In this report, we’ll cover everything you need to know about LLMs: what the competitive landscape looks like, how these models can be applied to your work, what changes with the release of GPT-4, and considerations for responsible use. After taking stock of the present, we’ll also look at what the future of text might look like as LLMs become more sophisticated and tailored for specific uses across industries. In addition, we’ll share our prototype built using GPT and Slack’s API that’s designed to jumpstart brainstorms among a group of collaborators.

In this report, you will learn:

• Why LLMs are so effective
• How GPT-4 changes the game
• Ways to apply this technology now
• Important considerations to keep in mind
• The upcoming impact of LLMs and how to prepare

Brian: A creative co-collaborator brainstorm bot powered by chatGPT.

To make GPT more accessible across our team and to integrate it within commonly used workflows, we created a chatbot that integrates into our widely used communication tool, Slack. Unlike typical chatbots that only engage in one-on-one conversations with users, Brian is designed to actively contribute and facilitate brainstorming sessions among teams.

Ultimately, the chatbot streamlines team brainstorming and collaboration within the Slack platform by guiding the conversation, sharing its own ideas, summarizing the discussion and more.

Indeed, the efficiencies and opportunities that AI enables can radically uplevel brand experience and output, though unlocking their true potential relies on understanding how to uplevel teams to use the technology effectively. Those who can fully leverage the power of AI and infuse it within every aspect of their business will dominate the market. But for those lagging behind, this is a Kodak moment: there will be no loyalty for businesses that are slow to deliver AI-powered experiences that make consumers’ lives easier.

Throughout this guide, we’ll showcase AI’s potential to transform marketing today and tomorrow, as well as the actions you can take right now to reap those rewards and lead in the new era.

It’s true that we’re at the peak of the hype cycle with AI, particularly generative AI. But this disruption is a significant one, one that has become a make-or-break moment for many in our industry. 

We’re not just bullish on AI; as a change agent in our industry, it makes our model inevitable. Automation has played a great role in helping us scale up our business and outmaneuver our more traditional peers since day one. Meanwhile, today’s rapid evolution of AI is reshaping the nature of the brand-agency in real time, along with how brands themselves can go to market. Winning in the new era requires a willingness to embrace all that AI has to offer.

Prior to the hype, generative AI sprung onto the scene about four years ago, and our innovation team quickly began to experiment with it by training our own models. One turned lines and doodles into foliage; another neural animation tool created original dance choreography based on simple input like stick figures. More recently, we made an entire short film with the help of AI at every step of the production process. These experimental prototypes anticipated a future in which AI would open a new world of creative possibilities—that future is now.

But AI will have other, far-reaching potential across our industry as brands set out on new paths to growth beyond just content creation; its rapid development signals the start of truly personalized digital experiences. Cookies are crumbling, and brands have taken that as a cue to recognize the people behind the numbers and provide a better value exchange for their data. Yet the tools that marketers have historically relied on have always failed to turn the ideal of 1:1 marketing across the customer journey into a reality. AI finally unlocks that ability—across content, data and digital media, and tech services—to build and deliver hyper-personalized, highly empathetic customer journeys at scale, and fast.

Some are wary of these tools, focused on the balance between augmenting human ability versus replacing it. That’s a noble and important conversation to have. But automation has always been crucial to the growth of our team, and we are confident AI will continue to lead to more and greater work for our people. As a tech-agnostic team of makers, one that has already enthusiastically adopted these tools across every part of our business, we are the best positioned to take advantage of the abundance AI enables.

There are incredible opportunities on horizon: making more with less, making marketing intelligence more intelligent, or even driving new value to intellectual property through the training of owned AI systems. Each possibility will challenge the role of a creative, data and digital media, or tech services partner to evolve—lest they be surpassed by any number of new startups and cottage industries that have cropped up designed around AI. We don’t shy away from the challenge; we’re poised and ready to lead in the disruption, because that’s what we’ve always been built to do.

10. Digital humans get more realistic.

Digital humans may have earned a spot on our list of trends last year, but we haven’t grown tired of traversing the uncanny valley to play with the technology. In fact, the recent explosion of conversational AI will likely inject new life into digital humans and transform the realms of customer service, entertainment and more. Whether used to hand-craft original characters or refine scanned-in digital twins, digital human creation tools are becoming increasingly complex to deliver lifelike avatars. 

“We’ll see more competition between Unreal’s MetaHuman Creator and Unity’s Ziva,” says Geert Eichhorn, Innovation Director. In fact, Media.Monks has used Unreal’s tool to create a digital double of our APAC Chief Executive Officer, Michel de Rijk. Because why not?

9. Motion capture becomes more accessible.

Last year, we released a Labs Report dedicated to motion capture and how its increasing accessibility influenced content production for both professional film teams and everyday consumers. New technologies available at consumer price points are helping to bring motion capture into even more people’s hands. Meta’s Quest Pro headset, which released late last year, features impressive facial tracking that will be key to expressing the nuances of human emotion in VR. Move.ai, currently in beta, enables 1:1 motion tracking with a group of mobile devices—no bodysuits, no markers, no extra hardware needed. Using computer vision, the platform allows anyone to make motion capture video in any environment.

8. Mixed reality and mirror worlds mature.

With smaller and more comfortable AR headsets shown off already at CES, we can expect augmented and mixed reality to become more immersive, accessible and practical over the course of 2023 (check out more of what we saw at CES here). The VIVE Flow, for example, includes diopters so that users can replicate their prescription lenses in the device, amounting to a more comfortable experience overall. 

But it’s not just about hardware. “One of the major advancements is not in the headsets, but in the software,” says Eichhorn, noting that VPS has the power to pinpoint a user’s exact position and vantage point in the real world. “They do this positioning by comparing your camera view to a virtual, 3D version of the world, like Street View.” We covered mirror worlds in last year’s trend list, but the development of VPS is now bringing this vision closer to everyday consumers.

