The conversation this week is with Sam Tyner-Monroe. Sam is an accomplished, applied statistician and data scientist with a PhD in statistics from Iowa State University. She has expertise in data visualization, and deep experience in developing and applying statistical modeling and machine learning techniques to the social and physical sciences, as well as within the federal government. Currently, she's Managing Director of Accountable AI at DLA Piper
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Resources and Topics Mentioned in this Episode
Justin Grammens 0:00
Hi, Applied AI Podcast listeners. This is your host, Justin Grammens. Just a quick note before we start this next episode, since recording this, my guest, Sam Tyner-Monroe has changed jobs. And she is now the Managing Director of Accountable AI at DLA Piper. We wish her the best in her new role and future opportunities. And I'm certain you'll enjoy the conversation that we had. So with that out of the way, on with the show,
Sam Tyner-Monroe 0:22
My big suggestion would be make sure you have some good solid statistics. Because when you take statistics, you really learn about the modeling you have, you need that linear algebra background, you need something that teaches you about the assumptions that models are making, so that you can apply those effectively. Because I find now that it's really easy, especially with all of the great you know, and free online learning tools that are out there, which I'm totally for, but it makes it really easy for people to apply things that they don't necessarily understand, and that they don't understand the assumptions that those models are making.
AI Announcer 1:01
Welcome to the Conversations on Applied AI Podcast where Justin Grammens and the team at Emerging Technologies North talk with experts in the fields of artificial intelligence and deep learning. In each episode, we cut through the hype and dive into how these technologies are being applied to real world problems today. We hope that you find this episode educational and applicable to your industry and connect with us to learn more about our organization at applied ai.mn. Enjoy.
Justin Grammens 1:32
Welcome everyone to the conversations on applied AI Podcast. Today on the program. We have Sam Tyner Monroe. Sam is an accomplished, applied statistician and data scientist with a PhD in statistics from Iowa State University. She has expertise in data visualization, and deep experience in developing and applying statistical modeling and machine learning techniques to the social and physical sciences, as well as within the federal government. Currently, she's a data scientist at Tri Tura, where her and her team enabled data driven lawyering through advanced data analytics, AI and machine learning ediscovery and fact developed. So very, very cool. Finally, at the little known fact, maybe that she is a co organizer of the art ladies of DC chapter. Thank you, Sam, for giving back to the community and supporting organizations like that and for being on the program today.
Sam Tyner-Monroe 2:18
Thank you so much for inviting me. Awesome. Well,
Justin Grammens 2:21
I gave a little bit of a background in with regards to where you're at today. But maybe you could give a little bit more with regards to how did you get to where you're at maybe their trajectory of your career.
Sam Tyner-Monroe 2:30
Yeah, absolutely. So I'll start way back in college. And that's usually where I start the story. I was one of those math people and there's big air quotes around math person there. And being a math person, I thought, well, I'll just go to college and major in math. And then being an overachiever type a type of person. I also majored in economics and French, French was just for fun, which like who takes on a college major for fun? I don't know me, I guess I'm just weird like that. And then while I was taking my math classes, I was really encouraged by one of my professors at Augustana. Tom Bankston, he really encouraged me to do a research experience for undergraduates or an REU. And so I ended up doing the REU that Valparaiso University has. And there I was working on a project in combinatorics. So very, very, like, quote, unquote, pure math. And I realized pretty quickly that I didn't like pure math, I didn't want to do pure Maths. But also, I learned about graduate school in that program. And before that, I had no idea that graduate school was really even an option. And so when they were describing the program, I think it was in Purdue, the math department, they were talking about how they get stipends. And you know, they just go to school, and they learn and and then I was like, I can get paid to go to school. I love going to school. Sure. So yeah, so from there, I decided that I wanted to go to graduate school. And then I took my last year of college, my senior year of college, I took all of this statistics courses that were available to us, which I think was a grand total of two statistics courses at my small, private liberal arts college. So yes, I took those statistics classes, and I decided I wanted to get a PhD in statistics from that. And so that's how I got the PhD in statistics. And then yeah, that was kind of a Gosh, that was a lot of talking. I just did.
