Alter Everything Podcast

A podcast about data science and analytics culture.
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Alteryx Alumni (Retired)

We're joined by Adam Blacke for a chat about the evolution of data culture, A.I. and M.L., and thought leadership.

 

 

Panelists

 

Adam Blacke - @ablackeLinkedIn
Brian Oblinger - @BrianO, LinkedIn, Twitter


Topics

 


Community Picks

 

 

 


Transcript

 

Spoiler

BRIAN: 00:05 

[music] Welcome to Alter Everything, a podcast about data science and analytics culture. I'm Brian Oblinger and I'll be your host. We're joined by Adam Black for a chat about the evolution of data culture, AI and ML, and thought leadership. Let's get into it. [music] Hey, Adam, welcome to the show. 

ADAM: 00:29 

Hey, thanks. Great to be here. 

BRIAN: 00:31 

Excellent. So I am so excited to have you on and we have some really interesting topics lined up for today. But before we get to that, why don't you give the folks at home and myself a little bit of an intro? Who you are, how you got into analytics, would love to hear about your journey. 

ADAM: 00:49 

Sure. So my name is Adam Black. I am the business intelligence analytics manager at Lake Trust Credit Unit now. Prior to that, I was at Ford Motor Company. I was part of the founding member of their global data insight and analytics team. Before that, I was in a financial analyst at Ford Motor Company also and before that, I was in IT doing application development, specifically financial systems. So I've got kind of a broad background in data and analytics and that's actually how I got into it, is I got very excited when I was being a programmer in all of the data that we had access to and all the interesting things we could do with that. And then as I rolled into my career as a financial analyst and leveraged those programming skills and my background to do things like financial forecasting and predicted modeling and just loved it and I've been doing it more or less ever since. 

BRIAN: 01:55 

Wow. So that's really interesting. So you kind of came to it I guess I would say organically. It seems like it just sort of came about and you seized the moment there. 

ADAM: 02:06 

Yeah. I started out thinking I was good with computers, loved them. I liked programming and kind of figured that's where my career path would take me, to be honest. And then I found the world of financial analysis because of the application development I was doing in financial systems and I was like, "Oh, there's so much more interesting things you can do with the data side." Which I frankly never been exposed to before. And I don't know. I just loved that you could tell so much with information like holidays affecting down time and things like that in ways that you wouldn't necessarily expect. And having to plan extra absenteeism around concerts in the area, which is just fascinating, right? 

BRIAN: 02:51 

So we talk about music a lot on this show. Seems to be a recurring theme, whether we planned or not. So which concerts negatively affected attendance the most, if you can tell us? 

ADAM: 03:03 

They've had a lot of concerts in Chicago during the summertime and those events, when they happen, tend to drive a lot of absenteeism because Ford has a plant in that area. And so it tends to impact those local plants pretty adversely. Yeah. 

BRIAN: 03:22 

Wow. Okay. And one question I always have to ask people around the music bit, what do you listen to while you Alteryx or while you do your data work? 

ADAM: 03:32 

It depends on the data, to be honest, and what I'm doing. When I'm doing market demographics, I actually tend to listen to Green Day. And ironically, when I'm starting to deal with account data and things like that that's more technical, it's Wagner or Beethoven. So I'm quite eclectic in my music choices there. Yeah. 

BRIAN: 03:58 

All right. Good. Well, the one thing I did want to talk to you about kind of going back to your history, you do have a very broad set of experience and places you've worked and different types of things you've done. I'm curious to hear from you, over that span of time and that span of experience, how have you seen data analytics culture change or has it changed, I guess, first of all, in your mind? And then, if so, how would you quantify that? What does that change look like to you and where we're at today? 

