[00:00:15] Speaker A: Hello, everyone. Thank you for tuning in.
[00:00:16] Speaker B: You're listening to the Sound Barrier, northeast State's official podcast, where we'll be breaking the barrier by getting to know some of our faculty, staff, students, and alumni. And today we've got a very special guest, mr. John Mean, who is an assistant professor.
We're grateful to have him here. He's a man of many interests as well as many talents, and you'll get to learn all about that today. This is your host, Matthew Poole. I'm also joined by co host Tom, as well as Mackenzie. So grateful that you all are listening in with us. And I tell you what, mr. Mcmean here, he has a story to him, and he is a man of many interests, so you're going to get to learn all about him today, and it's going to be a treat. So, John, if you don't care, just tell us a little bit about yourself, your background, and ultimately what led you to coming to Northeast State in the first place.
[00:01:04] Speaker A: Yeah, I can try to sum it up pretty quickly. I grew up in Elizabeth in Carter County, and of course, when looking at schools, wasn't really sure what I wanted to do as far as going to college and ended up at Etsu. And absolutely loved, you know, kind of flash forward through going to school and then leaving school for a while and pursuing music and then coming back to school and going to grad school. At some point, I accidentally got thrown into teaching a computer science class at Etsu. And I really enjoyed it. And so even after I left teaching there and was working in industry, I really missed my side gig. And so Dave Blair, professor here, he was one of my professors at Etsu years ago, and we met up at an event I do called Careerquest. And he was like, you know, there's some faculty position coming open you shouldn't should look at applying. So I got to come back to what I really like to do, teaching and sharing some knowledge and things on things I'm okay at. You said I'm a man of many interests. That makes you a jack of all trades, master of none. Right.
[00:02:17] Speaker B: I don't know if I'd go that far. I think you're a master of plenty. I've got a number of your lectures before, to me at least, you seem like a master of a lot of different areas of focus, at least just.
[00:02:27] Speaker A: A master of computer science. That's what I got at Etsu. And so I can at least say one but definitively, but then, yeah, I like to do a lot of things.
[00:02:39] Speaker B: That's cool.
[00:02:40] Speaker A: Yeah.
[00:02:40] Speaker B: And you're getting your PhD right now, right?
[00:02:43] Speaker A: I'm kind of considering it. I'm kind of shopping around and seeing I've got some research stuff coming up that I could either kind of publish or I could sit on and take to a school and maybe have some other brilliant minds look over it. Right now, I've got the brilliant minds of some of the folks out at Steel Creek Park in Bristol looking over me and keep making sure I stay within the bounds of what I'm actually researching. But yeah, so PhD might be on the plate. We've got to see. Sounds good. Sounds good. Yeah.
[00:03:17] Speaker B: So ultimately, though, I'm curious what got you interested in computer science?
[00:03:24] Speaker A: Computer science? Well, actually, another accident.
So I loved computers when I was a kid, and I spent a lot of time making websites, kind of playing around, getting comfortable with working on a computer. And even when I was a kid or really young, I remember playing math games and things like that at school.
So I always liked working with computers and playing around. And by the time I graduated high school, I looked at graphics and was kind of interested in doing animation and things like that. So when I looked at the catalog that was available at Etsu for 2001, which was the year before I graduated, there were a lot of courses in the computer science program on animation, different things. And so that's what I was like, oh, I need to be a computer science major. So I started in 2002, went for orientation, and it turns out that they built a digital media program, which is you're probably familiar with their program.
I can tell you exactly when they started because that's when I signed up for computer science mistakenly. So I got into an advising session, and advisors would never do this. They would never be like, Stay. Please stay. But he did talk me into staying, and a man named Bob Reiser and I got to thank him a couple years later for talking me into sticking around. Because what was cool about going into computer science versus going into the animation field and things like that is I learned to build the software that animators use so I can create any tool that I want, whether it be an animation tool or a tool to study frog sounds or whatever. So software kind of gave me software engineering gave me the expressiveness that I was kind of looking for with relating ideas and having fun with computers. So it's a little bit more not only am I consuming it, but I'm also producing it, for sure.
[00:05:34] Speaker B: And I remember whenever I would sit on your lectures, we'd go to different high schools, and you'd present about computer science program.
You always said, this one thing that stuck out to me, and it's whenever either Dr. Donna Farrell shout out would walk by the office and she may ask, what are you doing in there? It looks like you're playing video games. And you would just say, It's just research.
So tell me a little bit about because I think it's so neat that you did this, but you integrated artificial intelligence with Sonic the Hedgehog.
[00:06:06] Speaker A: Yeah. So when I first landed here. Dave Blair kind of coached me and said, listen, AI is coming and it's coming fast, and we have to be prepared to teach it at the two year level. So try to figure out how to teach an applied AI course. How do we take the tools and use them? And a lot of computer science programs might focus on, well, let's break them apart and study how they work and build new ones. Instead of consuming it, using it, applying it to things that are of interest. And that's usually how we start. We teach you how to use the thing, and then we teach you how the thing works, and you kind of dive in deeper and deeper as you go. And so at the tier level, it's a topic now that we can totally teach it at pretty much any level. I fully believe we can teach it at a high school level, at least consuming it and putting sets of tools together. But the way that I first introduced myself was, well, I need something fun to apply this to. And I was also looking for something that might, if I got it to work, catch my students interest. And so I start looking at packages that already existed.
We've heard a lot about Chat GPT. They developed the framework for the code that I put together. So they developed this tool called Gym Retro. And the Gym so training AI, they created a trainer that's a platform that basically allows researchers to all work in the same sets of tools. Because a lot of the research that was going on and being published was essentially ad hoc. Everybody was doing it their own way. And so when you're comparing results, the data wasn't coming in in comparable fashion. So the OpenAI was like, maybe if we just kind of put together a tool that everybody can use that helps build AI, then people start using it. And now when they compare their studies, the data is kind of put together in a better way. So I grabbed some of their code and there was actually a YouTuber that I learned all this. You know, I tell my students all the time, they're like they ask, can you just learn this online? Why do I have to sit through you yelling about it? Well, I'm an expert and I can make sure that what you're consuming on YouTube is correct.
[00:08:31] Speaker B: Exactly.
[00:08:31] Speaker A: That's what I get paid to do.
[00:08:33] Speaker B: Exactly. You can learn anything you want to outside of the classroom to actually receive a grade for it. To get the degree, you have to.
[00:08:39] Speaker A: Come through me 100%. Yeah.
Anyway, the YouTuber kind of did a great job of summarizing and putting together. And then he even admitted in the video, he's like, I'm not much of a computer scientist, so don't hold me accountable for some bad code. And I got in there and immediately was like, oh, I can make this better. And so I started hacking at it. And before I knew it, I had kind of redeveloped his code. And it's amazing. I did this in maybe less than 200 lines of code.
