Good afternoon. Good afternoon, Rochelle. How are you today? I'm doing well. Thank you. How about yourself? I'm okay. Thank you for agreeing to chat with me. I appreciate this very much. So may I start by asking you to tell us your name, a share, anything you feel comfortable sharing? Sure. Yeah. My name is Helen Norris. I'm the Chief Information Officer at Chapman University in Orange County, California. I've been at Chapman for about six years. I've worked in technology my whole life and in higher ed since about 1997. I've been at two other universities, UC Berkeley, and also at the California State University System at the campus in Sacramento. One thing that people don't always gather about me is I actually was born and raised in Ireland. I know I don't sound like it. I've been in America for a long time, but you know, so I feel like I have an interesting journey as an immigrant and working as a woman working in technology. They're very, very important to share that I think because for people that are coming behind you, it's good to know your path. So can you share your past? So from Ireland, how did you get to America? What was your interest in IT? How did that all come to be? So I grew up, as I mentioned, in Ireland. I grew up in the 70s and the early 80s. And I often joke with Americans that when Americans graduate from college, if they want to move away from home, they go to another city. Well, in Ireland, I mean, there's more than one city, but I was from the big city. And so when I graduated from college, I actually moved to Frankfurt in West Germany or it was West Germany at the time. It's Germany now. And my interesting choice to move to Germany because my German was not very good. So I actually ended up working for the United States Army as a computer programmer. I met my first husband there. When he was in the army, when he was restationed back in the US, I moved with him. And I think I kind of stumbled into computer programming. My degree is actually in mathematics. And I didn't, you know, I was the first of my family to go to college. So I thought, you know, that the path that was open to me was to be a teacher. So I kind of assumed I'd be a teacher. And so I was lucky that I stumbled into a field that that was really a great fit for me. So a lot of luck in success, I think. And I've been lucky, I think a lot through my life. Yeah. So you come to, you work for the US Army, your programmer. So now today you are CIO. That's not an easy path to get there. So can you share a little bit more about from being a programmer to who you are today? Yeah, yeah. You know, it's my title when I was working for the Army was a computer programmer. But it was a an interesting place to work because it was a small organization. And so, you know, you kind of got this was in the 80s. So you kind of got your hands on a lot of different things. I worked on technology. People in my age group might remember this. Wang technology. And so when I work to the I see some recognition. When I moved to the US first, I actually got a job working at Wang labs and worked on the customer service side. But over my career, I've been fortunate in being able to do a lot of different types of jobs. I've been a systems administrator and network administrator. And I think that that has helped me in preparation for becoming a CIO. I've worked mostly when I moved to the US first, I've worked mostly on the corporate side, but I moved into higher ed in 1997. I wanted to for personal reasons, I wanted to move. We when we moved to the US first, we lived in St. Louis, Missouri. But for personal reasons, we wanted to move to California. And I was hired at UC Berkeley in and I know you're at Duke. So this will resonate with you. You know, a lot of the big or ones have highly decentralized IT. I was hired as an IT manager for a local IT organization. One of the things that's great about being in a big organization or big or one university is you can move around and get lots of different experiences. So I actually was able to make a couple of different moves. I moved from the local IT group the first one I managed to another one. And then the second group that I was in was actually managing IT for the budget and finance division, I guess. And so that was really pretty high profile. And I got the opportunity to work on central campus systems, things like PeopleSoft and so forth. And then and I kind of say, I sort of went over to the dark side. I went to work in the central IT group as my final stop at Berkeley. So I was at Berkeley for a total of 12 years. One of the things, and I think my experience is that this happens to women, maybe a little bit more than men, when you're in an organization, especially at like a university where it can be really pretty political. When you're in an organization for a long time, sometimes you it's hard. It gets to the point where you sort of have some baggage if you've had to do unpopular things. And I did because you know you go through budget cuts and so forth. I think I got to the point where I knew I couldn't advance because I had too much of that baggage from some of the things that I had done. So I took a lateral move to move to the California State University system, CSU Sacramento.  And in my experience, that's not the case.  So for me taking that lateral move was probably the best thing that could have happened to my career. One of the great things I know you've been in higher ed for a long time. You move to a new campus, you know a lot of things, right? I mentioned that I'd worked on PeopleSoft. I go to a new campus.  But yet my experience has been that different universities operate differently. So you still have the opportunity to learn. So that was great for me when I moved to the Cal State system. And now that I'm at Chapman, which is a private university, again you come in with a great deal of knowledge but a lot to learn about different things that we do. Very, very interesting. Thank you for that. So as a CIO, how many other women, regardless of their ethnicity, do you see in that same role across the higher ed? I mean is that something where women are entrenched or is it we're still trying to find our way in? I think we're still trying to find our way in. I know when they EDUCAUSE and other organizations do some surveys and so forth. I think we see about 18% if I'm remembering correctly of women in the CIO role. And it's even much less for women of color. It's just much, much harder. The other thing, I'm very active locally and in Southern California and a lot of IT networking organizations and professional organizations. And I spend a lot of time, well, when we could network in person, you know, I went to a lot of events and I would say it's even worse outside of higher ed. I don't know the numbers off the top of my head. But I do know that I go to meetings all the time where I'm the only woman in the room, right? Or there might be one other woman in the room. So I think we still have a long, long way to go. And I think also that there are still it goes to, it comes from very early in our careers. You know, you do see, I think women coming into technology and then kind of not staying. And you also see this sort of funnel effect where as you go higher and higher in the organization, you'll see fewer and fewer women and definitely fewer and fewer people of color. So it's kind of like we funnel out as we go through the organization. So I think we have a lot that we have to do to, there's still a lot that we, there's still a lot of ground to make up for women and particularly I think for black women and black men, you know, I just don't think that the same opportunities have been there for people like us. Yeah, I agree with you completely. If I were to parallel my career to yours, I should be in charge of the CIO. That's how much I've done, but I'm still down in the trenches. And I was on a panel earlier in the summer, I think July, I don't remember when it was, but I was on a panel for CLAC, you know, liberal arts college. I was really surprised to see how little diversity was there. You know, there are a few black men, you know, a few women, but it wasn't really as diverse as you would think, especially considering liberal arts has a high uptake of people of color and rare as STEM has been the exact opposite. You know, we start off with a STEM career and end up in the humanities or something. Yeah. Yeah. And you know, it's interesting, I'm glad you raised that I just want to make two points.  I am aware that I bring so much privilege to the workplace on a daily basis because of how I look, because of the color of my skin. But also, you know, and I mentioned that I'm an immigrant also because of how I speak. So I look and I sound, I think the way people expect a CIO to look and sound, right? And that's, I, in some ways, I feel like I've been told I'm kind of the right sort of immigrant because I don't bring an accent, you know, I look like I've assimilated into American life. The other thing that's interesting is you mentioned that women start in the STEM fields and then they women and people of color start in the STEM fields and then, you know, we end up in different fields. But actually, I think we're discouraged a lot from being, from going into STEM. And I will tell you a story about when I grew up and I grew up in Ireland. And it was the 70s and 80s. It's a very different place now. It really is. When I grew up, you know, we saw the traditional, what we think of as the traditional models, right? My mother stayed home with us and my dad went to work and everybody in my community, everybody that I went to school with, that was the same experience. And we have a very different educational system to the US.  It may be different now, but you kind of go through the equivalent of high school.  And everything counts on that exam, right? If, how you get into college, they look at your grades in the exam and they calculate it and then you get accepted or not. And I happen to be very good at math. And I wanted, in the leaving search, you can do higher level or honors or lower level math. And I wanted to do higher level honors math. I went to an all girl Catholic school, and they didn't offer that. And so, you know, I talked to the nun that had none about it. And she just kind of told me, well, you know, girls don't need honors math. You know, and it was like, oh, well, so what I actually did is there were a lot of, there were places you could go to get tutoring, but the intent was really for people who were studying at school and to give them an extra help. But I can, I, that's what I did. I did that. And I studied honors math to go through that. Now, another interesting anecdote. So that was like the 70s in Ireland. So I, we have a student at Chapman, a young woman actually, and she is a young black woman who I've, I've done some work with. And she went to school in Los Angeles in obviously in the last five years, because she's still the last few years, she's still a student. And she went to some kind of fancy magnet school. And she was in a technology program. She's actually studying computer science at Chapman. And she actually had a teacher tell her he was going to assign her some of the more kind of administrative work, not the, the networking cause, and what he told her, you know, girls don't like to crawl around under desks. And so, you know, it's the same thing that happened to me in Ireland in the 70s happened to this young, young black woman in Los Angeles in the, in the 21st century.  Yeah, absolutely. And I will tell you, that's not unique for that black woman.    So coming in as a white woman, you have a lot on your plate, but I will say this, it's not so broad.  So that's my experience too. Yeah. And I think I may have shared this at some other time, but, you know, one of the CISOs in higher education came and did a talk at Duke and she told us this word, he peed, right? So you can be in a room and say something and no one responds to it. Right. Man takes what you've said and rephrases it. Yeah. And it becomes a thing, right? So if you say today is Sunday, it's three o'clock or three 30, nobody says anything, but if man comes and says, hey, today might be Sunday, all of a sudden it becomes a thing. It's gospel. Yeah. Yeah. You know, and so I think that that is true even for white women that there's still a little bit of finding your voice. And one of the things that I am a proponent of is advocacy, you know, like people talk about mentors and mentors are great to have as a mentor because a mentor is that person you share, you express what's happening to you and your job and kind of give you some advice because they've been there and done that, right? So they're great mentors, but advocacy is when you're willing to put some skin in the game, right? To put your name, your credentials, your cash, capital, whatever it is into the game and have other people respond to what you want, right? To you, Helen Norris were to pick up the phone and call somebody say, hey, I have this person who I think I made a great fit for your company. That's the whole traction than if I did the same thing, even with more experience and more opportunities to get the same thing. So I think that's really a tough road to hoe for people of color and some white women as well. Well, you know, a couple of things I'd like to say about that. The first piece you talked about our voices, right, that were in meetings and, you know, and I've had it happen where a man has taken credit for my idea and, you know, sometimes you just kind of go with it. But I know that there's research that shows as there are more of us in the room, if there are, and in fact, the kind of the tipping point is sort of three, if there are three women in the room in the meeting, you can amplify each other's voices, you know. So I do think that one part of advocacy is especially for me at this stage of my career and, you know, it's to be in the room with other women to amplify what they're saying and to make sure that they get credit for it is something that I can take on as a form of advocacy. The other thing you mentioned is, you know, whether it's kind of putting in a call or something for someone and you mentioned that I serve on a lot of boards and I feel really, again, I feel very fortunate about that. And one of the things that happened for me is a former boss of mine recommended me to be on a board. He was leaving the board and he was asked to nominate someone and he nominated me. And I really appreciate that for him. So one of the things that we can do as women, women like me, when I have the opportunity to nominate someone or for a position on a board or for an award or something like that, it's important for me and one of the things I've done for years is to nominate women. That's not to say I don't nominate men, I do. But, you know, every time there are plenty of deserving women out there who aren't getting noticed and that's something that I can do to do that and I've done that. But one of the things I realize I haven't done and that I really want to work on is to make sure that I especially do the same for Black women or other women of color or men of color because I have that. The person who nominated me to be on the board was a man. He was a Hispanic man and I really appreciate him lifting me up like that. And I think it's important for me to, how do people say pay it forward to do the same thing for other people who don't look like me and that's something that I think I can do better at. Absolutely. And so in your organization, when you go to hire or you have a vacancy in your organization, how do you deal with that? What are your steps that you take to make sure you have a diverse pool? You're picking the best person and you're really getting some buy-in from your leadership about that? So can you talk about that a little bit? Yeah, yeah.  I think you can only do that with the support of leadership and frankly with the support of the HR organization because you've got to know even how to find the right candidates. I know when I look at my organization, I've actually had this situation where I've thought, you know, I'm doing great. Our IT organization looks good. I have six direct reports for them or women. I have people of color and we implemented a new kind of a dashboard in our data warehouse and I was shocked when I really looked at the numbers and saw how much work I had to do, you know. So it's easy to kind of think that you're doing really well. But it's hard. I think you have to continue to do networking and reach out to people explicitly to try to bring them into your organization. You can't just put something on LinkedIn or on a job site and expect to get a diverse pool. So, you know, if you're working with recruiters, you've got to seek the right people. But I do think it's the networking. The networking, that's how people get jobs, right? It's who you know. And it's as you pointed out earlier, having somebody make that call for you to say you should look at this person. It's something I think we can all do better with. I recall when I was early in my career, I wasn't in higher ed at the time. I mentioned that when I moved to the U.S. first, I lived in St. Louis and I worked. I was like an IT manager, I guess, for a good size law firm. But it would be a small IT organization, right? And we needed to hire someone. So in those days, we didn't have the internet, you know, we put it in the newspaper at St. Louis Post Dispatch and an organization called me and asked me, there was a newspaper in St. Louis, the St. Louis American, who marketed to the African-American community and they would literally go through the job ads in the Post Dispatch, call the recruiting people and say, can we run your ad in the St. Louis American? And to me, that was a real eye-opening moment for me because I was very young, you know, and I hadn't been in the country very long. I didn't know anything, you know? But it was eye-opening for me to understand that you have to explicitly look. And in those days, that was the way, that was one way that you could look was to go, to meet people where they are. Don't expect everyone to always come to you. Yeah, exactly. And I think that one of the interesting things about what you just said really hits home. It's like, you know, someone came to you and asked you, could you put that ad in their paper? But like, you know, one of the key parts of that, it's like finding historically black schools and universities where there are candidates that are in these areas. It is looking beyond the typical answer of diversity. So, you know, there's, there are these stereotypes about people, right? So there's this thing, all Asian people are smarter than anyone else. They do really well in math. And so you have an organization that has a tremendous number of Asian people. You may have one or two blacks, one or two Hispanics or Latinos, but it's not like it's a diverse pool. So it is right. And we're hiring leadership dogs because every aspect of IT specifically, all ethnicities are represented. There is no, you know, if you're starting at the bottom, there's lots of diversity at the bottom. At the top, where the diversity doesn't, you know, it's really hard to try to make sure that that's the place it is.  And so I think that it's very important that we think about this in ways that are really inclusive because, you know, I tell this story often and I have two, I'll tell you, my first one is, is Google. So in 2015, Google released its facial recognition software. All white male team did the development of that. And so whenever this facial recognition software popped up, when it saw a person who was not particularly, you know, a certain schedule, it would put up a picture of a gorilla. And there's no way in the world people did that intentionally. That's not, you know, it would be bad business for them. But the problem is when you have homogeneous groups, you're going to get a homogeneous output.  And first out, you know, there's that piece right there. But, you know, I think in a lot of ways that, you know, this is one of the problems. And then the other part of that for me is like, you know, we assign stereotypes to people, right? So women are considered emotional. We're extremely passive. You know, we have all these things that, that people stick on us to say, this is who we are. Whereas men, if you were as in your role to go in and be extremely assertive, not even aggressive, just extreme, you'd be labeled by that. Where a man who goes and does that exact thing, that's not expected, right? So, it's one of those things where we're in a world where there's so much complexity and labeling of people that, you know, it's hard to understand why we're not making any progress, but it's also very simple to understand. Yeah. And you know, I think that's one of the things that we have to do. So a couple of things as individuals, I think it's important for us to understand, to acknowledge that stereotypes exist and to recognize them. You're right on the Google thing. They didn't go in and say, let's do, you know, but recognize that you're, if you don't think about it explicitly, you're going to unconsciously and use your bias in decision making or in producing a product, you know. And so, so it's important to keep that in your mind at all times. At the other point you made about, you know, when you look at organizations, you have a point to make again, then I want to ask you a question. You do see diversity in IT at the kind of at the entry level positions, but not as I said, you know, it kind of funnels off. So it gets back to that advocacy thing. There is some mentoring involved or sponsorship. It's important for the leaders in the organization to develop that, to develop that talent, right? And to bring it, bring it up in the organization because you're hiring great people and you give people opportunities in the organizations that they are. But I have a question for you that I related kind of to the pandemic. I think one thing in certain areas and a little bit where I am, even though it's in California, sometimes it's difficult to get the diversity because you don't live in a diverse area, right? You live in an area where it's predominantly whatever. With the pandemic, we've kind of proven that we can work remotely. So does it open up for us the opportunity to hire people from a more geographically, from different geographies? And can that help us to diversify the workforce? You know, that's a very interesting question. I think the fact that we have shown that we can work remotely, like many companies are going to remote work only, you know, are going to some some form of it. I know a lot of the companies in Silicon Valley have talked about that for a year or two. You know, they've said that people don't have to come into the office. I think that's a change of mindset, right? Because one of the problems that working remotely introduces is measuring the productivity of your people when you're not right there with you. And I think when you kind of juxtapose that to in-person work, so the person has basically an hour for lunch, two 15 minute breaks, you know, and because of the social nature of being in-person, so measuring productivity remotely has to be put against measuring productivity in place. When you measure those two things, it's kind of hard to say that the person is more productive in the office than they are at home, right?  