Conversations on Applied AI

Dave Mathias - Data Coaching and Artificial General Intelligence

• Justin Grammens • Season 1 • Episode 6

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In this episode, we are joined by Dave Mathias. Dave is a Data & Product Coach at Beyond The Data and host of the Data Able Podcast. He’s also a leader in the Data Science and Analytics community having founded many meetups, conferences and is on the board of MinneAnalytics. Dave shares with us his deep experience in using data within originations to make better product decisions. We also touch on AI and generalized learning along with what the world might look like if Artificial General Intelligence (AGI) becomes a reality and human relationships in movies such as Ex Machina and Her. We also touch on a few online courses that our listeners might be interested in taking to learn more about applying Artificial Intelligence to their particular domain.

Since recording this, I had a followup conversation with Dave specifically on Education and AI, which will be coming in a future episode!

Finally, if you are interested in learning about how AI is being applied across multiple industries, be sure to join us at a future Applied AI Monthly meetup and help support us so we can make future Emerging Technologies North non-profit events! 

Resources and Topics Mentioned in this Episode

Enjoy!
Your host,
Justin Grammens

Dave Mathias :

Do we feel like there's a strong need to have a generalized AI is that just because we were trying to I hate to say like be God like better like almost like pick out like where we can create a being like ourselves and you know I don't know if that where that comes from

AI Announcer :

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 in applicable to your industry and connect with us to learn more about our organization at applied ai.mn. Enjoy.

Justin Grammens :

Welcome everyone to the conversations on applied AI podcast. Today we have Dave Mathias. Dave is a data and product coach and as the co founder at Beyond the Data an organization that is helping individuals and organizations will be on the status quo by coaching and training teams to deliver amazing insights, products and services. He's also the host of the data abl podcast, a podcast that covers inspiring stories from real change agents, data champions, product champions, and data driven leaders. Finally, Dave has been very involved in the local analytics community by being on the board of mini analytics, and seems to always be out in the community running conferences, meetup groups and other events. Thanks, Dave, for taking the time to talk today. Great. Say, you and I have talked a lot in the past. We've been in this sort of tech community, and I know a little bit about your background, but maybe you'd be interested in sharing a little bit about what you've been working on, and maybe the overall trajectory of your career. Yeah. So all over the place is way

Dave Mathias :

off, right. So I consider myself in the tech community. Also, I also am in so many other communities that I feel like I don't know where I belong, and whichever day but like I tell people I feel I'm a recovering chemist and a recovering attorney. That became I'm a Product person that now has a couple of different entrepreneurial ventures more geared towards the intersection of data as certain analytics is certainly one of them, but also around customer experience employee experience. And certainly that is a core component, along with product strategy in product alignment, things like that, sort of that intersection of of those things are really where I play a lot. Certainly some of those things that I do just like yourself over the years, we like being part of a community one of the parts of being part of a community is to bring people together obviously we're in these Cova times we have a little less of the in person community, but at the same time been part of things like mini analytics and which is a big analytics or here in town will also lead the product camp here in town to product camp, Twin Cities started customer focus North a few years ago. Yeah, you know, so so some different angles from event side and of course, meetups. You and I both run different meetups over the years and right now I had laid both the date of his meetup here in town and also the data fluency meetup here in town, but also participate in many meetups like yourself. Sure. Absolutely. Yeah, no database meetup is awesome. And I'll be sure to include links to all these meetups and organizations and stuff in the notes for the episode. And and you're also an entrepreneur, right? So you started your own your own company focused on data storytelling, or how would you certainly data storytelling is a part of it. But I would say it's more than digital in analytics, adoption. visualization is a small part of that whole adoption efforts. So if you're going to harness data more in your organization, everything from you know, where do I start? And how are we going to start approaching leveraging analytics to and certainly like self service analytics platforms like Tableau or Power BI is or those types of platforms are certainly very popular. But then there's also efforts around Are you really looking to go down the very much the data sciency route and I say, data science, because I think there's these different terms that we use in different aspects and People get very sensitive have this means this. And that means that and at the same time, you know, if you're going to be more sophisticated and trying to build your own algorithms, your own models at a more sophisticated level, as opposed to repurposing more of what other people have done and doing slight tweaks to things, or are you simply just trying to basically be more of a reporting type of system of data that you have, so that you can understand, you know, what happened, maybe dive into why it happened, but you may not be building up those predictive models or prescriptive models that would help predict in the future, you know, so it's sort of, you know, we work in different areas with with organizations.

