00:00:01 Speaker 1: Welcome to the Wired to Hunt podcast, your guide to the White Tail Woods presented by first Light, creating proven versatile hunting apparel for the stand, saddle or blind. First Light Go Farther, Stay Longer, and now your host, Mark Kenyon. 00:00:19 Speaker 2: Welcome to the Wired to Hunt podcast. This week on the show, I'm joined by Darren Durham, an avid deer hunter and a program manager at Moultrie Mobile, to discuss the fast evolving integration of AI into trail cameras and how we deer hunters can best take advantage of this new technology. All right, welcome back to the Wired to Hunt podcast, brought to you by Moultrie. Today in the show, we are talking trailcams and specifically AI, artificial. 00:00:53 Speaker 3: Intelligence, and how. 00:00:55 Speaker 2: This crazy new technology that seems to be all around us and the rest of our lives is now being integrated into deer hunting and trail cameras specifically. You know, this is something that I have conflicting thoughts on when it comes to many of the AI questions of our day, right, How it's maybe taking jobs, How it's how data centers are impacting neighborhoods and wild places. How this technology is impacting all sorts of different things. I've got real concerns, but at the same time, it is a reality of our world and it is now part of everything right AI is part of how we send emails, as part of how we get our news. It's part of how so many different things are done across all parts of life, banking and business and writing and reading and music and your algorithms that develop your Spotify playlist, you name it, it's AI. So what I wanted to do and what I've been thinking about lately is just, you know, trying to understand where this fits in with our hunting world right now. And I think one of the main places that is is with trailcams. So I was asking around trying to find who might the experts be on this, who could speak to this from a position of authority and experience, And who I got to was Darren Durham. He is a program manager at Moultrue Mobile and his job, a big part of what he does is explores how to integrate AI into Moultree's cameras and their application, their app in which they help you or allow you to analyze and review and make predictions and learn all sorts of stuff based on those trailcam pictures and both sides of that, the cameras and the app. There is really deep AI integration, it turns out, and this is the case across many other camera companies too. 00:02:45 Speaker 3: This isn't just Moultrie. 00:02:46 Speaker 2: So what I want to get Darren on here to discuss is where is this what does this look like? And not just for Moultrue, but for many different camera companies. How can we as deer hunters best use this stuff? Where does it makes sense to use it? How can it help us as hunters? How can it help us take better photos or better understand our photos, or save battery life on our cameras, anything like that. These are the types of questions I eventually wanted to get out here was really the application side of this. What do we need to know, what don't we need to know? And how do we do this stuff? That is what I cover today with Darren. If you use trail cameras of any kind, there should be information here that can help you help you understand how this technology is changing, what can be done right now, and then also interestingly, what might be available here in the coming years. That's another final thing that I pressed Darren on is what's the future going to hold? 00:03:38 Speaker 3: So hopefully you guys will find this one interesting. 00:03:41 Speaker 2: Again, I think that there's plenty here to explore, regardless of the camera company you choose to use. Darren, of course is from Moultrie. That's something I've got experiences with as well. But I don't care what team you play for when it comes to the cameras. I think you're going to find this one interesting and I hope you do. So thanks for joining us here. 00:04:06 Speaker 3: All right with me on the line now, is Darren Durham. Welcome the show. 00:04:09 Speaker 4: Darren. Thanks happy to be here. 00:04:12 Speaker 2: I really appreciate you making the time, especially here on a Friday afternoon in the summer. I know you've definitely got better things going on than this, so thanks for making the televion. 00:04:21 Speaker 4: Then it's like ninety to one hundred degrees outside, so. 00:04:24 Speaker 2: Fair enough, this might be actually a good excuse than to stay in the air conditioning. 00:04:28 Speaker 4: Right. 00:04:30 Speaker 2: So I wanted to talk to you, Darren, because I have a set of questions and curiosities related to something that I just keep. 00:04:40 Speaker 3: Hearing about, keep reading about, keep. 00:04:43 Speaker 2: Wondering about, over and over and over, and I don't think I'm the only one on this topic, because AI, artificial intelligence, it's you know, for the last four years or so, it has been absolutely you know, a fury of news and changes and he in hysteria, you know, across all aspects of life, right, Yeah, And I feel like this is one of those things that some people are hyping it up is the best thing since sliced bread, and then other people are talking about how this is awful, and there's all these concerns and worries about it, and interestingly, there's this set of ways that it's starting to infiltrate into the hunting world that's gotten me curious about, Okay, you know all of those other things aside, how does this stuff impact what we do as hunters? 00:05:31 Speaker 3: And I've seen it. 00:05:32 Speaker 2: Popping up more and more in relation to traial cameras, and so that's why I wanted to talk to you as someone who's actually been involved in that side of AI use and application. And so what I wanted to start with was kind of high level, Darren, when you think about all of this, when you think about you know, all of this hype, all of this hysteria, good and bad, and you look at it with what you know and the way you've been involved in it. 00:05:57 Speaker 3: How are you thinking about it? 00:05:59 Speaker 2: You do you look at AI, you know, in the context of hunting and trail camera applications and kind of this world we're in. 00:06:06 Speaker 3: Would you say that AI seems to be overrated and overhyped right now? 00:06:10 Speaker 2: Or do you think that it's it's it is and or it's going to be everything that's hyped up to be, and that maybe we're just scratching the surface. 00:06:19 Speaker 5: Well that's I mean, that's obviously a tough question. But I do think that there's some parts of it we're touching the surface of. I think there are other parts of it that, you know, we we've pretty much gotten what we could get out of it. I mean, one way to you know, frame up AI on a camera real quick is that you know, there's there's two different things with AI. There's the device or the the app itself actually classifying images or tagging images from what the artificial intelligence sees. 00:06:53 Speaker 4: It's object detection of some sort. 00:06:55 Speaker 5: It's sees something in the picture, it tags it with what it's all. And then there's obviously the aggregated data you know that they does get lumped together as AI, you know, and like pretty much. 00:07:06 Speaker 4: Every app these days has some sort of way for you to filter pictures. 