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The rise of AIGC is closely related to the rapid convergence of AI technology, which is currently setting off a boom. This article will reorganize the original text and create a new article by changing sentence patterns, structure, and adding your own analysis, with a full text of about 2,000 words.
AIGC: A revolutionary step in AI content generation
introduction
Recently, ChatGPT has attracted widespread attention with its powerful natural language processing capabilities and text generation technology, and the emergence of this chatbot software marks a big step forward in artificial intelligence. ChatGPT was launched by OpenAI in November 2022 and attracted more than 1 million users in just 5 days of launch. This number is an all-time high in AI applications, with user growth far outpacing apps like TikTok and Instagram. But AIGC is not only ChatGPT, it also includes AI mapping, AI underlying modeling and other fields, which is detonating the market.
The multiple advantages of AIGC
The commercialization of AIGC is booming, such as Stable Diffusion, an open-source model published by Stability AI, which can automatically generate images based on the user's text description. Similar models include DALL-E 2 and Midjourney, which are leading the way in the field of AI painting, merging artificial intelligence with the field of art. These success stories have also boosted investment in the AIGC space, such as Stability AI, which raised $100 million in funding in October 2022 and became a unicorn with a valuation of $1 billion.
AIGC: A new way of producing content
AIGC is seen as a new generation of content production, which is distinguished from UGC and PGC/UGC. UGC originated in the Web 2.0 era, such as blogging and video sharing, while PGC/UGC is professional and user-generated content, such as Youku and Douyin. In contrast, AIGC has disruptive advantages in creation cost, content quality and personalization, which is expected to solve the problems in the PGC/UGC field, improve content diversity, and the price is relatively low, such as OpenAI's AIGC language model da Vinci only $0.02 per 750 words, image model even lower, only $0.02 a piece.
AIGC: A big step forward for AI
The rise of AIGC means that AI has made the leap from perceiving and understanding the world to generating and creating the world. Before the advent of AIGC, AI focused more on data analysis, such as personalized recommendation algorithms. But now, AIGC has the ability to independently create text, images, videos, 3D interactive content, and even unlock new scientific discoveries and create new value and meaning. This trend has also been recognized by Gartner and MIT Technology, which listed generative AI as one of the important technologies.
AIGC: The engine for data and content generation
AIGC is moving towards reducing costs and improving efficiency. Its application areas include social media, gaming, advertising, architecture, product design, legal, marketing, sales, and more. As AIGC technology matures, it is expected to influence or reform these areas, leading to more efficient content generation.
The application of AIGC in various fields
AIGC is not limited to text and image generation, it also has a wide range of applications in medical, auxiliary design and other fields. For example, AIGC speech generation technology can help people with aphasia "speak" and regain their ability to communicate. In addition, AIGC can also be used for cultural relics restoration, such as Baidu's restoration of the "Fuchun Mountain Residence Map" with the help of AIGC images.
AIGC: The driving force of the metaverse
AIGC is expected to be the driving force behind the development of the metaverse. One of the characteristics of the metaverse is its scalability, but how to build content has always been a difficult problem. AIGC's technology is gradually maturing, and it is expected to meet the content needs of a large number of users in the metaverse in a low-cost and efficient way.
AIGC: Model-as-a-Service Business Model
The industrialization of AIGC is accelerating, and the model-as-a-service business model is emerging. The pattern is divided into three layers: basic layer, middle layer, and application layer. The basic layer is built by leading technology companies such as OPEN AI and Stability, the middle layer is vertical and scenario-based models and application tools, and the application layer provides content generation services for C-end users. OPEN AI's business model is API interface fees, and by providing various AI model building applications, such as GPT-3, Codex, and DALL-E, users can customize access to relevant images, languages, and code adjustment services.
Introduction of industry-related companies
During the rise of AIGC, there were several companies that excelled. HKUST
At the inflection point, the pattern changes
We will further explore the reasons for the rise of AIGC, and attribute it to the catalytic role of AI technology integration such as computing power, algorithms, training multi-model, and multi-modality.
First of all, basic computing power is the key to the outbreak of AIGC. The core of artificial intelligence is data processing, and compared to AI algorithms, the massive processing of data is crucial for computing power. In the process of machine learning algorithms, training and inference are two key links. The training stage requires a large amount of data input to support complex neural network models. Due to the complexity of the neural network structure and the large-scale training data, the computing power and efficiency of the processor have become a crucial factor.
Secondly, algorithmic models are also an important factor in the rise of AIGC. As a deep learning model using the self-attention mechanism, the Transformer algorithm has played a key role in the development of AIGC. This self-attention mechanism allows the model to assign different weights based on the different importance of the input data. At present, the familiar ChatGPT and AI mapping are built on the Transformer algorithm.
Third, the emergence of pre-trained models has revolutionized the capabilities of AIGC technology. Before the advent of these models, the AIGC field faced problems such as high threshold, high training cost, simple content generation and low quality. However, AIGC's pre-trained models, such as Google's LaMDA and PaLM, and Open AI's GPT series, have qualitatively improved the capabilities of AIGC technology.
Finally, the application of multimodal technology has greatly promoted the diversity of AIGC. For example, the Open AI team's CLIP model correlates text and images, allowing the model to understand and process more versatile and diverse data. This multimodal application brings greater versatility and diversity to AIGC.
AIGC's application ecology and content consumption market are gradually prospering
AIGC has a better performance in learning general knowledge and understanding generalization, bringing the following features to the field of content generation:
First, the AIGC model can automatically generate content, including articles, blogs, social media posts, etc. This greatly increases the productivity of the content.
Second, the quality of the content generated by AIGC is high, because the AI model can learn from a large amount of data, ensuring the accuracy of the information. For example, DALL· The effect of the E model is close to that of a medium-level painter.
Third, AIGC models can generate many types of content, including text, images, audio and video, 3D content, etc. This diversity is able to appeal to a wider audience and increase the diversity of content.
In addition, AIGC reduces the cost of content production,
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