Machine Learning in the Film Industry - Episode #97 w/ Ruslan Khamidullin

Speaker A: Thank you for listening to the Data Is My Science Podcast, the show that makes data your passion, with your host, Dapper Data. What's up? What's up? What's up, everybody? You are listening to the Data Is My Science podcast, a show to make data science. I'm, um, I am your host, Dapper Data. Listen, today we are going to do something we've never talked about before, I promise you. We have never talked about this, right? We never talked about neural networks. We never talked about AI in the film industry, right? Neural networks in the film industry, right? There are some crazy things happening in the film industry. We got deep fakes, we got all this crazy stuff going on, and we never talked about in the film industry. I mean, you all are probably watching stuff all the time on social media where you literally are getting faked out, right? You. You're probably watching all kinds of movies where they're doing all kinds of crazy things in the background related to neural networks that you don't even know about. So I brought a special guest on, and, you know, I have to have a special guest because I don't know everything. I know high level, I read a lot, and I still don't know even high level of everything, you know, dealing with data science. So. So I brought a special guest on, uh, his name is Ruslan Rusland. Say what's up to everybody.

Speaker B: Hey, hey, hey there, Ruslan here. Nice to hear you. Um, please be with us. We're gonna tell you a, uh, few cool stories. Thanks.

Speaker A: Yeah, yeah, we are, man. You know, so, uh, when we talk about neural networks, right, in machine learning, specifically, I know we talked about machine learning in the past, audience. Uh, but, you know, when you look up definitions around, right, they're talking about how it is a computer system inspired by the human brain. So you think about, uh, how the human brain works. You think about, um, how it's designed to recognize patterns, and then, um, you're learning from experience, and then you're making all these decisions, all that cool stuff. And if you can imagine neural network as a team of people working together to solve a problem, right? Each person being what they call a neuron, right? So you look at a neuron, each person is called, is a neuron, and. And then they receive their own input, right? They have their own knowledge that they're gathering, and then they're sharing these results with the rest of the team, right? So you have a bunch of neurons come together as a team. Then you have the team members together, they collaborate to come up with that final Answer. And it's always adjusting. It's always coming up with a new answer. Right. Uh, or a better answer. Right. It's getting more accurate, and the performance is getting better and better over time. Right. So in machine learning, this is the same thing. Right. You got neural networks. Right. They're, they're used for tasks like image recognition, um, natural language processing, ChatGPT. He knows all about Chat GPT. Right. You know, I'm sure. Yeah. And decision making. Right. So now you have the film industry. So we're going to dive deep into that film industry part right there. I'm so excited. So, Rosalind is, is the Chief Technology Officer, CTO of, uh, Film Use Stage, and we're going to talk all about film, you, Stage. All right. A, uh, technology company that is streamlining the film production industry and process using neural network technology. So they're tying the two together. All right. That's what the industry is doing these days. And with 10 years of development experience, probably more now, and Ruslan has been able to develop the Film Use Stage technology to automate the script breakdown process. Right. Which is a painstaking process. Right. And, and, and I actually have looked, uh, through it, researched it, and I'm telling you right now, I don't even want any parts of that. I don't want any parts of that industry and dealing with the whole technology process they have going on because it is a lot. And, and that is necessary in the production industry. Right. And so another tidbit about, uh, Rosalind is that he is a music, I want to say, genius. Right. You've been a lifelong musician. Right?

Speaker B: Thank you, thank you, thank you. Perfect description. Yeah, yeah. Really, really love music.

Speaker A: Yeah. Ah, yeah, yeah. You know, he's well versed in sound design, you know, so thank you for coming on the podcast Resin. I really appreciate it. Tell them a little bit about yourself.

Speaker B: Thank you, thank you. First of all, thank you for this wonderful description and introduction of me. And also, I must admit that you gave a really nice description of what's, uh, machine learning and neural network. What is actually is like, it works. And you gave a really nice example. It's like a team of people trying to collaborate together, uh, sharing the results. And once you have a big enough team, you potentially can solve any issue and, uh, simulate lots of various systems and, uh, different kind of difficult stuff. So. Well, a few words about myself. Yeah. As I mentioned before, I'm a CTO of a company called filmustage. We started this business around four years ago. Three, uh, friends from the university we graduated together with Igor and Andrei back to 2012 in Minsk, Belarus. So we know each other for quite a long time. So it was around moment when I was. I was a bit uh, you know, tired of corporate uh, job and just um, normal development and we decided that let's, let's try. Let's try to launch our own business and we still, and we're still, we're still going so looks like we understood and got something. Something uh, in the proper way, did something, did something good.

