Shared todayAI large model seriesIn-depth Research Report:AI Large Model Topic: 2023 Artificial Intelligence Large Model Industry Innovation Value Research Report
Report Producer: Large Model House).
Report total: 75 pages.
Featured Report**: The School of Artificial Intelligence
This is the era of AI. At the end of 2022, large-scale models such as ChatGPT, Midjourney, and Stable Diffusion were unveiled one after another, setting off a boom in the development of large AI models.
Domestic enterprises are also actively involved in the development and implementation of large models. Well-known companies such as SenseTime, 360, Yunzhisheng, and iFLYTEK have joined this field and achieved important results, promoting the application of artificial intelligence technology in all walks of life.
From language understanding to image recognition to natural language processing, AI large models have shown great potential and rapid development trends in thousands of industries. The large model has begun to have a subtle impact on thousands of industries, and has also taken on the important task of upgrading the industrial structure and further promoting the deep integration of data and reality.
The artificial intelligence model will inevitably trigger a new round of efficiency revolution. In this era full of opportunities and changes, we need to deeply understand the development trend and application scenarios of AI large models to meet future challenges and seize opportunities.
Link the knowledge base to create a large industry model
A knowledge base is a static, structured representation of knowledge that stores and organizes expertise in a variety of domains. By linking the large model with the knowledge base, the large model can be equipped with stronger professional knowledge and inference ability, so as to improve the performance and adaptability of the large model in specific domains.
For example, in the medical field, large models can be linked to medical knowledge bases, so that large models can understand and use medical terminology concepts, rules, etc., so as to improve the accuracy and credibility of large models in medical scenarios.
Enhance networking capabilities, timeliness and flexibility
Networking is a dynamic way of knowledge acquisition, which can enable the large model to continuously obtain and update the latest knowledge and information from the Internet, and by enhancing the ability of networking, the large model can have stronger timeliness and flexibility, so as to improve the adaptability and innovation ability of the large model in different scenarios.
For example, in the field of search, large models can be enabled to obtain and analyze the latest news events, public opinions, trends, etc. from the Internet in real time, so as to improve the sensitivity and diversity of large models in news scenarios.
Artificial intelligence large models refer to huge neural network models built using large-scale data and complex network structures in the field of machine learning and deep learning. Large models are the most typical major innovations driven by intelligent computing power. Thanks to the strong generalization ability of the model, the low dependence of long-tail data, and the improvement of the efficiency of downstream models, large models are considered to have the prototype of "general intelligence" and have become one of the important ways to explore the realization of inclusive artificial intelligence in the industry.
Starting with breakthroughs in deep learning, large-scale neural network models have made significant progress in areas such as image recognition, speech recognition, and natural language processing. Subsequently, the rise of pre-trained models led to a revolution in natural language processing, with the advent of models such as GPT and BERT changing the way text is generated and understood. In recent years, the scale and performance of large models have been continuously improved, which has promoted the rapid development of large AI models and laid the foundation for more powerful intelligent applications and human-computer interaction.
The development of large AI models has gone through several key stages. The original AI relied heavily on rule- and statistical-based algorithm iterations. However, since 2012, breakthroughs in deep learning have opened a new chapter in artificial intelligence and entered a period of rapid development. In 2017, the advent of Transformer models led to the rise of pre-trained models, driving innovations in the field of natural language processing, such as seg2seg, GPT, and BERT, which changed the way text is generated and understood. Subsequently, the emergence of the GPT-3 model in 2020 brought breakthrough understanding and generation capabilities, and also gave the large model the ability to disrupt the emergence for the first time. Nowadays, as more and more players at home and abroad enter the large model, the "acceleration button" has been pressed for the development of the large model, which not only promotes technological progress, but also promotes the application and expansion of the large model for thousands of industries, opening a new chapter in the development of the artificial intelligence large model. With the evolution of technology, the large model industry will develop in the direction of promoting the automation process of building and deploying models, and lowering the threshold for industry users to obtain AI capabilities.
To a large extent, the development of large AI models is an engineering revolution, not a simple scientific revolution.
The success of large models does not depend solely on breakthroughs in scientific theories, and engineering factors play a crucial role in this. Through the continuous exploration and optimization of engineering practices, including the construction and cleaning of datasets, the expansion and optimization of computing resources, and the improvement of training strategies, large models are based on existing deep learning frameworks and technologies, and the scale, performance, and application fields of the models are continuously improved through engineering means such as model design, training strategies, and hardware optimization.
At the same time, the application scenarios of large AI models need to consider the needs of practical problems, customize and adjust the models, so that they can play the best role in real scenarios.
Since 2020, the excellent performance of large-scale pre-trained models in natural language processing, computer vision, speech recognition, recommendation systems and other fields has attracted widespread attention from the industry and promoted the rapid expansion of the market.
According to the estimation of the large model home, it is expected that by 2023, the global artificial intelligence large model market will reach 21 billion US dollars, and it is expected that with the further development of large models and the continuous innovation of technology, enterprises and organizations will be provided with more powerful data analysis, advanced capabilities and intelligent solutions, bringing more business opportunities and development space to the field of artificial intelligence, and continuing to contribute to the growth momentum of the market. This trend will drive rapid growth in the field of artificial intelligence and bring the large model market to $109.5 billion by 2028.
Report total: 75 pages.
Featured Report**: The School of Artificial Intelligence