Users input short text, and the large model automatically generates creative images and videos; Scan X-ray and CT images, and use large models to assist doctors in analyzing and diagnosing patients' conditions; Quickly predict the weather for the next 10 days within 1 minute, and issue early warnings if there is a possibility of extreme weather... Currently, artificial intelligence models are flourishing, and various new applications are emerging, accelerating the empowerment of thousands of industries. At the recently held 2024 China International Digital Economy Expo, a number of cutting-edge scenes in the fields of digital technology, digital services, and digital products were highlighted, shifting from general to vertical. Compared with previous exhibitions, this conference has opened a dedicated exhibition area for artificial intelligence big models for the first time, showcasing 32 vertical big model application scenarios and major achievements across the country, covering more than 10 fields such as healthcare, traditional Chinese medicine, steel, chemical industry, ports, and finance. A large model refers to a machine learning model with extremely large scale parameters and complex computational structures. Professor Li Piji from the School of Artificial Intelligence at Nanjing University of Aeronautics and Astronautics stated that large-scale models are divided into general large-scale models and vertical large-scale models. Compared with general models, the latter focuses on the vertical business areas where the enterprise is located, using business data to create vertical models and achieve cost reduction and efficiency improvement in their respective business scenarios. With the rapid evolution of the big model industry, its professional fields are constantly being subdivided. For specific scenarios, not all enterprises require the "omnipotence" of a universal large model, but rather the accuracy of the model. In contrast, the development cost of general large models is high, often accompanied by high costs of one billion or even billions of yuan. "Li Piji said that general large models are like" foundations ", with different training corpora, different" houses "can be built. For certain application scenarios, training a large vertical model at a lower cost can also effectively meet user needs. The big model has moved from language modeling to multimodality, which is an important stage in the landing of technology Lin Yonghua, vice president and chief engineer of Beijing Zhiyuan Artificial Intelligence Research Institute, said that at present, in the general field, the big model has initially shown a certain ability to apply scenarios. However, in the vertical fields such as health care and education, the ability shown by the big model is not enough to support professional applications. The main reason is that the model training lacks high-quality available industry data sets. 360 Group founder Zhou Hongyi stated that big models are not products but capabilities, and only by combining capabilities with scenarios can they be productized. By segmenting scenarios and breaking down business processes, corresponding professional models can be trained to solve the demand problems of government and enterprise professional scenarios. Wu Tienan, Deputy Secretary of the Party Committee of the National Industrial Information Security Development Research Center, stated that in recent years, domestic and foreign enterprises and institutions have quickly followed up and actively promoted the research and development of large-scale models. As of July 30th this year, 197 large model products have been registered nationwide, with industry large models accounting for 70%. Embracing production and life as a new technological highland in the field of artificial intelligence, big models are empowering thousands of industries at an unprecedented speed. Through its widespread application in various fields, it can achieve more efficient and intelligent services and decision-making processes, promote continuous innovation and development of the industry, and open up more new scenarios for production and life applications. Simulation training for tracheal intubation, virtual laparoscopic surgery, and digestive endoscopy diagnosis and treatment are important aspects of clinical teaching and practical training for various surgeries in medical schools. At the exhibition, a medical simulation training system launched by a company combined with a large model to learn and train case data from various departments in hospital clinical diagnosis, simulating more than ten common medical examination and surgical scenarios to help medical students integrate into clinical practice faster. Uploading satellite remote sensing images, the large model quickly completes image interpretation and analysis, generates detailed reports and results to assist agricultural production... The agricultural remote sensing large model exhibited by Hebei Agricultural University has impressed many agricultural enterprise operators and grain growers. The agricultural remote sensing big model is based on a massive amount of multimodal sample data such as natural disaster remote sensing images, pest and disease images, meteorological environment data, etc. It provides intelligent support for agricultural production through environmental monitoring and analysis prediction technology Wang Chunshan, Deputy Director of the Information Center of Hebei Agricultural University, said that its application scenarios cover natural resource investigation and monitoring, agricultural water and fertilizer scheme recommendation, accurate calculation of agricultural insurance, and other fields. Environmental pollution control is also one of the areas where vertical models are currently being implemented. Experts have stated that in the past, environmental pollution prevention and control relied more on on-site inspections and manual decision-making, while the implementation of vertical large-scale models provides technical support for environmental pollution control. By relying on data, more objective and accurate judgments can be made on pollution treatment, achieving scientific and precise pollution control. The further integration of digital technology and environmental protection models will promote the development of more intelligent, precise, and efficient environmental supervision in the field. The 'greatness' of a large model lies not only in its numerous scaling parameters, but also in the enormous potential and broad application scenarios it contains. Wu Tienan stated that big models are developing in depth and accelerating the empowerment of various industries. With the evolution of intelligent production, precise decision-making, and green industrial trends, the development of industrial big models is accelerating in practice, and big models are expected to become a new engine driving new industrialization. Coordinated development and safety science and technology are a double-edged sword, as the development of any technology has two sides. Nowadays, the development of large models is advancing rapidly, and how to coordinate the development and security of large models has attracted widespread attention from experts and various sectors of society. Wu Tienan stated that the rapid development of large models has intensified the security risks of artificial intelligence, causing more concerns such as endogenous issues such as "algorithmic black boxes" and unexplainability, technology abuse issues such as overuse and malicious applications, as well as more serious problems such as privacy breaches, data misuse, and "data poisoning" caused by data dependence. In the view of experts such as Wu Tienan, in order for large models to achieve long-term and sustainable development, they still need to break through multiple barriers. One is the fast iteration of technology. New technologies are constantly emerging, and cutting-edge technologies are changing rapidly. Although large-scale models have blossomed in many vertical fields, they have not yet produced substantial and large-scale applications. Secondly, it is difficult to obtain data. High quality information data is relatively scarce, and industry data has not yet achieved openness, flow, and sharing. The third aspect is that the scene needs to be explored. There are numerous industrial categories in our country, and currently, the application scenarios of large-scale models are only concentrated in a few highly profitable scenarios such as electricity and steel. The application of deep scenes is insufficient, and further exploration is needed for typical scenes that can be used for experience replication and promotion. Fourthly, there are high safety concerns. The industrial scenario has low fault tolerance and high security risk concerns. Currently, some enterprises still have insufficient awareness and capability of artificial intelligence security, which hinders the application of large models in related fields and scenarios. Lin Yonghua said that while bringing great changes to production and life, the big model still faces challenges in data, computing power and algorithms. At present, China's AI industry needs to solve not only the problem of resource reserve, but also the problem of ecological co construction. The upstream and downstream need to build an ecosystem with an open and open mind. The training of large models often requires the support of massive amounts of data. Song Jun, Vice Dean of the Shenzhen Research Institute at Hong Kong Baptist University, stated that due to the often disjointed systems between different departments and limited data that can be shared, he hopes to use big models as entry points and levers to organically connect these cross departmental data, obtain more effective calculation results, and enable more data to break the ice more effectively. (New Society)