Speaker A: So.

Speaker B: Yeah, also um. I'm really into music. I'm a lifelong position and uh, also I'm really passionate, passionate about movies. I'm uh, her movie geek to be honest. So I, I seen all of. All of 80s classics as well as.

Speaker A: Oh man, more, more.

Speaker B: More obscure stuff from, from. From German. From German stuff from uh, 1920s like Nosferatu, you name it. I'm a. I really like her. Her general.

Speaker A: Mhm.

Speaker B: Never source of inspiration.

Speaker A: What are you. Have you been in a band before?

Speaker B: Yeah, yeah, I had a multiple bands

Speaker A: so

Speaker B: I've been playing mostly punk rock and psychobilly music. So I taught myself to play double bass. Now I'm um, more into electric guitar and also probably hopefully I will start a new band this or the next. The next year when I will figure out well, where I get the band members and actually write enough songs.

Speaker A: Man. Man, that's amazing man. You know you, you've done some great things in your life and I know you're. I'm sure that you're going to do more amazing things. You know, being um, versatile, being uh, spread uh, across different domains. Man is always key. And you look at, when I look at geniuses, right, I look at people who, who study their craft, right? But they have multiple. They just want to experience and they love experience and being exposure to multiple things. So you know, it seems like that's what you're doing. On the verge of being the next Einstein man. Or close to it, man. That's what I'm talking about, you know. Um, so let's talk about film these days, man. I mean this is, this is something that, right, you know, is an amazing technology that you are doing within Film View stage, right? You all are doing something great, right? When I look at it, you're saying you, if you look at the website, right, you know, you, it says you're creating your project in two clicks and uploading the script and final draft or PDF format, right? And then you automatically create that script breakdown with all the categories you need. You got the cast, you got the props, location, makeup, etc. You have all those different things right, you know, going on. I mean, how is it changing the world? How is it making a difference in technology right now, now?

Speaker B: Oh, it's a good question really. So. Well, I want to start with how I actually got to this topic. It's all uh, possible because, um, the CEO of the company, Igor, he's a filmmaker with a really, really long story with an interesting um, background and he knows a lot about the processes. He was also filming a lot in the US So it came to our understanding that um, the way of doing things in industry, uh, is really conservative. It's kind of old school. They don't really like innovation. But it was, it's, it's 21st century. Okay. So when we really got some advanced technologies and computational power is, is more advanced and um, so we decided that we can deliver some innovation to this, to this industry, that's for sure. So we started to dive deeper and actually see what we can, what we can improve, what we can update. So, and like the, every, every film production, every video production, screen, screenplay based production, uh, starts with uh, obviously with a screenplay. It's a piece of text usually 100 pages long for a feature film, so which translates to something around uh, hundred minutes of screen time. And uh, what you have to do, you have to sit, basically sit down and carefully read the text and write down or mark down all the mentions of actors, say props, visual effects, makeup and stuff. And obviously it's a kind uh, of routine, repetitive task which takes anything from a couple of days to a couple of weeks or even longer depending on the complexity of the. So and it sounds like a great task for machine learning actually. So definitely you want to, you want to be able to automate this kind of, kind of stuff because it's not super creative. Okay. You just, you just, you're just operating, you're just operating with information. So we started to build our uh, model, started to build the data set, we started to run tests and actually we got really nice results uh, after a few experiments and we decided, okay, so we're going to uh, hit the market with this idea. That's okay. You don't need to, you don't need to break down to kickstart your project and basically understand once inside, you don't need to disassemble the dissected manual. You can, you can do it with a high uh, accuracy rate using machine learning actually. And that's what, what we actually offer to filmmakers. You literally Just upload your screenplay in a couple of minutes, we analyze it and you get the really quick, accurate and nice description overview of your project. Like you, you can understand like number of casts you need to hire, what kind of props you need, what kind of locations you need to potentially rent, etc. Etc. So this is a really huge time saver of, of at the early stages of pre production and business information you quickly can build even more, you can understand your schedule. So what you need to do to optimize your um, shooting schedule where you can actually save, how to build your budget, you can add to the references in our platform and quickly, quickly build uh, various m type of documents you would like to share with your colleagues and teammates. Also we're going to bring team access really soon which will be another huge step for us. So the way to collaborate in the same project for different, different professionals. So well the whole idea is to bring the best, the most useful, reliable tools to a modern filmmaker. Potentially democratizing the industry, lowering the entry level, the threshold and um, basically giving a chance to filmmakers to spend more time on creative tasks rather than manual uh, labor. And repetitive. Repetitive tasks, Exactly.

