Recently AI has exploded and been used for everything from writing poetry to drawing portraits and even creating music for musicians. AI has taken off in two major creative areas, writing, and art. One way it’s been able to do this is with predictive text software plugs into existing programs, like Grammarly and ProWritingAid. More recently, AI has been able to make its way into actual creative work—not just using text but also using data collected from sensors worldwide or Twitter feeds to create 3D models based on them.
The past decade, machine learning has been analyzing human behavior.
After years of feeding machine learning algorithms data, we see the results, especially in writing. You might wonder why AI has taken off in these two areas. The short answer is because the last few years have seen AI’s capabilities increase exponentially. As a result, it has analyzed music, writing, art, and other areas of human culture previously considered impossible for machines to compete with humans.
When I say AI has been around for a long time, I mean way longer than you may think. It was first invented in the 1940s by Alan Turing and John Von Neumann (who also helped develop modern computers) as part of their work on artificial intelligence (AI). Since then, there’s been no shortage of research into how machines can learn from experience to imitate some aspects of human behavior. However, these efforts were limited by computing technology until recently when advances like deep learning made it easier for machines to think more like humans do by breaking down complex problems into simpler ones through trial-and-error methods called machine learning algorithms (MLAs).
One way it’s been able to do this is with predictive text software and plugs into existing programs, like Grammarly. The rise of AI art is a relatively new phenomenon. One way it’s been able to do this is with predictive text software plugs into existing programs, like Grammarly and ProWritingAid. Many people have used predictive text for years, but only recently have we seen its potential as an art form. The predictive text uses machine learning to predict what you’re going to type next based on previous history and patterns in your writing—and then suggests words or phrases that are similar to what you’ve already written.
As a result, the predictive text has other purposes beyond just writing. If you want to write about a particular topic but don’t know which words should go where, for example (or even if you don’t understand how the context and spelling). Predictive text can provide suggestions so that your ideas come across clearly and effectively—even though they might not technically make sense sentence-by-sentence. This technology isn’t limited just yet; some kinks still need working out before it becomes usable in any medium other than prose fiction (though most experts agree this will happen within five years). For example, there isn’t enough processing power available at large-scale levels. Stabled Diffusion, for instance, cannot make large-scale art because it requires more computing power than the average user has.
Within a few minutes, you’ll be able to quickly plug in and generate a lot of content.
With apps and sites like Jasper.ai and Copy.ai, it’s amazingly fast to generate content for blog posts, social media, and website content. Upload images and automatically create the product description from the image. It can rewrite existing copy quickly as well. At this point, even though these machine-learning products are in their infancy, you can see how well these AI products work currently. If these are indicators of what the future holds for AI, then we will truly see wondrous things from this technology.
More recently, AI has made its way into actual creative work.
AI is used to create art: check out the work of Aiva (a music artist who has collaborated with musicians like Bjork) or Benjamin Lavoie’s AI-created pop songs. AI is also finding its way into music: see the work of Amper (an AI-based music composer that has collaborated with artists such as Hans Zimmer). And it can help generate videos and movies, too. Look at the short film “The Kiss” by director Oscar Sharp and his team of artists and coders who used an algorithm called Evolutionary Scripts to produce a movie.
An example is Jasper, a copywriting AI that takes the guesswork out of trying to figure out what your audience wants to hear. Jasper is an AI copywriter that uses your audience’s data to create content that resonates with them. Companies like Hotel Tonight use it to create targeted content for their customers, and it can generate hundreds of emails or ads in a matter of seconds.
The company trained an AI bot on an extensive collection of content marketing data, so it can generate copy that’s scored for engagement in real-time. It works by recognizing how people react to certain words, phrases, and images to deliver content that’s more likely to resonate with your audience. In short: The AI knows what people want to hear so that it can write it for you.
Then there’s Hypotenuse AI, another copywriting service that works with potential customers and buyer personas to generate targeted content.
One of AI’s most recent and popular applications in marketing is an automated copywriting platform called Hypotenuse AI. The company trained an AI bot on an extensive collection of content marketing data, so it can generate copy that’s scored for engagement in real-time. Machine learning knows what words are more likely to resonate with readers based on their specific demographics and buying behaviors. The bot can generate copy for all situations, from sales pages to landing pages (website visitors considering downloading your ebook or whitepaper) or even emails you’ve sent out in the past (so you can see how well they performed). It also offers recommendations on how you might improve your previous work, which could help you write better next time!
AI Artwork: Stable Diffusion vs. Dall-E
The piece uses genetic algorithms to create thousands of similar images but not identical to each other. Each image generated uses parameters like color palette, typeface, and composition style. AI Art generators work from a prompt, so the idea is to insert as much or as little as you can about what art you want to generate. In my experience, the more information you input, the better result. You
Stable Diffusion AI
Stable Diffusion uses genetic algorithms to create thousands of similar images but not identical to each other. Each image generated uses parameters like color palette, typeface, and composition style. AI Art generators work from a prompt, so the idea is to insert as much or as little as you can about what art you want to generate. In my experience, the more information you input, the better result.
The second type of AI-generated art is called Dall-E. This type uses a deep learning algorithm to generate images based on Google Images search results. These images combine into a single image as an interface screen saver for your phone or computer.
3D models based created by AI takes data from sensors worldwide and creates using people’s Twitter feeds to create text-based art.
In a paper published in the Journal of Science, researchers from MIT Media Lab and Harvard University describe how they created an AI system that can make art based on tweets. The artwork is a 3D world model built using Twitter feeds and sensors worldwide. The system works in three stages: first, it takes data from sensors worldwide, creates 3D models based on them, and uses people’s Twitter feeds to create text-based art. Then it uses these two inputs to create 2D images and 3D objects (viewed using VR headsets).
You can see crypto currency apps doing the same thing. They take the sentiment from projects in real time, and allow customers to use that data to make trades.
I’m excited to see where AI goes next. It’s already making a name for itself in the world of art and design, but there are still many more possibilities for it to explore. I see this as a tool, just like the internet. I remember that in the 1990s and early 2000s, many artists thought they would be out of work. As we know now, that did not happen. These are tools that artists shouldn’t be afraid of; instead, we should be embracing them. Copywriters can use this tool to push them to be better writers. Artists can utilize generative art to create the reference material they need to make what they want. Just my opinion, so take it for what it is worth. Still, I believe that AI and MLAs are here for good, so why not maximize their potential, to pull more from the human imagination? Because, after all, AI is just using historical data from what humans imagined in the past. Einstein has one of the best quotes: “Imagination is more important than knowledge.” More and more, I’m finding this to be true.
Creative Director, Coder, Open-Source Fanatic
Published: ~ 8 min read