road, they don't know if that crop will make some productive or what will happen because they have fewer migrant workers. the situation has changed. the mighty side were to acquire a company or what product to flaunt or what price to change and is a topic through, you hear much of what went on here, is an antecedent which is the situation and behavior, we will watch this product . and then down the road somewhere, windows media. he is a dog before us, and what makes us special is we can go

through long chains of consequences. but that is limited and we need computer help. so again, a decision is an imaginative process in our heads as we think through the actions in some context it will lead to some result. if you remember nothing else, remember this template. and what is cool about decision intelligence which is what the book is about is because we start with humans, you can take home and use this immediately. that is my promise if you stick with the talk. okay. how we make decisions today. i am sorry to say, i recently learned this. especially in a complex world. but going back from human element evolution, we don't really think through the consequent design our decisions very deeply. were much more likely not to think through these rationally, and instead use social stigma

looking signaling. we look at someone who is a successful in our company was dominant and prestigious and recopied the decision they have been making. it turns out that is very effective. it has been hugely successful for the human race in fact is what separates us from many other species. with the great copier. and that evolution theories say that we develop patterns that any individual can't understand but that the society like kind of the unconscious proposition of genetic evolution, we have this evolution to come up with these behaviors because it's what we are programmed for. we are programmed to look at some prestigious or dominant person and do with the do. as opposed to shrinking to the consequences. that was quite for a few millennia in. but the situation has changed. first of all, there is about actor here, or here, and they tell us what to do, and we're smart, they can subvert our behaviors. they can influence us to make

decisions that benefit them but not us. if they are smart about the situation. second, the context is massively changing. we need to be developing new ways of coping with this big ocean is fundamentally different. because it keeps changing. and back and forth and the old ways of thinking through problems and societal and crowd levels, are no longer working. these have complex system dynamics. the feedback effect, we see winner take all. all patterns, where large tiffany's martial artist at 90 percent of the benefit and his massive inequality. action at a distance, we talked about it. anybody who's work data, we tend to focus on what we can measure easily. money. price, and we tend to overlook reputations and happiness,

morale. and yet i've never built a decision model didn't have at least one feedback group that involved something intangible. a soft factor. we must start talking to the sociologist, the cultural evolution is, all of those other disciplines to understand those hot factors. in decision intelligence creates a roadmap for how to do that. to nothing and didn't say is the future is no longer like the past. and so you know the work of the problems, we estimate the best in the future the same. based on past and we don't realize our situations have changed. these big black notes white or white in no slack. and oddly nai, which i will tell you about, they can solve this problem. i grew up in a period of technology optimism. we are all sharing all of our code and the internet was going to democratize reality and we

going to collaborate. we had a dream. i don't think we have realized the dream. i think decision intelligence will help us go there. i think we created a number of police in the change, did the machines. collaboration in the internet and social media, and there is one garlic that we need. to start to make a big difference and to have a nonlinear impact. that is di which will start to target a little bit it will practically about right now. how do we do di. we start with people. we'll say where is the data. we don't say we can't do this ai without data. data is great but there's a huge amount of human knowledge premarket about knowing how our actions lead to outcomes pretty how he go home ask a friend who didn't come to the stock how they think about a complex decision. i promise you they will talk

about actions. and ultimately lead to outcomes in the context. so what i do as i sit down with a diverse group of experts. young gender race in different outcomes. i know so many companies who have drastically big projects never sat down and brainstorm through the outcomes. i go in a consultant fairly senior levels of organizations and i said what are you trying to achieve. in the listed outcomes is different for each person. let me tell you you don't need technology to get better. you just need to have a bearing brainstorming process we think through what are the outcomes we are trying to achieve as a team. is it higher revenues, net revenue after two years, is it some kind of a military

advantage. we want to create a military advantage that doesn't hit us ten years later. what are the outcomes that we are trying to achieve. make sure you ask that question. second, brainstorm through the action. many folks don't take the time to have an open brainstorming session where they allow that ideas for the actions we can take in to see those outcomes. do that, remove all of the blood to the creative side of your brain because when the blood is in the creative side of the analytical side of your brain. you don't have room for the creative side. so separate those two. spend some time being creative. and then spent some time being analytical. these triangles here are where ai intersect. in most precision models democratize ai, distinct patterns. this is how we will do it.