ADAM: 04:30 

Well, actually pretty significantly. When I first started doing it, it was more the realm of financial analysts or business analysts embedded in the skill teams and it was almost the-- you were the closet guy. You're down in this room, you're doing analysis and you're not quite a computer person. So the computer team doesn't really want to see or talk to you very much and you're not quite a finance guy. So they're not sure what to make of you either but, hey, they find you really useful. Until now, where data is everywhere and it's the hot thing. And so now it's integrated in everything and everybody is reaching out to you from HR all the way through the whole entire company and organization for just different data needs. And they're all excited about what they can do and how they can leverage data to change their business and make it more efficient. 

BRIAN: 05:24 

So it's more saturation, I guess, is the big change. People who previously weren't in it are now all about it. Is that kind of the big shift there? 

ADAM: 05:36 

I think it's saturation and just awareness. It wasn't that they didn't find the data useful, obviously, before. It's that they didn't know what to do about that or what you could do with that. And then the speeds at which you were operating before. When I first started, a query might take eight hours to run just to do a couple of gigabytes of data to process. To now, where that's seconds. And it required a very specific skill set to write your sequel queries and your programming code and your PL/SQL to do things. Whereas, now, the tools have developed such that, like Alteryx, you've got drag and drop functionality which expands it out so that they can understand what you're doing. One of the big hurdles when I first started is you show a business customer a sequel code or a programming code and their eyes glaze over and you've lost them. But with Alteryx, everybody understands a workflow. It's just a flowchart from their perspective. And so it's easier to have discussions around it and keep them engage. 

BRIAN: 06:44 

Got it. So the catalyst for the change then is access to these types of platforms and just in general ease of use. Because you were talking about before, your HRs of the world and your finance departments of the world, you were saying now they're excited about it and they're aware of it. Is it the access and the ease of use or is there something else that's sort of led them down that path? 

ADAM: 07:11 

I think it's the access, ease of use, and awareness, that trifactor. Even with ease of use and access-- I'll use actually my Trust experience. When I first arrived here, we had three active Alteryx users. It wasn't a very big density and it wasn't because they couldn't have it. Just no one knew to ask for it or what it did and it was this thing. They didn't know any better. Whereas, now, we have over 20 users and where we've gone and we've really started to democratize it and they know what it is and they're excited and we've held training classes. And so it's really getting that awareness out and the ease of use and the accessibility. 

BRIAN: 07:55 

Got it. And what's the next step? So that's where we're at today. Where are we going with all of this? 

ADAM: 08:02 

I'm really excited about products like data robot and driverless H2O and how that automated artificial intelligence is actually going to play into the industry in good and bad ways. It's going to be very exciting to see how that impacts everything. With such a constraint on talent for data scientists, that automation of AI and then the ease of use and the democratization efforts of getting it out to just people that are doing their day job, I think it's really going to be game changing. You won't have to necessarily wait for a super highly skilled, highly specialized PhD person to go and implement an AI solution. You'll be able to use some of these automated tools and processes and take the recommendations from the AI on what AI work do you need. 

BRIAN: 09:01 

Yeah. That's really interesting. One of the questions I have for you on the AI front is-- I'll try to find this tweet. I saw this tweet the other day that said, "Machine learning takes place on a computer at the Silicon level and AI takes place on a computer at the PowerPoint level." And I got a good chuckle at that but I'm curious. There's a lot of talk about ML and AI and automation and these kinds of technologies. I think what's hard for me and others to understand is what's real and what is sort of bluster or people being, I guess, pre-excited for those kinds of things. Where do you see that at? Where are we at, in your mind, in terms of AI today and how long do you think it's going to take until we get to that-- I don't know if it's utopia or maybe a dystopia, which we can talk about, but that world where, to your point, a lot of these tasks are just happening via AI and we're doing the higher order, more skilled labor, I guess, as it were. 