Justin Grammens :

That's awesome. So are you do you find just to kind of dive in a little deeper, I guess, are you finding that you're having to guide them through this journey sometimes, or you guys come in and they are very much like, we need to do this, this and this. I do those typically go or is it a little bit of both?

Dave Mathias :

Yeah. So bigger organizations have very experienced teams. They have a lot of great, great talent. It's a small world organizations, it tends to need more of that guidance along the way. effort, whether they're just starting out, or whether they're, they tried going down a route, and now they're trying to readjust, which oftentimes happens, oftentimes, you hopefully are learning from your mistakes that we all hopefully learn from our mistakes, etc, fail fast and learn fast. And so you know, how do you learn fast and, you know, update and really start getting better value out of the data that you have?

Justin Grammens :

Good? Well, the one question I've been asking a lot of people that come on the show is sort of how do you define AI? I'm not sure if you've thought much about it. But I'm always curious to hear what people say when I say you know, if you could give it a quick elevator pitch, say, I don't know nothing about technology, nothing about the field and I say, Hey, you know how I've heard this AI term? How would you How would you define it, Dave? Yeah, I

Dave Mathias :

mean, that's a great question. And certainly a question that brings up a lot, a lot of different responses that you say, it's interesting. So like, there's the terminologies, Ai, or I think machine learning has a little bit more agreed upon, type of or similar more similar type of response you get with machine learning versus data science, right? I think that response gets pretty broad similar to AI gets a pretty broad level of different responses. Let's just say this definitions, one that they basically use the machine learning definition as AI definition, which is, you know, basically anything that can program like sort of writing code, like I think that's a good one for for machine learning. In some sense, it's learned from itself. But I think as soon as you start saying AI there, I think AI can be other things that can mimic human side and other capacities. And that's why do you actually think RPA is not a bad because in essence, you're, you're trying to use a way of saying, hey, the human is doing this. Normally, they normally grab data from the spreadsheet, and then put in the email or they put in the database or whatever. And that's mimicking what humans do.

Justin Grammens :

Yeah, yeah, totally. And just mimicking what humans do is, is it? I don't know. I feel like it's not very intelligent. No Right, though, yes, it is artificial, but it is not very intelligent. So what you really want to try and build is a system that gets better over time that can optimize itself. And this generalized versus specialized learning. Yeah, the stuff that Andrew Andrew Yang was was sort of talking about is like, you know, we're not at a generalized level right now. But I do think it's either chess or go or like one of those things where they I was reading a book on this where it taught itself, right. It basically figured out the rules based and they didn't it wasn't human programmed it was it observed, yes, all these various combinations and found out how at one and what was fascinating was the book had mentioned that over the course of entire time, people have been trying to figure out chess and master it and this thing, figured it out, learned it in four hours. And so it was like it was fascinating, right? So how can you take that and that's a very again, it's very rules based. So it's very simple to follow. But how can you take that and build it into something that is, all of a sudden this car can learn how to drive, you know, for example, from an infant, you know, basically doesn't know anything and four hours later now it's basically racing around the track at 200 miles an hour.

Dave Mathias :

Yeah, so like you were saying that there is so many options in life in general, but there's so few. And actually, again, when we actually think of how many choices you get to make, whether it's chess, or whether it's go, or whatever it is, it's like, okay, you have a stone to place on a goal board and your X amount of squares. And yes, you place your first one where your starting point is. And then you see where there is, but there's just, there is a calculated double type of problem where you can calculate all different outcomes. And in essence, you can do that, as opposed to saying, Okay, if I'm using any type of hand gesture, and I have a bunch of different settings behind me, I could be out and outside in a self driving car that you know, so how do I understand that that person means this or is doing this or like, somebody is hurt and they're trying to flag down the car, they could be doing trying to point to something or they could be a blind person that's, you know, all these different aspects that become almost an odd processable amount. And so how do you how do you make the best guesses best calculation so that You can also have a level of safety as a pedestrian that's out there. And that's really, you know, it gets to the the question of problems where you can like, literally do the math and calculate everything, versus there's going to be a strong level of unpredictability that we can't calculate out all the options. Yeah, to sign up. So how do we how do we make decisions off that? And I think the better we get at that more, we can move along that and the more that we don't just have a computer system that is focused on just one task, but it's able to do like many, many different tasks, the more that gets people all these feeling like it's more generalized, but of course, the relevant to that generalized level you're talking just you think of all the immense amount of things that we do as humans on a daily basis. Right, so

