AIGC is the abbreviation of AI-generated Content. The Chinese name is artificial intelligence-generated content. It is a way of using artificial intelligence to create content. It is considered to be the next step after PGC (Professionally-generated Content) and UGC (User-generated Content). A new way of creating content.
AIGC is achieved by extracting and understanding intent information from instructions provided by humans, and generating content based on their knowledge and intent information. For example, users can enter a sentence and have the AI synthesize a picture associated with the description, or enter the description of an article or story and have the AI complete it for them.
AIGC is considered to be a new type of content creation after PGC (Professionally Generated Content) and UGC (User Generated Content). PGC refers to content created by professionals such as journalists, artists, or programmers. UGC refers to content created by ordinary users such as bloggers, vloggers or social media users. AIGC differs from PGC and UGC in that it does not rely on human labor or creativity, but on AI algorithms.
How AIGC works
AIGC relies on generative models that can learn from data and generate new data similar to the original data distribution. Generative models can be divided into two categories: Generative Adversarial Networks (GAN) and Natural Language Generation (NLG) models.
- GAN consists of two neural networks: generator and discriminator. The generator tries to create realistic images from random noise vectors, while the discriminator tries to differentiate between real images from the dataset and fake images from the generator. The two networks compete with each other until they reach equilibrium, at which point the generator produces images that are indistinguishable from real images that the discriminator is capable of.
- NLG models are based on transformers, a neural network architecture that uses attention mechanisms to capture long-range dependencies between words in natural language text. Transformers consist of an encoder that encodes input text into a hidden representation and a decoder that generates output text from the hidden representation. Transformer can be pre-trained on large-scale text corpora using self-supervised learning methods such as Masked Language Modeling (MLM) or Causal Language Modeling (CLM). Pretrained transformers can then be fine-tuned for specific tasks such as text summarization, machine translation, or text generation.
Currently, some of the more popular examples of generative models include:
- GPT-3 : A large transformer model with 175 billion parameters, pre-trained on a variety of text sources using CLM. Given some keywords or hints, GPT-3 can generate coherent text on a variety of topics.
- DALL-E : A converter model with 12 billion parameters, pre-trained on text-image pairs using MLM. DALL-E can generate realistic images based on natural language descriptions.
- Codex : A converter model with 12 billion parameters, pre-trained on source code using MLM. Codex can generate executable code based on natural language commands or comments.
- StyleGAN2: A GAN model with 50 million parameters trained on high-resolution facial images using style-based modulation. StyleGAN2 can generate realistic faces with fine-grained control of facial attributes.
AIGC application scenarios
AIGC has a wide range of applications in various fields that require writing or content creation, such as:
- Education: AIGC can help students learn new knowledge by generating explanations, examples, quizzes, or feedback
- Entertainment: AIGC can create engaging stories, poems, songs or games for entertainment or relaxation.
- Marketing: AIGC can create product copy and slogans, headlines or advertisements to promote products or services.
- News: AIGC can write factual reports, summaries, or analysis based on data or events.
- Software development: AIGC can generate code snippets, documentation, or tests based on specifications or comments.
AIGC Challenges
While AIGC enables more efficient and accessible content production, it also poses significant challenges related to bias discrimination, disinformation, safety, and trustworthiness.
- Bias and Discrimination: If the data used to train or generate content is not representative or diverse enough, AIGC may perpetuate harmful stereotypes and biases related to race, gender, ethnicity, and other factors. For example, AIGC has been used to create harmful content that reinforces race-related stereotypes. This can have a negative impact on society and the rights and dignity of individuals.
- Disinformation: AIGC can be used to manipulate and distort public opinion through disinformation and propaganda. For example, AIGC has been used to generate fake news, deepfakes, and other forms of deceptive content that can undermine public trust in media and information.
- Security: AIGC can pose security risks if the data used for training or generating content is not properly protected or encrypted. For example, if a data breach or hacker occurs, AIGC may expose sensitive or personal information of users or creators.
- Credibility: AIGC creates doubts about the authenticity and trustworthiness of content generated by AI models. For example, AIGC can make it difficult to verify the source or authorship of the content and its quality or accuracy.