Justin Grammens 4:32
Well, you are the guest on the show. I guess. I will say I think you and I are very similar in a lot of ways. I went to a small private liberal arts college here in the Twin Cities. I majored in math, applied math in particular, because like you I tell people, I got sort of sick of solving for x, like I wanted to understand, like, what does the x mean? Like, and I minored in physics, because because I really think that is a pure application of like, hey, how far is this ball gonna go when I throw it? I love solving those equations. I hate just solving equations. It's for equations sake. Right? So the pure math side kind of turned me off as well. And when I got done, it was more or less like, you know, do you want to be an actuary? Just you know, are an actuarial sciences, you know, do you want to teach, you know, do you want to go to go to Masters basically, like, move on to get a master's degree. And I decided to go into software, I decided to work it to basically kind of like use the logic that I think I've learned in moving more into software development, and software engineering practice, but, but I feel for you with regards to going through the math stuff. And I guess to back up, I feel like math is cool. Again, you know, I think this whole idea around AI and machine learning. Yeah, I feel like finally, finally, were getting what's due to us with regards to, you know, it's cool to be in this field.
Sam Tyner-Monroe 5:42
Yeah, it really is. And that's why I switched sort of, from a statistician to a data scientist, because I think that every data scientist is, at least a little bit of a statistician, or at least a good data scientist is a little bit of a statistician. And so that's why I sort of, I don't want to say rebranded myself, but like, that's sort of why I switched into calling myself a data scientist as opposed to a statistician, because I think that data science is statistics. And there's so much overlap there, not 100% overlap, for sure. But yeah, just that's sort of how I, how I decided to move into data science.
Justin Grammens 6:22
Yeah. Did you find your econ work kind of help in that place as well at all or? Not? Really?
Sam Tyner-Monroe 6:28
That's a great question. The econ piece, it really what it did was it it showed me again, it was kind of one of those things of, well, now now I know what I don't want to do. So what don't I want to do like so there were some graduate level courses in statistics about, you know, forecasting, financial things and whatnot. And so I sort of realized there that like, yeah, no, this is not really something that I want to do. I'm not interested in like, hypothetical numbers about like, markets that you can't really touch. You know, I want to I want to do things with, you know, people and things that you can actually, you know, see in the world.
Justin Grammens 7:10
Yeah, yeah, for sure. You know, I mentioned that you were a part of that you call organize the one in DC the R Ladies of DC. But prior to that you actually were one of the people that that did. The R Ladies in Ames, Iowa, right?
Sam Tyner-Monroe 7:23
Yes, yeah. And 2016. I started up that was maybe about four years after our ladies officially started. Or we, myself and another graduate student in Italia de Silva. We co started the Ames chapter of our leaf DC. And that was a great opportunity because we were able to get one of the original founders of our ladies, DC Gabriella to K Ra is to come out and do a visit with us at Iowa State. Wow. So that was really good experience.
Justin Grammens 7:51
That's cool. So you obviously were using our then when you were in graduate school and stuff, or even prior to that,
Sam Tyner-Monroe 7:56
yes, I first started using AR in graduate school, I had taken a math elective my senior year where we used it a little bit. But I really, really got into it our first semester. at Iowa State, there was a basically a one credit class where it was just Hey, everybody, let's just get you on the same page in terms of your our knowledge.
Justin Grammens 8:19
Gotcha, gotcha. You were just very interested in going deeper, I guess, or starting a community around this locally?
Sam Tyner-Monroe 8:25
Yeah, absolutely. That was one thing that really drew me in was, I think the fact one that it's, you know, focused around women and non binary people. I really appreciated that, as you know, I've been this math person my whole life. So I'm used to kind of being one of the only women or girls in the room. And, you know, statistics compared to something like physics is much more female dominated, but it's not. Overall female dominated. Yeah. And so I really liked that idea of having that space, where we can all just have like this safe space where we don't have to worry about, you know, people not listening to us, or, you know, various other side effects of being a woman in the world.