So there's that. And I think specifically for Black people, where that rubber hits the road is, is that we aren't given the benefited doubt in the office. So when we go home, you know, you're kind of... You really don't get it. Right. Because, you know, you're assuming that we're lazy, we're going to be late, we're going to do whatever it is that we're going to do. And there's these, again, it goes back to those stereotypes. I think that it is possible for it to be a better thing, but we really have to change our mindset. Really, you know, if you think about slavery and I don't know how familiar you are with American history, but, you know, first, if people come to America from Europe and they colonize Indigenous people, right? And then eventually slaughter as many of them as they could, then they go to Africa and they bring over another group of people and they colonize them as well. You know, so when you think about things like that, you know, it's kind of one of those things that you have to get buy in from white people to be willing to make this change.  You know, we didn't just walk out one day and say, we're done. You know, it took white people being in the fight with us to get us there. This is another one of those examples. White people being in the fight with us.  And I think, you know, I've said this in several of my chats, you know, like, when you're getting ready to hire someone, typically, especially for a senior role or a leadership role, typically what you do is you bring the people in that are just like that, right? People who are already CIOs, a CIOs or whatever senior directors or whatever it is. And the problem with that is group think it's the same. So everybody in that space has the exact same experiences. Where if you take that person that you just hired, he is first in your organization, he's desktop support or field support or whatever you want to call him, and you bring him into the meeting, he has things to tell you that you've long forgotten, right? You know, what it was like when you first started an IT, the struggles that you have, some of the issues that you experience, if you bring people from other parts of the organization into your, your interviewing or hiring process, you get a diverse pool of people. If you're only bringing in CIOs, you're going to hire someone that's just like that or CTOs or CISOs, whatever they are, they're going to look and that's what you see. And I think that working from home introduces a whole bunch of other pieces to that that are not necessarily accurate, but they allow us to feel comfortable that we did our due diligence, right?  It's not, the candidates they're going to do, they're going to start excluding people immediately that don't fit this model, right? So you create a job description that says you want this, this, and this, and this, and you want somebody who's had 10 years experience in this, people have done all these things, they then start looking for that person and that person almost inevitably is a white person, most likely a white people. So I hope my question wasn't too long, answer wasn't too long. No, it's, it's, it's, and I just want to kind of, you know, it's not even just at the leadership levels, it's in these highly technical jobs, right, that we go out and we say we want you to have five years experience with Oracle or whatever, that naturally excludes people. And it's not even necessary. And so, you know, what, what I've observed through my years in IT is often the route to the CIO is through some of these really highly visible, highly technical parts of the organization. So women in particular are often on the customer service side and you won't necessarily see people move from, from that part of the organization up to the CIO level, which is one of the reasons it was important for me to do some lateral moves to kind of fill in some of those gaps. So, you know, it's, it's, I think it's even important to change our thinking on roles that aren't at the leadership level, but that are highly visible technical roles in IT. And I want to also comment on one other thing you said, it's, it's, it's, you know, I think I probably saw this on a meme or something on social media. White people, we need to see the race issue as a problem for white people. As long as we look at it and go, well, we want to help black people, you know, we want to make it better for black people. It's our problem too. The Google example is a great example. You know, the, the exclusion didn't just impact the people who are excluded, although it impacted them the most, but it impacted the team that was working because they did a, they produced a less, a less, an inferior product. And so, you know, so I think that's the thing, you know, and it's, it's, it's something that we hear a lot these days. We have to, as white people, own the race issue as, as our issue. Right. And that's very important that you say there, because I think that the problem with where we are in our society as a whole, you know, so we've got all this, you know, law enforcement violence portrayed on, on black people, you know, you know, the first question you ask is, do you see me as human? You know, so I have two children.  And every time they leave me, my fear is really turned to me. So I think that law enforcement, to some degree, has this stereotype that's so embedded into what we are. So we're drug users. And I'll give you an example. So if you take the crack epidemic, I don't know if you really were. Yeah, I was here. Then you take them, the, the oxycodone, these, these opioids and, and methamphetamines, these are described when white people are in these epidemics as mental health issues. Right. But crack was a criminal issue. Yeah. Right. Yeah. So when police apply the logic that someone has come up with and said, this is who we are, then you're going to have the outcomes that we have. Yeah. The point is so valid that we need white people to join us in the trenches. You know, right.  We haven't known each other for a long time. But you know, I want to be a part of what you're doing, because I find it so impressive when I, like I said, when I looked at your LinkedIn profile, and I think even Chapman has a biology, a biography of you there. It's just, I mean, it's impressive is all get out. I know a lot of CIOs. I know a lot of people who have done some great things, but none of them compared to what you are doing and what you have done is just very impressive, you know. And so I think that you have the power to bring change. And I hope that you and people that think like you and have the same opportunities you have, that you think about how can I make my organization different? How do I make this world a better place? Because it's one thing to go to work and do your job, right? So you're the CIO, you make sure the organization has supplies, its needs, its customers and all that. But do you make an impact on the world? Do you change society? And with us in these roles, emerging technologies are coming fast and through. You know, so think about who's going to be hit by that. Another example. So I love Sam's Club. I go to Sam's Club. Okay. About a year ago, they introduced an app. You put on your phone, Sam's Club, right? And you go in, you open your phone, they say, yes, you're right. And you scan.  Because I don't know what California you are in, but you know, all these things that are coming up, you have to be purposeful and intentional about what we're doing. And you know, if we are not, then we are really doing a disservice to all because I think that Black and Brown people bring a unique perspective to all of technology, all of STEM, a very unique, right? So yeah, all of these things. So I just think that people like you really hold the chance for there to be better. Well, that's what I really want to do. And that's one of the reasons that I'm so happy to be connected with you. Because I do feel like for the last, you know, several years, I have focused so much on the issues of women and gender issues in technology to the exclusion of other issues. And it's hard in some way. It is hard. I have to put myself out there, right, to talk about race. And so that's why I so appreciate that you're having this conversation with me and willing to listen to what I have to say and to share your knowledge with me. But that's I think the thing that I really want to challenge myself to do better.  You have a lot of time here. I have a lot of time left. I hope so. I hope so. But it is, I feel for you, I, I'm obviously, but my children, I think I've mentioned this to you before, are African Americans. So I have a slight sense of what you feel. And, you know, I think as white people, though we can't, it's so much harder for us to internalize that or to, to understand what you said about it's the, the interactions with, with the police force, you know, that there's so much implicit bias built in.  And how do we kind of turn ourselves around? All I'm sure they're all really nice women who really just have this implicit kind of bias that they didn't even realize they had. And so how do we kind of work on that? I think the first part is to kind of say, I need to work on it, you know, and I need to work on it. So, so thank you for being there for me. Oh, absolutely. And I, and as I said to you before, is if you need anything from me, I am right that you don't even have to just call or text or email or something. I'm right there. I think the thing that's really interesting about you, you know, like so the younger people use a word called woke, like you're awake or a woke. I, I still haven't mastered what that means.  It is necessary for Helen Norris to be in the fight with everybody else.  This, this word I learned when I was getting my doctorate degree, but you know, as old as I am, you think I would have known this, but it has to translate as a whole more social reproduction. We prefer to be with those people who look like us, right? That in the old days called the old boys club, right? But you know, the problem with the firm to be with the people like us, is we don't grow as a society. We don't grow and mature as human beings, right? Because if I'm only with black people, my experience is only going to be what their experiences are. The lens they see the world through. Whereas I don't want to be stuck in a space that's all I know. I lived in Miami, doing the Muriel boat with, when the Cubans came in, and you know, there was a lot of talking, there was a lot of racism associated with that because, you know, they were told that these were the people who Castro led out of his jail, who were criminals. Oh my God, yeah. For all of that kind of stuff. And the problem with that, though, is that when we make these assumptions with people by some physical characteristic, that means absolutely nothing. That you have blue eyes and blonde hair, that you're six foot tall, or that you're thin, or whatever it is. These are physical characteristics that change over time, right? And if you look at any person when they die, they turn darker. So why in the world will we care about physical characteristics? Is the person a decent person? Is the person willing to help? Is the person contributing to the society as whole? You know, and I've been asked a lot about these diversity cats, right? So when Mr. Fuller was killed in May, it hit me so hard to see that. And again, going back to technology, technology makes these things, right? Because they were long time ago. We can watch you on TV and see them on TV. It's in our social media feed. What are we doing? What are we as a society doing to think about justice, race, opportunity, education, all of these places, right? I had a chat yesterday with a young man. He is in Pakistan. He is going to a predominantly white school here in America. And when the virus hit, he went home to see about his family and to help his family.  We just had 9-11 a couple of days ago. So, you know, all of these things kind of fluctuate. And so this young man is brilliant. I mean, he's got great skills and, you know, he will be a great contributor to America's all go. And he wants to work in America. He wants to help his family and his community in Pakistan, which to me is an example of a true human being. You know, I know I can contribute something to this society, but I also want to help my family, which is what we all want, right? You know, contribute to society. I shouldn't say we all, but there's probably some people do. Yeah, you know, so I think that there's just too much in the environment about race and gender.  