Justin Grammens :

Right, right, yeah. Do you believe we'll get to that level, generalized AI at some point,

Dave Mathias :

I don't know. I mean, I, I always like to think everything is possible. Boom. So but i do i think in my lifetime, no, I mean, I think certainly not in my lifetime. Now, I do know, I was at a conference a couple years ago. And I remember the gentlemen talking about cell phones and just said, computing power, and he was giving the idea of computing power and where things are, when you add up all the computing power around the world, and how many brains worth of computing power, there was like not storage, mental storage, but like just the ability to compute. Okay, because there's always like the storage and compute. Right, right. And I think he said, and I, of course, I'm probably gonna quote the number one, but with roughly, I think, by 2050, estimating that there was about 45 brains worth of compute power in a cell phone like size device. Well, now I think it was somewhere like, I think if we took all the computers in the world, and this is like a year, couple years ago, all the computers in the world I think it was in the single digits or maybe maybe in the team Have computing power in the whole world? And so then if you're able to like say, Okay, well, I have 45 human brains worth of computing power. And of course, like, doesn't mean that, as we know, like a computer can do certain tasks way better than a computer with just a lot less processing power, it's just really good at doing those small, repetitive tasks. That doesn't mean that it doesn't take all of our brain power to do some of those things, but at least a few if they could theory that we need to at least have the same or more compute power in a system to mimic to have generalized AI, along with the storage in that system. And at the storage is already at that level where I think we're probably not that I don't know what the the current standard storage term storage seems to be more advanced than on the compute side.

Justin Grammens :

Yeah, sure. Sure. Yeah. I you know, I'm not sure if you read much on neural nets or know about that, but and I'll probably get the stat wrong. In fact, I will probably win unquote, the stat per se but it was just phenomenal the amount of synapses we have in our brain and all the connections that Even the most powerful computers, at least, certainly not in your hand. But there is a lot going on in your brain that we still have yet to understand. Yes. Right. And so we've tried to mimic it. I think with a lot of these mapping the brain into a neural network and figuring out yes, it works in a lot of these cases. But there's things we still don't know about the human brain. Yeah, yeah, we've just scratched the surface of and, and we there's a lot of things we still can't explain. So I guess I'm kind of with you, and that if we can't explain the human brain 100% today, how can we say then that we're going to build a machine to replicate it in some way?

Dave Mathias :

Yeah, I think the challenge of course, as I as I think, you know, people hundred years ago, but they have said where we are today, they would have never imagined many of the things here. So that's where part of me just says is and the advancements especially in computing and all these even though obviously you like the term artificial intelligence and all those things were coined, I think, in the 50s. That you know, it's not that these these things just sometimes take a while to develop and I think Even 20 years ago, we wouldn't have thought for the generally the ability to speech recognition and facial recognition and all these other types of things that we have at a very accessible level right now. So so it'll be interesting to see what we can do. I mean, there's obviously a lot of fear on the generalized AI realm but do we feel like there's this the strong need to have a generalized AI is, is that just because we were trying to I hate to say like, be God, like better like almost like pick out like where we can sort of create a being like ourselves and and, you know, I don't know if that where that comes from.

Justin Grammens :

It's awesome. You mentioned being being godlike. I'm not sure if you read the book called Sapiens. Yes, yeah. And his second book, I'm gonna mispronounce his name, but he has a second book and called homo homo Deus. Yes,

Dave Mathias :

I had the audiobook. I have not finished that one, to be honest. Maybe a third of the way through.

Justin Grammens :

Okay, well, it just fits very much in line with sort what you're talking about and for anybody listening to this if you haven't read sapiens, homo Deus, and then he's got a third one out called 23. Something for the 21st century. really fascinating book.

