Paper explaining the history of Generative AI. Paper explained
ChatGPT, DALL – e, and Codex are all examples of techniques that use generative AI to generate digital content, such as text, images, and code. Recent advances in large-scale AI have improved generative AI’s ability to generate realistic content and understand intent. This text summarizes generative models, components, and recent advancements in AI-generated text, images, across modalities and other AI-generated content. It also highlights remaining challenges.
Artificial Intelligence Generated Content has received a lot of attention in recent years. The whole world is now interested in the content generation products developed by big tech companies. AIGC is a technical term that refers to generating content based on human instructions. The model can be taught and guided by the instructions. This process is usually divided into two stages: extracting information about human intentions from instructions, and then generating content based on the extracted intents.
The history of AI generative models dates back to the 1950s. Early models such as Hidden Markov Models (HMM) and Gaussian Mixture Models (GMM) generated simple data. Deep learning has seen major improvements with generative models. Traditional sentence generation in NLP relied on N-gram language model, but they struggled to handle long sentences. Recurrent neural networks with Gated Recurrent Units allowed for modeling of longer dependencies and handling up to 200 tokens. Before deep learning, CV generated images using hand-designed features that were limited in complexity and diversity. Generative Adversarial Networks (GANs) and Variational Autoencoders allowed for impressive image generation. Transformers were introduced in 2017 for NLP. They converged on the advances in generative models that followed different paths. Transformers dominate many generative models across domains. Transformers are used in large language models such as BERT and GPT. Vision Transformers (CV) and Swin Transformers (CV) combine visual components and transformers for images.
Source:
https://medium.com/artificialis/history-of-generative-ai-paper-explained-6a0edda1b909