Justin Grammens 9:13
Yeah, for sure. Well, like I say, I've started a couple of different community groups here. And I commend you for doing that it takes, you know, people want to give back and put energy back into the community. That's, that's a great way to kind of raise your hand and be like, I'm interested in doing this, who wants to support me in this role, or DJI has helped me in this mission. And so that's awesome that you did that, not only back in Ames, but obviously, part of the DC chapter and continue to move that forward. You talk about statistics, and then it's moved into data science, which you know, science is a great is a great word to use. Right, everyone, it sounds good. Because there is there's a lot of science to it. Not now, everyone's sort of talking about artificial intelligence and machine learning. And I guess, you know, the work that you're doing today now really much kind of fits into this fits into this new sort of vocabulary. Hmm, I guess how do you see it? Or how has it changed? I guess, you know, or has it not really changed? as much in your mind,
Sam Tyner-Monroe 10:01
I guess, in my work, my work hasn't changed a whole lot. And I took, you know, statistical learning classes when I was in graduate school. And so I learned all about all those machine learning models, and all of those things are still around, there's just even more now, you know, with all sorts of deep learning things like that. I don't really do any of that, to be honest, any sort of deep learning things. But yeah, the work I do hasn't hasn't changed a whole lot. But there are things that, you know, I'm staying aware of things, you know, and especially things that are really flashy, like, you know, chat GP and all of that,
Justin Grammens 10:34
for sure, for sure. So you're doing data science, you're a data scientist at this company that's doing a lot of ediscovery. And, and I guess, called it lawyering in some ways, at least, that's what the website said, to tell us a little bit about, like, what's a day in the life for you, when somebody's getting into this career? And what do you do?
Sam Tyner-Monroe 10:52
Yeah, so my team is a really small team. So in my, in our, in our company, the data science team is split into like an applications team and an analytics team. And so I'm on the analytics team. And there's various things that we do, as part of that team. One thing right now that we're really focusing on is improving internal software. So helping the applications folks are writing the programs using, you know, Python, spacey, to help write those programs, a lot of that is things like document processing, and looking for specific clauses and contracts and things like that, named entity recognition is a big one that we use. And I actually really don't do a whole lot of that, I tend to work more with the client. So as opposed to having internal tools, and I'm working on I'm getting client data, and I'm working with clients. And so I usually use are over Python, to do that, just because I'm more comfortable with it. But right now, I'm working almost exclusively with insurance companies, so primarily auto insurers and life insurers. And what we're doing is we're auditing their automated decision making tools. So whenever they sell for life insurance, for example, you know, traditionally, you would have to go get a doctor to fill out a form or something like that with all of your stats on it, you know, take some blood, some other bodily samples, do some testing there. But what they do now, a lot of the times it's called accelerated underwriting. And so they have these algorithms, models, you know, AI, whatever you want to call it, that they do to process other information about you. And instead of having to get your blood drawn, you just fill out a form form online, and then you find out whether or not you can get life insurance.
Justin Grammens 12:45
Interesting by pulling in data from all sorts of different sources. Yes, behind the scenes. Yes. But yeah, a lot a lot to think about there. I mean, the one thing that popped in my head is, as you were talking about what the system is doing, what does the future of work look like to you? It's kind of a kind of a follow up question. But the software you're providing is probably stuff that a lot of humans used to be able to do it right. So someone would come out some, some nurse or whatever, you know, some some RN would come out and do this. Now. They don't have to do that anymore. You think that's good, bad, indifferent? You know, I curious to know what you think.