So that's something that would be unthinkable years and years ago. And if you think about it, now, my AI platform for my frogs, I've actually abstracted that down to four commands that I can call from the command line and have my entire training pipeline set up. So that's all. Programmers really do is take really complex things and give them names that are simpler to use and then ask that thing to do it.
AI has been abstracted to the highest level. Just think for us.
[00:09:41] Speaker B: It'S definitely a tool that I can see us continuing to integrate into our world.
I can't help but think in the back of my head, like, what is this eventually going to evolve into?
How much is this going to be integrated to our life? I mean, is it just going to completely consume all that we do, or are we going to use it kind of like a calculator in a way, for a math class? For is long form writing a thing of the past? Do we just need to be really good short form writers and then it spits out this information for us? I don't really know what that looks like, and I don't know if anybody really does.
[00:10:16] Speaker A: Yeah, it's the Wild West right now. It's kind of like the Internet appearing. What's this going to do for our learning now that anybody can look anything up at any time? Are people actually going to remember things?
I have some arguments for and against. I think the Internet can really enhance our learning. I owe most of my knowledge to learning on the Internet. And so to say that I guess people were really scared when they first came out. Like, people could cheat. Now they can just look up answers. And so I was looking up answers not what we've always done. We're looking up answers to problems all the time.
Like any tool, I think it could be misused. And I'm probably more worried about really scary impacts of how it affects our political systems and things like that.
The impending robot apocalypse.
I've watched Terminator way too many times.
[00:11:20] Speaker B: I think we all have.
[00:11:21] Speaker A: Yeah.
I like wearing my Cyberdyne System shirt when I'm programming my AI stuff.
My friends say it's a little too on the nose when they catch me wearing it, but anyway, that's cool.
[00:11:37] Speaker B: Well, I'd kind of like to start talking a little bit more about how you have used more so artificial intelligence as a tool for your research.
[00:11:45] Speaker A: Yeah, of course.
[00:11:46] Speaker B: So as far as how you've integrated computer science and biology, when did this really all start? And your specific area I'll let you kind of describe, but what got you interested in that particularly?
[00:12:00] Speaker A: Well, I tell you, I think we've talked about this before, Matt, because your background's in psychology and so when I was in high school, before I landed on computer science, animation, any of that, I was interested in psychology quite a bit. And so I looked at the catalog and saw that psychology students at Etsu not only had to take a biology one, two, but a three. That scared. No. So I can't say that I've just immediately loved biology from the get go, but I tell you, the real inspiration is one of my best friends, jeremy Stout, the nature center manager at Steel Creek Park. And he just has such a passion for biology, and he shares it so well that he just gets me excited about it. And we were talking a few minutes before our talk here about getting excited about things and how it can take you in different directions and just being in love with science. And so this is a man that's just in love with science, and he inspired me. And really, Jeremy's probably the reason I went to grad school, too. I'm just like, this guy's so bright and he knows so much. Now he's teaching, and I grew up with him. I met him when I was 16 years old. And so he's really just kind of inspired me to do more. And us working together, I joke all the time. I was like, well, this data is kind of implying this, but I don't know, you're the biologist. What's it really telling you? And so as a data scientist and computer scientist, you're looking towards the people that you're developing software for or towards to understand how their processes and systems work. And as a computer scientist, a lot of times we're taught to be generalist, to be able to take sweeping ideas and put them and automate them and things like that. So as a computer scientist, you almost have to latch on to something else you're interested in to become an expert at, to apply your skill sets to. And some people specialize in building houses, and some people specialize in building industrial complexes, so everybody's got their thing they want to do. But yeah, I'm going to say Jeremy there. And that's specifically the project that I'm going to talk a little bit about today came from Jeremy, and I'll share the story real quick, but Jeremy, you met, right? Yeah.
Did you have him as I didn't.
[00:14:42] Speaker B: Have him as an instructor. Whenever I was a student here, I did a little bit of volunteer work for a group or a club that I was in at Northeast and we planted some trees.
[00:14:52] Speaker A: That's right.
[00:14:52] Speaker B: Still Creek Park.
[00:14:53] Speaker A: Okay, that's right. I remember you had said you met Jeremy. Yeah.
[00:14:56] Speaker B: Super nice guy. Oh, my goodness. And you're right. His energy and his passion for what he does is so infectious. It gets you motivated, right. And fired up.
[00:15:05] Speaker A: Exactly.
[00:15:05] Speaker B: I'm like.
[00:15:06] Speaker A: Yes.
[00:15:06] Speaker B: Let's go.
[00:15:07] Speaker A: Exactly. Have you all met Jeremy?
[00:15:09] Speaker C: Have not, no.
[00:15:10] Speaker A: So he is an adjunct here at and he teaches biology. And so I've always wanted to take one of his classes. I probably should, but just being his friend is kind of like taking a class in biology. You're going to learn something with Jeremy.
He called me up one day, and it's pretty rare that you can actually get me at my desk here. I probably shouldn't say that.
So my phone rang, and it's Jeremy. And he goes, hey, did you get that email I just sent you? And my computer just dinged right then. I was like, Well, I guess I did. He goes, just, hey, take a look at that paper. Do you understand any of the math? And so I'm like, on the spot, okay, open up the paper and start scrolling through. And he's asking me to synthesize and learn something in just a few minutes on this phone call. So I start reading through and putting it together, and he's talking a little bit. I'm like hold on. Give me a second. I'm looking at this, and I start to pick out some of the math. And I was like, yeah, okay. This is this, this and this. And explaining to me, he's like, Let me call you back. And he just hangs up on me.
I told you he's eccentric, right? He just hangs up on me. I'm like, okay, fine. And he called me back just a few minutes later, and he's like, hey, you want to work on a research project? And he was hanging up to ask one of his colleagues out at Steel Creek Park if it was okay for me to jump on the project, because Lance Jesse is a naturalist out there, and he was heading up this project, and so he couldn't just ask if I wanted to be on it without Lance's permission. So they pulled me in to their project, and we published a paper in the spring of 2022. Yeah, so we published a paper together, and it was really exciting. I did all the math, and I wrote four sentences for the paper that got cut out by the editing process. When you do all the math, you put it in graphs and charts, and I kind of said that. But anyway, I did have a hand, and there's a few sentences in there I think I had a hand in. But most of my work comes in the form of pictures and of course, the math behind it. But that was a really awesome experience. And then I talk about this when I go to talk about my project is what started this project is there were about three sentences of the paper I didn't like. And not that they were poorly written or slander or something.
