In-Depth Investigation and Analysis of the Current Development Status of the AIGC Industry in 2023
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With the continuous growth in the supply and demand for digital content, generative artificial intelligence (AIGC) technology has been increasingly implemented this year, further integrating the digital economy with the real economy. AIGC has opened a new frontier in human technological vision. Today, everyone is talking about AIGC, and businesses are placing greater emphasis on the importance of data. Large models have driven an exponential increase in computing power demand. Many technology companies are actively exploring and practicing how to effectively utilize large models to implement AIGC technology in real-world industries.
The number of artificial intelligence enterprises worldwide is rapidly increasing. In 2022, the global AI market size was estimated at $19.78 billion and is projected to reach $159.103 billion by 2030, with a compound annual growth rate (CAGR) of 38.1% from 2022 to 2030.
With the rapid advancement of technology, artificial intelligence is gradually permeating every corner of life. In the insurance industry, from computational intelligence and perceptual intelligence to cognitive intelligence, AIGC (generative artificial intelligence) offers unprecedented possibilities for innovation.
According to the recently released Evaluation Report on the Application of Large AI Models in the Insurance Industry by Yuanbao Insurance and Molecular Laboratory, current large models are highly suitable for use as intelligent robots to serve customers, answering inquiries about insurance, healthcare, and other areas. They can also empower insurance agents, serving as one of their business tools to enhance their ability to serve clients. Therefore, while the agent channel is still in a critical development phase, large models may help improve the average productivity of agents, reduce service costs for insurance institutions, and facilitate policy transactions.
In terms of evaluation subjects, the report systematically assessed ten mainstream large models in the market, including ChatGPT3.5, ChatGPT4, Claude-1, Claude-2, Tsinghua Zhipu ChatGLM130B, Baidu ERNIE Bot, Alibaba Tongyi Qianwen, iFlytek Spark, 360 ZhiNao, and Kunlun WanTian.
The report shows that, based on the average scores across various question responses, the ten mainstream large models generally performed well in medical knowledge, insurance basics, and insurance legal knowledge. However, their performance in intelligent underwriting, intelligent claims processing, and conversational optimization was less satisfactory, and their capabilities in marketing and service applications varied widely.
Specifically, because domestic large model providers have richer Chinese language data, leading domestic models demonstrated a better understanding of local contexts when answering basic inquiries in specialized fields, outperforming foreign models. ERNIE Bot and Tsinghua Zhipu ranked in the top two for insurance basics and legal knowledge, scoring higher than GPT-4. In medical knowledge responses, ERNIE Bot also performed the best among all models, with iFlytek also achieving high scores, both outperforming GPT-4.
In-Depth Investigation and Analysis of the Current Development Status of the AIGC Industry
This year, amid the ChatGPT boom, industries across the board have been actively exploring more possibilities for integrating AIGC technology. The financial sector is also continuously investigating how to leverage AIGC technology to meet the personalized needs of more customers and achieve innovation in financial services.
The cumulative integration of technologies such as GAN, CLIP, Transformer, Diffusion, pre-trained models, multimodal technology, and generative algorithms has spurred the explosion of AIGC. Continuous iterative innovation in algorithms, the qualitative leap in AIGC capabilities driven by pre-trained models, and the diversification of AIGC content through multimodal approaches have endowed AIGC with more versatile and robust foundational capabilities.
From the progressive development of computational intelligence, perceptual intelligence, and cognitive intelligence, AIGC has opened the door to cognitive intelligence for human society. Through training on large-scale datasets, AI has acquired knowledge across multiple domains. With appropriate adjustments and refinements to the models, AI can now accomplish tasks in real-world scenarios.
AIGC is a milestone for human society and artificial intelligence. In the short term, AIGC has transformed basic productivity tools. In the medium term, it will alter societal production relations. In the long term, it will drive a qualitative breakthrough in overall social productivity. In this transformation of productivity tools, production relations, and productivity, the value of data as a production factor is greatly amplified.
AIGC has elevated data as a core resource of the era, accelerating the digital transformation of society to some extent.
From the perspective of the AIGC industry's development background, the rise of AIGC originates from the rapid breakthroughs in deep learning technology and the increasing demand for digital content supply. On one hand, technological advancements have driven the continuous improvement of AIGC's usability. In the early stages of artificial intelligence development, although some preliminary attempts were made in AIGC, due to various limitations, the related algorithms were mostly based on predefined rules or templates, far from achieving the level of intelligent content creation. In recent years, AIGC technologies based on deep learning algorithms have rapidly evolved, completely breaking the previous limitations of being template-based, formulaic, and narrow in scope, enabling the rapid and flexible generation of multimodal data content. On the other hand, the massive demand has propelled the practical application of AIGC.