Sam Tyner-Monroe 13:14
So I don't know enough about like the life insurance industry say, in particular to know about, like, you know, jobs and the market there and all of that. But I really think that I find the discussion around, you know, oh, is AI going to, you know, get rid of jobs or things like that, I find that to be kind of a silly discussion, just because things are always changing. And humans are always making changes, and they're always adapting are some jobs going to change and or maybe even go away? With the advent of new AI tools and things and how you're going to change your job is going to be different? Yes, it's definitely going to with all these new tools, but I think that humans are generally very adaptable. And I think that, you know, we decide what society looks like. And we can choose to use AI in a way that makes our lives easier, and it makes our decisions more accurate. And things like that, as opposed to thinking of it as this this big specter that's going to just kill all these jobs.
Justin Grammens 14:20
Agreed. Yeah, I think anything can be dialed to an extreme, I guess. And the other question I'd like to ask people is kind of like, yeah, what what is AI to you? Everyone's got their own little definition of it, but, and no one's right or wrong at all. I'm just kind of curious. When somebody says, you work in the AI field. Tell me tell me what is AI?
Sam Tyner-Monroe 14:36
I love the definition of AI where it's split into two, right? You have general AI, and then you have narrow AI. So general AI is like the Hollywood type AI, where you know, you're watching like iRobot or Westworld or something like that. That stuff is totally hypothetical right now doesn't exist at all. And then there's narrow AI which is I think what most people are what most people are actually doing. So anybody says they do AI, that's actually what they're doing right is narrow AI. So where you are teaching a computer, you're you're coding up some code, and putting in some data to teach a computer how to make decisions or make predictions about something.
Justin Grammens 15:18
Yeah, yeah, totally. What do you see us reaching general AI? Do you see? I guess that's the bigger picture. Also, I'm just kind of curious to terms of where you see when your product the product that you're working on, you know, go in the future? Is it going to get smarter and better and more adaptable?
Sam Tyner-Monroe 15:32
I'm very skeptical that generally I will ever exist, simply because the connections that computers are capable of building are very different than the connections that humans are capable of building with their their neurons in their brains versus, you know, ones and zeros on a computer. So for that reason, I'm very skeptical. But I don't think that that means that AI is going to, it's not going to stop growing, it's not going to become less relevant. It's just going to keep on going. But I'm not sure that you know, AI robot or Westworld is ever going to happen.
Justin Grammens 16:10
Yeah, it certainly doesn't feel like it today. I mean, you know, you can show a two year old, you know, picture of something in the abstract, and they'll say, that's a dog. Yeah. And you show that to computer computers, I have no clue that that isn't a dog. So there's something there for sure that humans can see things and recognize things that computers need to be overly trained, the future remains to be seen, there's been a lot of things with regards to things that have happened, I guess, in my lifetime that I maybe never thought would actually happen. So tough to say it's always a good question. No, I always always like to ask people about that.
Sam Tyner-Monroe 16:41
Yeah. And some people are like, yes, absolutely. It's going to happen in the next 50 years or something. And I'm just like, okay, that's fine. I don't think so. But that's your opinion.
Justin Grammens 16:50
Yeah. Well, are there any things that you see, as you're looking around in this field that you're excited about? I guess, whether it's just even data science or statistics, algorithms, you know, AI, anything that you're like, Wow, this is very, very fascinated me,
Sam Tyner-Monroe 17:06
there's certainly a lot of new tools out there. And, you know, we're collecting more and more data, all of the time. And the more data that we have access to the more we can do with it. So I recently saw this particular tool, which I can't name, unfortunately, because I'm under attorney client privilege, but they basically have they said, they have over a trillion pieces of information about, you know, millions and millions of people. We're talking like, 10s of millions of people. And they're using this AI tool that they have to predict based on some behavioral information, and then some health information. Basically, how much are you going to cost an insurance company with your medical costs, and that's something that is, you know, fraught with all sorts of issues, right? One, just the sheer amount of data that they have on people, I think, is a huge concern for a lot of people. Because I mean, they've got, what did I buy at the store? When did I fill my last prescription? Did I go to my last doctor's visit, you know, all these sorts of variables that they have on people, that to me was probably what I took away the most is just the sheer amount of data. So I mean, the more data you have, the more AI is going to be able to do.