They're just assumptions. And so in science papers, sometimes we have to just state any assumptions we make and whether they're good or bad, and that's up to the kind of review process to be like, well, that's not a good assumption, or maybe that's a good okay, one. It's just setting your boundaries or limitations to how you framed your thoughts. And there were three sentences that I just these are assumptions that I don't want to live with, and I'm fine with putting them out there, but now let's go zoom in on this and figure out if we were right or wrong by making that assumption. So in the instant, we didn't have a way to kind of measure these.
So specifically, there's a couple frogs. There's one frog out there that hasn't been in Steel Creek Park. I guess I should mention that we're looking at frogs and toads. Our work, kind of separate from my project, is looking at amphibians as a whole in Southern Appalachians. And so I'm zoomed in on frogs and toads out at the park, and the sentence was basically looking for this one toad, the fowler's toad. And it hasn't been observed in the park in over 50 years. And so to include it in our data set that exists in the park, what I asked Jeremy was park records and things that we observe in biology. If we go out and observe something in a natural setting, what's the half life for that data? When do we say, yeah, it still exists here? And so one of the big problems that we see in those science fields is undersampling. We can't just be everywhere all at once, or can we? So that was part of my study, was putting these passive audio recording devices out. And though I couldn't be everywhere in the park all at once, even collecting data from these things, I could be in parts of the park that were under sampled and at temporal time points in the park. So one of the things is frogs and toads, they'll call at night. And some of these frogs are the size of your finger or half your forefinger, your pinky. They're so tiny, so they're hard to see. The best way to find them or observe them is through their calls. And a lot of them call at night. And coincidentally, the park's closed at night. So that's why there's kind of an undersampling at night in Steel Creek Park. And no systematic study had kind of ever been done on frogs and toads in the park. So that was just my whole rationale for, let's just throw some recorders out there and see what we got.
They generate huge data sets. So I've got almost, I guess, our sound engineer out there is probably thinking about the audio data. He's thinking, our hard drive big enough to hold this conversation from this long winded guy.
We run out of room out there anyway. So that's the thing, is that audio data, if you want really good quality audio data, it's uncompressed, it's big. It takes up a lot of space. And so I ate up about a terabyte. And if I had been super diligent with getting out there and getting all the data that I needed and we hadn't had some troubles with recorders and technical difficulties, I probably would have ended up with around two to two and a half terabytes just for the one summer. From four recorders. Wow.
[00:21:03] Speaker B: How many recorders did you have?
[00:21:05] Speaker A: I have access to four that I'm using, and I could put them on a timer, essentially a schedule to come on and turn off when we had them on at night. I can be out there during the day occasionally and other folks sharing data through programs like Inaturalist. So there's data coming in from everywhere. But for my recorders, it was just kind of imperative that they be out there at night and take all that in. So those huge data sets when you listen, I can't sit. I can't remember what the hours total was on that. I could probably scribble some math down here and based off the size of the files, but it was somewhere around a collective 1700 hours or something like that from all the recorders.
Even if it was 700, that's more hours than I have time to sit and listen to. And so audio software. There's a ton of audio software out there that does classification.
We'll go through and let you tag stuff and automate these things. But as a software engineer, of course, I wanted to solve the problem myself and build something very tailored to Steel Creek Park, build my own AI model for just the species out there and collectively add to it, because this is something unique that a city park might not have access or resources to. This is a custom AI model train for our park in helping identify the things that we know are here. And so, anyway, that's why I threw AI in on this. And it was kind of also that spark from Dave find a way to bring this back to the classroom. And that's what I've done. So I've got a really nice applied project with some really good goals and a ton of data to chew through.
Anyway, we found eight out of ten so far of the frogs and toads that we said were in the park. So there's two more that are kind of elusive, and one more that's even more elusive that we actually stated we don't believe is in the park. But there are records, so we kind of looked at them and we're not sure.
Well, I can't say for sure, but the way it was described to me, it was kind of like a blurry photo. It might have been a bigfoot type situation, so it couldn't be confirmed or denied.
[00:23:28] Speaker C: This is some cryptozoology possibly going on.
[00:23:31] Speaker A: Exactly, but that's what I wanted to know. Okay, well, if we can validate someone's photo, blurry bigfoot photo, and say that some of these frogs are out there, then I'd love to find a species.
[00:23:44] Speaker C: That'S never been documented was fowlers toad. We can confirm it's one of the two that cannot be confirmed in there. Fowler's toad.
[00:23:52] Speaker A: It just hasn't been observed. Hasn't been observed since, I think, 71 or 72 in the park. So that's something great about Steel Creek Park is they've had trained naturalists on staff for 50 years. And so that's why we have so much data about Steel Creek Park is we've got park records going back that far. And that was the kind of cornerstone of our study, was having all that data and getting it out there to the world to use.
[00:24:19] Speaker D: I'm so excited about this project because it actually just clicked.
My brain just made that connection. The Sonic the Hedgehog, it learned, I guess, how to defeat the game. And then with this machine learning it's learning the sounds and it can pinpoint the exact sounds like. So you heard that this species was making its call at like 02:00 a.m.. That's exactly right. Is this something that can be applied to other parks in the area? Eventually to, I guess, determine are these species here in these other areas or is this like a concept that you are looking to build on, I guess?
[00:24:52] Speaker A: Yeah, so it could definitely be applied at other parks and locally even just having a system in place. I've kind of been playing around with this idea and maybe I don't know if any angel investors are listening out there, but it'd be really cool. There's wildlife management systems and things like that in place to help city planners and park planners and things like that make good decisions on when they're building things. Not to destroy an ecosystem and things, but once those are in place. I've really been riffing on this idea. It'd be awesome to have more of these automated systems out at the park that were just kind of AI naturalists, so to speak. Now Jeremy and Lance would probably shiver at their jobs, know, we're not going to say replaced, but accentuated with these things, I guess.
It would be a really neat tool, I think, to have a board where maybe a screen or something when you come into the park and there's observations coming in from different places of the park with maybe pings on a map that just ping out, and you're like, oh, this bird is calling in this area of the park today. And so now let's walk out there and see it. Let's see if we can hear it ourselves. So the ability to kind of be in multiple places and take these in and oops, I hit the mic there. We'll get that post right. Anyway, so yeah, I think it could be applied to other parks and you can make it an interactive and fun kind of learning thing. And maybe I just gave someone a really good idea.
[00:26:27] Speaker B: That's true. We need to keep these still doves.
[00:26:30] Speaker A: Well, you heard it here first on the sound barrier.
[00:26:35] Speaker B: Give Mr. Mcming the credit if utilized.
