For those of you just entering the world of generative AI, we’re here to break it down for you.

Generative AI refers to machine learning models that generate new content based on what the models learn from their training data. These models produce original outputs like images, videos, and text. Generative AI can be image generators, video creators, or content enhancers; they produce output instead of just analyzing or labeling information. When you think of interactive AI, what do you think of first? If ChatGPT comes to mind, you’re definitely on the right track. In addition to being a fantastic tool, ChatGPT is a type of generative AI.

To create images based on text, use generative AI models such as DALL·E, Stable Diffusion, and Midjourney. Runway ML provides access to pre-trained generative AI models via an intuitive interface, allowing creators to use models like Stable Diffusion without writing code. It also offers a suite of AI models for video editing, image generation, motion tracking, and green screen removal. 

How did ChatGPT, Anthropic, Gemini, and Midjourney become so successful? One word: training. To train these models effectively, developers rely on vast collections of visual content. These visual datasets for generative AI teach the model how the world looks, moves, and behaves. The more diverse and high-quality the dataset, the better the model performs.

How Does Training Work?

Generative models are trained on large datasets of images and videos. They learn to recognize patterns, styles, structures, and relationships between pixels or frames. This is accomplished using techniques like, but not limited to:

• GANs (Generative Adversarial Networks) — two neural networks (a generator and a discriminator) are trained simultaneously in a minimax game. The generator tries to produce realistic outputs, while the discriminator tries to distinguish real from generated samples. Over time, the generator improves by learning to fool the discriminator.
  
  

• Diffusion Models — these models learn to reconstruct images by progressively denoising. Diffusion models typically use Convolutional Neural Networks (CNNs) to iteratively remove noise, and may incorporate transformers to guide generation with contextual information.
  
  

• Transformers — originally designed for text, transformers are adapted to handle visual data (i.e., with Vision Transformers, or combined with diffusion models for image generation) as well, enabling multi-modal generation when paired with other architectures.
  
  

• Variational Autoencoders (VAEs) — these models have two main parts: an encoder and a decoder. They are designed to learn the underlying structure of a dataset (like images, audio, or text) to generate new, similar, and unique examples of the data.
  
  

• Convolutional Neural Networks (CNNs) — trained as specialized building blocks within generative models, learning to recognize and generate spatial patterns crucial for processing and creating image and video data.
  
  

• Recurrent Neural Networks (RNNs) — trained to process sequential data; they are useful as components in generative models that handle text, audio, or ordered elements in images or videos.
  
  

• Reinforcement Learning (RL) — a training paradigm used to fine-tune pre-trained generative models, aligning their outputs with specific objectives or human preferences by optimizing for a reward signal.
  
  

• Hybrid and Multimodal Architectures — trained by combining various neural network types (like Transformers, often with CNNs) and diverse data sources to learn joint representations across different modalities (e.g., text, image, video) for competent understanding and generation.

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• Autoregressive Models — data is produced element by element in a sequential order. Each new element is explicitly predicted based on all the previously generated elements, modeling the data's joint probability distribution.

The goal? To reduce the margin of error between what the model generates and what it should have generated based on the training data.