Speaker A: Oh my goodness. I mean I didn't realize how um, uh, tedious and how meticulous you have to be in the film industry behind the scenes doing all of those manual tasks. Right? Like, like what you're telling me is that there's a whole process, people behind the scenes, writing stuff down and all that. And it's repetitive. And if it's repetitive, man, let's get this machine learning to work baby. You know, I mean we got to do, you know, I mean we got to automate. I guess there's a difference right in between the automation of things. You can write scripts and things like that, but something like this, definitely with a large data set out there, you need to be able to automate, you need to be able to look at uh, from machine learning standpoint, being able to analyze things historically, making better decisions, all that stuff man. So how's the, how the data stored? Is it in the cloud? Is it on premise? You know how you are storing the data and then uh, um, I guess analyzing it.

Speaker B: Mhm. So we decided to make our platform as accessible as possible. Basically accessible to anyone in the world. So we decided to actually run it on cloud and we use Google uh, cloud for our setup. So this is where everything is actually uh, deployed like front end, back end and of course uh, AI engine also runs on Google cloud. So I think it's, it's more it's more easy and more useful today to have uh, some kind of online tools you can use anywhere. Anywhere. Doesn't, doesn't matter where you actually, where you actually uh, at the moment. So we're also thinking about uh, having um, like uh, offline application desktop desktop solution for cases when. Well sometimes you are not in this, in the place where there's even an Internet connection or cellular connection and you still need to analyze your stuff and build your schedule, build other types of documentation or work on your project. So potentially in um, in the near future we're going to also have a like on prem. On prem solution.

Speaker A: No, that's awesome because I actually was thinking about how um, a lot of the people, the clients that I work with, they're interested in this whole federated data man, federated machine learning thing right now. And they're like, they love edge to core to cloud, right. They love some type of edge device closer to them like you were saying, or offline device or something. Because at some point they lose connection right to the uh, network and if they do they still want to be able to process and analyze. They want to be able to do everything they need and then when it gains connection and bam. You send it out to where you need to go. Right. Make the connection.

Speaker B: Exactly, exactly. Uh, there's one, there's one part of the, of the solution and also sometimes you just don't want your device to be ever connected to, to the world, to the Internet because well if you have a uh, one million dollar screenplay, you want to be absolutely sure and uh, make once, take all the precautions for it never, never to go anywhere and never leave just your laptop. This is also the case

Speaker A: man. No, no. That's amazing. So are you all using any type of tagging or anything? Uh, uh, in place like labeling or anything?

Speaker B: Yeah, exactly. So the core technology solves the um, thing called named entity recognition. So basically just that you want to distinguish different type of object in text. So you prepare data set with correct labels basically telling which, which type of what content means what. Say this is an actor, this is a proper location detail, so vehicles or whatever. And once you have a uh, big enough data set you can train your neural network and um, run it for prediction and actually for never seen before text and actually um, get the results for, for exactly that.