00:07:13 Speaker 5: I only want to see Bucks, or I only want to see Deer, or I only want to see Bucks from on this day, from twelve to three. You know, like like all of that is is now. It's a reality for us. So back to your to answer your question, you know, knowing those two things, I think we're just scratching the surface of how the data gets aggregated and delivered to us. You know, like what else can we scrape out of a huge data set that you know, we're as a as a camera company compiling for you as a customer, you know. And then the technology itself, which is like the the root of how it works, which is the one way to think about it, is like the it's kind of like an an ARC, you know that it's detect, classify, act, and predict, and each one of those is a phase that AI will help you do, whether that AI is on the device or that AI is encapsulated somewhere in in the in a server in a cloud server somewhere that you know, delivers it to you via via an app somewhere so so anyway, just bring it back around. I think it's I think we're just scratching the surface of of of the data aggregation and data delivery to the customer. I think the tech world kind of drives some of the other, you know, what the hardware self is capable of. But I think we're just scratching the surface of that too. You out actuality, how do you guys? Yeah, I know you mentioned a couple of different phases there. There seems to be the you know, detection, and then there's a analysis maybe ye when it comes to like teaching an AI or somehow getting this tool to be able to identify an animal or identify some detail, like how does how do you guys teach that stuff? Like? 00:09:04 Speaker 2: I understand how when I think of like chat, GPT or something. Right, they teach these large language models by feeding them millions and millions of data points of all of the texts that we've ever created on the Internet, and then you know, these models then use that to understand and predict what's you know, the next thing should be? 00:09:23 Speaker 3: Right? How does that work? When it comes to like photos or. 00:09:27 Speaker 2: You know, an animal, well, I have no idea how that might look is exact same. 00:09:32 Speaker 5: Well exact same, not exact same, but the method or the methodology or take you know, technology, whatever you want to call it, Like, the direction behind it is is that you know, we we have thousands of images taken by a device, by a camera. I guess I shouldn't say advice, but taken by a camera could be a device, but taken by a camera. Those thousands of images get put in and annotated, which means that somebody physically sits there and says yes, but yes deer, yes, kyo, yes. 00:10:03 Speaker 4: Duck whatever. 00:10:04 Speaker 5: Whatever it is that that AI engine is looking or that object detection model is looking for, there's somebody behind there at some point that has trained thousands of images in order to create that network of ability essentially for you know, the AI engine to say, yep, I am eighty percent confident that is a deer based off of the training that I've had. 00:10:28 Speaker 4: So you know, like. 00:10:31 Speaker 5: We're we're pretty accurate, I would say, you know, like we're within We're ninety plus percent accurate on a lot of images. You know, But it's all about the training. You know, if if that model's never seen a deer with a feeder in the background. It's it's a higher likelihood to get that wrong than you know, a model that has seen ten thousand deer with a feeder in the background, or with a particular are looking limb in the background that makes it look like it has horns or something like that. So I mean, it's it's It really just comes down to, like the camera. 00:11:11 Speaker 4: The same technology that. 00:11:13 Speaker 5: Has been used for years to tell the camera to take a picture as being used, but what's happening after that camera snaps that image. It's completely different now, so that that camera essentially goes and looks across that entire image and says, do I see anything that looks like something? And then it then the model says, oh, yeah, I see three things in there that could be something. 00:11:36 Speaker 4: Let's take, for example, like. 00:11:37 Speaker 5: An image that has a feeder in it, a deer in it, and I don't know, say a tall stump in it. 00:11:44 Speaker 4: That that object that object detection model. 00:11:46 Speaker 5: Might or the AI whatever whatever you want to call it. The technology sees that there are three different things. It sees a stump, it sees a deer, and it sees a feeder. It knows nothing of any of the. 00:11:58 Speaker 4: Three at first pass. 00:12:01 Speaker 5: So it draws a box around all of them, and then it says, okay, I've saw I saw three three things. I need to go look at those three things and see if they're in my training set. 00:12:09 Speaker 4: And then it goes it says. 00:12:10 Speaker 5: Well, I've never Darren didn't tell me I needed to look for a stump, you know, Darren didn't tell me I needed to look for a feeder up. 00:12:17 Speaker 4: But there's a deer. Darren told me I needed to look for a deer. 00:12:19 Speaker 5: And then it kind of runs through its bank of you know, if deer, then what is it a buck? 00:12:26 Speaker 4: Does it have horns? Does it not have horns? 00:12:28 Speaker 5: You know? And and I mean, you know, we're there were some things are being done now to like for like future future, to move it, move it further than the future, which are like is it a mature buck? You know? 00:12:43 Speaker 4: Is it a small buck? Is it a fall? 00:12:46 Speaker 5: You know? 00:12:46 Speaker 4: Whatever? 00:12:47 Speaker 3: You know? 00:12:47 Speaker 4: But but all in all, that's how it works. 00:12:49 Speaker 5: It Essentially, the device takes a picture like it always has, and then it runs through this big post program that says do I see anything in the picture? 00:12:56 Speaker 4: And what are those things I see in the picture? 00:12:59 Speaker 2: Is there you mentioned that you're going and training, you know, based on somebody actually annotating photos. Is there any kind of course correction after analysis too? Like, for example, sure we would go in and say, okay, the program said that this was a deer, a big we'll say, a mature buck like you mentioned, but actually the human looks at it and it's it's a young. 00:13:23 Speaker 3: Buck with a big tree branch behind it. That makes it look bigger. Right. Is there any of that kind of like teaching post happening as well. 00:13:31 Speaker 4: A lot of a lot of post teaching. 00:13:32 Speaker 5: Actually a couple of ways that that might that that might we might do that. Here's you know, one of us, you know, we're we're heavy users, you know, like any of us, multiple cameras, multiple accounts, We're constantly looking at them, and you know, if we see something, it's like why does this thing keep returning dose when I'm filtering, you know. And that's probably another thing I should add before we go into that question, is what the AI really allows the customer to use or the customer to do, is filter easily in the in the gallery, you know, like I don't have to look at the one hundred raccoon pictures that I got or the you know, whatever I don't, I essentially see what I want to see by just moving a filter. 00:14:13 Speaker 4: So when you're looking through your gallery of images and you. 00:14:16 Speaker 5: Filter it for bucks, let's say bucks as well as you used the buck, you know, and I see Doze predominantly Doze. You know, I might go back to our engineering team and say, hey, man, like the last two days, like for some reason, the model's pretty bad off, you know, and then they'll look at it and they'll say, oh, it's because you got a feeder in the background. 00:14:36 Speaker 4: You know, we haven't trained enough images. 00:14:38 Speaker 5: We'll out of our ten thousand image set that we ran through, only one hundred of them had a feeder in it. We now we got to go, well, we don't have to, but we would go build a set of images that has feeder. 00:14:52 Speaker 4: Ten thousand images that have feeders in it. You know. 00:14:55 Speaker 5: That's how the model improves is by you know, you identify it kind of a chink in the armor, and then you just expose it. You know, it's like, Okay, there's something going on here, maybe not sure what that is, but we're we're gonna dig into it, and then we're gonna spend another Another version of the model. 00:15:11 Speaker 4: We're gonna run it. 00:15:12 Speaker 5: We're gonna look at it and see is it increasing or decreasing. We monitor what they call IF one scores all the time, which is essentially the health of the object detection model, or not really the health, but more of the accuracy of the model. 00:15:26 Speaker 4: We monitor those scores a lot. 00:15:27 Speaker 5: When we start to see those scores kind of come down based off of our benchmark, you know, we might we might go back and retrain and evaluate where our where our weaknesses are to improve the customer experience. 00:15:40 Speaker 2: So on this, on this object recognition side of things, is this becoming commonplace across trail camera brands? Is this unique to you guys still or is this where everything's headed? 00:15:53 Speaker 4: Yeah, that's that's a that's a good question. 00:15:56 Speaker 5: So there are different levels of object detection or recognition, whatever you want to call it. There are different levels of that. To our previous point early, you know, like we are making advancements in that realm, you know, and and those advancements, you know, like generally speaking, I guess, let me simplify a little bit, slow down. 00:16:16 Speaker 4: I guess for myself to make sure everybody kind of gets it. 00:16:19 Speaker 5: So what what everybody gets generally for free. Is the ability to filter by object. You know, let me filter all my images to deer, let me filter all my images to Turkey, whatever. The part that they generally don't get for free, it's generally. 