[00:26:38] Speaker A: In the know, like I said, these things exist for making decisions. And so data science is all about using data to make really good decisions. But then I'm always looking at the angle, well, how do we get people excited about science with it, with what we have. And that would be a really fun thing, I think, to walk into a park and have this kind of heat map of where we're hearing things in the park that day, and maybe we can just go find them, launch our own expedition to go see the frog or toad calling or the birds and things like that.
There's already systems like Inaturalist that allow so I encourage you all to download Inaturalist and log different sites and sounds you might have. And so there's AI tools built in that take a picture of you see a bird outside and take a picture of it. It'll try to identify it. If you can't identify it and give you suggestions. And then people come along and they look at your photos and they make observations. And then a certain point, your observation gets marked as research grade. Once there's two or three people that come along and agree, oh, yeah, that's a spring peeper. That's a spring peeper. That's a spring peeper. That's now cataloged observation and your phone logs the GPS coordinates of where when you saw it and now adds it to this big heat map of where we're seeing things. So that's actually one of my next projects I'll be working on, will be taking Inaturalist and other data sources like that and bringing them together into a platform to cross analyze bioacoustic data.
[00:28:17] Speaker B: And so this is app is free on the App Store.
[00:28:19] Speaker A: Inaturalist is yes.
[00:28:22] Speaker B: Go download that.
[00:28:23] Speaker A: It's kind of fun. It's almost like catching Pokemon.
That's exactly what I was liking it.
[00:28:28] Speaker B: To, but I wasn't going to say it, so I'm glad we did my head. Pokemon go for science, but real actual.
[00:28:35] Speaker A: Creatures, real animals, living things. Yeah, exactly. It's kind of addictive, too.
When you're tuning into things and actually tuning into them, you'll start noticing them more.
I was telling you I was a whitewater raft guide over the summer, just kind of random, but we're going down the river, and I'm hearing the frogs that I'm studying out there, and sometimes from incredible distances.
Yeah, I hear it, and it's almost like my volume is turned down on my computer. So just using apps like that and then studying these animals has just made me more aware when they're in my environment. And it's like I said, kind of tuning into your landscape or natural environment.
So, yeah, download the app. It's fun.
[00:29:27] Speaker C: Speaking of frog sounds, John, we have a quiz for you.
[00:29:33] Speaker A: Okay.
[00:29:33] Speaker C: About we have acquired through means we won't talk about at this moment.
Some. Audio sounds of these frogs.
And let's see if you can okay, you can identify the frogs based on.
[00:29:48] Speaker A: Their calls, and you're going to grade me?
[00:29:50] Speaker B: Yeah, we're going to test that theory at the very beginning where we were stating how this will be the determining factor whether you're the master of all trades or not.
[00:30:03] Speaker A: And I know you all listen in. Can't see they're not holding up cue cards. There's nothing in here but us.
[00:30:11] Speaker C: No, it's all hidden away here.
[00:30:13] Speaker A: Well, I might have to let me load up Chat GPT here.
[00:30:17] Speaker C: Okay, this is going to be our first frog sound.
[00:30:49] Speaker A: Yeah. So that sounds like I think that sounds like Cope's gray tree frog. Ding, ding, ding, ding, ding. Yeah. Wow. I think so. This one's actually one of the harder ones to find at the park. At least it was for me. It didn't show up in the data as much, and I just tracked it down the other night. And where I actually monitored these frogs the most was my neighbor's pool. So sitting outside enjoying summer, and I hear a frog, and I'm like, I'm not sure I know that one. And then I was kind of embarrassed to find out. It's on my study list, and I hear it if there's any biologists at home and it's not a Copes great tree frog, I'm going to be embarrassed. But all sources point to that was a copes. But anyway, listening over the summer, it was, what can I do with this? And so one of my studies is going to involve the effects of noise on frogs. And at Steel Creek Park specifically, there's some airplane traffic that goes over because of Tri Cities Airport being close. I just kind of wanted to see do they change their behavior when the airplanes are around or because the airplanes are constantly in their environment? And so I'm sitting there, and I'm listening these frogs at my neighbor's pool, and they're not at Steel Creek Park. So in my head, it's like, well, these things aren't they're not doing me any good. I'm getting audio, maybe recording it with a hand recorder every now and then. And then this train comes blasting by. I live in Johnson City on the tree street, so that train comes blaring by, and I can't hear the frogs anymore. Here's my research study. So I went down and talked to my neighbor, and he let me put up a recorder. And we had some technical difficulties, but I got some audio data and just got to admire those frogs. All summer, they'd set up shop in their pool that was being repaired and worked on.
So they're gone now. They've moved them out, moved them on. But I got to hear it again in some of my data from the park. So I was really excited to hear copes.
[00:32:55] Speaker C: Sweet. Okay, we're going to try a second selection here, see what we can get. Okay.
[00:33:10] Speaker A: I'm going to quiz the people in the room. Everybody's heard this one before. Do you know what it is? I mean, if you're outside in the spring?
[00:33:24] Speaker D: No, I'm not even going to try to guess.
[00:33:27] Speaker C: Spring kermit.
[00:33:29] Speaker A: The Spring Kermit, that'd be a great name.
[00:33:31] Speaker D: I was thinking of grasshoppers or crickets.
[00:33:34] Speaker A: So that shows where I well, those are the sounds that all kind of blend together in that symphony that they play.
This is the spring peeper. And so when they come together in the it's the backdrop of sound in the spring in East Tennessee, in most of the United States. And so they're amazing to look at. They're so tiny. I mean, we're talking know the size of my thumb or a little bigger, but they really get going.
And when you hear them all, uh, sometimes they'll call later in the year and OD times if it's warm and in the southern states and things, but they're impossible. In fact, in some of my training algorithms, the spring peeper is classified as noise because I am not really interested in conservation for spring peepee. I mean, I am as a whole bad to say that I'm not interested, but there's tons of them and they're in all my audio data. So I actually take the AI and say, just treat that as noise. When I want to study the spring peeper, I'll let the AI know that that's what I want you to hone in on. But otherwise they will sometimes be louder than the things that are the frogs that I'm actually trying to record. And they drown out because they're just so loud in mass.
[00:34:56] Speaker B: We call this term in psychology sensory adaptation, where it's like so you're essentially training the AI to have the sensory adaptation of when a loud AC unit starts to kick on. At first you hear it, of course, but then over a few minutes, you just tend to adapt and your brain filters out that information is unimportant, so it's like you don't even notice it. So that's cool that you could really do that with AI and you say, okay, treat this as just noise and just ignore it. Filter it out as unimportant.
[00:35:26] Speaker A: That's right.
[00:35:27] Speaker B: That's really neat. It just, again, ties it all together with artificial intelligence being so closely linked to psychology, computer science, biology, I mean, it's all intertwined. So that's really cool.