Dave Mathias :

Okay, that's good one too.

Justin Grammens :

Yeah, yeah, it's really good. But but in the second book, that's what the whole point is, is, is basically, you know, we've evolved from apes. And now we're at the current state, where we're at right now. And a lot of the questions is how come we got so far as far as we did? How come we were blessed with all these things? How come our brains evolved a certain way? How come civilization and culture ended up coming together to where we are today? And then really about like, we are really pushing the envelope now on trying to become gods and in a lot of ways, you know, rewind the clock back, you know, hundreds of years ago, a storm would happen, a volcano would erupt. And people would point at it and say, the gods didn't like us. Over time, science has proven that there's not really these deities that are that are aiming to hurt humans in civilization. And now we're at the point right now with human genomes, we're basically you know, re splicing redoing stuff, we're going to the moon, we're going to go to Mars, you know, I mean, you see all the stuff that's going on, where we're basically rewriting the future. And you know, honestly, in a lot of ways, where if we didn't have this technology, we would have just been apes for the rest of it for the rest of time. And the his his book, I mean, obviously, as you've been reading it, it's, it's, it's not really a point of scaring people. He's really a historian, but it's really a fact of the matter is, is like, we're doing stuff that that mankind has never done before. And it's a lot of biological but it's also a lot of AI. I mean, it's a lot of the things with regards to mind melding, right? There's a lot, there's stuff going on where people are, I move my hand and it goes through my brain, and it goes through the internet. And it goes to you, Dave, and you move your hand, right. There's there's a lot of really interesting things where they're crossing the bridge between and I guess, in some ways, you know, more sort of thinking about this as thinking about AI and its bridge into biology, because that's where it can have a huge impact.

Dave Mathias :

Yeah, I mean, I think which is another field where we were Really at the infancy stage of really understanding a lot of these things. And so I guess the question is, and certainly I think there's a while I'm no expert in the field of AI and being used in, whether it's genetics or whether it's biology or any of those things, it does seem like there's at least a lot of folks in those spaces that see a lot of opportunities in those. And I guess the question is, is again, like, what is our purpose for using in those spaces? Are we using it for my motivation just to make lives better? reduce poverty, reduce climate change those types of things? Where we want to make the world a certain way? Or are we trying to just recreate different type of ourselves in Japan? I forgot that it's statistics. I mean, there there's a higher level of robotics in Japan than there are in the United States. And I forgot how many thousands of robots but there was so many thousands of robots that were companion robots to people, as opposed to basically they are they were surveyed for a different question. Relationship perspective. And I can, you know, that relationship. And I think of I forgot, I think it was a big bang theory or one of those big, big, big theory where one of the characters sort of like, almost fell in love with Syria on the phone. Right. So what are we trying to satisfy with this? with artificial intelligence is a big question.

Justin Grammens :

Yeah, yeah, totally. I mean, I think humans are just curious creatures, you know, and I think, you know, a century ago, you'd say, why would you go to the moon and, and I just, I think, in a lot of ways, we're just curious to do that. So sometimes we just build things just for the sake of building them I think just to explore but yeah, falling in love with a Syria that was a I have seen companion pets, you know, basically people that have Alzheimer's, or, you know, in advanced age they actually have and it's more of an IoT device and unlike a lot of ways, but yeah, it's a it's a pet that they can deal with and talk to, it'll talk back and it's like their own little, their own little companion. And maybe that's what we're getting at where we can use electronics with Basically intelligence, I guess, to keep them happy.

Dave Mathias :

Yeah. And I think they're, I think they're receiving a certain physical aspect and other things too, which is just trying to have an overall like, what are the things that are part of a relationship? You know, so I think I think it is interesting because folks of our generation data, you know, was a character in Star Trek in Yeah, generation and he was always trying to be more human, right. I mean, trying to, you know, mimic humans. And, of course, there's also a lot of those negatives of certain human qualities anger, rage, other things that that are not necessarily great qualities when they come out often. So, you know, are we trying to create better beings than ourselves where we see flaws? Are we trying to just recreate ourselves? Are we trying to, you know, what are we trying to do another movie that I like in that sort of sci fi space is Ex Machina. I think that's how you pronounce it. And and I think that's a you know, they're trying to create the generalized AI that that one's obviously a darker Few things. But yeah,

Justin Grammens :

what was the one movie where the guy, let's just call like her or something like, or maybe it was?