00:16:38 Speaker 4: Behind a paywall. 00:16:39 Speaker 5: Is that kind of prediction, you know, like in prediction may be kind of a. 00:16:47 Speaker 4: Way out there word. 00:16:48 Speaker 5: I mean, yes, we do predict some things based off of you know, certain data points and things like that that we're gathering. But that part piece of things, and we can talk about that in the future whenever you're ready. But that part of it is generally what costs, you know, what what the customer has to you know, pay extra for or you know, upgrade something in the in the plan. Sometimes you know, if you go to unlimited, you might get it for free or something, but the data plans anyway. 00:17:15 Speaker 4: But yes, generally speaking, the object detection piece. 00:17:19 Speaker 5: Is free on the app, and I guess it's but it's it's not every device does not have it, so I guess that's a good separation point, right, So devices also run those object detection models in the field. So up until you know, for Moultrie, it was it was the edge pro which was like a three year three. 00:17:41 Speaker 4: The camera was like three years ago. So that was our first device to. 00:17:44 Speaker 5: Carry onboard AI or onboard object detection. So what that means is that device was doing all of that compute that we were just talking about. 00:17:55 Speaker 4: You know, is there something in the picture? What is that something? It was? 00:17:59 Speaker 5: That device was those All of our devices, starting with the Edge Pro, are now. 00:18:04 Speaker 4: Capable of doing that. 00:18:06 Speaker 5: And what that does for the customer is it essentially saves them battery, you know, some some data cost. The biggest, biggest piece of that is the battery savings. You know, your device now is only only uploading what you want to see, meaning I only want to see deer. I don't care about turkeys or anything else right now. I just want to see deer. So my device is all they send me are deer pictures. You know, we've taken it to the next level and also said like all I want to see is bucks. I don't even care about those, you know, like I maybe I don't have a dough tag, maybe there's no dough seasons out in that particular state or whatever, you know, but like now you can you can really drill down per device and tell that device exactly what you want it to do. I mean, we we take it as far as you know, like a object based settings too. Now you know, multially allows you to say, you know, like hey, for for every picture other than a buck deer, send me a steal and upload them once a day. But for bucks, I want you to take a fifteen second video and upload. 00:19:11 Speaker 4: It immediately, like we can. 00:19:14 Speaker 5: We are it is available to the customer today on the multi side where we can actually you know, the customer gets what they want when they want it, essentially in holling. 00:19:24 Speaker 3: Yeah, so that's pretty coraod. 00:19:25 Speaker 5: Eaving tons of battery, you know, like it'll, it'll, it keeps your devices in the field for longer. 00:19:40 Speaker 2: Could you ever get to the point and I maybe there is a way to do this already and I just don't realize it, But would there ever be a way to do some of that specific kind of setting stuff to the individual buck level, Like are we to the point where we can train the algorithm to target a specific deer, like, Hey, this deer that I tagged as the white eight, could you automatically send me photos of that deer or give me an HD funnel automatically of that deer or any of these other things like are we to that point? 00:20:12 Speaker 5: We are not quite there yet, or maybe I should say we're not there yet, But that is the. 00:20:22 Speaker 4: That's the code, Gras. You know what I mean when I can say, show. 00:20:26 Speaker 5: Me the hammer, you know, year over year, yeah, you know, and like it's just putting it together, you know, and then you know, I mean as you throw the prediction piece on top of it, you know, and like you know, there there could be a day where you know, AI or objecttation, whatever you want to call it, it sees a deer that it knows from you know, from all of that training that we've given it, that that deer is gonna be a stud in three years. 00:20:53 Speaker 4: I'm gonna watch him. 00:20:54 Speaker 5: I'm gonna tell you everything I know about him, and then I'm just gonna deliver, you know, like I'm gonna Essentially, it's doing the things that we're doing. 00:21:03 Speaker 4: Mark. 00:21:04 Speaker 5: You know, we we spend a ton of time combing through pictures, combing through weather, combing through all kinds of. 00:21:12 Speaker 3: So many spreadsheets of stuff. 00:21:16 Speaker 5: You know, like that's where the benefit comes in, you know, that's that's again, that's that's where I think it'll be in the next you know, like three to. 00:21:25 Speaker 4: Five years, let's say. 00:21:26 Speaker 5: I mean, obviously I threw that number out there, and I don't know if it'll be three to five years, it might be two months. I don't know, but I definitely think that's the direction it's going is to give you. 00:21:35 Speaker 4: All of that. 00:21:36 Speaker 2: Okay, So so staying with the object detection side of things, just to continue to kind of drill down on that side. You mentioned that there's the on board side of it, in which the actual device itself can analyze what's in front of it right now. And is that happening like in the moment or does it does it trigger as I guess I'm saying, like, let's said, deer walks in front of the camp, Yeah, it triggers. Is it then doing this object analysis after that photo has been triggered? It is now it's on board, and then the camera analyze it and says upload or don't upload. I mean, it couldn't possibly be doing that object detection process as that deer is passing through and making a decision. 00:22:20 Speaker 4: It's amazing. 00:22:21 Speaker 5: It's it's doing it in milliseconds. It is actually not even doing it. It's actually not even doing it on like a I guess, a full resolution image. You might say, like it's doing it in a very very early in the process, like yes, the pr says, camera, wake up, there's something out there. But the camera doesn't fire up all of its systems until it keeps a very like a skeleton crew running where it could see just enough of that image to know, Yep, that's a deer, and yes it's a buck because we've trained it to. 00:22:58 Speaker 4: So it's using a very low resolution. 00:23:00 Speaker 5: Image to say, yes, that's a book, take a picture of it, or yes that's a book, take a video of it. 00:23:05 Speaker 4: So that's where you're getting the battery savings. You're not. 00:23:08 Speaker 5: All of your battery savings doesn't come from just the device uploading or not uploading the It's really the entire system, you know, like I don't. The device doesn't turn on certain subsystems if it deems it a null capture. It just leaves everything in idle state and waits for another one. 00:23:29 Speaker 4: Wow. 00:23:30 Speaker 3: Okay, so. 00:23:34 Speaker 2: How does someone how does the user make sure that they are fully enjoying that benefit that the battery saving side of this. It's kind of the way I'm thinking through this is like I want to understand each slice of the pie here, like the application, and then I want to understand, Okay, how do we make sure we're doing this to take advantage of that benefit? So we just we just covered AI will actually detect onboard immediately and any either fire up or not. So what will I need to do to make sure I am you know, have I'm utilizing that? 00:24:05 Speaker 5: For Multrie, you would go into your capture settings, and within capture settings, there's a general settings bucket and then there's a there's a smart capture bucket, and you really just essentially you can go into your smart capture bucket and you tell it what you want if you you tell it what you want to see, you know, and a lot of people I think you know to your point previously, you know, it's a little the confidence might wane a little bit. You know, it's we all have it. It's technology. You know, until you've been using it for twenty years, you don't tend to rely on it. But yeah, you just go in and tell it what you want and how you want it. 00:24:44 Speaker 4: And that I mean, it's it's simple, it's super simple. 