Get into this and discuss this whole issue of how history will be taught in the future. Im the managing director for center of cspan scholarship and engagement, a fairly newentity in the brian lamp school at school here at purdue and we use the cspan archives which is now over 250,000 hours of american political history in their classrooms and research. We do other things but thats what were concentrating on at this conference. I tweet at cj dubly and the center at center for cspan. We hope you will follow us on that and wed be interested in following you as we reach out to specifically history professors across the country who were interested in using the cspan archives in their classrooms and in their research. So heres what were going to do today. We have three excellent panelists with different areas of interest under this topic. Theyre going to speak for five to seven minutes and then were going to open it up and take a lot of q a. Were going to start with margaret omara. Now i hate to read introductions so theres her introduction. You can read it. I dont need to read it for you. I need to do what i was trained to could, which is in the brian lamb school of questioning, ask you the questions that arent on there. So margaret. Where did you grow up . I grew up in little rock, arkansas, connie. How did you make the move from little rock, saarkansas, where did you go to school . Northwestern university. How did you do that. Because i wanted to go to a big city, somewhere other than the south and i got in. [ laughter ] how did you choose history . You know, one of the reasons i chose history is my high school was little rock central high school. Ah. And i was in my senior year was the 30th anniversary the fall of 1987 was the 30th anniversary of the crisis at century central high. And it was a time in high school was a time we were all being made very aware of that history where, that certainly within the walls of the high school were reckoning with that history and by that point it had become a majority, minority very socially economic Diverse High School and really understanding my own personal connection to some place that played such a significant role in the civil rights story is one of the reasons i did this. Last bioquestion, what professor or teamer no matter in grade school, high school or university level, made the most difference in your career path . My graduate advisor, the late michael b. Katz, university of pennsylvania. So because we are the cspan archives i was able to and tickled to find that all three of our panelists are in the archives and have appeared on cspan. So heres margaret talking a little bit about the vietnam war and the protests. This is part of a program that cspan history does called lectures in history where they go across the country and look for professors teaching certain historical issues in their classrooms and they actually bring the cameras in the classrooms. And get a class. 1960s was a time yes when the modern liberal left comes together and you have strong leftist movements, both within and outside formal politics, a push towards more leftist solutions. But it is also the moment when the modern right is coming together. Because there are also young people on College Campuses and ins what it is and should be. Margaret has a book called the code in Silicon Valley. Will turn it to you. All right. Well thank you so much connie and katey for organizing this and meredith, its great to be on this panel with all of you and to be speaking with the people in the room and people who will be watching on cspan. So i set to writing my most recent book the code and approached it when i started about five years ago thinking about it as a political history of Silicon Valley and it morphed into something much, much broader, but that political spine is still there and in the course of writing about the evolution of the high technology, computer and Hardware Software industries particularry in california and west coast particularly from the 1940s to present, particularly in the last 25 years it also becomes a story about media. So im intensely interested in, you know, as scholars say, putting the state back in the story of Silicon Valley, a place that has for quite a while portrayed itself as a technolibertarian paradise in which politics and government were to be avoided, when government got involved they just messed things up and funnily enough politicians of both parties held as a beautiful examine of American Free enterprise and entrepreneurialism in action. Theres actual will you a very critical governmental and political story that runs throughout. There also is a media story or an information dissemination store yes. I think going to something that we see thats manifesting right now, you have these very Large TechnologyCompanies LikeAlphabet Google and facebook that are the media disseminatedors, media platforms through which so much information flows, yet they are companies that do not think of themselves as Media Companies. Not only, sort of a verb, say, theyre not in the business of media as if they were newspapers, but also their whole selfconception truly is one of not of being against traditional media, being something that media is, like, government, something to be that an old style institution. But when we look at this historically we not only see e the how the culture of Silicon Valley in particular, Business Culture that was based on growing fast at all costs, elbowing competitors out of the way, bringing products to market quickly. And so the growth mindset of Silicon Valley is something that is animating how these very Large Companies are working today and also why its challenging for, to change the Business Model to something that isnt about creating evermore powerful algorithms that can scrape information. But also a community i refer to as a glapagos a very distinct eco system in the 50s, 60s, 70s and 80s although very much connected to centers of finance and government on the east coast, notably through the flow of money through the military Industrial Complex which is why Silicon Valley came to be itself. But was isolated enough geographically and in terms of people paying attention, you know, if you run a story in the Washington Post or the New York Times that referred to Silicon Valley before 1980, first of all that term comes up very rarely, when it does, its Silicon Valley. Even when you didnt have National News coverage like fortunate who profile entrepreneurs in Silicon Valley, it was at if it was a strange species, a different types. When we look back to entrepreneurials like steve jobs and bill gate when they were first presented to the world, shaggy haired, iconic, classic, very, very different dis disruption of the largest narrative of american capitalism. But one thing we discover when we look back is both that theres a very distinctive Business Culture that grows in the Technology Industry, a Technology Industry that has come in our modern age to have an immense influence on politics and government and on media. Its very distinctive but yet it is deeply connected to old economy institutions. Whether they be the National Government or state governments or even local governments. Old money. Where did the money for the Technology Revolution come from . Where were the fund thats funds that flowed into the initial venture funds that started these iconic Entrepreneurial Companies and semi conductors, was the rockefellers, the whitneys, the gilded age, its where the money was. Wall street banks. The most establishment of the establishment is underneath. These Companies Like ample, for example, which presented itself in the beginning very successfully as a counter cultural dream of a company, place that thinks different. Was why did apple break apart from the pack of other personal computer makers in the late 70s . Well, they