ADAM: 10:13 

Well, I think that's happening now. A lot of different products have started to incorporate it into auto-generated insights. It's the current big thing. And it's only going to get-- we're only going to see more and more market penetration of those types of tools where the recommendations engines now make it very easy to see insights into your data that you might not even have noticed yourself. And so as those tools become easier to use and, quite honestly, more cost effective and the penetration hits, it's going to really change how people even think about and interact with their data sets. They're going to come to expect that. They're going to come to expect that, "Hey, the system will just tell me when it's sick or when I need this particular type of analysis." Now, I don't think that'll negate the need for data science professionals. There's a level of interpretation and understanding and making sure that the AI is right and, quite honestly, trust from your business partners. If the computer just auto-generates it and it'll be wrong, probably especially in the early days more often than it's right, you're going to want that human to really interpret the results and make sure you're getting what you expect. So I don't think it'll completely eliminate that type of activity but I think it will definitely change the expectations of your business partners. 

BRIAN: 11:43 

Got it. Okay. Yeah. It's really interesting stuff. We're in exciting times and I think everybody is waiting to see how this all pans out. And there's definitely a wide spectrum of opinions and sort of futures of where people think that's going to go. Like I said, the utopia versus the dystopia thing. So yeah. No, that's really great and I'm excited to see how, to your point, just basically machine learning even at that level can help us in the data science field because there's a lot of this work that it's getting easier all the time because of the platforms and technologies available to us outside of ML and AI but there's certainly a long way to go in that, like you said, I think helping people get information and producing insights or reports before they know they need it as opposed to some sort of reactive scenario. I know that IBM, for example, has had commercials for a long time about Watson predicting like, "The elevator's going to need service in 10 days. Go ahead and call the technician," I think was one of their commercials. Maybe we'll find that on Youtube and link it in the show notes or something. But yeah, anyway, it's fascinating stuff. 

ADAM: 13:02 

Well, it's actually funny because a lot of people in analytics get focused on the dashboards and the visuals and things along those lines. But my personal opinion is that when you get to that point, your analytics have actually failed because now-- it should just happen. It should be seamless and integrated into your business processes. You shouldn't have to go looking for the data and I think that's where the future lies as far as the ML and the AI. 

BRIAN: 13:29 

Got it. Okay. So as we're talking here, you're striking me as the kind of guy that it sounds like you're interested in enabling others, not just enriching yourself, which is great. So maybe we could talk about that a little bit. Whether it's at your current organization or in general, what's your take on the best ways to enable others and make that they have access to these-- not just the technologies but the knowledge so that they can navigate this world along with us here. 

ADAM: 14:04 

So when I was at Ford Motor Company, I implemented a process for doing an on-boarding boot camp for our data scientists that ultimately got rolled out to our general business customers as well, business partners. So I've trained over 400 data scientists and business partners in the last few years. I believe that the real powerful thing that you have to do is you have to have focused dedicated time to learning and you have to recognize that it's an ongoing process. You bring them in for the boot camp and say it's a week or two weeks long and you get them up to speed and you train them on the latest tools and tech. But it can't stop there because it keeps moving. The rate of change is phenomenal right now. And so you have to take and make sure you use dedicated time at least once a week, twice a week to go through and learn what's happening out there in the world and integrate it into your just daily business life. 

BRIAN: 15:03 

Yeah. Absolutely. And do you think that-- I don't know what your take is on this but we sort of have this mentality that if we're really going to get people to embrace change and embrace new things in terms of data and analytics, that we want it to-- not everything needs to be a party. It doesn't have to be the most fun thing you did all week but we at least want to make it engaging enough and interesting enough that people feel like they're having a bit of an experience while learning. Do you have that same idea? And if so, what are the ways that you try to keep it fresh or keep it light or keep it fun so that people are more apt to do it as opposed to kind of recoiling back into their chair and saying, "Oh, this is just one more thing I have to learn and do."? 