[00:35:39] Speaker A: That's really cool. Yeah, I always love talking to you about that because I told you I was interested in psychology at one point. That kind of leads into, oh, we're creating algorithms that learn and then adapt.
Yeah, it's really exciting stuff. Yeah.
[00:35:58] Speaker D: So have you all used your machine learning to I know that you said you have gotten, I think, logged eight out of ten of the frog. So have you all, I guess, tried taking the recorders of the machine learning and I guess making those eight that you've already found, like noise so that.
[00:36:12] Speaker A: I could that actually be one thing I could do. What I'm gearing up for now is basically I've got it. It's been set up for a while. I really could have ran this test, but I was kind of waiting until the last recorders are done. So I have one recorder still out at Steel Creek Park. And as of maybe a couple of hours ago, the batteries probably went dead on it. So it's out there. I got to hike out and get it, but that's the last one that I plan on and it's kind of bittersweet. I'm going to be walking out there, but it's also at the same time, okay, finally we got all the data that we need, and I'm going to stop this now. It's wintertime. Let's go inside. The frogs have all they're hibernating now. I'll go hibernate with my data. But anyway, we're getting ready to run a full scale sweep on it, and the two that are two or more that are left over, we'll run a full scan on. And with the current profiling that I've done on how long it would take to run the full scan of the data using the AI is going to take somewhere between a week and a week and a half, and that's pending. No failures in the code or failures in the software running. So these are really big processes, and once they've been gone through, we can kind of look at the data and say, okay, there were some instances of a certain frog here. We'll zoom in and actually listen to it and verify it. And so that's part of what my system will do is when it hones in on something, I could then just go pull that second or the three X seconds of audio around it or and have it make a composite file on the fly for me to pull up and see where that's at. And it does have false positives. I mean, there's times so there's this really one interesting one that came up, actually.
Tom, would you care to play the green frog for a minute? This is sometimes called the Banjo Frog.
I mean, I'm just assuming you have a recording of a green frog over there.
[00:38:24] Speaker C: I do, yes. Let's queue up the green frog. Yeah, here we go.
It sounds like a banjo.
[00:38:51] Speaker A: Yes. So they actually get the nickname the Banjo Frog, and it is just kind of a bonk. And anyway, so there was an instance in one of my data where it said that there was a green frog. So I went out there and looked and listened to it. And the best I can tell, it is some raccoon or bear or something scratching its back on the tree. And the recorder gets lifted up and dropped back down and it made a bunk. And it tricked the AI into thinking, oh, that was a green frog, because it made pretty much the same relative frequency with the plastic case of my recorder. I don't have them in here, but it was just enough resonance and just enough sound to make it go broke. And it thought it was a green frog. And it's funny. I probably should have sent you that recording, but it's definitely something scratching itself on the tree.
I guess it could have been a human. Homo sapiens are known to be out at Seal Creek Park, too.
But yeah, as an AI researcher, things like that can be a little disheartening. But how many times have you heard something? Did I just hear that? So we have auditory hallucinations a lot, too, and that's something that AI is known to do, is hallucinate from time to time. And so we're able to rationalize things after that. But going back and listening to it, I could totally see where it sounded like a green frog in isolation if someone had played it to me, I've been like, and they told me it's a frog. So that's where we get into how many ways should we classify all this sound if you're only looking at it in terms of, here's a sound, what ten frogs are these? Well, what if it's not a frog sound at all and you're looking at it through the lens of it has to be a frog sound, or it has to be this sound. So the bigger we make these tools or I guess the more things we can classify from these tools and build these hierarchies of basically 20 questions. What does it sound like? Okay, well, now let's go and cross analyze it. That's part of what my software is starting to do at this point.
[00:41:13] Speaker B: Any other quiz type sounds?
[00:41:18] Speaker C: We've got a few more frog sounds if you want to go with how do you all feel? Another frog sound.
[00:41:24] Speaker B: Let's do one more.
[00:41:26] Speaker D: Two more.
[00:41:27] Speaker A: Honestly.
[00:41:30] Speaker B: It'S impressive that you are able to pick up on it well so quickly.
[00:41:34] Speaker A: So we had to edit all the data that we were given from So McAuley Library, the Cornell Lab of Ornithology. We requested the data for our training model.
When I say we, myself and one of my former students, Zoe, she committed about 40 or 50 hours to the project editing sound files for me, which was great, and she's going to be continuing some of the research with me. But anyway, after listening to all actually editing all the sound and putting it and tagging it and getting it set up for my model to be able to listen from, I can't unhear them most of the time I've been trained.
[00:42:20] Speaker B: Noted it's ingrained in you forever.
[00:42:22] Speaker A: But sometimes I got that recall thing where it takes me a minute, like, oh, what is that?
[00:42:27] Speaker D: Would that be considered machine learning?
[00:42:31] Speaker A: Biological machine, yeah, exactly.
[00:42:35] Speaker B: I'm sorry I interrupted. It was funny that you always said that we were like meat robots.
[00:42:41] Speaker A: I get so many dirty looks for saying that, and I've actually had to stop saying it at some camp.
[00:42:46] Speaker B: We may have to edit that out.
[00:42:48] Speaker A: We are meat robots.
When you say that, though, people get a little mad. And I understand we believe in our soul and we understand that we have some special there's something special, and machines are special, too, in my opinion, but biological machines, we are advanced, and our brains are still the most advanced computing devices we have on the planet. And they're analog. And so it's quite amazing what we can do and reducing it down to meat robots offends people. But I like kind of describing it in that way. I love it. I'm pretty sure I heard that somewhere else. I'm not that clever. Yeah.
[00:43:37] Speaker B: There's a quote by Hank Green. It's like the most complex system in the universe, and we all get one of our very own, the human brain.
[00:43:44] Speaker A: It's exactly right.
[00:43:45] Speaker B: I think it's fascinating. But let's hit that sound. Let's test you again.
[00:43:50] Speaker C: All right. Okay, here we go.
[00:43:59] Speaker A: This is without a doubt, the wood frog.
[00:44:03] Speaker C: Yes. Again.
[00:44:07] Speaker A: They kind of sound like a bunch of ducks, like clicking around or geese, maybe.
So the wood frogs are one of the first frogs that you'll hear of the season.
