What You Need to Know About Generative AI

We have been witnessing a great production frenzy lately. The excitement of this fury started especially when generative AI tools such as ChatGPT and Midjourney became open to public use and gripped everyone. Social media platforms like Instagram and Twitter are shaken by the shares of everyone who comes up with new products, use case scenarios, and artworks by leveraging the creativity of AI. Maybe the technologies we're talking about aren't new, but it's likely that they have never been more approachable and easy to use. We can also give AR as an example of the widespread use of technologies on a global scale by being more reachable recently.

So far, we have mostly talked about the way AI learns. Even when we start off with the word "artificial", we can understand that this technology is based on imitating human intelligence. A technology that takes on new ways of learning by using the data created by us. However, when we look at the point where it reached today, it seems that it has taken its learning and perception abilities to the next level with its production capabilities.

Today, people all around the world are using ChatGPT to help them with their homework articles or create images with Midjourney or DALL-E within seconds. In this blog post, let's discuss the concept of generative AI, which plays a leading role in artificial intelligence gaining the competence we mentioned.

What is Generative AI?

Generative AI is essentially everything we talked about above. We can also say that it is a field of action that saves artificial intelligence from acting solely on existing data and enables it to exhibit its own reality and originality. In other words, the productive part of artificial intelligence creates different products by taking inspiration from the models, instead of evaluating the models. Ultimately, the artificial intelligence behavior we are accustomed to so far was to compile the entered data or to perform functions such as categorization based on the data. With generative AI, machines gain the ability to transform information and data in different formats from text to images.

To explain better, let's take Shazam, a popular app. Shazam, which can detect the song playing in your environment and tell you which song it is, scans its data library and brings you the most appropriate result for you. We can show the working principle of the Shazam algorithm as an example of the Discriminative AI model. Generative AI algorithms, on the other hand, compile the data obtained from all the songs it has learned and create for you a new song that has not existed before, upon your request. So, for example, an algorithm fed with The Smiths songs might produce an entirely new song that will remind you of Morrissey's style and voice.

2D Visual content production

In fact, we are witnessing more and more visuals being created with Generative AI tools, especially in the media sector. Visual production tools such as Midjourney, DALL-E, and Stable Difusion constitute an important source for written and visual media content. The narrow range of images offered by stock photography sites, and the copyright limitations confine the images used in the media industry to a limited area. We come across the same visuals very often, especially in a field that requires catchy or sometimes even abstract visuals. At this point, generative AI gives users the chance to come up with something new.

Think about this scenario. We are looking for an image for news about AR. When we enter Pexels, one of the most frequently referenced stock photography sites, and search for the keyword augmented reality, images like this greet us. 

Especially if you are someone who constantly follows blogs or newsletters and is familiar with technology media, you are probably familiar with these images. Seeing the same things over and over reduces the attention of your readers. Now with generative AI, we have unlimited options for our next blog post. Let’s enter the "augmented reality" keyword in Midjourney, one of the good examples of generative AI. As you can see below, we got unique images with just a simple keyword. By entering different prompts, we can produce our image in different styles such as more futuristic, or more dystopian.

Artificial intelligence will not reduce the value of the artworks created by artists, on the contrary, it will act as an assistant that will increase their creativity. The image below was created by a designer using Midjourney and won first prize in a competition in the USA. Although the basic lines of the image were created by Midjourney, the later changes and adaptations made by the designer ensured that it was finalized.

Text generation

Hi ChatGPT, we didn't miss you because we can hardly say we don’t have time to miss you. OpenAI's generative AI wonder, which has been in sight for a while, is actually a very advanced chatbot, as the name suggests. Today when we say chatbot, we are not talking about chatbots that are only used in customer service or that can answer simple questions. ChatGPT can design RPG games, find bugs in code, come up with your next article titles, and more!

If you are familiar with text-based AI applications, you may have heard of the GPT-3 model. ChatGPT is actually a customized version of GPT-3 as a chatbot. While GPT-3 offers a broader service, ChatGPT takes it one step further when it comes to “chatting”. After all, it is able to grasp and make sense of questions and concepts just like a human being. Thus, a fluent and customized chat environment is achieved, away from stereotypes. An example of generative AI that can be frequently used to support brands, especially in the era of personalized services.

Technologies such as ChatGPT can be used directly as the main source or as an inspiration for content producers. As a good tool for article title suggestions and compiling data on the topic to be written, generative AI can make stereotypical content a thing of the past. So let's run an experimental study for artlabs' next blog post.

We got 8 different article titles only by specifying potential keywords and our field of work. We even have an article on NFT interoperability that is written by ChatGPT, you can find it here.

3D model generation

The future of AR-ready 3D content generation is here with artlabs studio, and now the waitlist is open for our first users! At artlabs, we are also heavily focusing on generative AI when it comes to creating 3D models from image sets or even text. With artlabs studio, users will be able to convert their 2D product images into 3D models or upload their existing 3D models to optimize the files and make them AR & virtual try-on ready while keeping the interoperability of assets. They can choose the best solution for their businesses, digitize their existing 2D library and product catalogs, and use the 3D models on their own platforms with a few clicks

https://www.youtube.com/watch?v=IDF8XU22q-U

As the demand for 3D content continues to grow, artlabs studio is well-positioned to meet the needs of brands and creators with its advanced AI capabilities. Utilizing the power of artificial intelligence, we believe it will be possible to generate 3D assets from a couple of images with the latest developments in the Neural Radiance Fields area. Plus, our pioneering “Text-to-3D” tool is also coming to artlabs studio soon, making it possible for everyone to generate 3D models using only prompts.

If you want to try image-to-3D or text-to-3D yourself, you can sign up for our waitlist and be the first to try artlabs studio. You can also optimize your 3D models, and make sure they are usable in different virtual worlds with our presets. To access the waitlist, you can simply scan the QR code below: