The Most Common Types of Generative AI

Generative Artificial intelligence (AI) is revolutionizing various fields, from creative art to drug discovery. With the development of generative AI models like GANs and RNNs, AI can create, analyze, and interpret content in new and exciting ways.

Diagram showing the most common verticals we see and model types.

In creative art, GANs can generate new images, while style transfer can transfer styles between them. With image restoration, old or damaged images can be restored, and with neural doodle, new images can be created from sketches.

In marketing, chatbots and sentiment analysis can help businesses communicate with customers more efficiently, while recommendation systems and predictive analytics can provide insights into customer behavior and preferences.

In film making and VFX, RNNs can help with auto-editing, while deepfakes and face swapping can create realistic fake videos. With voice cloning, voices can be cloned, and with scene generation, new scenes can be created.

In fashion, GANs and style transfer can help generate new textile patterns, and recommender systems can provide personalized recommendations for customers. With body measurement, AI can measure body size for custom clothes.

In drug discovery, QSAR and DeepChem are used for machine learning-based drug discovery, while molecular generation and virtual screening can help identify potential drugs.

To get started with Gen AI in these fields, check out the most useful links for each use case in this graphic. Explore the power of Gen AI and see how it can take your work to the next level.

Creative Art

GANs: Generative Adversarial Networks Transfer: Transfer style between images - Restoration: Restore old or damaged images - Generate dream-like images - Doodle: Generate images from sketches -


Recommendation Systems - Conversational AI - Analysis: Analyze emotions in text - Segmentation: Group customers based on behavior - Analytics: Predict customer behavior -

Film making/VFX

RNN: Recurrent Neural Networks - Create realistic fake videos - Swapping: Swap faces in videos - Cloning: Clone voices - Generation: Generate new scenes -


GANs: Generative Adversarial Networks - Transfer: Transfer style between images - Systems - Measurement: Measure body size for custom clothes - Design: Generate new textile patterns -

Drug discovery

QSAR: Quantitative Structure-Activity Relationship - Machine learning for drug discovery - Generation: Generate new molecules - Folding: Predict protein structure - Screening: Identify potential drugs -