You'll sometimes hear spring peepers out. It doesn't necessarily mean they're out for the reasons that male frogs call for mating purposes. So the wood frogs are the first to kind of come alive and be like, all right, we're ready to find some mates. And at Steel Creek Park in the Wetlands area if you go out there early February. So I believe the first observation I made of them was February 10, but there was an observation on the 9th on. I naturalist from the 9th February, 9 on for just about two weeks. It's all you can hear when you walk through that area, and it is deafeningly loud on the recorders. I can set the gain of how high it is. The mic and I actually had to dial it back over a couple of weeks because the recordings were all just clipping. They were so loud and they're so tiny, and then they're gone at that point, once they've got about a month that they're really active and it's actually kind of hard to hear one at a time. And I noticed that you hear a and then they all start going together, and once they ramp up, they'll go and sometimes the audio recordings would be like three in the morning, and it's just dead silence, and then one goes and then it just takes off and they get so loud. So I love the wood frog, primarily because it's the first one I heard on my study. And I started going out to Steel Creek Park in about mid January when I got in the first recorder, and I knew that the wood frog would be the first one we'd hear. I just didn't know when exactly it would hit. And so early February is where I kind of got my first confirmation. Here we go. We're getting started.
[00:46:21] Speaker C: Okay, we'll do one more here. This one has an interesting name. It puts me in mind of something, but here we go. Okay.
[00:46:36] Speaker A: So this is, I think, turning into actually my favorite frog sound. It is the upland course frog.
[00:46:44] Speaker C: Yes.
[00:46:45] Speaker A: And yeah.
Okay. I really like the fowler's toad. If we end up playing it, I'm sorry, you don't have a fowler's toad recording.
But the fowler's toad I wanted to look out because they kind of sound like they're screaming. But this one I love how it's like an increasing clicking sound. The pitch rises up and some of the other frogs on my list, there's a pickle frog. It kind of sounds like running your finger across a comb. But with the upland course frog, it's like you're running your finger across a comb that all have different size teeth on it. And so it's increasing or decreasing as you go up with it and you're timing how much you release your finger across the comb. And so these frogs were over at another site across from the Wetlands Area, and they got really loud as well. And this was about the beginning of March when they started coming out. And very easy to hear at the park, too. They got very loud as well. Almost not quite as loud. I don't think there's as many upland course frogs as wood frogs, but they were just so fun to go listen to. And one great thing about the ones out there is I haven't looked at the data at night very much to see, but actually we go out about eleven or noon. I taught a 03:00 class last semester or last spring, and so I go out there before I came here and they'd be out and just going all day. And so they're one of the frogs that a lot of the course frogs of that type will come out during the day and call as well. They're not just nocturnal in that respect. So it was nice to be able to it was spring has sprung. It was the beginning of March. It felt great out. And here's one of the frogs that I'm looking for and able to put eyes on them and ears now to totally change gears. Okay, I'm down.
[00:48:42] Speaker C: What can you tell us about your music career? Because we know you've played in several bands, I guess going back to your college days.
What kind of got you interested in music and how was life on the.
[00:48:56] Speaker A: Road playing in the band? Well, I think the word career is definitely an overstatement, maybe DIY career.
I've been involved in a bunch of different bands since just before college. I guess I joined my first band in high school, and then I play punk and hardcore music and stuff that's not I mean, we're not selling out stadiums. We're selling out basements in people's houses, though.
Anyway, without a doubt, that's my mom.
She's a great musician, plays guitar, and just about anything that she picks up and can sing. And so music was a big part of growing up. And our taste may be different music. She's not maybe into punk and hardcore, but she'd still come to my shows and support me and things like that.
So, yeah, I played in a bunch of different bands and spent a good part of my 20s riding around in a van, making questionable decisions with my friends. And we had a lot of fun. I left school for a while. I like to actually share with my students that I dropped out of school when you're in your early 20s, part of it's finding yourself. And I was taking classes, and I was enjoying it, but I was distracted by a lot of different things going on socially and things. But music was fun, right? And so I guess I sit right in the middle of the introvert and extrovert. Was that amber? You're a psychologist, right? Yeah.
[00:50:48] Speaker B: You can have both traits, man. I as well share in that with you on the fence, depending on the situation.
[00:50:56] Speaker A: Exactly.
[00:50:56] Speaker B: Introverted and other times more ambivalent. Yes, I think that's what it's called. I need to refresh on my Myers Briggs We're personality here soon in my class. I need to brush up. Yeah, for sure.
[00:51:11] Speaker A: So, yeah, I really like performing. It was really fun. And my first show that I ever played well, not ever played, but with one of the bands that I was with for a longer amount of time, I got up on that stage and they had such a following that there was enough people there. And I remember playing the first show with them. It was out of town, and my knees just almost buckling underneath me and scared to death, and that's a rush, right? And I got over it, and I kept doing it and kept doing it. And then, let's see, what, five years after that, I taught my first college class, and my knees did the same buckling thing in front of people. And it kind of makes sense that I'm a professor now, that it's part of its performing, getting up in front of people and doing that. So I still have to have that me time every now and then.
Put me in a closet and I'll study frogs, and then I'll come out and give a performance. But, yeah, I owe a lot of that to music and whatever capacity that I really did that in.
We did tour a little bit southeast and a little bit up north. Didn't make it out to the West Coast very much, but I learned a lot, even kind of just being a slacker for a while.
You can learn a little bit from being a slacker every now and then. And I try to tell my students that you just got to take time to learn and grow and figure out what you like and grow into yourself, and sometimes you end up figuring out maybe ten or 20 years later, in my case now, that all of it can be connected in some way. So my love for music is how I can do these bioacoustic projects and know about recording and software to use and things like that. So it all kind of weaves together, for sure.
[00:53:12] Speaker B: Whenever you were in your 20s, your earlier 20s, did you ever think that you'd end up doing what you're doing today?
[00:53:21] Speaker A: Absolutely not. I dreamed of maybe going all the way up to, okay, maybe I could be a researcher or maybe get a doctorate. And then after two years in colleges, I'm like, well, that's not happening.
So, yeah. I never once even considered being a professor of computer science. It was definitely just kind of thrown on me. And actually, the man responsible for one of the men responsible for that I'm seeing tonight for coffee, dr. Phil Pfeiffer over at Tissue. He was the graduate, one of the coordinators that ended up getting the teaching position. And Phil will probably listen to this if I tell him I mentioned him. But the way this went down was I got an email on a Friday. It was like, all right, John, we did find you a graduate assistantship.
You'll be teaching CSCI 1100 on Thursday next week, the meetings on Monday.
[00:54:19] Speaker B: Oh, my goodness.
[00:54:22] Speaker A: So when I say thrown into teaching my first class yeah, I found out on a Friday and taught my first class the next Thursday. And I had great mentors over there.
Sam Burke was the coordinator over there and lots of great people to lift me up. And then I was like, man, I like doing this. This is cool. I'm kind of performing kind of sharing stuff. I get a little bit of that boost from being extroverted every now and then, and then I disappear in a mile.
