The development and evolution of AI

In the past decade, AI has made significant progress in the fields of text, image, and video generation, mainly due to breakthroughs in deep learning technology. Deep learning is a subfield of machine learning and belongs to the category of artificial intelligence (AI). It simulates the neural network structure of the human brain and uses multi-layer nonlinear transformations to learn the feature representation of data to solve complex tasks. The core of deep learning is neural networks , especially deep neural networks (DNNs), which achieve high-level abstraction and modeling of data by stacking multiple layers of neurons.

The application of technologies such as Generative Adversarial Networks (GANs), Transformer models, and Diffusion Models has promoted the development of AI in practical application scenarios. AI is no longer a virtual concept ten years ago, but has actually entered people’s lives.

Basic concepts of deep learning

(1) Neural Network

Neural networks are the basis of deep learning. They are composed of multiple neurons arranged in layers:

• Input layer : receives raw data (such as image pixels, text word vectors).
• Hidden layer : extracts data features through nonlinear transformation.
• Output layer : Generates the final prediction results (such as classification labels, generated images).

(2) Depth

“Depth” refers to the number of hidden layers in a neural network. Traditional neural networks may have only a few layers, while deep learning models often have dozens or even hundreds of layers, which enables them to learn more complex features.

(3) Nonlinear activation function

Deep learning models use nonlinear activation functions (such as ReLU, Sigmoid, Tanh) to introduce nonlinear capabilities, enabling the model to fit complex functions.

2.  Key technologies of deep learning

(1) Convolutional Neural Network (CNN)

• Features : Specially designed for processing grid-like data (such as images and videos).
• Core idea : extract local features through convolution kernels and reduce data dimensions through pooling layers.
• Applications : image classification, object detection, image generation, etc.

(2) Recurrent Neural Network (RNN)

• Features : Suitable for processing sequence data (such as text, time series).
• Core idea : Capture temporal dependencies in sequences through recurrent structures.
• Variants : LSTM (Long Short-Term Memory Network), GRU (Gated Recurrent Unit), which solve the gradient vanishing problem in long sequence training.
• Applications : machine translation, speech recognition, text generation.

(3) Generative Adversarial Networks (GANs)

• Features : It consists of a generator and a discriminator, and generates realistic data through adversarial training.
• Core idea : The generator tries to generate fake data, and the discriminator tries to distinguish between true and false data. The two compete with each other and ultimately generate high-quality data.
• Applications : image generation, video generation, data augmentation.

(4) Transformer model

• Features : Based on the self-attention mechanism, suitable for processing long sequence data.
• Core idea : The relationship between different positions in the sequence is captured through the attention mechanism, avoiding the sequence dependency problem of RNN.
• Applications : natural language processing (such as GPT, BERT), image generation (such as DALL E), and multimodal tasks.

(5) Diffusion Models

• Features : Generate data through step-by-step denoising.
• Core idea : Starting from random noise, gradually denoising to generate high-quality images or videos.
• Applications : Image generation (such as DALL·E 2, Stable Diffusion), video generation.

It is the development of deep learning technology, generative adversarial networks (GANs), Transformer models, and diffusion models that have laid the foundation for the development of AI generation tools that we are currently seeing. We can communicate with AI in natural language and produce the content we want. If you are a content creator, you will understand more about the current improvement of AI in content creation productivity. The following are the main developments and technical models of AI in the fields of text, image, and video generation:

1.  Text Generation

Text generation is one of the earliest areas in which AI has achieved breakthroughs, and is mainly used in natural language processing (NLP) tasks such as machine translation, text summarization, and dialogue systems.

Key technologies:

• RNN and LSTM :
  Early text generation was mainly based on recurrent neural networks (RNN) and long short-term memory networks (LSTM). These models can process sequence data, but are prone to gradient disappearance problems when generating long texts.
• Transformer model :
  The introduction of the Transformer model (2017) revolutionized the field of text generation. It solved the long-distance dependency problem through the self-attention mechanism and significantly improved the quality of generated text.
  • GPT series :
    OpenAI’s GPT (Generative Pre-trained Transformer) series models (such as GPT-3, GPT-4) are based on Transformer and can generate high-quality, coherent text through large-scale pre-training and fine-tuning.
  • BERT :
    Although BERT (Bidirectional Encoder Representations from Transformers) is mainly used for understanding tasks, its bidirectional attention mechanism also has an important impact on text generation.
• Few-shot and Zero-shot Learning :
  GPT-3 and GPT-4 introduced few-shot and zero-shot learning capabilities, enabling the model to generate high-quality text with very few or even no examples.