ADAM: 15:51 

Yeah. So one of the things that we've done is-- and it actually came off of the Alteryx community somewhere for advice but we did a-- several years in a roll, we did Halloween data challenges where I'd get all the data scientists together, I'd give them terabytes of data with different sets of data like - fake, obviously - a tame time keeping data and badge data and GPS data and they had to solve a murder or a crime that happened each one of the years and in little teams of five. And I think one of the things that was cool about that is, first of all, it was amazing what you could accomplish very, very quickly with highly skilled individuals. We're talking a timely [inaudible], four to six hours, they analysed a brand new data set they've never seen, applied it to the problem that they had, and came up with a presentation and presented their findings and made recommendations based on it. And they did it in that very, very tiny window but they had a lot of fun doing it too. And quite frankly, more often than not, they got the correct answer which was also powerful and passionate because the tools were just easy for them to use. And everybody always had a good time. It was just an exciting way to implement that training and that reinforcement of the skills to get them thinking new ways about the data that they see every day. 

BRIAN: 17:22 

That's really cool. It sounds a lot like the weekly challenges and things like that. And we've had similar discoveries that it's pretty insane how quickly someone can ramp if they want to on those kinds of activities. 

ADAM: 17:36 

Yeah. And they want to and they get excited about it. And it leads to lots of subsequent learnings and questions because when they didn't get something right, data scientists are the type of people they have to know why. They really dig into it. 

BRIAN: 17:50 

It's not that we want to know why. It's that we have to know why. 

ADAM: 17:53 

Exactly. 

BRIAN: 17:54 

It's who I am. I don't have a choice. Right. Okay. So all right. So we cover the enablement stuff. That's good. What about speaking out? I think you've done some speaking at some conferences and things like that, right? Tell me a little bit about that. 

ADAM: 18:10 

Yeah. So I've spoken at the Alteryx Inspire Conference in 2016. I was part of a team of three people, Alan Jacobson, Patricia Small, and myself, and we spoke actually on democratization and how we were getting the tools out and the processes out and the impact it was having on the business. And it was a lot of fun. I was amazed at the turn out. The room was standing remotely in the back to hear us speak about just our day jobs and I think that was actually eye opening and also fascinating. It was great to see all the data enthusiasts there, just like-minded individuals because you don't necessarily realize that there's so many of us out there. 

BRIAN: 18:56 

Yeah. Good. Yeah. Well, I'm not surprised it was a full room with the names that you just dropped. It's like the all star team there. It's not even fair. So very cool. So what about Inspire in Nashville? Roy is looking for good speakers. Are you tuned into that? Are you coming out? 

ADAM: 19:17 

We're definitely coming out. I'm debating on presenting, I'll be honest. I'm looking through what we can share and stuff from the projects that we have and things along those lines and what would be a good fit. But if I can find one, I think that it'll be actually a lot of fun to come out and do. 

BRIAN: 19:34 

I'm committing you right here, right now on the podcast. We want to hear some more cool stuff. So we'll all look forward to that. 

ADAM: 19:44 

Sounds great. Yeah. 

BRIAN: 19:47 

Okay. Great. And last thing on that topic, what do you-- because you had the dream team, it was probably a little bit easier, I guess, but what kind of tips do you have for someone who-- well, you just described one type of challenge with presenting like this which is what information do I have that I can share or I'd like to share or whatever the case might be. That's one challenge. The other challenge might be, "Hey, maybe I don't feel like I'm that good at public speaking or those kinds of things." Maybe what kind of tips do you have for someone who's listening to this and also debating whether or not they should speak at events or lead user groups or any kind of outward facing presentations or leadership like that? 

ADAM: 20:34 

Present on topics you're passionate about. I've gone to lots of presentations at different conferences where the presenter wasn't as passionate about their topic. They memorized it and they had their presentation but there wasn't the enthusiasm in the pitch if that makes sense. And so that you could tell their audience was kind of nodding off and things along those lines. And so you really have to make sure you bring your A game and you are passionate, enthusiastic, and you get your voice out there and pick a topic that gets you kind of revved up if that makes sense. 

