Editor’s Note:
Recently, the AI chatbot model ChatGPT launched by OpenAI has gained immense popularity, simulating human language behavior through a vast amount of training data. It generates text through semantic analysis, enabling realistic and natural interactions with users. It can even write poetry, scripts, essays, and code…
The rise of ChatGPT has also propelled the technology of AI-generated content (AIGC) into the spotlight. So, what is AIGC? What technical systems support AIGC technology, represented by ChatGPT? In response to these questions, we are launching a series of articles to interpret AIGC, and we recommend related reading lists for further exploration. This is the second article in this series.
AIGC, or AI-generated content, is considered a new form of content production following UGC (User Generated Content) and PGC (Professionally Generated Content), such as AI painting and AI writing. In recent years, with continuous upgrades in technical capabilities, AIGC is lowering the barriers to content creation and unleashing creative potential. In the future, it will drive a paradigm shift in content creation under the trend of digital and real-world integration.
Recently, the China Academy of Information and Communications Technology released the “White Paper on AI-Generated Content (AIGC)”, which systematically explores the composition of the AIGC capability system (i.e., the technical paths that empower content creation). This is of great significance for establishing standards in the field, building industry ecosystems, and attracting a broader range of developers and application scenarios. Enjoy the following:
Pre-Deep Learning Stage:The early AIGC technology primarily relied on pre-defined templates or rules for simple content production and output, lacking deep perception of the objective world and cognitive abilities regarding human language and knowledge. The generated content often exhibited problems such as emptiness, rigidity, and irrelevance.
Deep Learning Stage:The continuous iteration of deep neural networks in learning paradigms and network structures has significantly enhanced the learning capabilities of AI algorithms, thus driving the rapid development of AIGC technology. For instance, in 2012, the convolutional neural network AlexNet, known for its excellent learning ability, won the ImageNet Large Scale Visual Recognition Challenge, marking the dawn of the deep learning era. In 2014, the introduction of the game learning paradigm greatly improved the authenticity and clarity of generated content. Additionally, advancements in reinforcement learning, flow models, and diffusion models have also made remarkable progress.
Visual Large Models Enhance AIGC Perception Capabilities.Visual data, represented by images and videos, is one of the main carriers of information in the internet era. The ability to perceive and understand this vast amount of visual data is fundamental to enabling AI to generate digital content and achieve digital twins. However, new neural networks represented by vision transformers (ViT) are becoming the foundational architecture in the visual field due to their excellent performance, model scalability, and high computational parallelism. Joint learning for multiple perception tasks based on vision transformers is currently a research hotspot.
Language Large Models Enhance AIGC Cognitive Ability.As an important means of recording human civilization, language and text document the historical changes, scientific technology, and cultural knowledge of human society. Utilizing AI technology to mine information and understand content from vast amounts of language and text data is a key aspect of AIGC technology. However, in today’s complex information scenarios, the uneven quality of data and the variety of tasks make traditional natural language processing technologies face challenges such as difficulties in model design and deployment, and data reuse issues. In response, language-based large model technologies can fully leverage vast amounts of unlabeled text for pre-training, thereby endowing text large models with understanding and generating capabilities in small and zero data set scenarios.
For example, iFlytek, a company invested by Junlian Capital, relies on the Cognitive Intelligence National Key Laboratory established by iFlytek and the University of Science and Technology of China, focusing on the needs of providing quality resources in education/healthcare based on AI for a “Happy China,” upgrading the needs of human-machine intelligent interaction in “Made in China,” and creating a barrier-free economic and cultural exchange environment across major languages worldwide. iFlytek has achieved a series of leading technological research results and has realized large-scale applications in the industry.
In the field of smart education, iFlytek has achieved key technological breakthroughs in intelligent grading across all subjects and personalized education. In 2022, it won a total of 13 international cognitive intelligence competition championships, including the OpenBookQA, QASC, and ReClor in common sense reading comprehension challenges. It has surpassed human performance in scoring college entrance examination essays and IELTS writing tasks, providing a full-scenario personalized education solution for over 50,000 schools and more than 130 million teachers and students. In the field of smart healthcare, the “Smart Medical Assistant” system developed has passed the national physician qualification examination’s comprehensive written test and can now diagnose over 1,200 common diseases as a general practitioner assistant, providing 550 million AI-assisted diagnosis suggestions. In the field of human-machine interaction, it has achieved over 5 billion daily calls for its intelligent voice open platform AI services. In multilingual technology research, it has developed key technologies for speech recognition, speech synthesis, machine translation, and image recognition in 60 languages, leading in more than a dozen globally mainstream languages such as Chinese and English, strongly supporting the technical needs for the export of millions of units of products in the automotive and home appliance industries, with its machine translation technology winning the international spoken machine translation evaluation competition, and meeting the qualification standards for the national translation professional qualification (level) test in English.
Multimodal Large Models Upgrade AIGC Content Creation Capabilities.Single-modal content (visual/language) limits the application scenarios of AIGC and is insufficient to drive the innovation of content production methods. The emergence of multimodal large models makes integrative innovation possible, greatly enriching the breadth of AIGC technology applications. To some extent, AIGC based on multimodal large models represents an important step towards general artificial intelligence.
Specifically, multimodal large models possess two capabilities: one is to find the correspondence between different modal data, such as linking a piece of text with its corresponding image; the other is to achieve mutual transformation and generation between different modal data, such as generating a corresponding textual description based on an image.
● Intelligent Digital Content Twin: Compared to traditional content digitization, it can further mine effective information from data, achieving a series of efficient, accurate, and intelligent digital content twin tasks based on a deep understanding of data content. This can be roughly divided into two main branches: intelligent enhancement technology and intelligent translation technology.
● Intelligent Digital Content Editing: Based on digital content twin technology, intelligent digital content editing primarily achieves modifications and controls over digital content through two types of technologies: semantic understanding and attribute control, constructing an interactive channel between the virtual digital world and the real physical world, such as digital human technology.
● Intelligent Digital Content Creation:The aforementioned twin and editing capabilities of digital content mainly target real content in the objective world. Through intelligent twinning, understanding, control, and editing of real content, AIGC algorithms can quickly and accurately map real-world content to the virtual world, providing positive feedback and assistance to the real world through simulation control methods. Based on the development process of technology and the actual application forms, the creation capability of digital content can be divided into imitation-based creation and concept-based creation.
In the future, with the continuous development and iteration of core AIGC technologies, its foundational capabilities of content twinning, content editing, and content creation will be significantly enhanced. At that time, it will leap from the current “mainly assisting content generation” to “primarily self-generating content,” greatly satisfying future consumers’ dual rigid demands for both quantity and quality of content.
Reference Links
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