[00:54:55] Speaker B: I can relate to that. And you mentioned teaching kind of like reminding of performing similar to maybe not similar, but maybe the same feeling that you get whenever you are performing musically. Speaking of performances, are there any performances that you may remember most memorably or even some that you'd like to forget?
[00:55:15] Speaker A: No. Yeah.
[00:55:17] Speaker B: Or maybe both.
[00:55:18] Speaker A: Yeah. Well, we won't.
That's a good one. I'm trying to think. There's definitely ones I have forgotten. We played a ton of shows.
We were based in Johnson City, and we play a lot at the Hideaway. That's my home. I've been playing and hanging out at the Hideaway for almost 20 years now.
But we might as well have been the house band at one point. The band I was in, we were playing so much, even to our fault.
So we start playing shows. There's no one here. Well, maybe if we didn't play last week and next week people come out. So anyway, there were definitely ones I forgot because we played so much.
But I saw a picture the other day, and I hadn't thought about this in a long time, but one year it was a different band with some of the same guys, and we played Halloween and playing Halloween, I guess there's three holidays that are the best to play as a band. You got New Year's Eve, you've got St. Patrick's Day, and you've got Halloween. And I love Halloween shows. It gives me excuse to dress up in a costume and play.
So that year, we decided that the four of us would be Team Zisu. Do you remember that movie, the Life Aquatic? It's like a Wes Anderson flick.
[00:56:51] Speaker C: Bill Murray and Bill Murray.
[00:56:53] Speaker A: It's hilarious. You should check it out. And this guy makes nature documentaries, and so I hadn't thought about it in so long. And then I see this picture of me, I mean, 2022 years old maybe, and Team Zisu wears these all light blue jumpsuits and then the red beanie or red toboggan, and there I am wearing it and like, huh.
And I study wildlife is well, how did that all get tied together?
So I'm actually thinking about dressing up as Steve Z Sue this Halloween.
If you all haven't seen the movie, you got to watch it. It's very funny. It's very dry.
[00:57:44] Speaker B: Wes Anderson those are usually my type of movies. Bill Murray's just that's going to be up my amazing.
[00:57:48] Speaker A: Yeah, you should check it.
[00:57:52] Speaker C: Now, one thing I did want to also get to you were a panelist, I think, recently at the Education to Employment Summit in Medevie over there in Kingsport.
What was kind of that experience about as far as talking about education, what Northeast State's role kind of is in that for the region and kind of what you hoped people took an interest in for students here at Northeast State and here at our college.
[00:58:23] Speaker A: Yeah, the Ete Summit was really cool.
So that was put together. The First Tennessee Development District. Lottie Ryan's put that together, and Lottie and I have worked on projects for a while. Our first ever meeting was in the Science Hill cafeteria maybe seven years ago, where she first pitched the idea of careerquest to me and wanted to bring me on board. And that's something we've been doing now, the tissue, for quite a long time in the spring, which is a career fair for kids. They can come learn about all different fields and things like that. Will's group here killed it this year and brought a trophy home for us, even.
Yeah. And then anyway, so we have a lot of fun out there with the kids. So the Et Summit, I didn't really know what to expect going in. I'd never been before, and I believe I was invited last year and just didn't get to go.
And so this year, there was a lot of different focuses. And with our specific segment, AI is really new, and there's a lot of things floating around, and education is just we're upheaved. How do we ethically and intelligently integrate this into how we do things? Because it's not going away. And that's something we kept reiterating. It's here. It's already here. It's been here for a while.
We're just now getting the platforms to actually use it, and people consume it in mass. And so how what do we do for that?
Anyway, so our segment, I was panelists, we got asked some really good questions, and the speaker who was with us got to dispel some myths about AI and maybe even reinforce some things that we needed to understand.
And then there were a lot of other great speakers speaking on kind of all matters of kind of the education to employment pipeline. How do we get people that need training and skills? How do we get them the skills, and then how do we get them in the workplace? And something that I was actually really the point that I took home, I guess, the most from it was looking at veterans. So there were a couple of speakers that spoke on after people leave the military, they sometimes have a hard time integrating back into a normal job, and it takes some extra care from employers to kind of mentor and get them what they need and the resources and things. And a lot of times, military folks feel a little disconnected or purposeless when they get into kind of a day job.
And so they were really sharing a lot of tips for making your workplace veteran ready. And it was really a whole demographic of students that anytime I have a veteran in my class, I know that if I tell them to read something, they're going to read it. If I tell them to watch this video, they're going to watch it. The discipline there is something that we as professors really like to see in our students, but knowing that they had more needs and it wasn't just another student that, oh, I can count on them being disciplined and how to react and how to give them what they need. So the Ed Summit as a whole was just a really positive experience for me to learn things that I had never really thought about or put together in my classroom.
[01:02:09] Speaker C: Interesting.
I have another question about regarding AI and frogs and Steel Creek Park and our region in general. The region is obviously growing heavily in population development.
A lot of buildings springing up here and there and everywhere.
Can AI. Tell us what kind of long term effects this is gonna have on the ecosystem of Southern Appalachia? I mean, certainly the ecosystem of Southern Appalachia has taken a beating over the decades for various reasons. We won't get into here. But it's developing can AI kind of map out how we can protect the environment around here. And so tree frogs and upland church frogs can be another kind of wildlife. People in general can have clean water and that's right.
[01:03:12] Speaker A: So I definitely think know they're already being integrated into city management and know, we're even stepping back. So the work that I've done with Jeremy and Lance, we're working on another publication right now where we're actually using a hundred year old ecology model and it's no AI required.
This species area curve that kind of tells us what our bounds are for the amount of land that is habitable in our region versus inhabitable for frogs. So if you think about, well, we classify this whole area here as one big area, but how many places can they actually live? And so if we start covering it with massive amounts of city, we lose that land. So what our regression model can tell us is kind of where these thresholds are. For once we reduce the amount of land that they can live in, there is actually a species count and a richness that we can tie that to and we say, okay, if we lose 300 more acres we're probably going to lose a species of frog. It's going to migrate out of this area or just die off.
Now of course there's a little bit higher threshold. The will to survive is definitely higher than linear regression, greater than linear regression sometimes. But they are really useful models. And so what we're looking at taking in the next step is there's even more data than just, well, we've got this much area and this many species. How do we create regression from it or a model?
We can take all the data we're collecting from, say, my project and others and feed it into these larger models that can now look at not just the kind of metrics of where was this thing? And how do we tag or classify the land that it's in, but all the conditions that are going on along with it.
What's the temperature effect, how do these, I guess, planning management things affect the environment but also what's the state of the environment? And so these models take in very large amounts of data and can tell us some things that pick up patterns that we don't necessarily pick up on ourselves. So hopefully we can use some of this data. As long as I think that the biggest problem is I can sit here and we can spit out these models all day.