But I ask that, you know, you think about the entire spectrum of what a society is like, right? So I asked this question at a panel I was doing a while ago. May Jemisin came to my university and did a lot. And so I asked, how many Native Americans or Indigenous people does this university have? You know, how many Native Americans or Indigenous people are CIOs who are faculty? Where is that?  No one said a word to my question. I still don't know the answer to that question. So if we think just simply about that. So when you talk about true Americans, if there's such a thing, that's the true Americans. They don't care long enough all the rest of the same. But when you come to here where we are now, we don't even consider them. We don't even think about it. We don't make a concerted effort to hire them and bring them into our organization. So we consider the Indians from India or, you know, Asia, that's our new substitute for diversity. And you look at both colleges, there's a huge Asian population, faculty, all of those things. But no other group. And that's something I think that we should change. Yeah, I agree. I agree. And we have to do it all. We all have to do it together. And it's true. It has been a very hard time, you know, with the, there's the pandemic. And then, and it doesn't feel like when you watch the murder of George Floyd, which we get to see.  It's, it's, yeah, if I'm not always hopeful, it makes me sometimes feel even for me, just like, how, how can this be? And it makes me worry for the next generation, the next generation of black boys who are going to, who could experience this? I wouldn't even say for your daughters. I mean, you have, whether you want it or not, an obligation to take a path for them. Because though you identify as white, society is not going to identify them as white. I don't care. You're right about that. They're, what kind of hair they have. Society is not, you know, going to, No, it's true. So your role as a parent should translate into making sure opportunities are available for people, especially based on the fact that you have two daughters, Heidi is going to paint their own plans, right? So they're going to see them in this way, which may not even be true, may not even be factual, you know, but just automatically assume. And so I don't know if you've seen the movie, The Blind Side, but it's about this. Yes. Yeah. About that. When you think about that perspective, you know, it takes a white person to save a black person. You know, it takes a white person to, to, to make opportunities available. And I think there's a little bit of truth for that. But I think the problem is that we already assumed that this is not possible in our society for black people to get up and, you know, I've heard this story. So pull yourself up by your bootstraps. What happens if you don't have boots? What happens if you don't have the strings to pull yourself up? There is assumptions and so for black women, your daughters, my daughters, me and all the black women that you know, we're depending upon you to think about this and to engage people in uncomfortable conversations and they need to be very uncomfortable.  No, I know that, that makes sense to me. That, that makes sense to me. And it is, it is, it makes, that's the thing is we white people, we have to be willing to be uncomfortable because we have, we're pretty comfortable. And so, you know, that's the, the, the, that's what what we have to do. That's all. And I would just add this one thing you said earlier about privilege. You have privilege. You are visible in your IT community, probably in the overall IT community throughout. Yeah. You're, you're, you're visible. So when your children come, if they decide they want to be an IT or they want to be a doctor or they want to become an engineering or whatever it is, will they get that same opportunity and have the same luck that you had to get where you are? If you do not put things in place to make that happen. Yeah. Yeah. Yeah. It's, it's true. And it's interesting, you know, the word privilege, a lot of people, it, it, it are kind of offended or insulted to think that they have privilege. It doesn't mean you would, I mean, I've worked hard. It didn't mean I didn't work hard, but I have privilege. You know, it's, it's, it's just, it doesn't make me a bad person. I just need to understand that for a person like you who didn't come into the world with the, the same level of privilege that I did, it's so much harder. And what can I do to change that and to, to make, to make that better? Absolutely. And how can I use my privilege? I know my, you know, one of my daughters sent me a video one time of, it was after a police shooting and the young man, the victim's brother was, went to the city council meeting or something and she literally, he was really upset. Naturally, his brother had just been killed by the police. And she literally, the white woman literally put her body between this young man and the police. And that's, you know, to me, that was a, an example of how to use your privilege, right? And, you know, that's a pretty gutsy way. And, you know, that's the kind of thing that we have to do as white people is put our bodies, put our beings in the way of, of danger. So I think that's absolutely correct. We're running out of time. I want to ask you a question first. I want to ask you, have you gone back to Ireland since you left as a little girl? Yeah, yeah. Yeah, I go, my, my entire family, most of my family is there. So we go every couple of years or every, lately I've gone more often because my father has been sick and it has changed so much, you know, when, as I said, when I was young, it was very, the role of women you know, we were really in a different place, you know, we were not treated equally. And I, I see change, I see change at lots of different levels. Some of that is because it is a little island. And when I grew up, you know, we weren't like off completely isolated or something, but people didn't travel the way that they do now. I didn't get on a plane until I was like 15, you know. And so I think that that has helped us bring in some outside influences. We also, when I was a girl, we had a really difficult economy. And so we didn't have immigration, we had immigration, we used to kind of joke that our biggest export was our children, you know, we'd send our children all, you know, everybody in America claims to be Irish, right? And in the 80s and early 90s, we began to have immigration because our economy improved and the Soviet Union collapsed. And so you first, you started to see a lot of Eastern European immigrants and then immigrants from African nations.  And now we see more diversity because we have immigration and it's changing the country. We have a booming, well, we did have, it's not booming like it was, but we went through an economic boom as we went through this. We also, I think the role of the Catholic Church has really changed when I was a girl. The Catholic Church was really powerful. That's not true anymore.  But actually, I would say, you know, the Catholic Church reinforced a lot of those gender stereotypes.  It was actually illegal, you know, let alone divorce, you know.  So we have our, in fact, our last prime minister was a gay man. And so to me, that would never have happened. I'm shocked to see that it turned around in a single generation. But I think the church had a lot of influence that it's recently lost. Yeah, I went to Catholic school early on. And I remember the rigidity of the Catholic religion, you know, and I think that, you know, a lot of what we know about the world, people use religion to condemn, penalize, destroy whatever it is that suits their agenda regardless. And I think that's very unfortunate for us. That is indeed the case that we are, you know, I am not, I don't, I don't subscribe to any religion. I believe in God, I believe in the universe. And that's, that's as far as I can get. I can't get any further than that, because the fact that you can use religion to, you know, like, so black people, for example, there's a quote that the Klan and white supremacists use is about the story of Ham. So Ham supposedly went into a tent and had relations with his daughter. And so from that, God put a stamp on people and anybody that comes out black is because of Ham. You know, so that's Christian religion, right? So, and there's so many pieces and parts of that.  You can't see what's really happening. So, you know, I'm glad that Ireland has made that choice. And, you know, it's always been one of those places I wanted to go.  You know, and, you know, I traveled a lot when I was younger, but I've always wanted to go to Ireland, but I've always been afraid because like, I think what would a black person do in a whole wide country, you know, what would happen to me, you know, when I make it out, you know, and so. Oh, yeah. It's, I think, well, now it's, it's, it's way more diverse. I brought my children there when they were little and they were, and they were, and it was, it was fine. But, you know, they were, you didn't see many other kids who looked like them at the time. So, it's, it's changed quite a bit since then. Well, thank you so much for chatting. Thank you. We have a couple of minutes left. So, if you have anything you want to add, please, please, and as soon as the video converts, I'll send you a link and you can review it and let me know if I may upload it. That sounds great.  I don't know that I really have anything to add other than, you know, one of the things, as I said, I want to kind of rededicate myself in some ways to making IT, particularly in higher ed, a more welcoming place for people of color, as I've done for women for many years. So, I know you have quite a following and I just want to encourage people to reach out to me the way I'm sure people reach out to you at the way you reached out to me because I'd love to have a conversation and, and, and I'd love to get advice. And I, I don't know, I'll just say my email address is hnauras at Chapman.edu and I encourage, I'd love to hear from any of your, your connections. So, thank you again, Rochelle. I've really enjoyed this. Thank you very much. Stay well and you have my contact information. I do. And please reach out and I'm sorry, I normally don't use my phone over the weekends, so I turn it off before to somewhere so I'm happy to be able to do this today. So, I have a great rest of your day. But let me ask you one question before we go. Are you not susceptible or vulnerable to the fires that are in California? Oh, well, we're a little bit away from the fires. We, so actually I'm looking out the window now and it looks pretty nice, but the last few days it's just been really smoky. And so actually I was telling my husband, I felt it in my throat and my nose, you know, so to me, so I'm again very fortunate that I'm not somewhere where my home is in danger. Even my, one of my daughters lives in the Bay area and saying much smokier up there. So that's the worst that we felt. So then hopefully it stays that way. But man, it was smoky and it made, I was like, do I have the coronavirus or is it just the smoke? You know, hopefully it was just the smoke. Wonderful. Well, thank you so much again for chatting. I'm glad you're safe and your children are safe. Thank you. So thank you and stay in touch, please. Okay. Thank you. Bye. Bye.