00:24:50 Speaker 2: What is your confidence rating? I know you can't speak to anybody any other brands who are doing their own version of this, but at least from what you have built and what you've worked on with Moultrie, how confident do you feel in those you know on the accuracy there, because because I've I've looked at this and thought to myself, it seems amazing. Yeah, but I would be really disappointed if that big buck I've been chasing all season came through and was way in the back of the frame or in the back corner or something, and the AI did not detect it and so chose not to take that picture, when if it had been an old camera that I would have gone through every one of these and looked at I would have seen him in the corner and this would have been a great piece of information. 00:25:32 Speaker 3: How do you consider that? How do you think about that yourself? 00:25:35 Speaker 5: That's a that's actually a great example. I actually noted that as a as a conversation topic too. So that's where things get. So to answer your first question, ninety five percent certain that I'm going to the multie my Moultarie cameras, whether as an owner or a builder manager, whatever you know, like Moultie cameras are going to be, they're going to be ninety percent accurate at what they can see and what they know. That's where we come into play is to say, have we trained our model good enough with enough negative stuff. 00:26:09 Speaker 4: So it's ninety ninety five percent. 00:26:11 Speaker 5: Now that that example that you had where there's you know, like you know, oh, sad, Daddy's back in the you know, the left corner, you know, barely out of reach of the flash. Yeah, you know, like I mean we might catch him. I would say that I'm sixty percent confident. 00:26:28 Speaker 4: That we would catch that that one video. 00:26:32 Speaker 5: So really like that's the only place is those really edge cases, you know, It's it's really hard to teach that technology to identify the edge cases. Now. I think in the next couple of years, I think we'll be picking that deer up in the the shadows on the left, the far out left side, you know, cameras, our cameras like we we eliminate one hundred feet now or p i RS capture out to one hundred feet, you know, And that's that's pretty much the market got to do that or you're not a player, you know. And and I think we'll push a I to do better at what our take what our hardware is able to do. 00:27:08 Speaker 2: Yeah, okay, all right, so we use that smart detect setting, we can choose, you know, what kind of pictures we want to get. Is there any kind of middle ground with that in which I want to utilize some of these AI features to reduce my battery use or something like that, But at the same time, I don't want to. 00:27:31 Speaker 3: Yeah, I'm worried about what we just talked about. I'm worried about that edge case, you know. 00:27:36 Speaker 2: Can I think that what I've kind of looked at in the past, is you know, using the object detection to do you know, I think you guys have a feature in which, like you'll auto you know, deliver a video I think on a buck. But I'm still gonna get all my pictures. But I'm just going to get better pictures for bucks or better you know, assets when it's a buck. Is that the extent of the way to kind of modify and find a middle ground or is there any other that you guys are saying, Hey, here's here's a way you can get a better kind of experience for certain things. But you if you want to see it all, if you want to double check everything, you still have that option. 00:28:11 Speaker 4: Yeah, yeah, those are for MULTU. 00:28:13 Speaker 5: That's a smart capture uh smart capture object based settings, which essentially that's that's our you know, proprietary name order. That's what you see it as in the app, is smart capture. That's our that's our on device AI and it essentially does exactly what you said, you know. That's what I was speaking on earlier, is that you can tell it. 00:28:32 Speaker 4: I think we have. I don't don't quote me, I don't. I don't recall how. 00:28:35 Speaker 5: Many containers that we have and want to say containers, how many options we have in there, but we have definitely more than deer, and you can tell it, you know, like, hey, I want all of these and I want them to upload immediately or in a different fashion. 00:28:49 Speaker 4: I want them to be videos. 00:28:50 Speaker 5: I want them to be you know, I don't whatever other you know, image setting you might want, and then everything else. I still want those for what we were just talking about, that big monster that was back in the shadows. You know, you can still upload everything and you can set it to upload at a different cadence that also saves battery, you know, like an immediate upload every image uploading immediately. You can look at it like capture. It's the cost of the capture and the upload for every image, where when you bundle them all together, like all of these kind of secondary and tertiary images that I think. 00:29:26 Speaker 4: I care about but maybe I don't care about. 00:29:27 Speaker 5: I might go through them once a month or you know whatever, and whenever I have some free time. Like you look at that as now I've got every capture costs me power, but I only have one the cost of one upload. 00:29:40 Speaker 4: Yeah, so that's. 00:29:41 Speaker 5: Really where the savings comes from. Is you know, I'm willing to spend power on something that I want in the way that I want it, but I'm not willing to spend as much power to get the stuff I may or may not want, but I still want it. It's worth something to me, it's just not worth as much as these other things are. 00:29:58 Speaker 2: Yeah, and what about this, Uh, correct me if I'm wrong, But I think the new, the new, the brand new camera's coming out. 00:30:04 Speaker 4: Yeah. 00:30:05 Speaker 2: I think it's called buck shot right where there's some specific stuff for a buck Can you walk me through what that looks like? 00:30:11 Speaker 4: Yeah? Sure? 00:30:12 Speaker 5: Uh so, so Buckshot essentially uses everything we've been talking about today, and it just makes it easier for you to view those pictures of the buck. 00:30:19 Speaker 4: So how many of us sit. 00:30:21 Speaker 5: There on our phone, you know, and they're like, oh, man, look at that guy back there in the shadow, and we're pinching and zooming and. 00:30:26 Speaker 4: Like turn this out. 00:30:27 Speaker 5: You know, we want to see every angle or every color contrast that we can. Buckshot does that for you. So now with just a simple tap gesture in the app, you're like, oh, is that a buck back there? And there's a chip in the right corner that says it's Buckshot enabled. What that means is is that we have we see we've seen something in the image, and that something is something it's a it's a mature buck, or it's a buck, and then when you tap and hold on that image, it will essentially zoom it in and give you a secondary image of that buck, you know, from where we drew the box. 00:30:58 Speaker 4: Around it, you know, where that's like a modeloring the box around it. 00:31:03 Speaker 3: And so is that just a zoom in or is it also HD two? 00:31:06 Speaker 4: Right? 00:31:06 Speaker 3: Is that correct? 00:31:08 Speaker 5: Yeah, the thumbnail, not the thumbnail, but the HD image. It uploads an HD image automatically. If you have bush that turned on. Sorry, I skipped that. Thank you, thank you for bringing it back around of course. 00:31:22 Speaker 2: So all right, so we can do a lot of this onboard decision making, which is going to be the camera makes the decision in the moment and capture something or delivers something, and then there's some more of that happening in the app. Is there any other is are there any other sides of this AI equation when it comes to sorting things, analyzing things, finding patterns when I'm actually doing it in the app and trying to understand this stuff? 00:31:49 Speaker 3: What's that next level? 00:31:53 Speaker 5: Sorry you broke up right there at the end, But when you say next level, you mean like, what's the next level of AI in the app? 00:31:58 Speaker 4: Right? 00:31:59 Speaker 1: Yeah? 00:31:59 Speaker 2: So how you know this was which We've talked a lot about what the camera is doing on board in the moment. Now what about how does the app use this technology for me to better understand what I'm looking at and filter and and parse through these things? 00:32:13 Speaker 4: Sure? Yeah, so, uh so obviously there's the what we talked about. 