Justin Grammens 18:18
And that's just, that's just the health care stuff. It's, uh, it is amazing, like, what the Apple Watch might have people, right, once you start pulling in information that is more in the consumer space and location component to that, right, Google our phones, everybody knows where we're at our photos that we're taking, you know, and you know, some of it is just sort of tangential information. But it's, it all points to your right, a particular diagnosis, or, yeah, it's just sort of building the case against, yep, this person is going to do this next. You know, to me, it's all about prediction, right? And a lot of ways what we're doing Yeah,
Sam Tyner-Monroe 18:51
absolutely. It's all about prediction. I think that I found being a statistician and getting a PhD in statistics, you get very comfortable with uncertainty. And statistics is really all about Quantifying uncertainty. So you know how much you don't know. But your average person hates uncertainty, they really want to know, and I say this now, because I work with many attorneys. Attorneys hate uncertainty, they probably hate it the most out of anybody, just because they want to be able to see and apply a specific law or make a decision or whatever. But I'm very comfortable with uncertainty. So I don't need to know you know, everything about myself that a machine is going to predict. But a lot of people want to know that. And a lot of people can make a lot of money doing that type of predicting.
Justin Grammens 19:39
Yeah, for sure. There's an interesting book, I forget who the author is, but I think it's called prediction machines. And it's written by a guy who's an economist and, and really this whole idea that you know, AI is really sort of broken down to two predictions and basically, likelihood that one thing is going to happen more than the other right and it's not cut and dry. You're right if you show a computer revision model a particular thing, it's like, you know, 80% of the cat, but still 5% of might be a fish or 1%. You know, it's a it's a dog, a dose does come down to there is no black and white and some people just doesn't work well with.
Sam Tyner-Monroe 20:13
Yeah, the one example that really sticks out in my memory is, so I'm from the Chicago area initially. And so in 2016, when the Cubs won the World Series, my I like to think back to, I think 538. So 538 was doing is doing sports and politics at the same time, right? Because the Cubs won the World Series. It's the 2016 election. And in the same week, the Cubs had the same chance of winning the World Series that Donald Trump did to win the presidency. And I think it was around like 30% or something like that. And I just I just go back to that because the Cubs won the World Series. And Donald Trump, Donald Trump did it, isn't it, but neither of them were favored in that scenario. So you know, uncertainty really is the tricky thing to deal with.
Justin Grammens 21:01
Yeah, for sure. No, I do. I haven't tracked that website much a whole lot. Well, Nate Silver, and all that type of stuff, you know, it really popular during the political season. Yeah. And he was even calling it even as it got close to the ad. He's like, this is too close to call. But it wasn't until like, even with Trump it I think it wasn't even until like the final hours that it was like flipped, because it was just that it was just crazy with regards to the likelihood of it was gonna go one way or the other. So in probably same thing with the Cubs, too. No one really knew what the heck was going to happen. Because he was just so unlikely. That's cool. Well, so for people getting into this field, let's over wind the clock back, say I'm, I'm going to private liberal arts college today, right? And I'm going to be graduating or am I maybe not even graduating? I'm looking at maybe getting a career in this field. And what what are some classes you should you suggest people, you know, take Are there any books they should be reading, or obviously the PI ladies group, you know, they should be attending, I guess, you know, or the R group. Yeah, they should be attending,
Sam Tyner-Monroe 21:59
my big suggestion would be make sure you have some good solid statistics. Because when you take statistics, you really learn about the modeling you have, you need that linear algebra background, you need something that teaches you about the assumptions that models are making, so that you can apply those effectively. Because I find now that it's really easy, especially with all of the great you know, and free online learning tools that are out there, which I'm totally for, but it makes it really easy for people to apply things that they don't necessarily understand. And that they don't understand the assumptions that those models are making. So I think that would be really important. And then also getting some basic sampling, being comfortable with the idea of sampling would also be important.