BRIAN: 21:14 

Yeah. Absolutely. So what we'll do is I've issued you a challenge, so I should issue all the listeners a challenge as well. In the show notes, at community.alteryx.com/podcast for this episode, we'll put the link to the form that you can fill out to throw your hat in the ring to be a speaker for Inspire. I think that's really great advice and I hope a lot of people take that to heart and sign up. Because the thing I always talk about with the podcast specifically is everyone that we talk to about it, we say, "Hey, you got this great story. We'd love to hear it." And they go, "Oh, my story is not going to be interesting. The people you have on there are way smarter than me," which can be true because I'm on this podcast. But in general, what we tell people is, "Just come on and tell your story." And they say amazing things. And so I'm really working to get people over that hump and put themselves out there because we've just seen great results when they do that. So we'll put the link to that and if you're out there listening, go consider it because it's really awesome. 

ADAM: 22:22 

Well, and I'll give you one other actually tip for that. If they're thinking that, "Hey, I'm not smart enough. There's lots of smart people out there," things like that, do it for the reason that all those people usually come back and give you feedback on your project. People catch you afterwards and they talk to you and you make a lot of great connections. And they give you some phenomenal ideas that you might not think of. 

BRIAN: 22:42 

Yeah. Yeah. I think that's right. There's always opportunities for learning. We've definitely seen that especially at user group events where even the most seasoned of user can learn something new from someone in the audience. We see it all the time where they're like, "Well, I couldn't figure out how to do this," and someone who's been using Alteryx for six minutes is like, "Oh, yeah. You just go over here and you hit this hotkey and whatever." And even the seasoned consultant in the room is like, "Oh, wow. I didn't even know that was available." So that's actually a great segue. I'll ask you what's a good trick or tip that everybody should know that maybe they don't? 

ADAM: 23:22 

If you're running in database processes, inside of your formula tool, you can run subqueries and I don't think most people know that. And there's a few things that you can only do through subqueries. There's look-ups and things like that, especially when it comes to date ranges. And adding that subquery right inside in your formula tool, it simplified so many problems I was having while keeping the performance level of in database functionality. 

BRIAN: 23:53 

Wow. Okay. I'm going to go check that out. That sounds awesome. Great. What else? What's the coolest thing you've ever done? We'll just throw that one out there as a topic. 

ADAM: 24:05 

Outside of Alteryx, the coolest thing I've had an opportunity to participate in is so we do these volunteer days at Lake Trust and we get out into the community and we really help people and that's a lot of fun. And this year, I got to participate in a project called Operation Warm where we handed out coats to school kids in underprivileged areas in Detroit and in the Lansing area here at Michigan and it was just phenomenal to get out into the community and to see the impact and on a topic you wouldn't even necessarily think of. It was just kids needing coats. In today's day and age, how can that possibly be? The look of the happiness and excitement on the kids faces when they realized the coat was theirs and it kind of connected with them that, "Oh, I get to take this. This is mine. I get to keep it." And it was just amazing. 

BRIAN: 25:01 

Yeah. That sounds awesome and thanks for doing that. Here at Alteryx, obviously, we're really big on giving back. I'm actually sitting here wearing my Alteryx for Good shirt because after I wrap with you, we're going to go give some toys to some kids here locally. So yeah. It's just so rewarding. We talk about it all the time on the show. I just urge everybody out there, find ways, whatever way it is, your own way to give back because it's funny, you think you're giving back to others but then what you find out is it's good for your soul as well, so. 

ADAM: 25:36 

Absolutely. 

BRIAN: 25:38 

Very cool. All right. Well, that brings us to, I believe, community picks. You kind of just talked about what you're doing in your local community. What else is interesting out there that people should be paying attention to or taking a look at? 

ADAM: 25:52 

Just reiterating the opportunity to get out and do good in your community. There's so many people in need in so many situations where you can help and there's organizations that need the-- even if it's just time. And heck, even with just data. There are a lot of non-profits that need data science and need help optimizing their processes and things along those lines and they just don't have it and they don't have the expertise. And I know, for example, Alteryx, you mentioned one of your things is the non-profits have an opportunity to get access to some of the Alteryx tools to help them and make sure that people are leveraging that and taking advantage and getting out there into their communities and really just participating. Give back but not just once a year. Everybody does it around Christmas time and the holidays, it's the season. But do it as a part of your daily life and routine. I think that's really the focus. There's so many things, Habitat for Humanity, Operation Warm. Just get out there. 