Well, so I'd like to introduce David Mayer, Professor at Mathematics at the University of California, San Diego. So unfortunately, we lost a few weeks of David's visit due to the COVID, but still it's a period of time still to have a discuss with him at OIST. So today we have a seminar, very intriguing title, Quantum Data Science. So maybe you can start and we can ask questions afterwards. Thank you. So let me start by thanking Kai Sensei and the TSVP program for having me here. Everyone has been tremendously kind and helpful and I really appreciate this. This is not working. I'm using the one here. So as Kai says, I'm gonna talk about Quantum Data Science. Now it's a truism in academic talks that if the title has a question mark in it, the answer is no, but part of this talk is gonna be about how the answer to questions depends on the context. And so maybe the answer won't be no at the end. We'll see. My goal in the talk is to make it accessible to people who work neither on quantum mechanics or quantum computing nor data science, but at the same time have some things in it that will be new to people who do work on any of those topics. We'll see how well that works. I don't know. The part at the end will be the part that the experts may not have seen before and it's been done in collaboration with some people in my group. So David Rideout is a research scientist in my group at UCSD. Daniel and Grant were PhD students in my group and Maggie was an undergraduate who was working in our group for a little while. Okay, so with that, let me start. So quantum computing is in the news a lot. This is a story in Quantum Magazine a few years ago about Google and IBM clashing over this quantum computing experiment that Google did where they claimed to have accomplished or demonstrated a quantum supremacy. And IBM said, no, we could simulate that on a classical computer. They didn't actually do it. They just said they could do it. But I start with this because it illustrates the main point about quantum computing, which is this is Google's machine here. This is IBM's classical summit machine. And the point about quantum computing is maybe quantum computers can do things that classical computers can't do or at least they can do them more efficiently than classical computers can do them. And that's what the excitement about around quantum computing is about. So since then, both Google and IBM have continued on their paths. You might think that part of the reason IBM was critical was that they were also working on their own quantum computers. Here's a paper from earlier this year. This is Google's quantum group. And in this paper, they demonstrate that they can correct for noise that happens in their device. So part of the problem with the not yet usefulness of quantum computers is that they're very noisy and it's hard to get the answers that you want to get out of them, out of them. And one of the things that we would like to be able to do is have error correcting codes. And that's what they demonstrate in this paper. They demonstrate experimentally that they have error correcting codes and they scale in a certain sense. Even more recently than that, so less than a month ago, IBM had this paper where they said, well, we have our 127 quantum bit processor that has noise in it, but we understand the noise quite well. And so we don't need to have an error correcting code to correct the noise. We can just sort of figure out what the signal was before the noise because we know what the noise is. We can sort of subtract it out. I'm speaking very roughly here, okay? And they say, we argue that this represents evidence for the utility of quantum computing in a pre-fault tolerant error. So fault tolerance is the words that people use in the subject to say, we're going to have a fully quantum error correcting machine where we can correct the operations and deal with all the noise. And they're saying, well, if we understand the noise well enough, we can make some progress anyway. Okay, so these machines are the product of roughly three decades of science and technology development motivated by this result of Peter Schuart, completely theoretical result, which showed that if you had a quantum computer, you could efficiently factor large numbers. And this was important because our current crypto systems are based on the difficulty of factoring large numbers. So that got everybody excited and people started spending money on this and scientists started working on it. And now we talk about budgets and the billions of dollars for building quantum computers. But we haven't made that much progress in 30 years if they're still talking about 127 quantum bits. That's not very many bytes, right? That's pretty small. So what's the historical comparison here? People often make a historical comparison to classical computing, the development of classical computing, but I think there's a more apt historical comparison and it comes from this. So for those of you who don't read Latin, this is the 12th year of Queen Anne's reign. So this is Queen Anne, Queen of Britain, who became Queen in 1702. So this is 1714. And this is an act of parliament in the UK. So it's an act of providing a public reward for such person or persons that she'll discover the longitude at sea. So this was a real problem and they explain why in the preamble to the act. So whereas it is well known by all that are acquainted with the art of navigation that nothing is so much wanted and desired at sea as the discovery of the longitude for the safety and quickness of voyages. I'm reading this because the letters are so hard to read but I've read it before so I can do it. The preservation of ships and the lives of men. And whereas in the judgment of able mathematicians and navigators, several methods have already been discovered. True in theory, though very difficult in practice some of which there is reason to expect may be capable of improvement. Some already discovered may be proposed to the public and others may be implemented thereafter, hereafter. So this was an act which offered some large reward for the success of being able to measure the longitude at sea. Okay, so the methods have been discovered true in theory. That kind of sounds like quantum computing, right? So already 200 years earlier, Johann Werner had figured out how to determine longitude using the position of the moon. And Petrus Apianus had figured out how to measure the position of the moon. Here's a picture of how they do it by triangulating against the fixed stars to figure out exactly what the position of the moon is. Okay? Gemaphysius had described how to determine the longitude if you had an accurate clock in 1533. But these methods were still very difficult in practice. Again, sounds like quantum computing. When the longitude act was passed by the British parliament in 1714. So not until accurate clocks were constructed. So here's a page from the website of the Seiko Museum in Tokyo, which so they have a whole display on the evolution of clock technology. And here they had talked about the invention of marine chronometer by Pierre Rois. So it's actually funny to read this because of all the technical jargon in it. So he contrived an innovative détente escapement with a mechanism to reduce the impact imposed on the balance by the escape wheel during dramatic shifts in temperature, which is a problem when you're on a boat. Okay? As well as a fusée to keep the torque of the mainspring fixed. Unforeseen difficulties have been in his attempts to build an actual precision chronometer for many years. And then in 1764 equipped with an automated temperature compensated balance using mercury and then later he invented an innovative bimetal detached balance taking hints from the bimetal compensated mechanism developed earlier by Harrison, an Englishman. And then this really sounds like current science. Regrettably for France, Lois was forced to abandon 20 years of research for a want of funds. Unlike contemporaries across the channel, the French government offered no support for Lois' work. Okay, so it's a great museum, we should go there. Okay, so not until the accurate clocks were constructed and then the nautical almanac and astronomical ephemeris for the year 1767 was published by the Royal Greenwich Observatory, could these methods be implemented? So here's the nautical almanac from 1767, 250 years after the whole thing started. Okay, now I'm not saying that quantum computing is gonna take 250 years, but it's taken more than 30 so far, so okay. All right, so meanwhile, while the engineers and experimental physicists have been working on building quantum computers, mathematicians, computer scientists and theoretical physicists have been trying to devise good quantum algorithms for which you need a quantum computer to implement them. And hopefully solve some problems that people are interested in, maybe other problems then factor it. So to get sort of to the topic of this talk, here's an article from a few years ago, 2017, in Nature where they talk about prospects for quantum machine learning. And they say, machine learning techniques have become powerful tools for finding patterns of data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently. So it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. Had I written this paragraph, I would have not have said to postulate, I would have said to hope. I think that might be slightly more accurate, but. And then they conclude the review, this is prospectus on future work, they conclude, however, the execution of quantum algorithms requires quantum hardware that is not yet available, okay. But that's not the only problem. The problem is also the algorithms, okay. So here's a standard data mining problem or machine learning problem, so recommender systems. So this is something that you interact with every time you watch Netflix and they recommend a movie to you or a show to you. So the problem is to take information on ratings by end users about end items to suggest new items to users, okay. And that's what happens on Netflix. So 2017, Karenidis and Prakash wrote a paper where they said, so here we have M users and N items, you can arrange that in a table or a matrix that's M by N. And if you assume that there's a, what's called a low rank approximation to that matrix, which basically means you can compress it into a smaller number of pieces of information than M times N, so good rank K approximation. Then there's a quantum algorithm that provides a recommendation in time, which is polynomial in K, like K squared or K cubed and polynomial in the log of M times N. So the log of a number is much smaller than the number and there's gonna be polynomial in that log, okay. So it's, you know, fast, okay. And they say, this is the first algorithm for recommendation systems that runs in time poly logarithmic in the dimensions of the matrix and provides an example of a quantum machine learning algorithm for real world application. Okay, so this paper came out and the story is that a computer scientist at UT Austin, Scott Aronson asked one of his undergraduates, can you please try to show that this is really better than the best classical algorithm for solving this problem? So she worked on it for about a year. She couldn't show that it was better. She worked on it for a little longer and Ewin Tong, that's her name, still an undergraduate at that time, now she's a grad student, and she said, ah, there's a classical algorithm that also runs in the same time. So it was unknown before she did this, but then she did this. And so this was the first example of what we now call de-quantizing an algorithm. There's a quantum algorithm and it got de-quantized, it became classical, okay. All right, so, but all is not doom and gloom for quantum computing here. So here's a more recent paper from 2021 by Huang, Quang, and John Preskula, Caltech, where they find some bounds on quantum advantages in machine learning.  