While VPS currently works only outdoors, we’ve already seen the power of the technology with Gorillaz performances in Times Square and Piccadilly Circus in December 2022.

7. More enterprises embrace the hybrid model.

For many businesses the return to the office hasn’t been a smooth transition; while some roles require close collaboration within a shared space, others enjoy more flexible setups that support childcare, offer privacy for focus work or greater accessibility. Given the benefits of flexible work setups and the development of technologies that build presence in virtual environments, Luis Guajardo Díaz, Creative Technologist, believes more enterprises will embrace the hybrid work model.

Media.Monks’ live broadcast team, for example, built a sophisticated network of cloud-based virtual machines hosted on AWS to enable people distributed around the world to produce live broadcasts and events. Born out of necessity during the pandemic, the workflow goes beyond bringing teams together—it’s designed to overcome some of the challenges traditional broadcast teams face on the ground, like outages or hardware malfunctions. It stands to show how hybrid models can help enhance the ways we work today.

6. Virtual production continues to impress.

Virtual production powered by real-time become popular in recent years: the beautiful environments of The Mandalorian or grungy urban landscape of The Matrix showed what was possible by integrating game engines in the production process, while pandemic lockdowns made the technology a necessity for teams who couldn’t shoot on location.

Now, further advancements in game engines and graphics processing offer a look inside the future of virtual production. Sander van der Vegte, VP Emerging Tech and R&D, points to Unreal’s Nanite, which allows for the optimization of raw 3D content in real time.

Localization of content is one example. “In 2023 we’re going to see this versatility in the localization of shoots, where one virtual production shoot can have different settings for different regions, all adapted post-shoot,” says Eichhorn.

5. TV streaming and broadcasts become more interactive.

With virtual production becoming even more powerful, TV and broadcasting will also evolve to become more interactive and immersive. “Translating live, filmed people into real-time models allows for many new creative possibilities,” says van der Vegt. “Imagine unlocking the power to be the cameraman for anything you are watching on TV.” 

It might sound like science fiction, but Sander’s vision isn’t far off. At this year’s CES, Sony demoed a platform that uses Hawk-Eye data to generate simulated sports replays. Users can freely control the virtual camera to view the action from any angle—and while not live, the demo illustrates the power of more immersive broadcasts. The technology could be a game changer for sports and televised events that let audiences feel like they’re part of the action.

4. Metaverse moves become more strategic.

“2021 was a peak hype year for the metaverse and Web3. 2022 was the year of major disillusionment,” says Javier Sancho, Project Manager. “There are plenty of reasons to believe that this was just an overinflated hype, but it’s a recurring pattern in tech history.” Indeed, a “trough of disillusionment” inevitably follows a peak in the hype cycle.

This year will challenge brands to think of where they fit within the metaverse—and how they can leverage the immersive technology to drive bottom-line value. Angelica Ortiz, Senior Creative Technologist, says the key to unlocking value in metaverse spaces is to think beyond one-time activations and instead fuel long-term customer journeys.

A great example of how brands are using Web3 in impactful ways is by transforming customer loyalty programs, like offering unique membership perks and gamified experiences. These programs reinforce how the Web3 ethos is evolving brand-customer relationships by turning consumers into active participants and collaborators.

3. Large language models keep the conversation flowing.

With so much interest in bots like ChatGPT, the Labs.Monks expect large language models (LLMs) will continue to impress as the year goes on. “Large Language Models (LLMs) are artificial intelligence tools that can read, summarize and translate texts, and generate sentences similar to how humans talk and write,” says Eichhorn. These models can hold humanlike conversations, answering complex questions and even writing programs. But these skills open a can of worms, especially in education when students can outsource their homework to a bot.

LLMs like GPT are only going to become more powerful, with GPT-4 soon to launch. But despite their impressive ability to understand and mimic human speech, inaccuracies in response still need to be worked out. “The results are not entirely trustworthy, so there’s plenty of challenges ahead,” says Eichhorn. “We expect many discussions over AI sentience this year, as the Turing Test is a measurement we’re going to leave behind.” In fact, Google’s LaMDA already triggered debates about sentience last year—so expect more to come. 

2. Generative AI paints the future of AI-assisted creativity.

If 2021 was the year of the metaverse, the breakout star of 2022 is generative AI in all its forms: creating copy, music, voiceovers and especially artwork. “Generative AI wasn’t on our list in 2022, although looking back it should have been,” says Eichhorn. “The writing was on the wall, and internally we’ve been working on machine learning and generating assets for years.” 

But while the technology has been embraced by some creatives and technologists, there’s also been some worry and pushback. “These new technologies are so disruptive that we see not only copywriters and illustrators feel threatened, but also major tech companies need to catch up to not become obsolete.” 

In response to these concerns, Ortiz anticipates a friendly middle ground where AI will be used to augment—not erase—human creativity. “With the increasing push back from artists, the industry will find strategic ways to optimize processes not cut jobs to improve workflows and let artists do more of what they love and less of what they don’t,” she says. Prior to the generative AI boom, Adobe integrated machine learning and artificial intelligence across its software with Adobe Sensei. More recently, they announced plans to sell AI-generated images on their stock photography platform.

1. The definition of “artist” or “creator” changes forever.

Perhaps the most significant trend we anticipate this year isn’t a tech trend; rather, it’s the effect that technology like generative AI and LLMs will have on artists, knowledge workers and society. 

With an abundance of AI-generated content, traditional works of art—illustrations, photographs and more—may lose some of their value. “But on the flip side, these tools let everyone become an artist, including those who were never able to create this kind of work before,” says Eichhorn. This can mean those who lack the training, sure, but it also means those with disabilities who have found particular creative fields to be inaccessible.