Justin Grammens 22:47
Good. Okay. So look for that with regards to classes and stuff like that. And, yeah, you know, it's like I say, I just was thinking back when I had an applied math degree, when I came out, it felt very narrow with regards to what I could go into, but it feels like kind of, yeah, we're, we're using statistics and Applied Maths sort of everywhere these days. So probably,
Sam Tyner-Monroe 23:04
yeah, definitely. Yeah. And another thing I would suggest is getting some comfort with reproducibility. So making sure that you can write code and share it with others, and they can reproduce it, maybe, you know, take a course on Docker or something like that. And then also, exploratory data analysis, I think is really important. Make sure you have a good grounding and have to do that. Because so often you you'll, you'll fit a model, and you'll get, you know, all the way down to the finish line, and then you realize that you missed some, some key piece about what what's actually in the data. So make sure you start with getting to know the data. Well.
Justin Grammens 23:41
Interesting, interesting. Sure, sure. Yeah. I mean, it's like someone is just the, the, I guess, understand the problems that you're trying to solve, or the problem you're trying to solve, rather than getting so so deep into, I'm just going to do the statistical analysis, just for the sake of it, you're probably gonna need to understand the real world application in some ways.
Sam Tyner-Monroe 23:58
Absolutely. And the 8020 rule totally applies where 80% of your time should be spent thinking about the problem and about how you're going to go about solving it and getting to know the data versus the 20%. That's actually fitting the model.
Justin Grammens 24:12
That's great. That's great. Well, how do people reach out to you, Sam?
Sam Tyner-Monroe 24:16
Yeah, so the best place to find me is probably on Twitter. I would say I'm on there more than I'm on LinkedIn. But I am getting more into LinkedIn these days. But yeah, Twitter for sure. LinkedIn a second.
Justin Grammens 24:27
Well, there are other things that you wanted to talk about that maybe I didn't cover, this was kind of a pretty generalized conversational talk on, you know, artificial intelligence and machine learning. And that's what I just love to do is just kind of bring people in that are working in the field and just anywhere around this sort of whole area. But yeah, there there are other topics maybe we didn't talk about.
Sam Tyner-Monroe 24:44
I'd love to just talk more about some other resources. I do teach quite a bit. I have taught quite a few workshops and things and so I do like to you know, give resources to folks. So definitely, some conferences to check out would be like the posit conference familiares to con if you can afford that, that's a great conference to go to. They also have lots of scholarships and things you could apply for. And then women in statistics and data science is a good one. And then also meetup groups, you know, our user groups, Python user groups, statistics groups, or ladies pi ladies, those things. Shout out for one of my favorite books in recent memory, which is data feminism, which is totally free and available online. It's just absolutely one of the books about data that has probably blown my mind the most, it makes you think about the assumptions that you're making implicitly, when it even like the ways you record data, or the words you use to describe data, talking about data cleaning, or binary gender and data, things like that. She just really, really makes you think about how so much of data needs a little bit of a feminist lens.
Justin Grammens 25:54
Okay, well, cool. Yeah, we'll definitely, definitely search for that. And we'll put a link to that. It's just called Data feminist said it's free.
Sam Tyner-Monroe 26:01
Yep. It's totally free online. They have like a nice coffee table book copy of it, you can get too
Justin Grammens 26:06
The applied AI group. We're on meetup, we just had a meet up last night, actually. So we meet the first Thursday of the month, but I found meetup.com to be is phenomenal. He just got there typing anything you want. And now, you know, with everything being on Zoom, you can pretty much attend a meet up anywhere in the world in some ways.
Sam Tyner-Monroe 26:21
Yeah, absolutely. Yeah. For our ladies, dc, we, one of our CO organizers for a while was living in Italy, because she wanted to start up a chapter in Rome. And so she wanted to help us co organize for a while. So she can sort of get her get a feel for it. And before she started up the chapter in Rome, so it's a great opportunity to meet people from all over the world. I've met tons of folks, people show up to our meetings from Indonesia, California, Montreal, all over the place.