so let me talk about the decision i am facing today. i saw greta on tv. and she was so compelling. she said we've got a climate crisis. and though i resolve this, it is really simple. stop worrying about analysis and then at the very least, pavers and trees. the organizations all over the world that will take your money and buy trees. in the streets will grow and so there will be more biomass and that will have some carbon and if enough people do this perhaps, but i don't know if she said this trees alone will do it but she said it would make a big difference. i haven't sent any money to a tree organization yet. i can't visualize how this money that i might send needs to achieve to some outcome. if i am going to use ai to benefit me, i want interactive fun experience. and so this is what i think the

future is of ai. it will look like a videogame. and i hope we can do some of this in the basement because we can do this through the vcr. we can walk through this. and what are we doing in the spaces. we are experienced inc. with actions we might take and we are letting the computer help us understand the chain of events that sets in motion. and lead to the consequences. valuable personal level, also highly available an organizational level. let's see if this works. i've been having a lot of fun coding. i really like it. business at two for the future, and has no purpose whatsoever except to show you there's a physics running through here. what is going on here. we are trying to make a decision as to how much money am going to pay each year to kuester carbon. as a change in this decision, this data here is telling me about the future that puts in motion. iceman some money, i change my

decisions, and changes the number of trees that i will purchase. and changes note biomass. here is the total atmospheric carbon. so i can see the chain of events. now linda is the expert in the background list and some research and by the way, you might've also but none some research for each expert has an opportunity to seek how these actions connect to the outcomes. and here it not only i change my decision but i can also change whether samhan is right. and who i can trust. because i can see different people, different experts, playing different things. ultimately i can also click on the names, and i can go to the site, where i can see where they are making the case. that where there models are leading to outcomes that can help us. in this like wikipedia) can oversight that is curated that we can use to understand people. i'm sorry to understand the

situation. this sort of like a business intelligence dashboard. like stuff we have been building for a long time. but it is telling you it's not. we're not looking at a data set here. we are looking at the future. really summarizing it in bar chart so you can understand it and in the background, depending on our choices, we also have a generating applications of those choices. i did a lot of investments, and as i've changed my investment or i use linda's opinion, i can see how my decision interacts with the situation as they characterize it. in order to impact the outcomes that i care about. this universal pattern. this example here is an example of something that you do in your head 500 times a day. and that large organizations really struggle with understanding impacts of today's decision tomorrow.

so we'll give you a couple of examples, these are machine learning models here. these little triangles. we might have built a machine or model that detects whether a computer system has occurred and shows you what is happening right now. that is a coming machine learning. it's pretty widespread use. and he gives score. in my be 20 percent, goes up to 95 percent rate is pretty sure there's an intrusion thing right now. yet another model, the sense here the type of intrusion, dd os attack or something else. and in this picture is typical of many decision model situations. it has some spaghetti in it. but that's getting this really how people naturally think. i promise you if the spaghetti is how you are thinking about things, is a lot better to have it on paper and try to keep it in your mind and explain it when using invisible mechanisms today words, and tax which inherently linear to communicate these decisions. we need a blueprint like this

legions of being an architecture diagram for machine learning system. it shows how machine learning begins and then we have some choices. and like it's been my intrusion information to the police, i can investigate the intrusion. it will force you to cost-benefit and have an outcome. if i call the police every time, it might be pretty costly. we need to try to call the police only when necessary. this is a decision marla built with a bunch of farming experts as part of that big project he talked about. and as 23 phd's in seven institutions and were just finishing up this week. crusher fingers that we win this friend it would national center for excellence in ai and agriculture . was important about this is i didn't have to explain the intelligence to anybody. i simply sat down with my diverse team of experts and i said what is the farmer trying to achieve. the first they wanted to do crops at the end of the year.