When everyone can be an artist, what does being an artist even mean? The new definition will lie in the skills that generative AI forces us to adopt. Working with generative AI doesn’t necessarily eliminate creative decision-making; rather, it changes what the creative process entails. New creative skills, like understanding how to prompt a generative AI for specific results, may reshape the role of the artist into something more akin to a director. 

Eichhorn compares these questions to the rise of digital cameras and Photoshop, both of which changed photography forever while making it more accessible. “The whole process will take many more years to settle in society, but we’ll likely see many discussions this year on what ‘craft’ really entails,” says Eichhorn.

That’s all, but we can expect a few surprises to emerge as the year goes on. Look out for more updates from the Labs.Monks, who regularly release reports, prototypes and podcast episodes that touch on the latest in digital tech, including some of the topics discussed above. Here’s to another year of innovation!

Since its launch in 2021, Epic Game’s MetaHuman Creator, a cloud-based app for developing digital humans, has extended its range of possibilities by adding new features—such as Mesh to MetaHuman. Using Unreal Engine, this plugin offers a new way to create a metahuman from a 3D character mesh, allowing developers to import scans of real people. In other words, it makes it easier to create a virtual double of yourself (or anyone else) almost immediately.

Inspired by this significant update and following our tradition of enhancing production workflows using Unreal Engine, our team of dedicated experts decided to build their own prototype. Needless to say, they learned a few things along the way—from the practical possibilities of metahumans to the technicalities of applying motion capture to them. As explained by the experts themselves, here’s what you need to know about creating and unlocking the full potential of virtual humans.

Be everywhere at once—at least virtually.

If you ever fantasized about cloning yourself to be able to comply with all your commitments or complete your pending tasks, metahumans may be just what you were looking for. Virtually, at least. As digital representatives of existing individuals, metahumans offer endless possibilities in terms of content creation, customer service, film and entertainment at large. Sure, they won’t be able to do your dishes—at least not yet—but if you happen to be a public figure or work with them, it’s a game changer. 

By lending likeness rights to their digital doubles, any influencer, celebrity, politician or sports superstar will be able to make simultaneous (digital) appearances and take on more commercial gigs without having to be on set. As John Paite, Chief Creative Officer of Media.Monks India, explains, “Celebrities could use their metahuman for social media posts or smaller advertising tasks that they usually wouldn’t have the availability for.” Similarly, brands collaborating with influencers and celebrities will no longer need to work around their busy schedules.

The truth is, virtual influencers are already a thing—albeit in the shape of fictional characters rather than digital doubles of existing humans. They form communities, partner with brands and are able to engage directly and simultaneously with millions of fans. Furthermore, they are not stuck in one place at a time nor do they operate under timezone constraints. In that regard, celebrities’ digital doubles combine the benefits of virtual humans with the appeal of a real person.

A new frontier of personalization and localization.

Because working with virtual humans can be more time-efficient than working with real humans, they offer valuable opportunities in terms of personalization and localization. Similarly to how we’ve been using Unreal Engine to deliver relevant creative at speed and scale, MetaHuman Creator takes localization to a new level. As Senior Designer Rika Guite says, “If a commercial features someone who is a celebrity in a specific region, for example, this technology makes it easy for the brand to replace them with someone who is better known in a different market, without having to return to set.” 

But not everything is about celebrities. Metahumans are poised to transform the educational landscape, too, as well as many others. “If you combine metahumans with AI, it becomes a powerhouse,” says Paite. “Soon enough, metahumans will be teaching personalized courses, and students will be able to access those at a lower price. We haven’t reached that level yet, but we’ll get there.”

For impeccable realism, the human touch is key.

To test how far metahumans are ready to go, our team scanned our APAC Chief Executive Officer, Michel de Rijk, using photogrammetry with Epic Games’ Reality Capture. This technique works with multiple photographs from different angles, lighting conditions and vantage points to truly capture the depth of each subject and build the base for a realistic metahuman mode. Then, we imported the geometry into MetaHuman Creator, which our 3D designers refined using the platform’s editing tools. 

“Because Mesh to Metahuman allows you to scan and import your real face, it’s much easier to create digital doubles of real people,” says our Unreal Engine Generalist Nida Arshia. That said, the input of an expert is still necessary to attain top-quality models. “Certain parts of the face, such as the mouth, can be more challenging. Some face structures are harder than others, too. If you want the metahuman to look truly realistic, it’s important to spend some time refining it.” 

Once we got our prototype as close to perfection as possible, we used FaceWare’s facial motion capture technology to unlock real-time facial animations. While FaceWare’s breadth of customization options made it our tool of choice for this particular model, different options are available depending on the budget, timeline and part of the body you want to animate. Unreal’s LiveLink, for example, offers a free version that allows you to use your phone and is easy to implement both real-time and pre-recorded applications, but focuses on facial animations only. Mocap suits with external cameras allow for full-body motion capture, but with mid-fidelity, and recording a real human in a dedicated mocap studio unlocks highly realistic animations for both face and body. 

At the same time, the environment we intend the metahuman to inhabit is worth considering, as the clothes, hair, body type and facial structure will all need to fit accordingly. Naturally, different software may adapt better to one style or another. 

While this technology is still incipient and requires some level of expertise, brands can begin to explore different ways to leverage metahumans and save time, money and resources in their content creation, customer service and entertainment efforts. Similarly, creators can start sharpening their skills and co-create alongside brands to expand the realm of possibilities. As Arshia says, “We must continue to push forward in our pursuit of realism by focusing on expanding the variety of skin tones, skin textures and features available so that we can build a future where everyone can be accurately represented.”