BRIAN: 26:53 

Yeah. Yeah. Absolutely. I agree 100%. Okay. Great. I have a couple of picks I'd like to put out there. The first of which is Santalytics. Have you participated in Santalytics, Adam? 

ADAM: 27:06 

I have not. I did not even hear about it. Sorry. 

BRIAN: 27:08 

So every year for the last two years-- so this is year number three now as we record here in the winning moments of the year 2018. So this is year three of Santalytics and it's really cool. It's basically at this point-- well, I'll read the description here. It's an all inclusive, non-denominational secular festive excuse for some friendly competition using Alteryx. So basically, it's a set of challenges that we put out that are related to Santa. So in past years, for example, you had to help Santa navigate the globe and distribute the presents and find the optimal route and you kind of see where all this is going. And we always come up with some really interesting gift or swag because people love free stuff. I know you know that. And so in the past, we did cold weather kits that had Alteryx beanies. We did a rap video last year which is super interesting. And if you haven't seen that, definitely check that out. We'll put the link in the show notes to the rap video. This is who we are as a community team at this point. So Santalytics is great. I definitely recommend participating in that. It's a great fun time here and you'll learn some stuff along the way. So that's my first pick. And then my second one-- and I don't know how much people outside of Alteryx know this. So I might be breaking some news here but one of the things we decided to do as a company this year is get everybody together and we're doing it here next month in January. We're going to get our whole company together in one spot and we're going to kind of do-- you can kind of think of it as an Inspire but for Alteryx employees. And a lot of it is just us getting on the same page about where we're going, what we're doing. But one of the cool things we're going to be doing is we're doing an internal grandprix. 

BRIAN: 29:03 

So Adam, you've been doing Inspire-- you know all about the grandprix. So we decided that, hey, we need to do one of these internally. And so for the last several weeks, we've been having preliminary rounds and we're weedling it down and the idea is that there's going to be one participant from every department in the company who's going to participate in the grandprix and we're going to crown this year's champion live at the event. So I bring that up because I think it's really cool. I'm really excited that we've brought this internal and we're going to get our internal champion for this year. And I think once it's done, I'm going to be interviewing whoever the winner or winners are live on the podcast. So stay tuned to the show for more on that. And just because I feel compelled to say this, people have asked me, "Who are you rooting for? Who are you rooting for?" And the basic assumption is that I'm, of course, rooting for the person on my team, which I am. But the real answer is that I'm rooting for whoever is competing against Tony Moses in any of the particular rounds because that's just who I am at this point and Tony-- no, I'm just kidding. Tony is a great guy but we got to burn Tony on every episode of the podcast. And so this was my way of getting it in. So yeah. Stay tuned for more internal grandprix goodness. 

ADAM: 30:30 

Awesome. 

BRIAN: 30:30 

Very cool. All right. Well, Adam, thank you for being on the show. This was great. I had a wonderful time talking with you and I can't wait to see your presentation at Nashville. 

ADAM: 30:43 

Excellent. I look forward to it. It'll be a lot of fun. [music] 

BRIAN: 30:50 

So Adam, I want to circle back with you on something. So a little earlier on, we had talked about AI and machine learning and I made some over references to the end of the world as it comes to AI. I was reading this article on TechCrunch that was actually just posted just looks like yesterday and the title of this article is, "US intelligence community says quantum computing and AI pose an emerging threat to national security." And when you read the article, and we'll link this in the show notes, it goes on to talk about how all of the intelligence agencies within the US government, they did a survey and they asked them what is the number one threat to the world or the country as it were and, of course, you have things like terrorism and climate change and some really narly, obviously, topics in there. And believe it or not, AI was voted by those agencies as being one of the top things that they're concerned about. And it sounded like, earlier when we were talking about this, you have a particular, I guess, maybe thought or kind of view point on this. I'd love to kind of hear based on that, what you kind of think is coming up with AI. Let's put our tinfoil hats on here, Adam. Let's really dig in deep to what's going to happen going forward with all this AI mambo jambo. 