susan is not all. and they said well you know what, they wouldn't want to take actions that would put them out of business in a few years. so that is a second goal that they need to balance against that. and we talked about the actions. those farmers might take. to make choices about how they spring their crops for the spring schedule and the choice of crop and hybrid in the cultivar. this gives us a map to understand how all of the imt and ai machine learning technologies were together because there's a couple of models here at the telus how precision spring will impact the yield is on the model this is the amount of type of diseases or contaminants that we might have, they set off frames. iot, sensors that farmers might have on the drawing or somewhere in the field that they can use again with ai to interpret the data to know what their path starting in this early warning if it is possible so that we can spray as little as possible in

order to achieve our goals. both from a cost and long term viability and a climate and pollution point of view. so the type of spaghetti, but i promise having you this began getting on paper is a lot better on what's going on right now which is in the visible and people said pretty is artifact and we talk about value thinking. we design a decision. and it's something that we can design. that is pretty radical. but when we do it we realize we can bring all of this engineering best practices to decisions. we can qa this thing. we can continuously improve it. it acts as a blueprint that connects the end-users. my stakeholders about this. this is their mental model. it connects and up to the ai people so they know where they fit in. there's a wonderful moment. mother cell before we did this diagram, and i don't think anybody can anew how to put something together.

we don't have to explain it in words anymore we have a map. we know how it all fits together and we know the ai go. so it's going on with this decision intelligence. you've only known me for a few years and you know that it's been quite a thing. i'm flying all of the world. sometimes there are three people in the audience) trying to tell people the story. i am really happy to the extent that this matters. they have this on its ai cycle now. and that is a big accomplishment. where also, ibm, google, has trained 20000 people using this. and she is the other big evangelist in the space. she is awesome. google her. there is a bunch of companies. i started to identified as di companies. most of them in specific areas like your company i think is a

water company and these guys, hr, labs, analytics is a medical devices. these are all people who recognize that if they could be on the machine learning model and into a decision model, it's running the machine learning model in the connect some actions to outcomes, but that will help them be more successful. it is really cool. i love this work and is starting to get them. this is how kathy defines di. discipline and turning information into better actions and any skill. in slightly different way of saying it but is essentially the same thing. i sent di answers the question. if i make this decision today, which leads to the section, will be the outcome tomorrow. lots of people have different approaches of di. my decision diagram is mine. other people have other approaches. some people don't even come from tax sociologists, economists,

and more. what is common to all of us is that we taken seriously the action to outcome path. as the technology and science that interact humans. we also made it to hollywood. what shows this. it's the good place. this is attendance in. i'm not going to do the story line but you haven't seen season three. this is episode ten. i recommend you take a look at it. basically discovers di by the end of the series. it is the big reveal. the mystery. which is so cool right. and what does ten half. most of this is pretty is janice. what a sheep. issue human no she is in ai. so here is ted trying to understand why there are all these negative unintended consequences. and jenna is helping him. how cool is that.

i hope to read my book. i hope they call me. that would be your response to keep his feet seen going because it is so important to the future. what's with great power comes great responsibility. you have the ability to take action as an individual, and as an organization and have a giant impact. i'm an optimist and i believe we are in the midst or the beginning actually was solution of renaissance. or it's not just ai, but all of these technologies will come together under a common blueprint. system dynamics complexity computational neuroscience, cultural resolution, all of these many of them traditionally view within their little spirals, a thousand years of bank specialists. i think we are entering the age of synthesis, and those of you who are in and di, you know that

we are the experts focus on the outsides of these boxes. we know what goes into it and what comes out of it. and we start to know using what i think some version of di, ago that together to crystallize solutions for we understand that the solution to honor impacts poverty. a solution to poverty impacts the status of women. in the impacts hit all of us. they had government, democracy, they had companies and we call these the sdg sustainable development goal. i think there are all the same problem. i don't think we can solve them separately. i think 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 to the best outcome. thank you.