Angelica: Hey everyone. Welcome to Scrap the Manual, a podcast where we prompt "aha" moments through discussions of technology, creativity, experimentation and how all those work together to address cultural and business challenges. My name's Angelica, and I'm joined today by a very special guest host, Sam Snider-Held

Sam: Hey, great to be here. My name's Sam. We're both Senior Creative Techs with Media.Monks. I work out of New York City, specifically on machine learning and Generative AI, while Angelica's working from the Netherlands office with the Labs.Monks team.

Angelica: For this episode, we're going to be switching things up a bit and introducing a new segment where we bring a specialist and go over some common misconceptions on a certain tech.

And, oh boy, are we starting off with a big one: Generative AI. You know, the one that's inspired the long scrolls of Midjourney, Stable Diffusion and DALL-E images and the tech that people just can't seem to get enough of the past few months. We just recently covered this topic on our Labs Report, so if you haven't already checked that out, definitely go do that. It's not needed to listen to this episode, of course, but it'll definitely help in covering the high level overview of things. And we also did a prototype that goes more in depth on how we at Media.Monks are looking into this technology and how it implements within our workflows.

For the list of misconceptions we’re busting or confirming today, we gathered this list from across the globe–ranging from art directors to technical directors–to get a variety of what people are thinking about on this topic. So let's go ahead and start with the basics: What in the world is Generative AI?

Sam: Yeah, so from a high level sense, you can think about generative models as AI algorithms that can generate new content based off of the patterns inherent in its training data set. So that might be a bit complex. So another way to explain it is since the dawn of the deep learning revolution back in 2012, computers have been getting increasingly better at understanding what's in an image, the contents of an image. So for instance, you can show a picture of a cat to a computer now and it will be like, "oh yeah, that's a cat." But if you show it, perhaps, a picture of a dog, it'll say, "No, that's not a cat. That's a dog."

So you can think of this as discriminative machine learning. It is discriminating whether or not that is a picture of a dog or a cat. It's discriminating what group of things this picture belongs to. Now with Generative AI, it's trying to do something a little bit different: It's trying to understand what “catness” is. What are the defining features of what makes up a cat image in a picture?

And once you can do that, once you have a function that can describe “catness”, well, then you can just sample from that function and turn it into all sorts of new cats. Cats that the algorithm's actually never seen before, but it just has this idea of “catness” creativity that you can use to create new images.

Angelica: I've heard AI generally described as a child, where you pretty much have to teach it everything. It's starting from a blank slate, but over the course of the years, it is no longer a blank slate. It's been learning from all the different types of training sets that we've been giving it. From various researchers, various teams over the course of time, so it's not blank anymore, but it's interesting to think about what we as humans take for granted and being like, "Oh that's definitely a cat." Or what's a cat versus a lion? Or a cat versus a tiger? Those are the things that we know of, but we have to actually teach AI these things.

Sam: Yeah. They're getting to a point where they're moving past that. They all started with this idea of being these expert systems. These things that could only generate pictures of cats...could only generate pictures of dogs.

But now we're in this new sort of generative pre-training paradigm, where you have these models that are trained by these massive corporations and they have the money to create these things, but then they often open source them to someone else, and those models are actually very generalized. They can very quickly turn their knowledge into something else.

So if it was trained on generating this one thing, you do what we call “fine tuning”, where you train it on another data set to very quickly learn how to generate specifically Bengal cats or tigers or stuff like that. But that is moving more and more towards what we want from artificial intelligence algorithms.

We want them to be generalized. We don't want to have to train a new model for every different task. So we are moving in that direction. And of course they learn from the internet. So anything that's on the internet is probably going to be in those models.

Angelica: Yeah. Speaking of fine tuning, that reminds me of when we were doing some R&D for a project and we were looking into how to fine tune Stable Diffusion for a product model. They wanted to be able to generate these distinctive backgrounds, but have the product always be consistent first and foremost. And that's tricky, right? When thinking about Generative AI and it wanting to do its own thing because either it doesn't know better or you weren't necessarily very specific on the prompts to be able to get the product consistent. But now, because of this fine tuning, I feel like it's actually making it more viable of a product because then we don't feel like it's this uncontrollable platform. It's something that we could actually leverage for an application that is more consistent than it may have been otherwise. 

So the next question we got is: with all of the focus on Midjourney prompts being posted on LinkedIn and Twitter, is Generative AI simply just a pretty face? Is it only for generating cool images?

Sam: I would definitely say no. It's not just images. It's audio. It's text. Any type of data set you put into it, it should be able to create that generative model on that dataset. It's just the amount of innovation in the space is staggering.

Angelica: What I think is really interesting about this field is not only just how quickly it's advanced in such a short period of time, but also the implementation has been so wide and varied.

Sam: Mm-hmm.

Angelica: So we talked about generating images, generating text and audio and video, but I had seen that Stable Diffusion is being used for generating different types of VR spaces, for example. Or it's Stable Diffusion powered processes, or not even just Stable Diffusion... just different types of Generative AI models to create 3D models and being able to create all these other things that are outside of images. There's just so much advancement within a short period of time.

Sam: Yeah, a lot of this stuff you can think about like LEGO blocks. You know, a lot of these models that we're talking about are past this generative pre-training paradigm shift where you're using these amazingly powerful models trained by big companies and you're pairing them together to do different sorts of things. One of the big ones that's powering this, came from OpenAI, was CLIP. This is the model that allows you to really map text and images into the same vector space. So that if you put in an image and a text, it will understand that those are the same things from a very mathematical standpoint. These were some of the first things that people were like, "Oh my gosh, it can really generate text and it looks like a human wrote it and it's coherent and it circles back in on it itself. It knows what it wrote five paragraphs back." And so, people started to think, "What if we could do this with images?" And then maybe instead of having the text and the images mapped to the same space, it's text to song, or text to 3D models?

And that's how all of this started. You have people going down the evolutionary tree of AI and then all of a sudden, somebody comes out with something new and people abandon that tree and move on to another branch. And this is what's so interesting about it: Whatever it is you do, there's some cool way to incorporate Generative AI into your workflow.