Speaker A: Yeah man, you all are doing some amazing things right. We have to get you out there. We got to get the world to know what you're doing in technology. This is amazing. I want to talk about the future of technology, right. The future of not technology but neural networks in the film processing industry. And I remember reading the article recently, right. I'd like to research a little bit prior to um, uh, interviewing guests and you know it was an article talking about generative AI, uh and how it's bringing the biggest disruption to the filmmaking industry in like 100 years. Right. And when you're using general to AI, right. And they're doing things like removing profanity post production, right. You know there is getting a little intense and I didn't know they're doing these things right. So you know, it's also transforming film and video. You know, for example, um, there was something in Berlin they talked about where it helps companies make corporate videos, right. Using AI and machine learning to generate real life actors. I'm like what real life actors? What are you talking about? To create, to create like hyper realistic or synthetic videos. Right. We need a lot of more synthetic uh, videos out there for testing. But videos uh, in minutes, right. By inserting just a script. And I was like I have to talk to Rosalind about this. So what, what's going on in the future of neural networks in the film industry? What kind of use cases are there they talking about that's going to be out there?

Speaker B: Oh, it's a really interesting topic about the whole generative hype and stuff. So uh, lots of, lots of difficulties in here. Lots of problems potential of course works like magic. Let's, let's, let's be clear here. You. The things, the things you can do with Mid Journey or GPT4 or ChatGPT is just insane. You can literally talk to a computer or you can ask your machine just to throw, throw anything. Never existing before potentially so. But the, the trouble, the problem is that it said it's a generative AI, okay. It's generate stuff it doesn't actually

Speaker A: like.

Speaker B: It's not a library uh, of hundred percent uh proven facts. It's uh, a really complex neural network which generates response to your question basically. And well quite often um, ChatGPT just creates never existing stuff for you. If you ever tried to let's say create some code with GPT, it tends to tell you about never existing frameworks or some really weird stuff. The same about historical events and uh, etc. Etc. Um, image generation is also a bit funny because the way how the machine understands the visuals basically it's a set of characteristics and features, really complex one and of course it translates human um, language to those sets of features and then tries to construct an image out of that and you definitely have ah, some, some unbelievable, really inspiring results. But it still struggles with stuff like hands. Sometimes they update majority to generate better hands of course, but still there are, there are. Sometimes they just, they just look terrible. Basically that's because the machine doesn't understand how, how the hand works and what's the purpose, how, how your fingers move and why it's all, why it's so important how, how it looks from different angles and, and stuff. When you, when you try to generate say um film camera using uh midjourney it will generate you a really actually funny looking abomination of all sorts of metal uh parts and devices. But it looks like a camera. Okay. But if you take a closer look you can tell it, it's not designed to work. So once we will be able to actually make machine understand a bit more about the functionality of objects uh and stuff it tries to generate, we'll put it to the next level. Yes, it's a really hot topic in filmmaking industry because you can generate imagery, you can generate videos, the fakes and stuff. You can have for instance a digital copy of uh, of an actor or a famous one or maybe not existent anymore and you can continue to create, create visuals using, using this person and his voice.

Speaker A: Right.

Speaker B: I personally find even more satisfying and more interesting the uh audio generation uh networks and generative uh AI which helps you create like never, never existing before. Tracks by Drake. Like that was like a recent case of uh feed uh by Drake and uh Weekend if I remember it correct right, which was immediately put down by the label, the publisher because it violates all. Well it's probably violates all possible, all possible regulations on copyrights.

Speaker A: Uh was it the Weekend?

Speaker B: Yeah, and Drake and uh, some guys, some guy generated uh, a new song with their voices in it. It sounds really realistic. So it definitely definitely changes filmmaking uh as well because you can, you can create voicing with some dead actors potentially who already passed away.

Speaker A: Yeah. So scary man. A little creepy.

Speaker B: Oh yeah, it's really creepy to be honest. But it's you know, it's, it's funny. Maybe, maybe we'll see more of this coming coming really soon. Yeah, the very same will be about Diminger actually. Well for now it still looks a bit um. Well not, not ready for production let's call it. But the technology will change and we'll get the better, better results. Nowadays there was a, there was a project on YouTube uh where the VFX studio, they generated an anime based on video. They actually, they actually shot themselves In a really cinematic poses and you know, reading all these lines from their screenplay and then they, they frame the by frame. They generated basically anime styled images and combined it into a uh, video so it looks okay. Still. You can see how it wouldn't m. Can't replace real animation where everything is precise. Well, it's all about precision actually because when you shoot a movie or draw animation you want to control the things and actually it's a huge part of your vision like how the frame is built, how, how the characters are ah, animated and you cannot do this with neural networks. Still with generative AI you just, you just, you just describe some really high level. Well of course you can go deeper in details but still it's a really high level description of what you want to get. And then it's uh, it's for the machine discretion how it's, how it's gonna, how the machine is going to interpret it. Interpret it. So it's all about precision.

Speaker A: No, precision is definitely important man. So let, let's, I wanna uh, and, and thank you for giving that insight into everything because uh, like I mentioned before, you know, um, the film industry, the future of the film industry is getting very, very highlighted. Right? You know, and it's, and it's getting, getting into some stuff that may be unethical, you know, in certain people's eyes as well, where we have to start keeping track of that. I mean AI moves so fast, right, that we almost don't have time to say slow down and let's think ethics here, right? Let's think uh, um, you know, when you think about creativity, right, and AI can automate some things of the creative process but it cannot replace, replace the human creativity entirely.

Speaker B: That's, that's the point exactly. Yeah. So film industry is a, is a huge venture, venture machine actually. It's every, every film looks like a startup to be honest. So you have an idea, you try to get financing, you hire the team and you want to produce it as cheap as possible to, to you know, to be as tight in budget as possible and at the same time you want to get the box office as high as possible. And what they really excited about is to wait about the ways to actually predict the potential box offices and ways uh, to save the budget and all the ways actually to optimize the production and stuff like this. So it's film industry and filmmaking is a huge, huge business and it's a venture and uh, they need tools like this.

Speaker A: No, they do need tools like this, man. I mean the use of AI in the filming industry is raising a lot of ethical concerns, you know, such as the potential for AI to kind of, I, um, guess perpetuate those, um, stereotypes, right, or manipulate audience emotions and things like that. And it's, you know, the question is, is this, Is it, Is that right? You know, is that ethical? You know, I mean, as technology is moving faster and faster, we don't sit down. I mean, who was it? Uh, the guy who created Tesla. Tesla stuff. You know, he has, like, this whole, uh, petition that went out, right? He wanted to stop AI especially came out, you want to stop AI from going for like, six months at least, because he said we're going to probably die if we don't do that. You know, I don't know how intense that can be, you know, but, uh, you know, what's your thoughts on that, man?

Speaker B: Well, it's interesting, but. Well, it. It definitely can feel scary sometimes, but you cannot. You just can't stop the progress once. Once it was started. You can. You can slow it down even. So, uh, about that. That petition about, like, the potential threat of machine learning and AI engines advanced, like, chat GPT. It's a funny story because not. Not very. Not very long from that, um, from that. When this petition was published, Elon Musk, actually, he hired a whole new division of, uh, machine learning specialists and data scientists actually, to potentially to build something. Something like. Like OpenAI's GPT4. Google got their own bard, uh, engine, which was not so long ago, uh, published for public access. So things are not going. Things are not looking like it will. Like it's going to slow down or will be stopped or something. It's not going to happen at the same time. Of course, the ethics are questionable. What the machine can generate, we cannot really. We cannot really control what's. We don't. We don't know what's inside. Uh, the model of, uh, any neural network we don't understand because it's too complex to ever analyze it, actually. And, uh, it's like a huge maze, okay? You can enter in one part, and you never know where you're gonna. When we're gonna exit. And you can potentially block some dangerous entrances, which leads to potentially dangerous points or places in your maze, but you'll never be able to block all of them. And there always will be some explorers who will find, um, some. Some new backdoors.