[applause]. >> do we have a chair transporter. thank you. >> i am shocked that you actually finished under the time. one question i have is you have a case study or a story to tell us shows how this works in practice. >> i think the example showed with the farming is a good one. it also built initial model for government decision. regarding the conflict in african country. when the model showed studies but is should you need to take action from two places at once. you have to do some work with the rule of law and you also had to simulate the place.

another things had happened. if you do with those two things that was enough force in system to take official stop cycle of conflict to a virtuous cycle. >> how long is the test case. >> i would say was maybe four to five weeks long. it was more complex than this one. it was just initial preliminary study. it took a few months. because the spinal lot time viewing experts. >> one of this actual things about people as they see metaphors and models of how things are light of the things. and whether you can actually imagine things in that way. >> i think the future are part of the future of di is to understand that their systematic patterns that happen in one place and then happen in another one of them is the pattern where

were taking an action which is good and is good in the short term. and then that action in the short term actually leads to a negative consequence. but our visibility horizons does not tell us that. i see that pattern everywhere. the example i think is vending machines in kids schools. mexicans happy. but down the road, and be diabetes and obesity are such good things. >> i like to look at a couple of other people talk as well. people's comments. [inaudible]. can i ask a quick question. >> good to see you again. i also have a background in the

science department. and right now parts are really interesting when everybody is kind of weighing in on this. and i find it interesting because you have a lot of people want to have these links. naïvely they're putting them in the legs. there saying that there having an idea. you want to see spaghetti) and yet i think about your metaphor and this challenge about teaching science. would you be able to not have to teach a science but send them straight to complexity of thinking as a way to start to appreciate, i feel like they are seeing the complexity. and just to be able to show it. have you thought about ways to make this tool to influence peoples thinking without them

having to painfully go through it. >> i use a metaphor to answer your question. i cannot drive a ferrari that seeing what is under the hood. for certain people are really impressed when you open up the hood and you see all that complexity. so some of us don't want to hear about that at all. we just want someone who we trust to tell us if we take this action it will lead to the southern arkham. some people we want the ability to open up the hood and so we have to have a multilayered approach to this. we have to have beautiful immersive video games which really grab your attention. but then you also have to be able to click on that expert and see the thought process that led to the mechanism of that model. >> they don't know but it is even in there. if they look in the hood, they didn't think they could build

it. it. >> i think we need both. as a technologist, we are obsessed with under the hood stuff we haven't paid enough attention to the user. >> the chief scientist, nai frames, so i think is much for a wonderful talk. thank you for a great book. very great talk. i love when using the vision you have with the internet is just getting started is going be positive and bring humanity together to sort of see people of the world and understand them. and we could increase empathy in connection friend i think the reality in the last years has been much more divisiveness and sort of tribalism and breaking up people. dementia tried to imagine a world in which di is everywhere. if where everybody has tools to sort and better ways.

i wonder if you think that will help people understand one another bring them together or will that to beat divisiveness. when you think the effect is pretty. >> i think the first thing you will do is a lot of people feel overwhelmed the information executives attack to say this in particular. i do want talk to the sky. i can't even understand were the same. and if they have interfaces like this, they will engage with the evidence in a more solid way and with the data in the ai and all of this great janet assistant. they can do better. i think as individuals and i'm not a sociologist but it sure does seem that we've all gone two oh my gosh this is too complex and i can't figure it out. someday we will balance out to help inequities that come to a technologist sort of dominate the world and send us things and make us do things and make us click on things. and we start to use it for our own needs and personal needs. i think we will hit and

equality. i'm an optimist. i think we have a democratized computer. let's democratize ai's complex technology. >> steve jobs, it was black and white. >> let's this do that for people. [inaudible]. >> you really empower people and enable the kind of interesting markers the sensation of complexity. >> let me repeat myself. if i still need to read the book or learn tech, just make sure you brainstorm your outcomes and your actions. if you do nothing else. there is so much value in that and we're not doing that. we are so blessed in her specialties.

>> it became somewhat of an argument. what he said was you're actually designing a tricycle. and i'm designing what he is saying is that mountain bike. and it brings real attention because steve jobs thing was just too simple. he wanted it to be much more robust. i think the good news from what you are talking about and one part is the tenure forecast is that most of the big changes takes 30 to 50 years to be an overnight success. so in a real sense, you may be on the verge of that now. when was this going to 67 years ago. ai 1956, 2012 for di. >> and as i recall again, artificial intelligence are augmented.

alongside one. i think that so that the evolution. but the good news is now i think it is possible that is coming together and actually work. and you can have both a tricycle and a mountain bike. >> she is a brilliant technologist so she wants speaking on her behalf, you might resonate with the one view that this is a tricycle. you need to be worse sophisticated and complex but we don't have to be either or. if we take seriously that we are low friction and humans understand that, and engines, ai feels like we are building cars for 50 years but nobody is ever built inc. every engine has a different set of controls. you can have a common set of controls easy for everyone and then we can be fancy under the hood. his classic information. it is computer science. thank you.

[inaudible]. implicit in understanding all of this, but also wondering about science and perceptive science. so when there is a wikipedia expert about one of these people, not rules that are set up for how people can edit those entries or not. would be possible or should be possible to have multiple revisions of these different characters rather than having only one set of perception of their outcomes. >> so this is the challenge. i think this will happen in the next year or so, there will be a new kind of wikipedia. which has rules.

how to get reasonably accurate information from crowds sourcing. but instead of giving facts, this is what they's called warm data. get connections. we did this, it causes this. when you get this money to this tree, charity, they will buy ten trays. and then will have some one to open that link and then they will curate that. and then they say when you spend $10 with us, you get one trade. and somebody else will say something different. any street will have this bio map. and another expert will say this tree has its biometric. it's critical that we create a warm data version which talks about the links. something as opposed to wikipedia is great but it is not saying that when you do this it causes this. the connection between things

that causes other things, is what we have been susan systematically missing. prayers. and that will be what happens. >> so this is where bias gets help. so one of the things that is happening with ai is forgetting on to the consequences because were not modeling the context. in which that day i as. so when i start a new ai project the first thing that i say, they will disable we have the data. so we are ready for ai. and i say put the data aside pretty timid the decision is. that this ai will be used corporative the actions lead to some outcomes. i might build the wrong ai system. maybe could do this without data. data might help . this a software engineer and if you don't understand the requirements we going to be just their encoded bill things the right. there's been research lately some kind of intent projects fail. the spotify.

i like your points, one topic that came up that i'm aware of his strong correlations of help access they have two breath control. siblings. all of the world. they can note the size of the family they want to support. this is like a missing link i was not aware of. and yet so the talk around it pretty will they do about the overpopulation. we need to educate women. what is a secondary effects. they're just seeing the primary link. so i'm curious in your envisioning it what technology can do. i would love to see the technology somehow to be able to resolve when you have these multiple interpretations.

and somehow, you see a way to build a system. because imaginative people can see that. that is a pattern link. >> i don't think we have a lack of research that demonstrates these kinds of things. i think science is giving us randomized controlled situation. and we get this result. you've got a lot of results. i think what is missing is taking those individual links and connecting those two actions that i could take today. so estate i believed you i care care about the status of women globally. can i do about that. show me some evidence and a picture . on evidence but in the video games and virtual reality. make it fun, immersive, cool. then i can see is a move this labor but i my expert maybe jill would curate that particular link. and then you have other people curating that. i can see women's happiness on television. i need some good designers.

we need to have these immersive displays that show these future immediately is the play out. that is the piece that is missing. we have great science, great ai data but we don't have this last piece will be democratized ai the chain of events to give you a sense of agency. i feel like i should speak to people on the side. >> i would be interested in. [inaudible]. honey behave on is unpredictable. that is the difference in having a brain controlling a body. and you can sort of see it as a

cultural religion. so people know what your decision will do. it's quite a powerful adaptive model. [inaudible]. >> i think we been through 2000 years in ai was an extreme example of this. if you can verbalize it, it has logic. there is massive amounts of subconscious rationality's this radar. this emergence intelligence where a group, none of them can explain what they're doing. none of them even know what they are doing on their own. instead somehow the sickness schools that they are giving each other and turns out cultural evolution news that you. what i think we are in a fish pond. i think were in a situation where our natural instincts for how we behave like birds, doesn't work anymore.

you asked how di fits into this. i think it surfaces in secession it creates a challenge to the cultural evolution us to really study the emergence behavior in humans as they make decisions. and it's an invitation to that part of the world to work together with us ai and did the people of the world. in a coherent way. the great point is that we do have these behaviors and these model complex decision. he builds assimilation models to help us understand we get better emergent behavior. hopefully you can help. [inaudible]. in a certain sense i hear you

articulating something that i've been thinking about my whole life. i am curious, what is you find in terms of when you go into an organization, maybe teaching a class. honey get these ideas across. also particular tools you find are useful. i go in, i don't want to bring my biases to the picture. i go in with the methodology where you storm the outcomes. then you elicit people's innate sense to get you from one to the other and then only after you've done that, i will go back there's usually this really ugly picture and i cleaned up and we look at it together.