Angelica: Yeah, that reminds me of another question that we got that's a little bit further down the list, but I think it relates really well with what you just mentioned. Is Generative AI gonna take our jobs? I remember there was a conversation a few years ago, and it still happens today as well, where they were saying the creative industry is safe from AI. Because it's something that humans take creativity from a variety of different sources, and we all have different ways of how we get our creative ideas. And there's a problem solving thing that's just inherently human. But with seeing all of these really cool prompts being generated, it's creating different things that even go beyond what we would've thought of. What are your thoughts on that?

Sam: Um, so this is a difficult question. It's really hard to predict the future of this stuff. Will it? I don't know.

I like to think about this in terms of “singularity light technology.” So what I mean by singularity light technology is a technology that can zero out entire industries. The one we're thinking about right now is stock photography and stock video. You know, it's hard to tell those companies that they're not facing an existential risk when anybody can download an algorithm that can basically generate the same quality of images without a subscription.

And so if you are working for one of those companies, you might be out of a job because that company's gonna go bankrupt. Now, is that going to happen? I don't know. Instead, try to understand how you incorporate it into your workflow. I think Shutterstock is incorporating this technology into their pipeline, too.

I think within the creative industry, we should really stop thinking that there's something that a human can do that an AI can't do. I think that's just not gonna be a relevant idea in the near future.

Angelica: Yeah. My perspective from it would be: not necessarily it's going to take our jobs, but it's going to evolve how we approach our jobs. We could think of a classic example of film editors where they had like physical reels to have to cut. And then when Premiere and After Effects come out, then that process is becoming digitized.

Sam: Yeah.

Angelica: And then further and further and further, right? So there's still video editors, it's just how they approach their job is a little bit different.

And same thing here. Where there'll still be art directors, but it'll be different on how they approach the work. Maybe it'll be a lot more efficient because they don't necessarily have to scour the internet for inspiration. Generative AI could be a part of that inspiration finding. It'll be a part of the generating of mockups and it won't be all human made. And we don't necessarily have to mourn the loss of it not being a hundred percent human made. It'll be something where it will allow art directors, creatives, creators of all different types to be able to even supercharge what they currently can do.

Sam: Yeah, that's definitely true. There's always going to be a product that comes out from NVIDIA or Adobe that allows you to use this technology in a very user friendly way.

Last month, a lot of blog posts brought up a good point: if you are an indie games company and you need some illustrations for your work, normally you would hire somebody to do that. But this is an alternative and it's cheaper and it's faster. And you can generate a lot of content in the course of an hour, way more than a hired illustrator could do.

It's probably not as good. But for people at that budget, at that level, they might take the dip in quality for the accessibility, the ease of use. There's places where it might change how people are doing business, what type of business they're doing.

Another thing is that sometimes we get projects that for us, we don't have enough time. It's not enough money. If we did do it, they would basically take our entire illustration team off the bench to work on this one project. And normally if a company came to us and we passed on it, they would go to another one. But perhaps now that we are investing more and more on this technology, we say, "Hey, listen, we can't put real people on it, but we have this team of AI engineers, and we can build this for you.” For our prototype, that's what we were really trying to understand is how much of this can we use right now and how much benefit is that going to give us? And the benefit was to allow this small team to start doing things that large teams could do for a fraction of the cost.

I think that's just going to be the nature of this type of acceleration. More and more people are going to be using it to get ahead. And because of that, other companies will do the same. Then it becomes sort of an AI creativity arms race, if you will. But I think that companies that have the ability to hire people that can go to their artists and say, "Hey, what things are you having problems with? What things do you not want to do? What things take too much time?" And then they can look at all the research that's coming out and say, "Hey, you know what? I think we can use this brand new model to make us make better art faster, better, cheaper." It protects them from any sort of tool that comes out in the future that might make it harder for them to get business. At the very least, just understanding how these things work and not from a black box perspective, but having an understanding of how they work.

Angelica: It seems like a safe bet, at least for the short term, is just to understand how the technology works. Like listening to this podcast is actually a great start.

Sam: Yeah.

Angelica: Right?

Sam: If you are an artist and you're curious, you can play around with it by yourself. Google CoLab is a great resource. And Stable Diffusion is designed to run on cheap GPU. Or you can start to use these services like Midjourney, to have a better handle on what's happening with it and how fast it's moving.

Angelica: Yeah, exactly. Another question that came through is: if I create something with Generative AI through Prompt Engineering, is that work really mine?

Sam: So this is starting to get into a little bit more of a philosophical question. Is it mine in the sense that I own it? Well, if the model says so, then yes. Stable Diffusion, I believe, comes with a MIT license. So that is like the most permissive license. If you generate an image with that, then it is technically yours, provided somebody doesn't come along and say, "The people who made Stable Diffusion didn't have the rights to offer you that license."

But until that happens, then yes, it is yours from an ownership point of view. Are you the creator? Are you the creative person generating that? That's a bit of a different question. That becomes a little bit murkier. How different is that between a creative director and illustrator going back and forth saying:

"I want this."

"No, I don't want that."

"No, you need to fix this."

"Oh, I liked what you did there."

"That's really great. I didn't think about that."

Who's the owner in that solution? Ideally, it's the company that hires both of them. This is something that's gonna have to play out in the legal courts if they get there. I know a lot of people already have opinions on who is going to win all the legal challenges, and that is just starting to happen right now.

Angelica: Yeah, from what I've seen in a lot of discussion, it's a co-creation platform of sorts, where you have to know what to say in order to get it to be the right outcome. So if you say, “I want an underwater scene that has mermaids floating and gold neon coral,” it'll generate certain types of visuals based off of that, but it may not be the visuals you want.