ADAM: 32:13 

Well, I definitely don't think it'll be the end of the world. Humans are amazing at adapting to situations and I don't think it'll come to that. But what I do think is it's going to change the way we think about data and privacy. It's already started to and I think that it'll actually get harder to be a data scientist in coming years as different data privacy groups and laws getting active worldwide. We'll take the European data privacy act that just went into affect earlier this year. 

BRIAN: 32:47 

GDPR. Yeah. 

ADAM: 32:48 

GDPR. Yeah. It made it a lot harder, actually, to do global analysis on data from a data science perspective. Even when anonymized, there were so many rules and regulations around it, you have to be very, very careful and make sure you're using that data in compliance with that regulation. And so as each one of the countries comes up with their own version of that going forward, that's going to definitely impact how data science is done. But I do think that there's good in there, that the AI will become easier, it will take over more and more of these tasks that we think about, monitoring tasks and things along those lines that, right now, we're manually doing and sometimes not particularly effectively and not cost effectively, for sure. It's going to change that type of labor and that type of work that needs to be done. And I think that society is going to have to adjust to that as more and more things like-- heck, you don't even necessarily have to drive your car. It just takes you to where you need to go. Well, as that really reaches its natural conclusion over the next - I don't know - 50 years, we'll call it, there's going to be an adjustment to society related to that. And again, I don't think it's the end of the world. We'll adapt. But it's going to be interesting to see how that plays out. 

BRIAN: 34:16 

And yeah. I've been reading a lot and listening to a lot of folks talking about this. As I mentioned earlier, there's definitely a couple of very different camps on this in terms of, "It's going to be wonderful. It's going to change everything." And then there's the, "We're all going to die," crowd. I think the truth probably lies somewhere in the middle and I've been hearing-- there's folks out there like Elon Musk and folks like that that have been saying, "Hey, this isn't bad but we need to put some decent regulation or some decent rules around this stuff to make sure it doesn't get out of control." What do you think about that? Where is the line, I guess, in your mind and how much should we be kind of trying to protect ourselves to some degree? 

ADAM: 35:04 

I think that we absolutely should be protecting ourselves and I think that we should care probably more than we even do about our data and our privacy. We sacrifice an awful lot of private data just for convenience and I think that has a negative impact. Look at the targeted adds and the things that take advantage of essentially our perceptions. The way that we can be swayed by arguments that are targeted specifically to the way we think is a huge, huge problem. And I think that those data privacy rules, even though they make the life of a data scientist a little more challenging, I think that's actually really, really important. Because, again, AIs, they can process a lot more information, see patterns of data that we couldn't see before and they can do it fast. And that's not going to slow down. That's not going to change. So yeah. We need to be concerned about how people are using our data. 

BRIAN: 36:07 

The last thing, I was just reading this article too and it's so funny, the last line of this article, the statement that they got from "the US government"-- they don't even say it which agency, which is so scary. The quote is, "The nature of conflict has changed. And so the United States must evolve." That's not ominous or anything. It's like the last line of the article. That just leaves you with a nice, warm, and fuzzy feeling there. Yeah. 

ADAM: 36:36 

Well, it really is scary if we think about it. My favorite one is the Google AI. When asked what thought about world domination and things like that and people, and I think the quote was something like, "Don't worry. I'll keep you in a people zoo," was its response. 

BRIAN: 36:52 

Yeah. I think we just found our show title. Episode 20 whatever. Don't worry. I'll keep you in the people zoo. 

ADAM: 37:02 

Exactly. Yeah. 


This episode of Alter Everything was produced by Maddie Johannsen (@MaddieJ).