had a beautiful product and they also had a singular, they had the two steves, Steve Wozniak who designed a beautiful, powerful elegant mother board inside the computer but steve jobs could tell a really good story and understood how to present this device to the world. They also had management expertise coming from other company thats were much more traditional and wellestablished that kind of took these two guys in a garage and turned it into a real operation. We see this again and again and again. So recognizing, a, that this whole eco system has a history, that it is both singular and distinctive but it is a product of the last 75 years of american political history. And American Social history. Its really critical to understanding and grappling with the immensity and immense influence of these companies today. And ill leave it at that. Thank you very much. Mer Meredith Broussard from New York University has a book called official unintelligence how computers understand the world. Where did you grew up. Just outside of philadelphia. How did you make it from philadelphia to nyu . Well, i was at penn before this and tumble before this, and is your mic on . F so before i was at nyu i was a professor at temple and a professor at the university of pennsylvania. And i study Data Journalism. I practice Data Journalism, a practice of finding stories in numbers and using numbers to tell stories. And new york is really the emcenter epicenter of people working on Data Journalism and people working on major issues around ethics in technology. Especially ethics in Artificial Intelligence. Which is my other specialty. So what teacher moved your life . One of the stories that i tell in the book is about when i was in high school and i was in an Engineering Program for kids go ahead. Do we need to start over . No. Absolutely not. I will just ask you the question, what teacher actually changed your life . One of the really important educational experiences i had in learning to use technology happened when i was in high school and i was in an Engineering Program for kids. So we could get taken once a month to the rca plant in this small town where i grew up. And it was rumored they were buildi Building Nuclear weapons there but what i did, i went on a bus to this Engineering Program and they gave us spare computer parts and said, here, build a computer so i actually built my own first computer and it was great. And so i learned from that that i i had the power to create technology also that there are a lot of wasted spare parts laying around at tech companies, which seems like useful information, and i learned about power. I learned that i had the power to build things. I learned that as margaret said, theres a lot of economic power behind Building Technology. And so that was really important knowledge that it took me into becoming a data journalist. So in looking for you in the cspan archives i found you at the yelp headquarters in washington, d. C. I didnt know they had headquarters much less in washington, d. C. , but here you were. Technology is not going to save us from every social problem. So lets take homeless nls, for example, homelessness, for example, the fix is not making an app to connect people with services better, the fix for homelessness is giving people homes so we need to push back against techno chauvinism and use the right tool for the task, sometimes that tool is a computer, sometimes its not. Meredith broussard. Thank you. So i want to talk a little bit today about understanding Artificial Intelligence. So my Book Artificial Intelligence is about so my book is about the inner workers and outer limits of technology. Uintelligence is abt so my book is about the inner workers and outer limits of technology. Nintelligence is abt so my book is about the inner workers and outer limits of technology. Intelligence is abt so my book is about the inner workers and outer limits of technology. I wrote it because people were having a hard time understanding what i do. I build Artificial Intelligence for investigative reporting. I would say this and people would say, you mean, its like a robot reporter . And id say no. And theyd say, okay, so, its like a machine that spits out story ideas, id say no. So i realized that if i wanted anybody to understand what the had heing i was talking about heck i was talking about and what i was working on there needed to be more basic understanding of Artificial Intelligence in the world. So i started researching the book. And i realized that we dont often get good definitions of a. I. We talk about a. I. A lot. But theres kind of this fog that descends when we try to talk more precisely about it. Theres a lot of confusion. Often when youre having a conversation between two people about a. I. One person is actually talking about the hollywood stuff with the killer robots and you know, a computer that is going to take over the world. And then the other person is talking about computational statistics. So its really important if were going to have policy discussions about Artificial Intelligence, about the role of technology in society, that were all talking about the exact same thing. So one of the things that i do in the book is i give a really concise definition of Artificial Intelligence. And i show readers exactly what it looks like when somebody does a. I. Specifically, i look at Machine Learning. Which is a form of Artificial Intelligence. So Artificial Intelligence is a subdiscipline of Computer Science, the same way al gallinari algebra is subdivision of mathematics. Theres also natural learning, expert systems, natural language processing, natural language generation. But an interesting happened where Machine Learning is the most popular subfiled of Artificial Intelligence. So a linguistic slippage happened where people say im using a. I. For business what they actually mean is im using Machine Learning for business, but the two terms have become conflated so its really important to keep this distinction in mind. And then another point of confusion is that Machine Learning, like Artificial Intelligence, sounds like theres a little brain if inside the computer. Nside the computer. So i was once at a science fair doing a demo of an a. I. System and this under grad came over and said, is it real, i said yes, just kind of confusing. And then he starts looking under the table. Like, like theres something hiding under the computer. Like, as if theres a little brain in there. So i realized that this this linguistic confusion is really profound so we need to talk about the fact that real Artificial Intelligence real Machine Learning is not actually about sentients in the computer. Its a bad term, honestly. So what Machine Learning is, it is computational statistics on steroids. Machine learning prediction is is essentially making statistical predictions and its amazing that it works so well. Most of the time. And its amazing that we can use math to figure things out about the universe. But math cannot tell us everything. Prediction can tell us likelihood but it cannot tell us truth. So we need to keep these ideas in mind and we also need to think about hollywood. Because hollywood ideas about Artificial Intelligence color our beliefs and every student who comes into your classroom and starts learning and starts thinking about technology and starts thinking about history is also simulcataneously thinking about hollywood and hollywood images of Artificial Intelligence. So we need to make that distinction that hollywood imagery a. I. Is totally imaginary. Researchers call it general Artificial Intelligence. And that is the sing lairity that is machines that think, thats the robots who will take over the world, and its all totally imaginary. And real Artificial Intelligence what we actually have called narrow a. I. So Machine Learning, though it sound