Dave Mathias :

Yeah, I mean, it is interesting. I mean, as we're thinking of like, like I think like, voices is become a bigger thing, right? Like voice technologies like whether it's Siri or Alexa or Google assistants or things like that, right? And where they started really becoming much more popular. Part of it was they know how good the language sounded from the device itself, but at the same time, how do we treat that voice? Do we do we treat it as another person that we're we're asking a question, or we're sort of having a conversation or are we, you know, as our behavior just like Google search, where you would just, you know, Hey, tell me this now and I want to quick as possible and so, it is, it is interesting how people interact with those types of devices as a proxy of you know, where, where this might go. Yeah,

Justin Grammens :

yeah, absolutely. Absolutely. Because Yeah, voice can be very transformational in a lot of ways, you know, people, people thought that radio was gonna die off because it wasn't as interface. You know, there wasn't a lot of visualization cues, but radio is just as powerful now it feels like as it has ever

Dave Mathias :

been, I think podcasts are another element of it. I mean, we're still humans, we still have the same senses. I mean, people like to think we're evolved and we're so much different than we were even thousands of years ago. I tell people, we're pretty much the same, right? I mean, we, we have we have a little more legacy knowledge that we get to start with, we have different technologies that we get more tools that we get to start with and access over the same genes were the same, our systems work the same, we're so susceptible to viruses and to other things like that out there. So if we're not that different, and certainly one from a sensory sensory perspective, you know, sound or taste or those types of things are really strong emotional cues that we take in and why I think podcasting has done so well is because of voice is one of the more intimate, you know, senses for us to, you know, hear somebody's voice and You know, very distinct?

Justin Grammens :

Sure, definitely. Well want to bring it back a little bit to data, I guess. Which is great. No, man, I really I love these conversations to go wherever they're gonna go. And you mentioned about voice and you mentioned about, I mean, we touched a little bit on the beginning around sort of storytelling, it feels like at the end of the day, you've got this ocean of data. And what you've been doing in your career, at least most most recently is sort of helping businesses a make decisions on it, but then also maybe explain it better

Dave Mathias :

and stronger at being able to do that themselves. Right. Like we're we do a lot of stuff in the education space, and a lot of, you know, teaching people how to fish as opposed to just doing the fishing, which is I think, a core thing and I think I would like to see more effort. Like that. I think part of it's just the nature of we're always reacting to things and how do you take a step back and be more strategic and say, where do we want to go and what does it take to get there? It's it's easier said Have them done for organizations around because there's a lot of expectations and and i think Andrew Yang we were talking about before, I think he does a good job for education wise his AI for everyone, if you haven't taken that, as a person who's newer to this space, definitely recommend you take that on Coursera AI for everyone. Okay. And it's it does a good job of really laying it down at a like, I think, at a very basic level. And of course, he goes into some of those concepts of generalized versus, you know, specialized AI and all those types of things, but just even thinking about what things is this type of stuff good at, or what is it not good at? You know, when you talk about artificial intelligence is I guess, there's certainly a big technical aspect and you certainly, you know, if you're going to do neural nets, you need a lot of data generally, I mean, generally speaking up not all the time, but oftentimes, you'll need a fair amount of data. And so there are certainly that aspect, but there's also a huge aspect of just understanding the domain and picking the right types of problems to go after. No matter what you're doing and really finding value. I mean, even the idea like creating dashboards, like, you can create a ton of dashboards, but you know, are those dashboards used? And are people are actually taking action on those things? Are those actions what you expected those to be? And how do you sort of decompress that stuff from the start to minimize? Again, learning fast as opposed to failing fast? How do you? How do you quickly say, Okay, wait a sec, we built this, it says, getting the action that we thought we need to scrap this and get something else out there because you can't just stick 100 dashboards in front of an average person and expect that they're going to be able to just like you can't, you know, say, hey, these are the hundred metrics that you're going to be judged by. They don't know what they're going to like what matters, right where people so you know, how do you know each individual is going to say, okay, what's important to be from an incentives perspective as an organization, how am I going to be judged, whether you're in government, whether your nonprofit whether you're a for profit you're working in, and so and even in your own life, like I think of one of the better things out there from a using a combination of they use data visualization, but just even giving good insights is Fitbit. Not just because it does a good job of using good visualizations and you know, good practices like that, but it also gives good insights and thinking about, okay, here, what are the things that are important to me? Here are some insights like, okay, you're, you're on average, sleeping this much. After you've worked out like this, like, have you thought of doing this like, and giving people those actual insights and just thinking of, like, whatever you're doing with data, taking a step back and say, Okay, what are we trying to do? Like, why are we collecting this? What what are we hoping to get out of this? And how do we get people to, you know, help us unleash that value? And while it sounds simple, sometimes where people are like, Oh, yeah, we're just gonna, like, we're just gonna collect all this data, we're going to throw it into a shoehorn model, and it's going to like, tell us the answer. Like that doesn't. That's not how it works. And so, you know, really taking a step back at the early stage of plan. It's no different to be honest than a lot of things like the more you can app the upfront perspective. Start thinking about these things, start asking the questions. I like what Amazon they do the whole practice of saying, okay, I write the press release at the beginning and out about it. And at first, and you're trying to really figure out how I'm wrapped that problem.