i use any special technologies. only after we have an agreement to the picture, what i bring in, one of these immersive at that point they can get their intuition going as other actions lead to the outcome. and we have these decision dashboards which show data the given these decision that we are making in situ and that we are in, here is where our decisions are going to go. as all know. there's a lot of machine learning more than anything. >> is real thought to how you present to people. especially the visualization. i think is not likely done and we should partner with people

like carradine more. part of it makes it an arsenic craft. i do think the people can understand parts of the graphics. it is not very clear sometimes what to do. >> i want to double underline what you just said. i don't have this career that you have. so i'd like to make an invitation to you as well as anybody who is in this. i'm not claiming that these environments are the place to go. that is untested. i'm not the person to do this. though those of you who know this, they need to be invited into this picture. it is so new that that just hasn't happened yet.

i'm so they think squatting right now. the intelligence to figure out how to get through it. i've been working on innovations, and the last talk i gave, as i'm going to stop using and profitable is asian becaus. [inaudible]. i think a lot of things you talk about, and is really came together of these pieces and portions that we are working on. the technology saying no incentive using responsibility ai, i say just use that process.

as the decision-making. in the is how do you decide. i've been thinking about how to make these decisions. when you made the comment about technologists who have kind of taken this way. actually find business mindset be caught in the ways that we have designed. how do we make it based on that structure. for example if you can't have these tools, these amazing tools to the same people who have the desire for the same outcome, make more money. the outcomes of change. the people don't get the change to get to the outcomes. amusing tools, for the people to create the sound same outcomes and much more delicious even maybe way but maybe a large group sort of way. this is what i have been thinking about.

we get this amazing conscious we have to make change. it's been a so there is a lot there. and you're doing amazing work. on the respond one point. the reason i believe in this, is because it is making the invisible, cement no matter what it does, even if we have got bad actors using this, and invite them and insist that they draw a map. before we made any big government decision that we as a sensibly drawing decision models. and we have a collaborative diverse team with multiple races and ages. then the allstate that they agree to that model. i don't know if you've seen it, but like the lettuce guy in the room front to consider the best toy. and i think we have to about that. in a way to do it, is by taking invisible and making it visible.

jeff mentioned something to think what you are saying is the challenges and i really want to work with you and figure out to figure it out. the challenge is that when you have that perspective, you talking about 25 different people becomes like an overload. you're talking about decision in intelligence. you get overloaded by all of the variables. all these perspectives and then how do you kind of say, put this into this now. to a point where again, allow this to have the platform and you all good say your piece. now this is what we're going to do. >> a good point. let me speak to just one aspect of it which is a principal. amazing, read the book and that will answer your question. one of the keys in the space is the outside of the box person inside of the box.

you talk about this thing, this technology, this agent based complex system model. people talk about the math inside of it. that will fill your brain. information hiding is moot good architectural process. our goal here is to glue these technologies together ineffective ways. you don't need anymore inside for answers, we need more science but we've got an awful lot of unused sciences because nobody has connected them to the action to connection outcome. we get overwhelmed but if we keep using these best practices and focusing on surfacing them into models, we can start to overcome that overwhelmed. that is the exciting thing. i let them go on for a day and that i say, were going to draw a map which shows how all of you are thinking together the moment they see it, there's this little thing. they don't have to keep it

invisibly in the head. is now rendered in the picture. is just a mapping technology in many ways. so let's talk. >> have you had a chance to look at this book. >> that's a hundred and 40-dollar book. thank you. [inaudible]. decision intelligence. logical approach. there is the interesting side,

do you think there is a way for them to work together. and how is that. >> was take the economics thing. let's look at a bias that we might make. that behavior economist, fits into this diagram summer and will help us learn how when we do this, or when this is true, that will cause that thing to happen. so the way i see these expertise areas opinion is they form the links. so the picture is integration for how we see different understandings of how people work into the picture. so whether we are modeling the game theoretically, another company will behavior another person will behave. the becomes very important.