00:32:17 Speaker 5: Majority of camera companies now give you free access. 00:32:20 Speaker 4: It's just through the app. 00:32:21 Speaker 5: Is as I can filter by tags that have automatically been placed on the image for me. You know, and then the next level like what might be behind a paywall? You know, is there some predictive stuff too, you know, like we we do like multi doesn't activity charting, which I think might be free to quote me on that. I can probably dig through my notes if it's something you want to know. But we do activity charting, which essentially says, for this specific camera, what were where was the peak activity you know, throughout the day. You know you can modify that, you can say what was the peak activity in the month or the week? I think you like there are there are triggers on that that that allow you to kind of you know, set what you want. But yes, we we do activity charting and then we do predictions too, and those predictions that those are kind of the cool one. And I'm not really sure how much the secret sauce I should let out there. But you know, it's not just pictures from your camera mark, It's it's aggregated data that we have access to that you know, we don't we don't share with anybody else. 00:33:29 Speaker 4: I mean, let me clear that up. 00:33:30 Speaker 5: You know, it's like it is our data, it is it's your data that that you know in that that we we have access to and we use, you know, a lot of different things like that to put into our predictive models that essentially, you know, when you look at it, it'll it's looking at not only activity from your devices. It may be looking at activity from devices in the you know, within thirty miles of your your device or something like that, or maybe one hundred miles of your device, you know, and it's factoring in what your property is doing as well as you know what surrounding properties might be doing. 00:34:06 Speaker 4: You know, like. 00:34:08 Speaker 5: Might might be an indicator of like a small second rut you know, or a small like follow up rut at the end that you know, maybe you didn't even know anything about, but you know, a I picked up on this weird you know one seven day peak or three day peak or something, you know, and it's there, been there for the last two years. 00:34:27 Speaker 4: You know that that's the I guess. 00:34:29 Speaker 5: That would be the other piece to put in that predicted piece is that you know, you can go back and look year over year in both activity and our prediction models are looking at that. But in activity, you know, if you were curious, like how you know my landing strip food plot, how much activity was in it last year or the year before, compared to how much I've. 00:34:50 Speaker 4: I have now. 00:34:51 Speaker 5: You know, maybe I don't know, maybe you changed the change the food plot planning, or you know, there's a clear cut now that runs right up the side of it where it used to be, you know, a mature stand of hardwoods, you know, mass producing stuff. 00:35:05 Speaker 4: So you know, like there's a lot of stuff that goes into it. I mean you can. I don't think we could touch on everything. Those models are complex. 00:35:15 Speaker 5: You know, there's somebody's way smarter than me out there thinking like what if we added that into it? 00:35:20 Speaker 4: What if we did it like that? 00:35:21 Speaker 2: Yeah, So so give me some more of like the how it's, how it's actually factored into, like the way you use it in your own hunts when you're when you're looking at this, and I guess one of the things I'm also wondering wondering about is are you able to it all separate between just your data versus the collective data. Is there a way to say, hey, tell me predictions based on just what my cameras have said over the last six months or six years, and then can I also then expand that and say, hey, give me a broader sense of things based on everything else you mentioned that secret sauce. 00:35:58 Speaker 5: Yeah, I can so. Like A good example is is I guess, like two years ago, I hunt here in Alabama, you know south, I guess South Alabama, maybe Colin just south Montgomery. But anyway, what you see a lot of times is or what I what I like to do is all leading up starting now starting you know, when the velvet start, this horn start to develop, and you know early on in the season, you can kind of tell like, oh, that guy's probably gonna be a good one right there, you know, And what you're doing as you watch him grow is you're kind of. 00:36:28 Speaker 4: You're putting him in a box. You know. 00:36:30 Speaker 5: Does he like it when it's hot? Does he like it when it's cold? Does he like it when the wind blows out of the south or wind blows out of the Norse, like like, all of that stuff is stuff that you and I as hunters, we we. 00:36:43 Speaker 4: We have to we had to do that ourselves. You know, it was probably three years ago, two years ago. 00:36:49 Speaker 5: I had to take notes and note, you know, hey, he shows up on the north wind when the temperatures you know, between fifty and seventy degrees. 00:36:57 Speaker 4: Or something like that. 00:36:58 Speaker 5: You know, but all of that now can you can you can pretty much look at that in in your you can go back and look at statistics for that. So in your like activities you can go in and look for that particular device. You can't get down to the deer though, Like what it what AI is doing for us today is giving us We're not looking at this, We're not looking at say, twenty four hours worth of data. 00:37:24 Speaker 4: We're we're looking at two hours worth of data. 00:37:27 Speaker 5: Where AI said the deer are all moving in the afternoon, or maybe they're moving from nine to twelve, you know, which is a weird you know movement there's a ton of pressure in the area or something. Don't move in the mornings and afternoons and move middle of the day or something. But that's what AI does for you to day. I'll tell you that, like in that spot, your deer like to move between. 00:37:46 Speaker 4: You know, I don't know, eleven and twelve. 00:37:49 Speaker 5: And so and then you can you can you can feather a temperature piece on top of it. You can feather you know, the wind. Like all of that is available in the app for you to kind of all stick it all together somehow, So it's it's still somewhat manual. Like to what you're asking, you know, is can I look at it like a specific deer or deer movement? No, we can't do that yet, not very well anyway. You know, that still involves human intervention. But what we can look at is is site specifics. You know, what was the wind, what was the temperature at that particular site? You know, I mean, you know how it is. You might have ten food plots, you know, and two of them are going to produce almost every year. You know, there's something about it where those maturity are just like to move through there that piece we can do now from the aggregated data provided by A do you. 00:38:48 Speaker 2: Feel that hunters or even more specifically just your customers, what's like the adoption rate of using all this stuff? Like are people Do people get it and are doing all these things you're talking about and enjoying these benefits or has there been a lag or folks not understanding it? Or do you get what I'm what I'm. 00:39:08 Speaker 5: Yeah, I do, Yeah, And that's kind of a that's a tough one. I think it plays into that confidence thing more than anything. From what I've seen sitting on the I guess sitting from the outside looking in, because that's that's what we listen to. Our customers. Our customers drive a lot of our roadmap. You know, we're not just sitting back here as you know some guy that hunts twenty four to seven. You know, like in fact, we're lucky to get to go out sometimes. But you know, we're not thinking like I like it, so they'll like it. You know, we're asking our customers a lot of questions. You know, anytime we can interact with the customer, that's what we're doing. So from from those customer interactions, I think the confidence wanes a little bit. And you know, like how good is that technology that we've been talking about at AI or object detection technology and then. 00:39:57 Speaker 4: The aggregation piece. 00:39:58 Speaker 5: You know, I think the confidence there, and I think with that confidence, I think that's kind of what keeps people. 00:40:06 Speaker 4: You know, that's the entry, that's the barrier of entry. 00:40:08 Speaker 5: You know, Am I confident enough in it to spend time to learn it, to spend time to use it? 00:40:12 Speaker 4: You know? And I think I think. 