Generative Artificial Intelligence (AI)- An example case study
Generative AI is a subset of artificial intelligence approaches that are intended to produce new data or content, such as photos, language, music, and so on. Unlike standard AI models, which are employed for specialized tasks such as categorization or prediction, generative AI models are concerned with creativity and the generation of new data.
Deep learning architectures, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are at the heart of one of the most prominent methods to generative AI. Here’s a quick rundown of both:
Adversarial Generative Networks (GANs): GANs are made up of two neural networks, the generator and the discriminator, that are trained concurrently in a competitive process. The generator’s job is to generate data that resembles genuine data, while the discriminator’s job is to distinguish between real and created data. Over time, the generator improves at providing realistic data, while the discriminator improves at identifying genuine from phony. This back-and-forth training process eventually leads to the development of realistic data samples.
Variational Autoencoders (VAEs) are a different class of generative AI model that makes use of encoding and decoding concepts. In VAEs, input data is mapped into a latent space representation by an encoder network, and the original data is reconstructed from the latent representation by a decoder network. The latent space data’s probability distribution is learned by the VAE model, which enables it to create new samples based on that distribution.
Numerous uses for generative AI exist, including but not restricted to:
- Image synthesis and generation: Producing lifelike pictures of objects, environments, or faces.
- Text generation is the cohesive creation of tales, poetry, or articles.
creating new musical compositions in a variety of styles. - Making deep fakes or video sequences is known as video synthesis.
Data augmentation: Creating more training data to enhance the performance of a machine learning model. - Drug discovery is the process of creating novel molecular structures with potential medicinal uses.
- Helping artists create original pieces of art or designs is considered creative art and design.
It is important to remember that while generative AI has many intriguing uses, it also poses ethical questions, such as the potential for technology to be abused to distribute false information or make profound fakes.
Example Case study:
A new picture in the manner of the well-known Dutch artist Rembrandt van Rijn, who lived in the 17th century, was the goal of the ING Bank, Microsoft, and several technological partners initiative dubbed The Next Rembrandt. An entirely new picture that closely resembled Rembrandt’s work was produced as a result of the project’s use of generative AI and deep learning techniques to study and mimic the master’s aesthetic.
What was done to complete the project?
1) Data Gathering: High-resolution pictures of Rembrandt’s extant paintings, including still-life and landscape scenes, were gathered and scanned by the team. The training data for the generative AI model came from this set.
2) Machine Learning Training: To study and comprehend Rembrandt’s style, the team employed deep learning methods, specifically convolutional neural networks (CNNs) and generative adversarial networks (GANs). The AI model picked up on a variety of creative details, including brushwork, color schemes, composition, and themes that were typical of Rembrandt’s work.
3) Feature Extraction:The AI algorithm identified patterns and common components in Rembrandt’s paintings and retrieved their important traits, revealing his distinctive stylistic characteristics.
4) Composition Generation: The AI model created a brand-new piece that was influenced by Rembrandt’s aesthetic using the knowledge it learned from the training data and feature extraction. The model was able to incorporate many Rembrandt painting aspects into a seamless and original piece of art.
5) Physical Painting Creation: After the AI produced the digital composition, the team printed a three-dimensional version of the painting using a 3D printer. Layers of paint were added by the printer, replicating the texture and brushstrokes seen in Rembrandt’s works.
The outcome was a brand-new picture called “The Next Rembrandt,” which perfectly encapsulated Rembrandt’s aesthetic. Even though it is important to note that this artwork is not a genuine Rembrandt creation, it showed the ability of generative AI in imitating creative trends and producing new works that looked like the works of well-known painters.
This case study demonstrates how generative AI may be used to the fields of art and creativity, pushing the limits of what technology is capable of in terms of reproducing and creating aesthetic expressions. It also triggered conversations about the link between human creativity and artificial intelligence (AI)-generated art and highlighted concerns about the validity and worth of these works in the art world.