>> i often see those things make the wrong decision. then i realized that you need things not necessarily inside of this approach. and therefore that can help you understand work together. >> he talks about now grid make model and shepherd tour this woman aspired to be a great teacher. it's an ai system. if they had built a decision model listen here's the variable this thing is using and this is how the prediction will be used in this larger context, i think they would surface that unintended consequence much faster. it's not perfect but at least it makes it visible and subject to critique and continuously improve it.

around the context of the ai and also technology solutions that we use. thank you for the question. [inaudible]. i'm wondering if you could talk a little bit about conflict. and how that sort of or the role that might play in outcomes. there is so much on the above thinking. i was making at the table making the decisions to define the outcome. and if in your experience, in developing this methodology if you thought through, what you do when outcomes are in opposition. >> i believe that in many situations, outcomes that appeared to be in conflict, actually part. and that if we do these kind of maps, and resting this is, in

fact this is preliminary work. the whole purpose was to resolve conflict in africa. if you have two people knew of different opinions and the outcomes were trying to achieve and you capture those, not, then ai and some of the technology, in many cases can find the holy grail. the divine set of actions that help you achieve everybody's outcomes. because we are better at doing this and understand these links and at the same time will arguing with a buddy having to keep these compass links interest and we can't but we don't want to admit that we can't keep them in our heads we fall back on arrogance and yelling. and we don't have to do that, when were all cognitively and we have a map. and it is not to you against in our opinions, as you me working on a common model. as of this conflict, let's join together to use your view. this kind of a map, facilitates

the process. great question thank you. >> and you like being heard they do. >> they do and when you brought picture of the right they are really hard. [laughter]. >> does make you more open to the future. >> thank you for that edition. any last comments from anybody. >> thank you all for the decision to be here today. i hope this is a positive outcome. exceeded the negative outcomes. in your conscious effort to drive here . >> having to make decisions together, and i also think now

and this is what sometimes to engage with each other. we do continue this conversation with each other. thank you so much for having us. thank you bill for bringing us together wherever he is. in the nbc friends for the recorder. and for being very in the background. thank you very much. [inaudible]. [applause]. the american enterprise institute in washington dc, washington examiner political analyst michael, examine how america's political parties haven't have not changed. there's a portion of the program. >> the democratic party is always been a coalition of a groups pretty different groups of people regarded by themselves and others is not typical

americans but when united, to make up the majority. andrew jackson party was a coalition of southern whites and roman catholic immigrants in big cities of the north. a good combination if you keep them separate the democrats took a hundred and three ballots in nominated candidates for president in 1924 and that convention, but just like something of 2000 votes declin declined, just four of them, the ku klux klan. in effect there. today's democratic party is the coalition's most loyal members unusually black americans and unusually secular liberals. they are often going to work together to impeach donald trump. so it appears when it comes to argument of toxic junctions for church that don't perform same

marriage. i predict they will be disagreed with their between these two groups. i think these two enduring characters, help to explain their longevity. that is been important in the nation that has always been diverse. and you will hear a lot of commentary that says when the last three years for the last 12 years, we become a diverse country. we have always been a diverse country. the british colonies in the atlantic seaboard were diverse group of colonies. the founding fathers recognized that when they created the constitution, that the retained power in the states, the provided for freedom of religion but also said there was not going to be federal government established religion. you can continue to have them in the states for some of the states wanted to massachusetts and connecticut.

they recognize things about that religious wars of york. they knew like the european countries, the united states was a religious diverse country. and they provided a framework in which we could do that. my proposition here is the existence of these two parties, one always concentrating on court can situate near the wind a coalition of selected out groups. they've given a large majority of voters and always diverse country, diverse economically, religiously, regionally and ethnically. racially and so forth. they have an avenue express. tend to be congenial to them. and that that has accounted for the persistence of our two parties two-party system. something fundamental about the united states. not necessarily transferable to the other countries. is something that our two

political parties which like to both of which are have been - i've been hearing about that. nonetheless, have performed over time. ... ... before we get started i would like to remind everyone to please silence your cell phones. we will be doing the recording as you can see so please make sure to ask any questions into the microphone that will be passed around after the talk. with that i am pleased to introduce tonight's