00:40:14 Speaker 5: Our goal as you know Moultrie are Yeah, our goal as Moultrie and what what our customers deserve from us, is for us to just give that to them, you know, they shouldn't really have to learn anything. 00:40:28 Speaker 4: And I think that's the part that's coming. 00:40:31 Speaker 5: I think, you know, we've been nailing down technology, making sure everything works like it's supposed to, and now we're trying to figure out how to bolt it, bolt it all together, you know, like all. 00:40:39 Speaker 4: The components to build the house are here. 00:40:41 Speaker 5: Now we just need to raise some you know, raise some walls and put some nails in it. 00:40:45 Speaker 4: And I think that's what's happening now, you know, is we're putting those walls up. 00:40:49 Speaker 5: We're nailing everything together now to deliver something you know that that's going to be extremely useful. It's going to be easy for the customer to use, you know, it's not going to be is difficult, and I use difficult loosely. It's not difficult now, it's just kind of you know, you do have to poke around in the app. 00:41:07 Speaker 4: You have to use your time to do that, and you could be honting or doing something else. You might each other. 00:41:12 Speaker 2: What's for someone who is hesitant about this stuff? What would be your entry way suggestion? So for somebody who said, ah, I don't know about this. I'm sticking to what I know, and they have not gone down this road yet. What would be that first thing you would suggest, like, hey, at least try this filters the filters, okay, at. 00:41:33 Speaker 4: Least try every day when you go into your gallery. 00:41:37 Speaker 5: Don't just thumb you know what I mean, like say, okay, okay, I'm off work. 00:41:42 Speaker 4: I got off work at five. 00:41:43 Speaker 5: I'm gonna sait in the easy chair, you know, for thirty minutes an hour this afternoon. When you're doing that and you're looking through your pictures, filter, say show me all the Bucks. You know, go in there and set a filter, just a single just a single filter, don't try and get too crazy, and show me Bucks, those turkeys, you know, and then see if it works. You know, just just hone into what you want, you know, if you want to see Bucks, ask the app to show you just Bucks, and then look through that and say it's pretty turned good. 00:42:13 Speaker 2: You know. 00:42:13 Speaker 5: Yeah, it might have missed one or two, but it was it was. I got enough information out of this to say that now. 00:42:19 Speaker 4: I don't need to. 00:42:20 Speaker 5: I don't I don't have to if I don't want to thumb through all of those pictures to just see bucks, you know, or I don't know, say you're just in for activity, you know, taking taking your. 00:42:30 Speaker 4: Son, your daughter hunting or something like that. 00:42:32 Speaker 5: Taking a just to just any kid hunting or some nine hunter. Maybe you know you're introducing them. You just want them to see deer, you know. Go to go to activity in Maultri app. It's a free thing. It's a free free feature. Go to activity and look and say when when should I go hunting? And say, well, it tells me I should go in the morning. I know I should go in the morning. And then if you got multiple food plot options or multiple camera options camera sets, you know, go look at one or two of those that you think are good good and kind of see what the app tells you. You know, yeah, okay, it definitely says that there's a lot of activity at this. 00:43:07 Speaker 4: Camera set from ten to twelve. 00:43:10 Speaker 5: And I know that's true because I've been hunting that camera set for like the last four or five years. 00:43:14 Speaker 4: You know, like I kind of know when to go. But you know, we know our properties, we know our sets, we know our cameras bys the time. 00:43:20 Speaker 5: You know, we know a lot about how the animals generally move about our farms and properties we have access to. 00:43:28 Speaker 4: So you can use that to build your confidence. 00:43:31 Speaker 5: You know, I recommend that Actually that's what That's what gives you that confidence level to say, you know, like, I feel confident I can go in here and I can filter for bucks because I just want to see bucks right now, I see those bucks, or I go to the activity charting and I see that, you know, these are my three favorite spots, and the AI agrees with with the way that I like to hunt my three Fravoran spots. 00:43:55 Speaker 4: You know that that gives you the confidence to say. 00:43:57 Speaker 5: I really don't I really hardly ever hunted on this this south end, you know much. I think I'm gonna see I'm gonna put a couple cameras out on that south end and I'm gonna see what see what the app what Moultrie tells me about the south end, and I might have to go try it out. You know, now that you've kind of used you know, then now that. 00:44:18 Speaker 4: You've used your you're one. 00:44:21 Speaker 5: Hundred percent confidence of I've hunted this place for that long and then look looked at the AI and the AI essentially agrees with you. 00:44:29 Speaker 2: Yeah, so you're kind of using your You're able to confirm the AI with the place you have experienced, right, which gives you confidence in the tool to then go put it in a place you don't have personal experience, and then you can get an assessment of that with the camera, and now you can trust it because you were able to kind of fact check the camera, right. 00:44:48 Speaker 5: Yeah, I mean there's some like ancillary like things that you get from the AI too, you know, like all of that, especially behind a paywall, but like we do like buck to dough ratios, we can now we can now do that from the. 00:45:03 Speaker 4: Object detection stuff. There might be a couple others that. 00:45:05 Speaker 5: I'm missing, but you know, like things that just like regular guys like me or you, you know, I may not have I mean I do spend time doing some cameras I raised, but you know a lot of people don't, you know, so they don't really know what their buckt to dough ratios are or what their fawning success was or something like that. So you know, like a lot of that. Now can we can use AI to kind of hit around it. You know, it may not be as exact as me or you annitating images, but it definitely gives you a good feel of should I take those or you know, like am I healthy? 00:45:40 Speaker 4: Is my property healthy? Or something? 00:45:52 Speaker 2: How far away are we from, you know, just broadly as an industry, how far away do you think we are from being able to do you know, accurate antler scoring, age, you know, estimation, all that kind of stuff. 00:46:08 Speaker 4: Is that? 00:46:08 Speaker 3: Is that pretty close? 00:46:10 Speaker 4: I think it's pretty close. 00:46:12 Speaker 5: I mean, you know, I think everybody's working on that now, you know, I think we all know that, you know, out out of the handful of camera competitors that are out there now, I think that market. I think everybody in that market's trying to solve that. I don't know how. 00:46:28 Speaker 4: Close it is. 00:46:29 Speaker 5: I think that that would be a super hard, you know thing to put a date or even a timeframe in are on. 00:46:36 Speaker 4: But I definitely think everyone is working on it. 00:46:40 Speaker 5: Everyone's trying to get that, and I think we're closer now than we ever have. I mean, you saw the you saw AI go from I can see a deer to I can see a dough or buck to I can see a mature buck or an immature dough. And I think that's kind of where our an immature book, I think, right Now that's kind of where it gets a little you know, little little cloudy, you know. And I think that's that's the natural progression you're going to see. You know, I could see deer first. Now I can see enough to delineate between buck and doe. Now I can see enough to delineate between mature buck and immature buck. Next level is I can see enough to know that that is the wide a you know, or you know, and and that's the hardest part, you know, and you know, like you still can't lose track of like we still have to do our part, march, Like our part is to make sure that our cameras in a good spot. 00:47:31 Speaker 4: You know, it's it's it's as it's as stable for the trigger as it can be. 00:47:36 Speaker 5: It's it's tuned the way that the way that I want it for that particular spot. Like we still have to do our spot. Are are part of, you know, getting it ready to go. But I and the better we do it that the better. 