What is synthetic media?
Synthetic media is the blend of technology and creativity, using AI to generate realistic images, videos, or audio. It can entertain with mind-bending visuals or make us question what's real. So if you encounter a too-good-to-be-true piece of content, it might just be synthetic media at work.
Synthetic media fast facts
Deepfake videos, like the one featuring David Beckham speaking 9 languages, showcase synthetic media's ability to create highly realistic content.
Virtual influencers, such as Lil Miquela, demonstrate how synthetic media blurs the lines between digital and real-life personas.
Synthetic audio technologies are advanced enough to realistically mimic human voices, and are usually used in video games and films.
CGI, a form of synthetic media, is integral in creating realistic environments and characters in movies and video games.
Synthetic media has rapidly evolved since the early 2010s, particularly with the development of Generative Adversarial Networks (GANs) in 2014 and the release of ChatGPT in 2022.
Demonstration of the face mapping technology in action on David Beckham’s viral deepfake video. Photograph: PANOPTICON.
What are examples of synthetic media?
Synthetic media covers a wide range of AI-driven content. Think of deepfake videos, where someone's face or voice gets swapped out so well that it's hard to tell it's fake. Remember the deepfake of David Beckham speaking in nine languages? That's a classic example. It shows how powerful and tricky this tech can be.
Then, there are virtual influencers, like Lil Miquela. She's a digital creation with a huge following on Instagram, and she's not even real. This kind of synthetic media really blurs the lines between what's real and what's not. Virtual influencers are a big deal for content creation and advertising because they look and act like real people.
And don't forget about synthetic audio. This is AI that can mimic voices so well, you'd think it's a real person talking. This tech pops up in video games for personalized audio or in movies for voice overs.
Whether it's for sound or visuals, synthetic media is changing the game, making us all think twice about what's real and what's AI-made.
Is CGI a synthetic media?
Yes, CGI (Computer-Generated Imagery) is definitely a form of synthetic media. It's all about creating visual content using computer software, and it's everywhere in movies and video games. Think about those mind-blowing effects in blockbuster films or the super detailed environments in video games — that's CGI at work.
CGI has evolved a lot over the years. It's not just about making cool visual effects anymore. Nowadays it can create whole characters or environments that look super realistic. Movies like "Avatar" or games like "The Last of Us" show just how far CGI can go. It's a big part of why today's entertainment feels so immersive.
What are synthetic and non-synthetic media?
Synthetic media is all about creating content with AI and tech. It's when you use algorithms, like in deep learning, to make something new or alter something that already exists.
Deepfake videos are a classic example. They can make it look like someone said or did something they never actually did. There are also virtual influencers like Lil Miquela, who are entirely made by computers but seem real. It's about crafting content that's so good, it's hard to tell if it's real or not.
Non-synthetic media, on the other hand, is the traditional stuff. It's content created by humans directly, without heavy AI intervention. Think of a regular photo taken by a photographer, a documentary capturing real events, or a news article written by a journalist. This kind of media relies more on human input, like a writer's words or a photographer's eye, rather than algorithms or AI.
In short, the big difference is that synthetic media is AI and technology driven, where you’re creating or altering content so it looks real. Non-synthetic media is all about human creativity and direct creation, without AI doing the heavy lifting.
What are the problems with synthetic media?
Synthetic media, while innovative, comes with its own set of challenges. First up, there's the trust issue. Deepfakes, for instance, can be so convincing that they make people doubt what's real and what's not. This can lead to misinformation spreading like wildfire, especially on social media. Imagine a fake video of a politician saying something they never did — it could really mess with public opinion or even an election.
Then there's the ethical side of things. Using someone's likeness or voice without their permission is a big no-no, but it happens in the world of synthetic media. It raises serious questions about consent and privacy.
There's also the legal landscape, which is still trying to catch up with this fast-moving tech. Laws and regulations around synthetic media aren't fully developed yet, so there's a bit of a gray area about what's okay and what's not. This makes it tough to tackle the misuse of synthetic media, like deepfakes used for fraud or to spread fake news. As synthetic media keeps evolving, these problems keep getting more complex.
An illustration of AI-generated synthetic media that highlights the ease with which convincing fake news can be created. Photograph: Steven Rosenbaum via MediaPost.
How do you synthesize content?
Synthesizing content hinges on a blend of AI technology and detailed data analysis. It starts with defining the goal, like creating a realistic deepfake or a virtual influencer. The creation of synthetic media is usually powered by deep learning algorithms and neural networks, which are trained on extensive datasets to produce satisfying results.
Technically speaking, in order for you to create synthetic media content, tools like Generative Adversarial Networks (GANs) come into play. In a GAN setup, one part of the AI,the generator, creates the content, while another, the discriminator, judges how authentic it looks. This iterative process enhances the synthetic content's realism and minimizes errors. For example, in video synthesis, every time the AI generates a frame, the discriminator evaluates its realism.
If you're a developer, you can access open-source algorithms on Hugging Face — but if you're a creator or business owner, you're better off exploring user-oriented products such as ChatGPT to help synthesize content.
What is a synthesized image?
A synthesized image is a picture created or altered using AI and deep learning techniques. It's not just a simple edit or a filter you might use on a smartphone app. Instead, it involves complex AI algorithms that can generate entirely new images or modify existing ones in very detailed ways. Think of it as AI playing the role of an artist or a photoshop expert.
These images can range from completely artificial creations, like landscapes or portraits that never existed in the real world, to directed alterations on real photos. For example, a synthesized image could show a person in a setting they've never actually been to, or it could combine features from multiple faces to create a new, unique face. This is where the term "synthetic" comes in — these images are synthesized by AI, not captured through traditional photography.
To make these images, AI uses techniques like Generative Adversarial Networks (GANs), where one part of the AI system creates the image and another part critiques it, until the image looks realistic. Tha leads to images that can be so lifelike or artistically impressive that it's hard to believe they were generated by a machine.
A collage showcasing synthetic media in action, from deepfakes to voice cloning across different industries and use cases. Photograph: Sudharshan Chandra Babu via Paperspace Blog.
How does synthetic media work?
Synthetic media operates using a blend of advanced artificial intelligence (AI) and machine learning techniques, primarily through deep learning and neural networks. These AI systems are trained on vast datasets, allowing them to learn and replicate complex patterns in images, videos, or audio. Here's a breakdown of the process:
- Data analysis and learning: The AI analyzes extensive data to understand patterns. For instance, in creating a synthetic image, the AI might study thousands of real photographs to learn how to generate a new, realistic-looking image. This process involves recognizing and replicating details like textures, colors, and lighting.
- Content generation: Generative Adversarial Networks (GANs) are a key tool here. A GAN consists of two parts: a generator that creates content and a discriminator that evaluates it. The generator produces an image or audio clip, and the discriminator assesses its realism. If the discriminator deems that the content isn't convincing enough, the generator tries it again. This iterative process continues until the output meets a high standard of realism.
- Human involvement: Despite the heavy reliance on AI, human input is crucial. Professionals in the field guide the AI, ensuring the content is up to standard. For instance, in the case of AI-generated music, composers might input basic melodies or rhythms, and the AI then expands upon these ideas to create a full composition, for example.
What is the purpose of synthetic media?
Synthetic media has several uses, thanks to AI and deep learning. First, it's great for telling stories in a new way. Imagine making ultra-realistic videos or characters that can deliver a message or tell a story effortlessly. In advertising, this means brands can create super engaging content without needing a full film crew.
Then, there's the training and education side of things. Synthetic media can create lifelike simulations, perfect for training doctors, pilots, or even soldiers. This tech lets people practice in safe, controlled environments that feel like the real deal. It's like having a sophisticated training ground at your fingertips.
Lastly, synthetic media is big in the art and creativity world. Artists and designers use it to push boundaries and explore new ideas. It's not just about making things look real — it's also about creating what's never been seen before. AI-generated art or music is a great example, where the AI takes hints from humans and then goes off to create something totally new and unique.
When did synthetic media start?
Synthetic media's roots can be traced back to the early days of computer graphics and digital media, but it really took off with the rise of advanced AI and machine learning. The groundwork was laid in the latter half of the 20th century with the development of computer-generated imagery (CGI) in movies and video games. However, the kind of synthetic media we talk about today, such as deepfakes and AI-generated content, started gaining momentum in the 2010s.
The big game-changer was the evolution of deep learning and neural networks, which saw significant progress in the early 2010s. Tools like Generative Adversarial Networks (GANs), introduced by Ian Goodfellow and his colleagues in 2014, were pivotal. They made it possible to create more realistic and sophisticated synthetic media than ever before.
Since then, the field has exploded, with rapid advancements and increasing accessibility of AI tools. The 2010s have seen a surge in applications, from deepfake videos to virtual influencers on social media and user-friendly synthetic media applications like ChatGPT.
A table detailing the various applications of synthetic media for brands and retailers, including images, videos, audio, text, and more. Photograph: CB Insights.
What are the applications of synthetic media?
Today, synthetic media has found its way into a variety of exciting applications:
- Entertainment and media: Here, synthetic media is a game-changer. In movies and video games, it's used to create realistic synthetic characters and environments. Think of the lifelike creatures in modern films or the stunningly detailed worlds in high-end video games.
- Advertising and marketing: Brands are using synthetic media to create more engaging and personalized content. For instance, AI can generate tailored advertisements that change based on who's watching — without spending fortunes on a film crew and multiple setups. It can also be used for creating virtual models or spokespersons, offering a new level of creativity and customization in marketing campaigns.
- Education and training: In educational contexts, synthetic media is revolutionizing how training is conducted. Medical students, for instance, can practice surgeries on realistic, AI-generated human models, pilots use flight simulators with synthetic environments for safer and more efficient training, and architects can use synthetic media to create and explore 3D models of buildings and structures.
- News and journalism: AI-generated content is starting to be used in journalism too. It can help quickly generate news reports, especially in data-driven journalism, where AI can analyze and report on large datasets faster than humans.
- Art and creative expression: Artists are tapping into AI's potential to expand their creativity. With synthetic media, they're crafting everything from paintings generated by AI to music compositions, blending traditional artistry with cutting-edge synthetic media content. This fusion not only opens up fresh, unexplored paths for artistic creation but also allows artists to infuse their unique style into AI-generated works.