Speaker A: No, absolutely, Absolutely. Man. So, Rosalind, man, you have. You have said so much on every one of the topics, right? We talk about epic AI. We talked about the future of neural networks and how um, it is really increasing all the greatness that already comes with the film industry, but it's now increasing it. Um, and we talked about Fenby State, right? Which I mean I advise all the audience to please, please check that out. If you are in the industry, if you, if you have a need for something like that, you know, um, I'm definitely going to pass it on to my buddy who lives in California and I'm going to say look, a lot of scripts, he does script writing and this will be valuable to me. Here's why. You know, so, uh, but I like to leave the audience what I call a summarization, right? Or dope nugget or Jim of everything. And what I've learned today is I think AI is so important in things like that pre production phase, right. You know, I know it's, it's coming to post production as well. But you know you can be used, it can be used to streamline the pre production process, you know, including casting and you have the uh, the location scouting, you know, you got storyboarding, right, you know, and I only know, all right, who does it and you know, even think about special effects, right? You know, creating that more realistic, immersive special effect that's out there, right. Reducing, possibly reducing the need for practical effects and saving time and money. I'm sure it does all that, right. So I advise people who are in the industry, um, or not even industry, I actually advise people who are data scientists, who are like movie buffs, right, who are interested in movies, who are interested in the film industry so much that uh, there is always a place in this AI or data science path for you right now. It doesn't matter where you go, everybody is probably going to be in need of it in the near future, you know, if not now, uh, to be more competitive in the industry and to take advantage. So thank you again. Russell, is there anything that you want to leave the audience with?

Speaker B: Well, yeah, the world is changing and changing really fast. So I recommend everyone to read something about uh, machine learning on all AI engines. So go and try it by yourself. Try to generate some images with mid journey. Try to talk to OpenAI's ChatGPT. It's gonna very soon is going to be a really huge part of our life. It's so massive. It's something like Google actually how Google changed everything, like change the whole Internet and generative AI like this again is going to change the way of doing things. It's going to be, I don't Think it's going to replace like, people and uh, some professions. But it will be a really nice tool for those who want to be competitive, who want to stay with the latest tech and always be on top of. Well, also I really want to invite, uh, all the filmmakers out there to go to our platform, filmstage.com and try to use it on your own platforms, on your own projects. Uh, we are available to chat 24 7. Just shoot us a message on our support, uh, using our support bot on the, on the webpage. We'll be really happy to help you. And also, uh, to invite you even in the most special way, I want to give, uh, a, uh, discount code. It will be called diaper data 30 and it will give you 30 off of every subscription we have on our website. So please go and try it. We have, we also have a seven day trial and after that you can decide whether you like it or not. So no strings attached.

Speaker A: Yeah, definitely, definitely. Thank you. I appreciate that. You know, and it's www.film use stage film.com film got you, got you. Um, audience, definitely check that out, right? And is it forward slash? Uh, dapper data 30. Is it. Would that take them to the promo? Uh, or you just put it in at the end?

Speaker B: Um, let's, let's, uh, we can, we can have it here. And also I will, we'll put into the description, I guess.

Speaker A: Okay. Yeah, yeah, definitely, definitely. Uh, okay, so look, let's play a game, right? Really quick. Um, we're running out of time. I um, want to play this game called Overrated. Underrated. All right. And this game is pretty fun unless the audience know that we think about things other than our geeky stuff that we do every single day. Right. You know, there's 5% of our brain does care about or have an opinion about ice cream. We do care about that, right? We have an opinion about cheese man. We haven't about, uh, basketball or, or different things. Right. We, we have an opinion. We might not care for it, we might not watch it on a daily basis, but, but we have an opinion about it, if you ask us. We just don't give our opinion out right all the time. So this is a chance for us to talk about things other than our geeky stuff that we do every day. Right? Are you ready?

Speaker B: Um, yeah, I'm super ready.

Speaker A: All right, Russell. All right, first one, I want to call it real football, uh, soccer.

Speaker B: Okay.

Speaker A: Is it overrated? Underrated. All right. Um,

Speaker B: uh, I'll related. Okay.

Speaker A: You said overrated?

Speaker B: Yeah.

Speaker A: Oh, man, What? Eat the overrated, man. Most people say that it's, ah. It's underrated, man. Most people don't. Don't, ah, really, um, uh, I mean, mo. Most people would say it's underrated. Right. Coming from, uh, where. Where are you located at right now, Russell?

Speaker B: In Europe? Lithuania. So here it's a kind of big thing.

Speaker A: Yeah. You don't, like. You don't even watch. Watch soccer like that.

Speaker B: Well, m. You know, it's, um, in our part of Europe, it's, uh, always was like a default kind of sport and everyone is supposed to play football. And I was, uh. I just never got this. So the championship. The championship is really huge, but it's just not my cup of tea.

Speaker A: Um, all right. Okay, next one. I hope I say it right. Sepuline.

Speaker B: Uh, I think it's underrated.

Speaker A: Okay. Okay. So if people don't know, what am I saying it right?

Speaker B: Zeppelins. You can, you can. Well, it's. If you want to call it a moratorium, where you call it, uh.

Speaker A: Okay, okay.

Speaker B: But Zeppelin is absorbed. Absolutely fine.

Speaker A: Okay. So Zeppelin is this potato meat dumpling, uh, format. It's. It's from my understanding, is very delicious. You know, I have a couple friends that are from Europe area. And so Rosin is saying definitely check it out. It's underrated, so check that out.

Speaker B: Yeah, yeah.

Speaker A: All right. Uh, but it is heavy on the stomach, right? Are we saying it's heavy? It's a little heavy, yeah.

Speaker B: Ah, it's super heavy. So, you know, have heavy appeals somewhere close to you.

Speaker A: Uh, uh, okay. All right.

Speaker B: Hardcover, uh, books, uh, underrated.

Speaker A: Underrated. You still like them?

Speaker B: Yes, I try to. I try to buy, well, everything I find interesting. Especially I'm a huge fan of like, illustrated. Illustrated books for. With lots of photography. Especially the kid, something. Um, well, yes, it's. It's also really nice. But, um, with, um, photo reproductions or art reproductions really, really nicely so they. They have to be hard covered.

Speaker A: Okay. Okay. All right. What about YouTube?

Speaker B: Oh, man, it's like an air, you know, you can.

Speaker A: You can.

Speaker B: You cannot say his air is a rated or underrated. I know. It's just. It was just fine.

Speaker A: Okay. Right where it needs to be. Right?

Speaker B: Yeah.

Speaker A: Okay, I get it. I give you that last, uh, two. The printer.

Speaker B: Uh, overrated. I hate printers.

Speaker A: I hate printers, man. I hate it. We should do like a petition on stock printers, man. And then somebody always wants to ask. Once a year they ask you to print something.

Speaker B: It's so stupid. Especially when you go to Some governmental structures, they all the time, they need printed papers, so it's so. It's so stupid.

Speaker A: All right, last one. Beer.

Speaker B: Oh, um, tough question. Uh, well, I'm a really huge beer lover, so let's say it's underrated.

Speaker A: Okay.

Speaker B: Must be even more exposed.

Speaker A: Yeah, I would say the right ones. I don't know if people would call me. I, I, I. It's a. It I. It either has to be very strong, right, Like a strong Guinness, or it has to be light, like a summer light. Yeah. In between, for some reason, I just, it just, I just can't do it, man. It just takes me over, man. I don't know.

Speaker B: Guinness is fine. So you know what m I gonna do for after the recording session? I'll go straight to the bar and order a pint of Guinness.

Speaker A: Oh, nice, nice.

Speaker B: Thank you.

Speaker A: It's good. Yeah, yeah,

Speaker B: I have a spot where they have a really nice Guinness on draft. So thank you for the idea.

Speaker A: No problem, no problem. We'll. Well, thank you again for being on the podcast. I really appreciate it. Rosalind. Thank you audience, for listening to Data is my sign podcast show to mix dat Passion. I'm your host, Dapper Data. As you know, you can follow me at Mr. Dapper Data. That's at percent sign M M R D A P P E R D A T A or any one of the social media platforms. Definitely check out, um, filmustage.com. right. And, and when you go on there, the promo code, uh, that you use is Dapper Data 30. That would be great. I appreciate it. Um, and, and Resin, is there any, any place they can reach you at and, and anything you're promoting right now?

Speaker B: Uh, you can find me on LinkedIn, you can find me by my name or you can find me by, by our platform name and you will instantly find me. I'm um, a CTO of Film Stage, so please connect. I'll be more than happy to talk to anyone.

Speaker A: Well, thank you. I appreciate it, man. Definitely check out Resident. I'll put it in the show notes, everybody. The, uh, contact to LinkedIn and if you have any questions, just reach out to me. You know how to reach me. Thank you again, Resin, for being on the podcast and audience. I love you all. Peace. Thank you for listening to the Data is my science podcast, the show that makes data your passion with your host, Dapper Data. Mhm.