00:47:50 Speaker 4: The better. The answer to your question that you just gave me is winter we going to be able to. 00:47:54 Speaker 5: Know that that is a specific deer regardless of where that camera is while it's on the north end or the south end. If that deer crosses one of those two cameras, because he's done, because. 00:48:05 Speaker 4: I've seen him before, I will know that that's him. 00:48:08 Speaker 5: I think the better we set up our cameras and give these really smart engineers the opportunity, you know, the best best leg to stand on. 00:48:17 Speaker 4: You know that, I think we'll be there in no time. 00:48:19 Speaker 5: But if you know that technology's ability to filter the chaff out is not that it's not that good yet, you know, maybe there'll be a breakthrough in that which will speed up the delivery of Can I identify a specific deer, our specific buck? 00:48:38 Speaker 4: But you know, if we. 00:48:39 Speaker 5: Don't get any better at that, then you know it might might be some time where the object dictation itself, it just has to see more. 00:48:47 Speaker 4: We just have to give it more. 00:48:48 Speaker 5: It might have to see that same deer ten times to get ten different angles of it and then I know that that's that deer. 00:48:54 Speaker 2: Yeah, that seems like I mean from the little I understand about how these others or trained, right, it's just for a long time there's been have been in putting more and more training data. And the more training data you put in there, and the more compute more power you put behind. 00:49:09 Speaker 3: It, the more the more these things are able to do. 00:49:12 Speaker 2: So it seems like that would that would hold true with something like you know, image detection and analysis, right, yeah, do you know of any interesting ways or have you played around with any interesting ways yet yourself of using AI from other providers to kind of take another step with trail camera data or hunting dead, like, is there some way to integrate with chat GPT or Google Gemini or anything like that to to take your analysis further or to I don't know, maybe there'd be some way to upload your photos that your that your camera has told you were you know, of interest, and then you go and could I upload those then to chat GPT and have them do a next level thing? 00:49:57 Speaker 3: Like is there some way to to go further. 00:50:02 Speaker 5: So on the stuff that's outside of like camera apps, you know, like GPT vision or something. 00:50:09 Speaker 4: I think that's chat GPT's. 00:50:11 Speaker 5: Version of of you know, like an object detection kind of you know vision model. 00:50:18 Speaker 4: I mean, really we have messed with uploading images to those. What we see a. 00:50:25 Speaker 5: Lot of times is is that those models aren't specifically trained for deer or for turkey. They're trained for animal or people or car or something like that. 00:50:36 Speaker 4: So what we generally. 00:50:37 Speaker 5: See is that that that type of stuff can tell you that there's a deer in it, yeah maybe, or maybe it can tell you if it's a deer in it, but it could definitely tell you there's an animal. 00:50:45 Speaker 4: There, you know, or you might call a kyotea dog or something. 00:50:49 Speaker 5: But what we see is that our models are much more accurate because we actually train them on the same number of images I would assume as their models are trained. But what our model, what our training say do is it's all deer. It's ten thousand pictures of deer, not you know, a thousand of a dog, a thousand of a person thousand you know. And I don't know how they do theirs. I'm you know, I'm just kind of riffing on that. But we see that our models are generally speaking more accurate for what we want to see as hunters because we train our models to show what we want. 00:51:21 Speaker 4: To see as hunters. As far as like data aggregation goes, you know, like. 00:51:27 Speaker 5: I mean, you can upload images into the Moultrie app now, so like if you want to use all of the tools that I've been referencing from Moultrie. You know, like we allow you on the on the free side to upload a certain number of images to our to our app so that you can use our AI based tools. It'll run through all of our tagging, you know, if there is data with the image, we'll pull we'll scrub it and pull it in. But but generally speaking like, there's no, there's no. I don't know that there's anything I have not experienced or experimented with anything that would that I feel would give me anything more than we already give you in the app, Like with the prediction models, you know, the activity charting and stuff like that. I'm sure you could take that to the anth degree with some of these A platforms. 00:52:23 Speaker 4: I mean, they're amazing. Yeah, it's amazing some of them can do. 00:52:28 Speaker 2: I've never seen this, so I'm assuming the answers no, and probably nobody would actually want to do this, but maybe a nerd like me would. Is there any kind of export function within your guys' app in which I could export the data. 00:52:45 Speaker 3: So, for example, if I were to. 00:52:47 Speaker 2: Say, all right, I've I've tagged every single time that I've got a picture of the white A, We'll say right, And so for three years, I've been tagging in my in my app every time I see them, and you're your cameras have got all this, you know, time and date and wind and temperature, all that data is there, right, So I'll collect on those photos and you guys do a certain amount of analysis in the app on it. 00:53:13 Speaker 3: But is there any way that I could export. 00:53:15 Speaker 2: That data and then have like an Excel document that shows two thousand data points that have all that information tied to each one of those photos that then I could then take and feed into chat, GPT or Gemini and have it create for me, you know, further analysis in the way I want to see it. 00:53:34 Speaker 3: Is that technically possible. 00:53:37 Speaker 4: Not that I'm aware of. 00:53:39 Speaker 5: I think if it were, I think you and I probably having a slightly different conversation about using some of those outside AI platforms you know that have a lot more compute than say, the device does, are even I mean, our cloud servers have, you know, plenty of compute. If we don't have enough, we get more. But you know, like just their ability to crunch those numbers might be. 00:54:01 Speaker 4: Better than what we can do. But not that I'm aware of. 00:54:04 Speaker 5: We there's nothing that says we can export, you know, a whole bunch of images with the data that comes with those images and put it into some CSV file or something and uploaded to the you know, to a third party man model interface or whatever. 00:54:21 Speaker 2: I'm probably I'm probably the one in a million crazy person that would be interested in doing that. 00:54:26 Speaker 3: So I don't think. 00:54:27 Speaker 4: We're doing well. We can do someone. 00:54:29 Speaker 5: We do some of that for you mark to a certain extent, you know, like when you when you go look at our prediction model, like you can kind of see things in our prediction model as you as you pay over like the days and the hours of when you might want to go hunting. You'll see that the weather changes as you move it, the wind changes as you move it, you know, and and then even at even at the activity level, you know, like we're we're we're putting some of those data points out there for you to see. You know, like when when you look at peak movie at a particular camera set, we're not only showing you like, hey, there was a bunch of deer that this camera took at this time and this time, we're also showing you on other you know, a little further down the scroll is what was the wind doing whenever those peak moments were happening, you know, or what was the temperature doing at those peak moments or something like that. So again like really the only slice we're missing is is that what dear was it that we saw at in the morning and in the afternoon. Like, if we can win, we break through that, it'll definitely be. 00:55:36 Speaker 4: Amazing. Yeah, it'll be super cool. 00:55:39 Speaker 2: So okay, So the the individual buck detection, the Antler scoring age estimation, those are things that you mentioned that probably everybody's working on to some degree, and that's coming at some point. What else might we be able to imagine in the future when it comes to how AI might you know, integrate with this with this technology we're using now, I don't know, like five years from now, ten years from now, if you're just putting on your your imagination hat and thinking about where this maybe could go, what might be possible? Uh, what else might be on the horizon? 