Are people willing to do anything about it or actually, do we care about how many species we have in our environment or do we care about building a new building and making so we have to be really smart with our decisions and that's up to people that make decisions. So I guess, can we be told, can we make models that tell us things about it. Yeah, we can, but how we apply it and use it is up to the next part of the chain. So I hope that we start taking a look at the effect we're having on our know. I spent last night reading papers on the effects of noise, and so I don't know if there's a documentary that came out several years ago. I remember watching it when I was working for Etsu at the time, and I was on a business trip for them and was in my hotel room, and this documentary came on, and I was fascinated by it, but it was basically all these whales are turning up on beaches all over the world and they're dying. And they started tracing it back to noise in the ocean. Think about if someone came in your house and just blared a loudspeaker of shipping freight boats or something. That's what they're hearing. But maybe I'll come to your house and play some of my music for you.
So I come to your house and I play noise, and now I'm doing it. Twenty four, seven. And you don't have a way to turn it off. You don't have hands to plug up your ears. Maybe you don't have ears. Maybe you can feel it through your body, it's being played so loud. So the noise we generate from everything we do now, there has been push, for instance, in the oceans, to limit how many boats can go through a certain area at a certain time or to make ships quieter.
We often do things and then see their effect, and we're reactive way too often, which is fine, as long as we're willing to react. And in these cases of noise, it's everywhere. So we consider ourselves lucky out here. We live in the middle of the Appalachians.
We're separated from all that, but roads run right past wetlands all the time. Planes fly over wetlands. Actually, I need to look at the map. There's a rumor that there is a pond right here behind Northeast State, and it might be on Tri Cities airport's property.
And someone was telling me that they could look over their house fence and see that pond behind the airport and it'd be a great study site. But I don't know if it's on airport property that anybody be welcome to let me do an ecological study on their airport site because of the negative implication of what could be going on at that pond. Behavior changes. Now, obviously, if there's animals in it, it's surviving to some degree, but noise can drive you crazy and they can drive animals away.
It can lead them to move to different areas, and it interrupts their ability to hear their mates call, and so it's a disruption of the ecosystem. Now, obviously, as a musician, I love noise. The noise is better, but we just got to be smart about how we release it into our world environment. Wow.
[01:09:31] Speaker B: That is so true.
[01:09:32] Speaker A: Oh, my gosh.
[01:09:33] Speaker B: Yeah. The way that you just described it all just clicked for me right there.
[01:09:37] Speaker A: Yeah. So that's what I'm going to be looking at next, is I'm not trying to say we shouldn't fly airplanes or drive cars, but if all it took was well, we noticed that planes coming in on this runway fly over this one wetlands, and all you've got to do is maybe fly in from a different angle or something like that. It's a small incremental change that can maybe help. Unfortunately, maybe that route costs more fuel. Now, are we willing to spend that money to not disrupt an ecosystem?
I don't know. That's some of the tougher questions I don't want to have to, I guess, deal with.
As someone studying these things, though, it's pretty optimistic for me to believe that all of my research will get people to change their ideas. But if it's out there, someone might kind of look at it one day and be like, well, we can make smarter decisions when we're going into these processes to make sure that we're not having to be reactive to an ecosystem and crisis.
[01:10:41] Speaker B: Yeah, you're starting the conversation and getting it out there, and these are necessary things we need to talk about for the future, I really hope to see, because it's outstanding research. I mean, I'm just impressed in general with it, and I really hope this comes to fruition in a lot of different ways for the betterment of our really impressed.
[01:11:01] Speaker A: Yeah. Thank you.
[01:11:03] Speaker B: So appreciate you, john, for taking the time out to be a part of the podcast.
[01:11:08] Speaker A: Man thank you. I've got just a couple of other plugs here. Please give me just a minute.
[01:11:15] Speaker B: We've got plenty of time.
[01:11:16] Speaker A: Okay.
I'll be speaking October 14, and I believe the event is October 13 and 14th at steel creek park. It's wildlife weekend. So this is primarily aimed at a younger audience, but kids, families there's usually nature walks and things like that, hikes, exploratory stuff.
So, of course, I'll be talking about frogs, and we might do something similar to what we did here in the studio today.
Name that frog where someone might I might put jeremy on the spot and have him compete with my AI. Can you identify it? I joked with him about it, and he goes, well, you know, the frog sounds better than I do, man. Come on. Don't put me on the spot like that. But, yeah, there's a bunch of little tools and stuff I thought about building for that. But I will be talking out on wildlife weekend and then the atme conference in Atlanta, association of technology management, applied engineering. I'll be speaking at that conference, so that's more for atme members. But with wildlife weekend, that's right here in our community. Bring your kids. Come on out and learn a little bit. I think after doing this project with them, I might end up being kind of a regular addition to Wildlife Weekend if I can make it fun enough and have continual progress to show at the park. So it'll be a fun thing to do.
Just a couple. Thank yous. So this research, I appreciate you talking about my research with me and everything, but I definitely have to thank some people for that. So of course, Jeremy, we've already thanked him earlier in the show. He got his plug. But no, I got to thank him and Lance for getting me involved with their project. And it was just a spark. It was one spark. This was, I guess, the fall of 2021 when I got that phone call from him. And so here we are two years later with whole new ideas, whole new papers, and just taking it in that direction.
My former student Zoe, she's been great, has moved on to another college now and will hopefully be taking one of these projects with her. So she did a lot for helping me get the groundwork of training the AI. And there's been a couple ideas we've come across, and so we're looking at the airplane noise problem. So I think that she's going to kind of spearhead some of the efforts on that. So I want to thank her for helping me get this off the ground. I wouldn't have had any of the AI done by the summer. I would have went into the summer without any training stuff. So big thanks to Zoe and then all the way back around the Friends of Steel Creek Park. So they are the ones that provided me with the tools through a small grants program they have. And so I purchased one of the recorders myself to just be able to, at the very least, get the project started or done with one recorder. And so they provided me with three more that they purchased. And so they're the ones that put together Wildlife Weekend. And I really appreciate the Friends of Steel Creek Park for putting this together for whole I wouldn't have over a terabyte of data without them. So I appreciate every bit of that.
[01:14:39] Speaker B: Amazing. Well, yes, I second that. Shout out to you all. Appreciate you supporting John. We get to benefit by having him here on campus at Northeast State.
Again, John is an assistant professor. Congratulations on your promotion, by the way.
[01:14:54] Speaker A: Thank you. Thank you.
[01:14:55] Speaker B: The assistant professor of Computer Information Sciences, we're grateful to have him part of the podcast today. Thank you so much for your time. We'll talk at you next time as we continue to break the barrier.