Then that's where it gets nitpicky into styles and references. That's where the artists come into play, where it's a Dali or Picasso version of an underwater scene. We've even seen prompts that use Unreal...

Sam: Mm-hmm

Angelica: ...as a way to describe artistic styles. Generative AI could create things from a basic prompt. But there's a back and forth, kinda like you were describing with a director and illustrator, in order to know exactly what outcomes to have and using the right words and key terms and fine tuning to get the desired outcome.

Sam: Definitely, and I think this is a very specific question to this generation of models. They are designed to work with text to image. There's a lot of reasons for why they are this way. A lot of this research is built on the backs of transformers, which were initially language generation models. If you talk to any sort of artist, the idea that you're creating art by typing is very counterintuitive to what they spent years learning and training to do. You know, artists create images by drawing or painting or manipulating creative software and its way more gestural interface. And I think that as technology evolves–and definitely how we want to start building more and more of these technologies to make it more engineered with the artist in mind–I think we're gonna see more of these image interfaces.

And Stable Diffusion has that, you can draw sort of an MS paint type image and then say, "Alright, now I want this to be an image of a landscape, but in the style of a specific artist." So then it's not just writing text and waiting for the output to come in, I'm drawing into it too. So we're both working more collaboratively. But I think also in the future, you might find algorithms that are way more in tune with specific artists. Like the person who's making it, how they like to make art. I think this problem's gonna be less of a question in the future. At one point, all of these things will be in your Photoshop or your creative software, and at that point, we don't even think about it as AI anymore. It's just a tool that's in Photoshop that we use. They already have neural filters in Photoshop–the Content Aware fill. No one really thinks about these questions when they're already using them. It's just this area we are right now where it's posing a lot of questions.

Angelica: Yeah. The most interesting executions of technology have been when it fades into the background. Or to your point, we don't necessarily say, "Oh, that's AI", or "Yep, that's AR". That's a classic one too. We just know it from the utility it provides us. And like Google Translate, for example, that could be associated with AR if you use the camera and it actually overlays the text in front. But the majority of people aren't thinking, oh, this is Google Translate using AR. We don't think about it like that. We're just like, "Oh, okay, cool. This is helping me out here."

Sam: Yeah, just think about all the students that are applying to art school this year and they're going into their undergrad art degree and by next year it's gonna be easier to use all this technology. And I think their understanding of it is gonna be very different than our understanding of people who never had this technology when we were in undergrad. You know, it's changing very quickly. It's changing how people work very rapidly too.

Angelica: Right. Another question came relating to copyright usage, which you touched on a little bit, and that's something that's an evolving conversation already in the courts, or even out of court–or if you're looking in the terms and conditions of Midjourney and DALL-E and Stable Diffusion.

Sam: When you download the model from Hugging Face, you have to agree to certain Terms and Conditions. I think it's basically a legal stop gap for them.

Angelica: Yep.

Sam: If I use these, am I going to get sued? You want to talk to a copyright lawyer or attorney, but I don't think they know the answer just yet either. What I will say is that many of the companies that create these algorithms–your OpenAIs, your Google's, your NVIDIAs–a lot of these companies also have large lobbying teams and they're going to try to push the law in a way that doesn't get them sued. Now, you might see that in the near future because these companies can throw so much money at the legal issue that by, in virtue of protecting themselves, they protect all the people who use their software. The way I like to talk about it is, and maybe I'm dating myself, but if you think about all the way to the early 2000's with Napster and file sharing, it didn't work out so well for the artists. And that technology has completely changed their industry and how they make money. Artists do not make money off of selling records anymore because anyone can get them for free. They make money now primarily through merchandise and touring. Perhaps something like that is going to happen.

Angelica: Yeah. When you brought up Napster, that reminded me of a sidetrack story where I got Napster and it was legitimate at that time, but every time I was like, "Oh yeah, I have this song on Napster." They were like, "Mmmm?" They're giving me a side eye because of where Napster came from and the illegal downloading. It's like, "No, it's legit. I swear I just got a gift card." 

Sam: [laughter] Well, yeah, many of us now listen to all of our music on Spotify. That evolved in a way where they are paying artists in a specific way that sometimes is very predatory and something like that could happen to artists in these models. It doesn't look like history provides good examples where the artists win or come out on top. So again, something to think about if you are one of these artists. How do I prepare for this? How do I deal with it? At the end of the day, people are still gonna want your top fantasy illustrator to work on their project, but maybe people that aren't as famous, maybe those people are going to suffer a bit more.

Angelica: Right. There's also been a discussion on: can artists be exempted from being a part of prompts? For example, there was a really long Twitter thread, we'll link it in the show notes, but it was pretty much discussing how there was a lot of art that was being generated using her name in the prompt, and it looked very similar to what she would create. Should she get a commission because it used her name and her style to be able to generate that? Those are the questions there. Or if they're able to get exempt, does that also prevent the type of creative output Generative AI is able to create? Because now it's not an open forum anymore where you can use any artist. And now we're gonna see a lot of Picasso uses because that one hasn't been exempted. Or more indie artists aren't represented because they don't want to be.

Sam: I don't think the companies creating these exemptions are really going to work. One of my favorite things about artificial intelligence is that it's one of the most advanced high tech technologies that's ever existed, and it's also one of the most open. So it's going to work on their platforms because they can control it, but it's an extremely open technology. All these companies are putting some of their most stellar code and train models. There's DreamBooth now where you can basically take Stable Diffusion and then fine tune it on specific artists using a hundred or less images or so.

Even if a company does create these exemptions, you can't create images on Midjourney or DALL-E 2 in the style of Yoshitaka Amano or something like that, it wouldn't be so hard for somebody to just download all the free train models, train it on Yoshitaka Amano images, and then create art like that. The barrier to entry to do these things isn't high enough that this is a solution for that.