00:56:16 Speaker 4: I don't know. 00:56:17 Speaker 5: If I don't know if my employer would like for me to say too much about that, Mark, I. 00:56:22 Speaker 4: Guess that's a good point, but I can What I. 00:56:26 Speaker 5: Will say is that if you can think it, I think in the future there will be an easier way for it to be done. You know, the days of looking at a picture and or let's say, looking at a camera pan on a map and equating that to what you see in the gallery by saying, you know, I saw this big deer moved across this camera when the when the temperature was fifty degrees and the wind was out of the north, and barometric pressure was you know, I don't know, twenty eight and all of that. You know, like the days of that are not necessary anymore. I mean, maybe I went out on a limb that'll break by saying it's not necessary, I don't know, but I definitely think that you know that is is here now. Just tapping into. 00:57:18 Speaker 4: It and learning how to use it in a way that. 00:57:20 Speaker 5: You feel comfortable and confident with it is is kind of the the mud we're stuck in right now. 00:57:27 Speaker 3: All right. 00:57:28 Speaker 2: So I'm going to ask you to repeat a little bit of some stuff you've already said, but I want to kind of tie a bow on this. If you were to give me the three things that every deer hunter should try to be using when it comes to AI and their trail cameras, what would be like the like the three things that hey, everybody, you should try this. You should wrap your head around this. You should be trying to take advantage of this. And if you can think, I mean you can you can speak broadly like across brands, or if there's some stuff that you need to you guys, that's fine too. But I'd be curious to hear you know, everybody out there, these are three things you really should give a shot. What would those three things be. 00:58:07 Speaker 5: Be filtering in the app Obviously that that is the lowest level, and the thing that's that's really the tip of the spear. 00:58:15 Speaker 4: That's what's gonna set some confidence. 00:58:17 Speaker 5: You know, that's gonna it's gonna it's essentially just confirming what you see by using the filters. 00:58:22 Speaker 4: You're already looking through your gallery. Look through your gallery, go out, take a wild guess. 00:58:28 Speaker 5: And say, you know, let me see if it sees but the same bucks I just saw. 00:58:33 Speaker 4: Do that. It's easy, it's not gonna take long. 00:58:36 Speaker 5: It's definitely gonna give you some confidence in the technology. The second piece is is probably using like those the for multum I almost speak to Moultrial on this one. The previous comment can be done across a lot of different camera apps. Yep, this one the smart Capture OPPS based stuff obviously based settings like that's gonna be from what I know. It's a multie you know, you need it's you need tumultuary we And that's the one that saves you power, saves you a little bit of cell you o their you know, data for your uploads and stuff. 00:59:06 Speaker 4: But that's the one where. 00:59:08 Speaker 5: You get what you want, when you wanted, how you wanted essentially, so like you know, now that you've filtered a little bit. 00:59:13 Speaker 4: Got some confidence in it. 00:59:14 Speaker 5: Next level is let me, let me tell my device to just take videos or just take pictures whatever it is, whatever whatever you're viewing media is, whether that's pictures or videos, tell it. 00:59:26 Speaker 4: I just want to. 00:59:27 Speaker 5: See bucks in the form of a fifteen second video. I want those to upload immediately, and I want everything else that my camera takes to upload once a day. Just I mean, you know, just try that for a couple of days. You know, did it show me as many bucks as I was seeing when I was uploading everything? You know, now between eight and five pm, you know, like I'm looking at my app and I'm not having to look through the five or ten images that just came in from three or four five different cameras. I'm only seeing the bucks, so you know, like I'm not having to weighe through everything, but it's X when I get home at night. Maybe my cameras have started seeing showing me the pictures of the of the pesky raccoons that I have to look at every day, or you know, the squirrels that are chewing holes in the top of my feeter because I haven't put any corn in it in a or something, you know you And then I guess the third thing is is that is that data aggregation, you know, and like it's really hard to say, go look at activity or go look at prediction or something like that. You know, I guess if you've done the two things that I just mentioned, you're you're becoming a super user to some extent. You know, like you're now you're saving battery. You're using AI to upload what you want when you want it. You're using the filters in the gallery to only see what you want while you're in there, regardless. 01:00:45 Speaker 4: Of how they got uploaded. 01:00:47 Speaker 5: Now you're going to say, like where do I need to be and when do I need to be there? 01:00:51 Speaker 4: On my property. 01:00:53 Speaker 5: You know, can the app tell me that, Well, the app maybe can't put a pin in that specific field to hunt or something like that, but we can definitely lead you in the direction of what time of day if you're like me, like I seldom get the hunt Monday through Friday. You know what I mean. We have jobs, we do what we do. You know, So I am in the field on Saturdays and Sundays come hell or eyewater, and it doesn't matter if it's raining, it doesn't matter, if it's freezing, sleep and snohing, it doesn't matter. Like I'm out there, and when I'm out there, like I just want to know what's the best time for me to go get wet or go freeze or something like that, you know, and those activity those activity things, like the activity charting and the prediction models can start to help you hone in on you know, like I'm a weekend warrior, I only have two days to hunt. You know, maybe maybe I only have two days to hunt two weeks a month, you know, because my job or my family keeps me from doing that. You know, that activity charting and prediction model within Multrie's app will now increase your odds on those two days twice a month that you are able to hunt. 01:02:01 Speaker 2: Yeah, all right, Well, i'd say those are three marching orders that all of us can take out there in the field and give a shot, and we'll come back to you a year from now as AI power users. 01:02:13 Speaker 3: And I'm going it back, all right, sweet. 01:02:16 Speaker 2: Yes, I appreciate you, Darren, walking us through this, talking through where things are right now with AI and maybe where they're headed soon. I'm excited about that future when all of my nerdy questions can be answered without me having to spend seventeen hours a week analyzing spreadsheets. 01:02:36 Speaker 4: Yeah. 01:02:37 Speaker 5: No, I appreciate the opportunity to come on and talk about it. I mean, I do think it's a maybe underserves not the right word for it. Maybe underutilized is more of the word, but it could be a little of both. But you know, I do think it's important for the customers to start to use the technologies this out there. It'll definitely increase their odds, you know, in the field, and it'll definitely make it more pleasurable to look at pictures. 01:03:02 Speaker 2: Love it all right, Darren, Well, thank you for being here, and let's stay in touch as all of this continues to evolve because if there's anything that I can say about AI, it is that it is always. 01:03:12 Speaker 3: Changing and growing, and the news. 01:03:15 Speaker 2: Is always new, So I'm sure there'll be something interesting to talk about next year or the year after that. 01:03:20 Speaker 4: I hear you all right, sounds good, Mark. 01:03:23 Speaker 2: Thanks all right, that's going to do it for us today. Thank you for joining me for this conversation. If you'd like to learn more about what Dared was speaking specifically to with Moultrie, you can visit multiemobile dot com to visit to explore their offerings and some of these specific feature sets we've talked about Otherwise, Until next time, I hope you get out there try some of these new things on your cameras, regardless of what kind of camera you're running, have some fun with it, do some tinkering, do some experimenting. Until next time, stay wired to hunt