Justin Grammens :

Phenomenal. Yeah, totally true. Completely. I completely agree. Kind of people, they get so lost, they don't see the forest through the trees in some ways, right. So they're not they're losing aspect of the big picture. And maybe that's a lot of what your current consulting work is, is keeping them focused on what the end goal is. Yeah. I

Dave Mathias :

mean, certainly the end goal is is that I mean, in purely it's just helping people also be better at thinking about that in themselves like and helping technical people be better people to work with the business side and vice versa, and how the business people can have enough technical aptitude, I mean, because there's there there is a need for both sides of folks. And some people will say, well, we'll, we'll hire you know, translators is a popular, more popular term that's been used, like they've translators and its parents, some of those have talked about that. There's also a just okay, like, you know, you can have technical people that can, you know, understand the domain have the ability to interacted and do that. Certainly that doesn't work for everybody. And some people are going to be better at that than others. I do think, though, that business people should also be asked to have some basic knowledge around some of these things that are important around the technology, and hence why you know, product teams and other things are always encouraged to say, okay, you should understand you're going to be working with developers, you have you taken a programming course before not that you need to be the programmer, but you should at least understand programming, understand some of the challenges understand that things aren't as easy as, as they seem. Just like what kind of things have you done in the user experience base or other things? So that's like a for a product manager. But if you're a finance person, and you know who are your stakeholders that you work with, and have you sat in their shoes a little bit and had some exposure What they've been, you know faced with that you can better relate to them.

Justin Grammens :

That's great. That's great and kind of makes you think about the value of a liberal arts degree in some ways, which is, which is, which is what I did consider getting multiple perspectives. So, well, I want to give enough time here for you to promote your contacts, you know, how do people get ahold of you? I know you have a you have a podcast, of course, as well. If you want to mention any of that. I just know how do people look you up online and find you and maybe connect with you?

Dave Mathias :

Yeah, the website is go beyond the data.com. You can also just connect with me on LinkedIn. I'm not on social media that much, but the platform I'm on the most is LinkedIn. And to be honest, I'm really not any others anymore. So I'm David Mathias one and LinkedIn after the linkedin.com slash n slash Dave Mathias, one, feel free to reach out I do a fair amount of virtual coffees I've been trying to do more of that now with COVID have been doing interacting with people trying to help especially folks younger in their career. So if you're a person that's looking to take Get into this make a career switch or looking to, you know, accelerate your career. I'd love to just grab a coffee with you virtually and we can chat. And I'd say there's there's a lot of opportunities out there. I think there's a lot more opportunities for folks that may have some strong data skills, but also have those those other skills too. So thinking about how you can be more of a blended, blended person is good.

Justin Grammens :

Well, cool. Is there anything else you wanted to finish? No.

Dave Mathias :

I appreciate that. All as always, Justin, great to see you despite these brand nowadays, but now appreciate being on today

Justin Grammens :

for sure, Dave, I appreciate it. Thank you so much. Thanks.

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