So what they looked at was if they did an experiment on a quantum system where they had some quantum state and they did things to it and then they observed the results incrementally, then they compared how well from those intermediate results they could predict what was gonna happen next, okay. And they showed that on average, a classical machine learning algorithm could do as well as the best quantum algorithm that they found. But if you wanted to get it right for every possible input, if you wanted to get a good approximation of every possible input, then the quantum algorithm was provably better than the best classical algorithm you could do. And this is based on information theory, that's sort of how a lot of these proofs work, okay. This is, it's a bit complicated and it required me to introduce too many ideas to explain this result. But this is a positive result for the ideas of quantum machine learning. Okay, so what I'm gonna talk about instead is sort of motivated by this paper which is posted in the archive in March of this year. So these two authors that are at IONQ, which is another quantum computing company. It's a, I don't know if it's considered a startup anyway. It uses IONTRAPS, hence the name. And the third author is in the Department of Psychology at the City University of London. And just checking the time here, that's what that 20 means, okay, I'm good. In this paper, they say, well, Patoz tells us that there are some models of human cognition which are quantum mechanical. I think we can justify our investment in quantum hardware by saying that we can implement these models quantum mechanically on our device, okay. So that's basically what this paper is. So what I'm gonna explain for the rest of the talk is what is this model for quantum cognition that they're basing here, basing this upon and should we take it seriously, okay. All right, so this has to do with this kind of problem. So this is a page from the Pew Research Center. So the Pew Research Center is a survey and polling company in the US that does polls constantly about various political and religious things. And they several times a year or annually, I don't really remember, they have this national information social security survey and they ask questions like this. So in 2005, the relevant question was, do you approve or disapprove of the way George W. Bush is handling his job as president? And then they ask, all in all are you satisfied or dissatisfied with the way things are going in the country today, okay. But then this part that I've highlighted in yellow, it says on form one, question one precedes question two and on form two, question two precedes question one. And that's instructions to the polling person. And the person is answering the questions, doesn't see that, okay. Okay, so why do they have this weird different versions of the poll? Well, it turns out that people who study polling scientifically recognized long ago that question order can be important in a survey. So these guys, Rugg and Cantrell, I feel like I left out an L there, I'm not sure. In constructing a ballot, care must be taken to place the questions in such a way that the answers to one question do not unduly influence the answers to succeeding questions. Okay, well, why would that be? Why would that happen? Well, you can go back way earlier than that. You can go back to Francis Galton in 1879. So Galton was a half cousin of Darwin and he's responsible for a lot of ideas in a lot of early ideas and statistics like correlation. But he was also someone who did psychometrics. He measured things, some not so appropriately, but anyway, avoid that. And here he's talking about an experiment he was doing on himself, okay? So he says, he was trying to understand his own thought processes. And he says, the plan I adopted was to suddenly display a printed word to allow about a couple of ideas to successively present themselves. And then by a violent mental revulsion and sudden awakening of attention to seize upon those ideas before they had faded and to record them exactly as they were at the moment when they were surprised and grappled with. It was an attempt like that of men allows in the Odyssey to constrain the elusive form of Proteus. Nice writing. He says, additional exertion and much resolution are required in carrying on the experiment to maintain the form of the ideas strictly unaltered while they are vivified as they have a strong tendency to a rapid growth both in definition and completeness. So the point is, as you answer a question or in this case, think you have the thoughts that were presented to him by this word, it can change his mind. It changes what you're thinking, okay? Now, 100 years later post the existence of quantum mechanics, these guys who are psychologists, they have sort of physics entity and they talk about, well, we understand since Galton at least, if not before that trying to pay attention to your thoughts, one thing can, or in other settings, one thing can influence another thing if you absorb it. And oh yeah, in physics, for instance, Heisenberg's Uncertainty Principle links basic physical processes to the consequences of measurement operations. Their goal was to develop a similar language in psychology relating theories of the generation of judgment and behavior to the effects of measurement, okay? So how well did they do that? Well, this is a good source of data to try to make such a theory, okay? So let's look at some data before we try to make a theory. So in that particular survey that I showed you, when people were asked question one and then question two, so Bush approve or disapprove and then country direction satisfied or dissatisfied, this is the numbers of the different kinds of answers that were returned, okay? And then when the, no, it's not me, okay. I don't know how to get rid of that. No, it's definitely not me, we'll just deal with it. And then if you ask the questions in the other order, this is the results that they got. So it's a little hard to compare these two because the numbers aren't, the totals aren't exactly the same. So let's look at the percentages. So I'm gonna call this arrangement of numbers turned into percent or fractions, I'll call that P and these numbers turned into fractions, I'll call that Q. And we can see that these numbers are different, right? This number here, which represents a lot of the answers is smaller than this number here. And similarly, this number here, which is the biggest one of these is bigger than that one. Okay, so the numbers definitely change under the two conditions. Now, of course, it was a different set of people. You can't ask the same person the questions in opposite orders, right? Because you would have already screwed them up, but statistically you might hope that this represents something about what happens to an individual person. So how are we gonna compare these two? Well, one way to compare them is to just look at the difference. So we're gonna take these numbers and subtract them from those numbers and we'll call that Z. And so that's this arrangement of numbers here. Now, if you think about this for a second, there had to be some negatives here because these numbers add up to one since they're fractions, these numbers add up to one since they're fractions. And if you take the difference, either all of these are gonna be zero or if some of them are positive then some of them have to be negative because the sum of these numbers has to be zero. But there's something more interesting than the fact that some of these numbers are negative. And that is that these two numbers are very close to the same, but with opposite signs, okay? So people who think about these things, notice this, okay? And these people, Wong, Soloway, Schifrin, and Boosmeyer, who are psychologists, they looked at a bunch of examples of these kinds of paired questions in survey data, all from Pew, included that one that I just showed you. And then they plotted these two numbers. So this is N1, this is N2. So N1, N2, they plotted those two numbers and they noticed that they tend to lie along this line which says that one of them is the negative of the other or that they add up to zero, okay? And when you compute the correlation of these, you get negative 0.82. And if you're skeptical about this one and this one because they're kinda outliers and throw those away, you still get a correlation of negative 0.73. Now, for any of you who've ever worked with real social science data, this is astonishing. You never get correlations that are that big.  So getting something like that should make you suspicious, okay? And it made these people suspicious. They said, well, maybe there's some structural reason, some constraint here, which is making this happen. Let's see if we can understand what that might be, okay? To do that, they went back to this idea about physics that these guys had expressed. So I'm gonna tell you a little bit about quantum physics now. So this is the five minute lesson on quantum physics but without any math. So well, there's gonna be a little trigonometry at one point, but that's all. So this is a famous experiment in physics. I took this picture from Wikimedia. This is the Stern-Gerlach experiment. So what this experiment is, is you have a source of silver atoms here. The silver atoms get pumped out in a beam and then they go into this magnet where this is supposed to represent the fact that the magnetic field is not constant but it has a gradient. And then you have a screen here and you look to see where on the screen the silver atoms end up, okay? And the reason they did this is because silver has a magnetic moment.  The reason it acts like a little magnet for those of you who care is that it has 47 electrons and 46 of them are paired up. And so their magnetic moment is zero but the 47th electron isn't paired. And so the spin of that electron which is plus or minus a half if you like is a little magnetic moment. Now, classically, if you pass little magnets through a inhomogeneous magnetic field like this, you would expect them to sort of make a blurry blob in the middle. They'd just be spread out depending on how the magnet was oriented when it went in. But in fact, that's not what Stern and Gerlach discovered. They found that half of the silver atoms went up here and the other half went down here, okay? So this is a, I kind of think this should be on the first page of quantum mechanics textbooks instead of talking about the hydrogen atom. It's much simpler, but historically this came long after people thought about the hydrogen atom. So most physics textbooks don't talk about this until later on. But it's a beautiful example of quantum mechanics and the explanation is quite simple. So here's the explanation. So this is quantum mechanics on one page. So the state of the system is represented by a vector which in this picture is red and has length one. So its end point is on the unit circle, okay? So that means with respect to a particular coordinate system, it has an X coordinate, which I'm gonna call the down coordinate for the down beam and a Y coordinate, which I'm gonna call the up coordinate for the up beam. And the components of this vector in that coordinate system are of course cosine of theta and sine of theta if this angle is theta, okay? And then what quantum mechanics says is when you do an experiment on a system which is represented by such a vector and the experiment is to make a measurement with respect to this choice of directions, you're going to see down with probability the length here squared and you're gonna see up with probability the length here squared. And of course because cosine squared of theta plus sine squared of theta is one, that tells you that the two probabilities add up to one and so you get an answer, okay? That's quantum mechanics right there. Okay, now we're not asking one question, we're asking two. So maybe we should chain together two Stern-Gerlach apparatuses where the second magnet is rotated by some angle relative to the first one and make a second measurement on what comes out of the first one, okay? So this picture comes from Feynman's Lectures. So now we have to complicate our picture a little bit. The second measurement is represented by these blue, this blue coordinate system which is rotated by some angle in this picture it's rotated by angle omega relative to the first coordinate system. And then the result of that second measurement is that if the first measurement was down then you take that point and you project it up to the new coordinate system and you'll get down