Justin Grammens 26:49
So awesome. And since you guys are focusing a lot on an AR in particular, I was just kind of kind of curious, have you seen any of like, code writing tools? You know, have you seen anything with regards to you know, that's the whole thing is like, you know, AI being able to write anything? You know what I mean? Yeah, Java to Python to probably our I haven't tried it with any AR stuff. But I've asked like chat GPT to write me some stuff. And I have a lot of opinions with regards to not only the quality of the code, but really, okay, fine. You've gotten a little free piece of snippet, right? I mean, I could have done that, you know, off of Stack Overflow. Yeah. past 10 years. Does that come up in conversations at all with your meetups? Like, what do you typically sort of talking about in your group?
Sam Tyner-Monroe 27:27
Yeah, so we haven't talked about anything like cogeneration, or anything like that, yet, I haven't used it myself personally. So I have no idea about what the quality of it is. But yeah, mostly when we when we have our meetups, and we have one next week, we focus on you know, we can we can do, you know, one single art package we can talk about, or we'll talk about career or career oriented things, or we'll bring in a couple of people from a local graduate program. So we had the Georgetown Master's in data science folks come out and talk to our group, we get a lot of people who are exactly like what you were saying, you know, they're sort of starting out, and they want to know, you know, what, what should I do? What classes should I take, you know, should I get a master's? Should I get a PhD? And so, you know, all of the organizers at that point, we're all PhDs. And so we were like, We gotta bring somebody in from the master's program to get sort of that flavor of it. And because we'll just tell them to go get a PhD, but I don't think you need a PhD.
Justin Grammens 28:25
What was your PhD? I mean, do you focus on a specific area thesis?
Sam Tyner-Monroe 28:29
Yeah. So primarily, I did social network analysis for my thesis, but it's one of those things that I don't do a whole lot anymore. Unfortunately, I had a package on cran for a while, but it's gotten taken down multiple times. And it's just one of those things. I just don't have the energy or time to maintain anymore, unfortunately.
Justin Grammens 28:49
Yeah. Well, it sounds like is your group virtual? Is it in person? And you do a little bit of both? Have you done both?
Sam Tyner-Monroe 28:55
Yeah, right now, we're still virtual. And again, that's mostly because we can attract people from all over the world and get people from all over the world to attend. We are talking about going back in person, and we'll probably do that over the summer, I would imagine.
Justin Grammens 29:09
Yeah, yeah. No, it's been tough. We were in person, the applied AI group did did meet in person. And then, you know, basically, march 2020 was the last, you know, first week of March was the last one we had. And then we went virtual. And I think, like you just said, like, we can attract either both a wide audience and also a wide range of speakers. Right? Yeah, I have the person that spoke with us that was on our meetup last night. She is from Northwestern versity. And so dealing a lot with AI and healthcare as like, boy, I couldn't ever have that person buy into Minnesota and do that presentation. Right. So clearly, you're able to get such a wide range of speakers but I do miss the in person component. And yeah, our intention is to kind of mix and match to a little bit of both do stuff in person because it is nice to have pizza and and hang out. A lot of it's just networking. I mean, I've known so many people that have gotten jobs and changed careers and just, you know, built new and cool applications. and started companies because our you know, group getting together so there's a huge component to that, that you just don't get if you're just online and just don't call
Sam Tyner-Monroe 30:08
Yeah, cuz you can kind of be, you know, off to the side doing your own thing and another browser window or whatever, and may I still myself and I'm getting used to, like interacting with people out in the world again, that's something that I'm still I need to re accustom myself to still,
Justin Grammens 30:26
ya know, pretty easy just to sort of stay in your own basement or stay in your own office or whatever it is to go back out again so well I appreciate you taking the time and and taking the time to reach out and beyond the applied AI conversations on applied AI podcast today. Yeah, thanks for having me. Yeah. And again, thank you for all the work that you're doing and look forward to keeping in touch and good luck in your future endeavors. I'm sure there's a lot of interesting things going on and data science and and statistics and it's kind of kind of a never ending feels it feels like to me,
Sam Tyner-Monroe 30:55
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