Application scenarios:

• Chatbots (such as ChatGPT).
• Content creation (e.g. news, story generation).
• Code generation (like GitHub Copilot).

2.  Image Generation

Image generation is one of the fastest growing areas of AI in recent years, mainly due to breakthroughs in GANs and diffusion models.

Key technologies:

• Generative Adversarial Networks (GANs) :
  GANs consist of a generator and a discriminator, which generate realistic images through adversarial training.
  • DCGAN :
    Deep Convolutional GAN ​​(DCGAN) introduces convolutional neural networks into GAN, improving the quality of image generation.
  • StyleGAN :
    The StyleGAN series (such as StyleGAN2, StyleGAN3) can generate high-resolution, high-quality images through style control and hierarchical generation.
• Diffusion Models :
  Diffusion models generate images by gradually denoising, and in recent years have surpassed GANs in quality and stability.
  • DALL·E series :
    OpenAI’s DALL·E and DALL·E 2 are based on a diffusion model and can generate high-quality images based on text prompts.
  • Stable Diffusion :
    Stable Diffusion from Stability AI is an open source diffusion model that supports text-to-image generation and allows users to run it locally.
• CLIP model :
  CLIP (Contrastive Language–Image Pretraining) associates text and images through contrastive learning, providing strong support for text-to-image generation.

Application scenarios:

• Artistic creation (such as MidJourney, DeepArt).
• Advertising design (such as DALL·E 3).
• Game development (such as character and scene generation).

3.  Video Generation

Video generation is the latest frontier in AI, which has been relatively slow to develop due to the complexity of video data and computational requirements, but has also made significant progress in recent years.

Key technologies:

• Video generation based on GANs :
  Early video generation was mainly based on GANs, which created videos by generating consecutive frames.
  • VGAN :
    Video GAN (VGAN) attempts to generate simple video clips, but with low quality and resolution.
  • MoCoGAN :
    Motion Conditional GAN ​​(MoCoGAN) improves generation results by separating content and motion to generate videos.
• Video generation based on diffusion models :
  Diffusion models are increasingly used in video generation, and can generate higher quality videos.
  • Imagen Video :
    Google’s Imagen Video is based on a diffusion model and can generate high-quality videos based on text prompts.
• Transformer model :
  Transformer model is also used for video generation, generating coherent videos by processing spatiotemporal data.
  • VideoGPT :
    VideoGPT combines GANs and Transformer to generate high-quality video clips.
• Neural Radiance Fields (NeRF) :
  NeRF generates high-quality videos through 3D scene reconstruction, especially for dynamic scenes.

Application scenarios:

• Short video generation (such as TikTok, Instagram).
• Movie special effects (such as dynamic scene generation).
• Virtual reality (such as 3D scene reconstruction).

4.  Multimodal Generation

Multimodal generation is the latest trend in AI development, which aims to combine text, images, and videos to generate more complex content.

Key technologies:

• CLIP and DALL·E :
  The combination of CLIP and DALL·E makes text-to-image generation more accurate.
• Flamingo :
  DeepMind’s Flamingo model can process joint inputs of text and images to generate multimodal content.
• Phenaki :
  Phenaki is a multimodal model that can generate high-quality videos from text.

Application scenarios:

• Cross-media content creation (e.g. advertising, films).
• Virtual assistants (e.g. generating responses with images).

Future trends of AI generation tools

• Higher resolution and higher quality :
  As hardware and algorithms advance, AI-generated images and videos will become more realistic.
• Real-time generation :
  Real-time generation techniques (such as real-time video generation) will become possible.
• Multimodal fusion :
  The fusion of text, images, and videos will drive the diversity and complexity of AI-generated content.
• Personalized Generation :
  AI will be able to generate highly personalized content based on user preferences.