on the second one with probability the product of these cosines, right? So this length here was, sorry, not probability that point has length product of the cosine. So this length was cosine of theta and then this length here, the green length is gonna be that number times cosine of omega. And similarly over here if you wanna see what the probability of getting up on the second one if you got up on the first one is you take this length here which was sine of theta and then this little angle here is also omega because it's a coordinate system so it has the same relative angles to the axes times that cosine of omega. So the probability of blackup followed by blue up is sine theta, cosine omega squared and probability of black down followed by blue down is cosine theta, cosine omega squared, okay? Now of course there's also, you could get up on the first one and down on the second one down on the first one and up on the second one I didn't make the picture too complicated so I didn't include that, okay? Okay, so this is the idea that Wong and Busmeyer decided to use to model quantum cognition, okay? So they said a binary question is gonna be represented by a choice of coordinate axes for those of you who know linear algebra a basis in a two-dimensional vector space the axes or the elements of the basis correspond to the answers, okay? And the probability of each answer is determined by the length squared of that component and when a question is answered the state vector becomes the corresponding axis vector, okay? So it's quantum mechanics, okay? So let's go back to the data, okay? This was the data I showed you before. Now notice that we are interested in this phenomenon which I said must come from some constraint or it's just too unlikely to get such a big correlation. So let's compute it in this model, okay? So this is the up-up if you ask the black question then the blue question and the down-down if you ask the black question then the blue question but we can also ask the questions in the opposite order which is what they did in the survey, okay? So okay, so the picture is a little more complicated but if we ask the blue question first we project the state vector red onto the blue axis and onto the blue axis, so this line here and then we project onto the black axis or project back onto the black axis. So what are those lengths gonna be?  So this tells us what P11 is, what Q11 is, what P22 is and what Q22 is. If the initial state is theta and the relative angle between the questions the coordinate systems is omega, okay? So now we can compute what the difference is, okay? So, or we're gonna compute the sum. So Z11 remember was the difference between P and Q so it's P11 minus Q11, Z22 is P22 minus Q22. We can just look up here and plug them in. Notice that there's a cosine omega squared in every one of them, so I'm gonna factor that out. And then P11 has this sine squared theta, Q11 has this sine squared theta minus omega, P22 has cosine squared theta and Q22 has cosine squared theta minus omega, okay? I've kept them in the same colors so we can notice that we have sine squared theta plus cosine squared theta, which is one and minus sine squared theta minus omega minus cosine squared theta minus omega which is also one. So we get one minus one times cosine squared omega which is zero. So that says that this quantum question model explains that the two diagonal elements in the difference probability matrix should add up to zero. In other words, things should lie on that diagonal line. So these axioms imply what the authors call the QQ identity, the quantum question identity, namely that n two is equal to minus n one. Let me check the time. Okay, I'm good still. All right, so a little tiny digression for those of you who've done quantum mechanics before and you don't recognize this identity and you're wondering if maybe it's not a real thing and it's just because I set everything up in real system instead of in a complex vector space. No, this is a real thing. We just didn't bother to ever learn it in quantum mechanics. So here's the argument. Again, those of you who haven't done quantum mechanics before ignore the next two slides or something. So if psi is a qubit and we have two bases A and B, then P11 is gonna be the projection on A1 followed by the projection on B1 norm squared. Q11 will be projection on B1 followed by A1 norm squared, which we can write out like this. So that means the difference Z11 is gonna be this expression. And then this composition of projections, we can rewrite by noticing that A2, the other vector of A1 of the A basis is the identity minus A2, et cetera, write this out, multiply it out, lots of things cancel and you just end up with the negative of the same thing in the opposite order. So this implies that Z11 is equal to negative of Z22. I wasn't cheating by doing things in real. This is something from quantum mechanics that we just never bothered to learn. And this quantum question identity is approximately satisfied empirically. Wow. Okay, so clearly people answer questions quantum mechanically, right? I mean, we've explained this very surprising result by this quantum mechanical model. Well, not so fast. Yeah. Differently ordered questions were given with differences of people. Yes, yes. Differently ordered experiment were given with the same set of target responders and prospective teams that seem to be very similar each other more than two groups of people. Well, I mean, each individual atom goes through, right? You think? Yeah, I agree with you, of course. Yes, I mean, this is a good reason to be skeptical, right? I mean, it's really hard to imagine that there are any true quantum mechanical processes going on in an individual person which cause this to happen, right? There are a few people like Penrose who believe that, but most of us are skeptical about that. So, and to give the psychologist credit, they don't say that. They say, look, this is a model which generates the data as we see it and we don't see a classical way of getting this result. So despite not believing that people are like atoms, we're gonna go with it, okay? And what I'm gonna say next is basically, well, not so fast, okay? We should think a little harder about what we mean, what's possible classically, okay? Okay, so a little reminder about probability distributions for those of you who aren't economists. Economists think about probability distributions this way all the time. If I have a probability distribution over two things, so one outcome or the other outcome, the two numbers have to add up to one, which means that they have to lie on the line, P one plus P two equals one and they have to be in the first quadrant, okay? Similarly, if I have a probability distribution over three things, they have to lie on the plane in three dimensions where P one plus P two plus P three equals one and they have to lie in the first octant, okay? So this is the point a half, one third, one sixth, which lies on that plane, okay? Of course, we have four probabilities in P and in Q. So I need to draw a four-dimensional picture, which I can't, but I can draw a four-dimensional triangle because that's only three-dimensional, right? It's got three vertices and it's flat, it's two-dimensional. So it's a tetrahedron, okay? So a probability distribution over four things is a point in a tetrahedron, okay? And the data that I showed you, P is that point, okay? Q is that point, which is close but not the same, okay? And then the difference between them is that little red line that I showed you. And so it's P minus Q, so it's the second one minus the first one, okay? All right, so my picture of a tetrahedron shows us what all the possible probability distributions are. What are all the possible difference vectors between probability distributions? So that should also be some shape. It's a great problem to work out for yourself what that shape is. It's just sort of visual geometry to figure it out, but I'm gonna show you. It's this cube octahedron. So it's a cube with the corners cut off at the halfway point, okay? So all the little red vectors, which represent the differences between one probability distribution and another probability distribution over four things, correspond to single points in this cube octahedron, okay? So our difference arrangement Z is that point right there, okay? Now, we're interested in these two components of Z. So I wanna take this picture and project it into two dimensions, okay? So here it is, here are N1 and N2, which are these two components of this picture. And I've drawn this green hexagon to represent the range of possible values of that red point when it gets projected. And if you look at this, you can see going across here, that's this piece, then going down and then down, that's this corner, then going across the bottom is this, and then going up and then up, that's this and this, okay? So the projection of this thing is this hexagon and when we project a point, we get a dot there, okay? Now this is already suspicious, right?  It's not just a blob, if they're high on this coordinate, they tend to be low on this coordinate and conversely. Of course, I've cheated slightly because I picked the two on the bigger diagonal. That's really only half of this picture, it's the half that I've shaded in here. The part that I haven't shaded in, those are the difference matrices where the off diagonal ones are the bigger ones, okay? So really I'm just projecting half of this thing onto here. And if it turned out that these two are the bigger ones, then I would sort of rotate it and do the projection of the uncolored parts, okay? Okay, so what happens when we do this projection? Well, if it turned out that we got sort of a uniform distribution on that hexagon, we could compute the correlation and we would find that the correlation is minus a half. That's a little calculation, it's not difficult, okay? Now that's not as big in magnitude as the observed correlation. But why would we think it would be uniform on here? There's no reason really to think it should be uniform. What if it's uniform over the whole cubic dehedron? So it's uniform in the red picture and we project it.  And we compute the correlation of this as a little harder calculation, but still not that hard. And we get something which is approximately minus 0.54. Still not what we're aiming for, but an improvement, okay? Of course, why should it be uniform over Z, right? Maybe it should be uniform over P and Q, right? Okay, so let's do that. I did that by sampling. We get this arrangement of dots. Turns out that the correlation in that case is actually the same, that doesn't change anything, okay? All right, so it looks like maybe we're stuck, but we're not, okay? Let's go back to the data. Why did we bother to ask these questions in two different orders in the first place? Or we, Pew Research. They did it because they suspected that the answers were gonna be related, right? It wasn't like, do you prefer hot dogs or hamburgers? And do you think George Bush is doing a good job or not? It was the country going in the right direction and is George Bush doing a good job or not, okay? So we expect there to be some relationship between these answers, okay? How do we measure that? Well, we can measure the, if these were not categorical variables, but numerical variables, we would look at the correlation between the answer to the one question, the answer to the other question, we would expect it to be positive, right? But these are categorical variables, so that doesn't really make much sense. But because it's a two by two setup, a good proxy for that is the product of these numbers minus the product of these numbers. That's the determinant of the matrix. And if these were real valued variables, the determinant here is proportional to the correlation, okay? The determinant of this one is 0.149. The determinant of this one is 0.153. They're really close, okay? In fact, when you go through the Pew data set, the