Angelica: Yeah, the mainstream platforms could help to get exempt, but if someone was to train their own model, then they could still do that.

Sam: It's starting to become kind of a wild west, and I can understand why certain artists are angry and nervous. It's just...it's something that's happening and if you wanna stop it, how do we stop it? It has to come from a very concerted legal sort of idea. Bunch of people getting together saying, "We need to do this now and this is how we want it to work." But can they do that faster than corporations can lobby to say, "No, we can do this." You know, it's very hard for small groups of artists to combat corporations that basically run all of our technologies.

It's an interesting thing. I don't know what the answer is. We should probably talk to a lawyer about it.

Angelica: Yeah. There's other technologies that have a similar conundrum as well. It's hard with emerging tech to control these things, especially when it is so open and anyone's able to contribute in either a good or a bad way.

Sam: Yeah, a hundred percent.

Angelica: That actually leads to our last question. It's not really a question, more of a statement. They mentioned that Generative AI seems like it's growing so fast and that it will get outta control soon. From my perspective, it's already starting to because of just the rapid iteration that's happening within this short period of time.

Sam: Even for us, we were spending time engineering these tools, creating these projects that use these and we'll be halfway through it and there's all these new technologies that might be better to use. Yeah, it does give a little bit of anxiety like, "Am I using the right one? What's it going to take to change technology right now?" Do you wait for the technology to advance, to become cheaper?

If you think about a company like Midjourney spending all this investment money on creating this platform, because theoretically only you can make this and it's very hard for other companies to recreate your business. But then six months later, Stable Diffusion comes out. It's open source, anyone can download it. And then two months later somebody open sources a full on scalable web platform. It's just that sort of thing where it evolves so fast. And how do you make business decisions about it? It's changing month to month at this point. Whereas before, it was changing every year or so, but now it's too fast. It does seem like it is starting to, again, become that singularity light type technology. Who’s to say that it's going to continue like that? It's just so hard to predict the future with this stuff. It's more what can I do right now and is it going to save me money or time? If not, don't do it. If yes, then do it.

Angelica: Yeah. The types of technologies that get the most excitement are the ones that get different types of people more mobilized that then makes the technology advance a lot faster. It just feels like towards the beginning of the summer, we were hearing like, "Oh, DALL-E 2, yay! Awesome." And then it seemed like it went exponentially fast from there based on a lot of the momentum. There was probably a lot of stuff behind the scenes that made it feel exponential. Would you say that it was because of a lot of interest that brought a lot of people into the same topic at one point? Or do you feel like it might have always been coming to this point?

Sam: Yeah, I think so. Whenever you start to see technology that is really starting to deliver on its promise, I think, again, a lot of people become interested in it. The big thing about Stable Diffusion was that it was able to use a different type of model to compress the actual training size of the images, which then allowed it to train faster and then be able to be trained and executed on a single GPU. That type of thing is how a lot of this stuff goes. There's generally one big company that creates the, "We figured out how to do this." And then all these other companies and groups and researchers say, "Alright, now we know how to do this. How do we do it cheaper, faster, with less data, and more powerful?" And any time there's something that comes out like that, people start spending a lot of time and money on it.

DALL-E was this thing that I like to say really demonstrated creative arithmetic. When you say, I want you to draw me a Pikachu sitting on a goat. And not only does it know what Pikachu and a goat looks like, but it understands that in order for us to believe that it's sitting on it, and you have to have it sitting in a very specific space. Pikachu's legs are on either side of it.

The idea that a machine can do that, something so similar to the way humans think, got a lot of people extremely excited. And at the time it was just, I think at the time it was like, 256 pixels by 256. But now we are doing 2048 by 24... whatever size you want. And that's only two years later. So yeah, a lot of excitement, obviously.

I think it is one of those technologies that really gets people excited because it is starting to deliver on the promise of AI. Just like self-driving cars–AI doing protein folding–you're starting to see more and more examples of what it could be and how exciting and how beneficial it can be.

Angelica: Awesome! Well, we've covered quite a bit, lots of great info here. Thanks again, Sam, for coming on the show.

Sam: Yeah, thanks for having me.

Angelica: Thanks everyone for listening to the Scrap The Manual podcast!

If you like what you hear, please subscribe and share! You can find us on Spotify, Apple Podcasts and wherever you get your podcasts. If you want to suggest topics, segment ideas, or general feedback, feel free to email us at scrapthemanual@mediamonks.com. If you want to partner with Labs.Monks, feel free to reach out to us at that same email address. Until next time!

Sam: Bye!

The impressive output of generative AI has led some to voice concerns about whether their livelihoods are in jeopardy. Creativity, after all, has long been considered a strictly human skill.

But creatives aren’t about to lose their jobs to robot overlords who can spin strings of text into pixelated gold. To the contrary, these tools—which rely on human input and some level of artistic aptitude to really shine—are unlocking creative potential and helping people bring their concepts to life in new ways. This outlook prompted the Labs.Monks, our research and development team, to explore how generative AI can uplevel the work of our teams and our clients.

“We’ve been playing with this technology for a while, and after it began to trend, we’ve been getting more and more questions about it,” says Geert Eichhorn, Innovation Director and Head of Labs. For instance: a lot can be said about the future of content creation aided by AI, but how could today’s tools integrate into a present-day production pipeline? 

Looking for an answer, the Labs.Monks collaborated with animators and illustrators on our team to develop a prototype production workflow that blends traditional animation methods with cutting-edge AI technology. The result is an animated film trailer made using a fraction of the time and resources that a typical, frame-by-frame animation of its length would require.

Learn to live with the algorithm.

Ancestor Saga is a 2D-animated side project focused on a central question: what if people in the Viking Age realized they were living in a simulation? After learning that their purpose in life is to entertain the gods, will they accept their new reality, or put an end to the world by bringing about Ragnarök?