signs of the determinants of P and Q are always the same. They're either both positive or both negative, okay? So now let's sample uniformly in P and Q, but only keep the cases where they both have the same sign determinant, okay? You get this picture, and the correlation is, hey, look at that, minus 0.769, which is very close to what they saw empirically, okay? Actually, we can do better. So this is the hardest part of the talk, which I'm gonna ignore. We can look at what the probability distribution for the determinant is over random choices of points for the probability distribution in the tetrahedron, okay? And we get this black curve here. The histogram shows the histogram of the empirical data, what the determinants are in the actual questions. And we can see that even though the probability is biggest close to zero, if we pick a point at random, the probability is biggest at pretty substantial values, bigger than 0.1 when we look at the actual data, okay? In particular, the determinants in the Pew data are bounded away from zero, okay? So let's look at the cases where the product is actually not just bigger than zero, but bigger than 1.100, just a tiny bit bigger than zero, okay? Now we get these, and hey, look, the correlation is minus 0.879, okay? Which is even bigger than what they got when they threw out the outliers, okay? So we have reproduced the empirical negative correlation between the diagonal elements of Z using entirely classical probabilities. We didn't use any quantum stuff at all, okay? So your question is, well, maybe no, maybe we don't need to think about it quantum mechanically because look, we can do it classically, okay? All right, so I talked about a bunch of different things and it's time to wrap up, so let me give you a quick summary. So quantum computing is exciting because it promises to solve some problems more effectively than is possible classically. This has been demonstrated experimentally for some not so natural problems like the Google experiment, what that experiment was, I didn't tell you, is sampling the output from a random circuit, which is pretty useless. But it turns out that that sampling problem can't be reproduced classically, so okay? It would be nice if some data science problems could be solved quantum mechanically, that'd be great, okay? But no classical data science problem has yet been shown to have a more effective quantum solution. For example, the one I talked about was recommender systems do not, okay? But there are quantum algorithms that have been shown to solve some quantum data science problems more effectively, okay? So that's good. And that motivated this investigation of classical data that's maybe generated by a quantum model, but it's still classical data, okay? Namely this quantum cognition model. But if we take reasonable priors on classical probability distribution pairs for these two binary questions, this QQ identity is as close to being satisfied completely classically as it is in the empirical data. So we don't need the quantum model, it doesn't help us. So we need some stronger evidence if we really wanna judge that the quantum question model is what's going on rather than something classical, okay? However, the fact that this quantum question model was proposed led us to a better classical understanding of these survey data because my group and I did these calculations which had never been done before, okay? And the proposed quantum algorithm for recommender systems led to Ewin Tong writing down a classical complexity analysis which again doesn't seem to have been done before. So thinking about these quantum data science problems maybe eventually something useful will happen, quantum mechanically, but at the very least we're learning stuff classically that we didn't know before, okay? All right, so I will stop there and answer questions that people have. I thank you very much. So, just questions, any questions? I think this one works, right? Thank you very much for the nice talk, for example. In addition to the kind of confusion or skepticism that I shared earlier after the talk, there was somewhere else that there was an assertion along the lines that we still do not have the technology to do quantum computation. And the way that I understand it, maybe I misunderstand it is we know what we're expecting, we don't have the tool. And so is that what really the experts believe that we know what you're expecting but we don't have the tool and you're searching for. So the situation is that there are by now many noisy intermediate scale quantum devices where noisy means what you think it does, they have lots of errors. Intermediate scale means they're not very big, they don't have a whole lot of quantum bits in them. But they do something which is quantum mechanical but affected by noise. So, Google has one that's roughly 100 quantum bits, IBM has one that's maybe roughly 1,000 bits, I'm not sure right now. D-Wave does a slightly different model and they probably claimed several thousand bits at this point, I'm not really sure. All of these devices, they're physics experiments, right? They do some sort of quantum thing, it's a quantum system and you can set it up and you can control it and you can read things out. But none of it reaches the desired level of fault tolerance, meaning that it supports recursive quantum error correction to keep the results close to what they would be if there were no noise, okay? So all these companies which are spending lots of money on this are, well, they're trying to improve their machines to the point where they can get to that fault tolerant level. But they're also trying to figure out things that they can do usefully even with these noisy intermediate scale quantum devices. And that's kind of where the subject is at this point. People are sort of searching for useful applications of these. And if you read what the individual companies say, some of them say we can do useful things now. I'll only speak for myself, I'm skeptical that any of the things that can be done currently are useful in the sense that they can't be reproduced by a big classical computer. But it's improving, I mean, every year it's better, so. Very nice talk. So in data science, there's some problem called the feature section or variable section. This is like basically we want to say which feature is important from the data. We have the adventure vector, we want to say which feature is important. So this is basically the combinatorial program. So the best one is of course like most the easiest one is the brute force, but brute force is like we need to compute two to power the computation. This is very computationally expensive. So therefore in data science, so Michelle and we tends to use like, relaxation to the continuous and solve it. But for example, like I'm just wondering, I'm not super familiar with quantum computing, but I just wonder whether if you use the quantum computing whether we can solve these kinds of combinatorial problem or efficiently compute the current state of the art in classical of the machine learning approach. I just want. So I give sort of two answers to that. So one is there's certainly some hope of solving combinatorial kinds of problems with quantum algorithms. So there are examples of that. They're not directly data science examples, but they're sort of combinatorial question. The difficulty with taking any of the problems that people care about in classical data science and porting them over to a quantum machine is that most of the problems we care about in classical data science involve large amounts of data and all that data would have to be entered somehow into the quantum device. So really the only way you can expect to get a quantum improvement is if the classical algorithm that you want to run on the classical data is at least some high powered polynomial of the length of the data or maybe exponential as you suggest in the length of the data. If that calculation is large classically compared to the length of the data, then maybe it makes sense to input it into the quantum computer and run a quantum algorithm that might be more efficient. But in many cases, like if you think about a lot of image processing problems, for example, it's really inputting the data. It's really the size of the data that matters and the actual calculation you run, like a template matching, for example, to take a simple example. That runs linearly in roughly, I mean with a log factor I guess, but in the size of the data entry. So you shouldn't expect to get anything better quantum mechanically in that setting. So we're really looking for either data science problems which don't involve large amounts of data which seems like that's probably not going to be something we can't already do classically except factoring a large number or problems where the computation that you want to do classically is very expensive compared to the amount of data. So if you know examples like that, maybe that's a case that there's a hope for. Nobody has worked out such an algorithm at this point, but. Thank you very much. Thanks very much, very nice talk. Can I ask about the determinants in the empirical and the model? So it was interesting to see that in the empirical data the determinants were always positive where in the model you had to restrict it to be both positive. So in the empirical data, is that biased somehow because the questions they ask are so polarizing that you can't get a flip in the answers when you flip the order of the questions. No, it's just because of the order we write the columns in, right? So if we wrote the no answer and then the yes answer that would change the sign of the determinant relative to yes answer and then no answer. So it's just because they always write them in the order that makes it positive. But it might not have been. So that's why I was careful to say the product of the determinants is positive in case they happen to post me negative. Any other questions? Just ask one. So you talked about data, but still classical data on the sort of computation will maybe in quantum. But what is if really quantum data science? Yeah, so there's sort of quantum data, quantum science or quantum data, classical science. So I didn't talk too much about the Preskell and Huang and Quang result. So there's a really important detail there which is they have this quantum experiment where they make measurements intermittently. Now, when I say make measurements I might mean one of two different things. One thing might be I make a classical measurement and I pull classical information out of it like the Stern-Gerlach experiment. Another thing I could mean is the state of the quantum system, I have that interact unitarily with my quantum computer and then run some quantum analysis coherently with the experiment. And that's what they show gives a exponential improvement. So the data never becomes classical in that case. The future if we have the quantum internet, my computer my quantum computer generating data and then quantum data and it can send it to you. And maybe doing things like analyzing quantum Facebook at that point maybe is something you can do. That's right. As long as it never becomes classical. Any other questions? No, okay. So let's thank again.


In this presentation, we will take a look at multiple choice questions related to the statement of cash flows. First question. Cash equivalents are A. Investments readily convertible to a specific amount of cash. B. Investments that mature in less than a year. C. Malot monopoly money. D. Our assets that are not very liquid. E. Cash in the bank rather than physical money. So we'll go through this again and see if we can use the process of elimination to narrow this down. Cash equivalents are A. Investments readily convertible to a specific amount of cash. So that seems fairly reasonable. If we don't know exactly what it is, I'm going to keep that for now and go through the rest of them. B. Investments that mature in less than a year.