The theme might feel familiar to anyone trying to make sense of the increasingly algorithmic world we’ve suddenly found ourselves in. “We wanted to tell a story that could integrate with the tech we’re using: virtual worlds and virtual people,” says Samuel Snider-Held, Creative Technologist. Associate Creative Director Joan Llabata takes this thought further, citing some of the challenges faced when humans and AI don’t quite connect. “There’s some space where we need to find the best way to communicate with the machine effectively,” he says.

When using generative AI, a bespoke approach is best.

That challenge of getting humans and AI to play nice demonstrates the need for a team like the Labs.Monks to experiment with the tools that are available. While off-the-shelf tools are great for empowering individual creators, integrating them into team pipelines requires a more custom solution.

AI is designed to do specific tasks very, very well. Projects that involve multiple capabilities and phases call for a workflow that can integrate a variety of generative AI to fulfill different goals throughout. With an animation project, this means plugging into creative concepting, storyboarding, sound and of course animating the visuals.

In our case, says Snider-Held, “We wanted to explore how AI could allow us to do the work we really want to do, even if the time or the budget isn’t there.” He found that while our animation team loves classic, frame-by-frame animation, the method is often overlooked because it is slower to produce and less cost-efficient than other ways of animating. 

Now the team had a clear goal: orchestrate an AI-based workflow that could output a frame-by-frame animation in record time, without compromising quality. They took inspiration from rotoscoping, a method used by animators like Ralph Baskhi, in which an artist traces images over existing footage. This task of translating an existing recording from one style to another was ideal for image-to-image generative AI. In addition, the team used AI technology to develop background designs and read out the animation’s voiceover.

Generative AI isn’t a radical departure from tradition.

The team began by recording a 3D character model in a virtual setting, capturing a variety of poses for an illustrator to trace over. These visuals were then used to train the AI model on how to draw the character in different movements. “If you draw about five frames, you have enough to teach a neural network how to paint the others,” says Snider-Held, noting that it’s important to select frames that are different from one another so the AI can pick up on various forms, shapes and poses.

In addition to rotoscoping virtual production, the team also experimented with live-action stock footage. Being able to use two different types of visual source material baked extra flexibility into the process; teams could mix and match the different methods according to their specific needs or abilities. Fantastical creatures might be captured more easily in virtual production, while a team lacking in their ability to animate lifelike movements may prefer using live-action film as a base. “You get better acting from footage versus a 3D model, but the visual output is ultimately the same,” says Snider-Held.

Much like how that process emulated classic rotoscoping by hand, other ways of integrating AI followed a traditional animation process, albeit with some additional steps here and there. For example, the storyboarding phase is important for visualizing which types of shots or animations are needed for a specific sequence. In addition to pondering that, the team also planned which kinds of AI would be best for generating this or that shot.

Using Stable Diffusion—a kind of generative AI that translates a text prompt into an image—allowed the team to create a large volume of backgrounds that they could swap in and out to test how they looked. “You can explore a lot in this phase,” says Snider-Held.

As for developing backgrounds in particular, “It’s like describing the shot you want to a director of photography,” says Llabata. He was able to test hundreds of different environments, camera angles, artistic styles, lighting and more, all with relative ease.

Unlock efficiencies and long-term gains.

The findings above hit on perhaps the biggest gain that a generative AI-powered workflow can provide: greater flexibility throughout the life of a creative project. Being able to generate 60 frames in one minute—rather than one frame in 60 minutes—makes it incredibly easy to pivot or change things up in the blink of an eye.

It doesn’t require a sophisticated hardware setup either, further making content creation accessible to teams of all sizes. “You don’t need a giant server or cloud computing,” says Eichhorn. “A reasonably good gaming PC can churn out assets like backdrops quickly.” Still, more complex uses of AI like rotoscoping may require more power.

The flexibility unlocked by integrating generative AI into a team’s pipeline continues to pay dividends beyond the life of a single project. “If you have a project whose scope is really big, the effort and money you spent in that R&D is compounded in value over time,” says Snider-Held, noting that whether a brand wants to make 10 animations or 30, the steps to lay down an AI-powered foundation will be roughly the same.

Experiment to find an approach that suits your needs.

Tools like stable diffusion aren’t meant to replace those in the creative field. “An AI will not achieve anything by itself,” says Llabata. Instead, these products will give teams the ability to chase more ambitious projects with fewer constraints in time and budget. Just consider how closely the creation of the Ancestor Saga trailer follows a traditional animation process, just with more efficiencies baked in. 

Such flexibility afforded by generative AI can go well beyond traditional animation.

Whatever your use case for generative AI, understand that while building tools from scratch can be challenging, the result is extremely powerful. “Our approach is that if an off-the-shelf tool is mature enough, use it. If not, create it yourself,” says Snider-Held. In addition to ensuring a tool is calibrated for their specific needs, teams who go the bespoke route will also be better poised to future proof as the technology continues to evolve at a rapid pace.

So, think you’re ready to explore what generative AI means for your field? Learn more about the ins and outs of the technology in the latest Labs Report exploring the rapid evolution of digital creation.

In this Labs report, we'll uncover how Generative AI is impacting digital creation today, and will explore how to keep ahead of where the tech is going next.

In this Labs report, you’ll:

• Learn what Generative AI is and what’s currently available
• Understand how the tech works
• See the technology in real-world action
• Peek into what the future holds
• Learn how to harness its power now

Generative AI supercharges production for higher quality creative output.

In this animated trailer for an original series called "Ancestor Saga," we demonstrate how Generative AI can be applied to film production. This prototype leverages Stable Diffusion AI for generating background scenes and custom Img2Img neural networks for AI-based rotoscoping of virtual characters.

See our findings about the benefits of using generative AI, including time and labor reduction in production, in the report.