AI applications such as video generation and voice interaction have continued to be popular in recent years, providing more and more users with efficient and convenient experiences by 2024. What is even more remarkable in the industry is that in 2024, various traditional industries will begin to embrace AI technology, endowing industry data and computing models with AI with deep learning capabilities, and opening up a path for large-scale applications in the industrial production field. Zhao Guohua, Chairman of Schneider Electric Group, recently analyzed the current situation of industrial intelligence development and stated that industrial enterprises need to increase their deployment and application efforts in the field of digital intelligence, which is the only way to ensure global competitiveness and achieve sustainable development. He Kaibo, CEO of AVEVA Jianwei Software, which recently launched a new AI product, believes that the prelude to the large-scale application of AI has begun, which will provide industrial enterprises with more extensive and in-depth intelligent services and achieve productivity improvement. Relevant institutions predict that global industrial data will significantly increase from 147 zettabytes in 2024 to 175 zettabytes by 2025, and the future digital transformation of industry is expected to bring a growth potential of $75 trillion. The composite application of AI and other technologies in important digital links will significantly accelerate the transformation trend of global industry. Li Kaifu, CEO of Zero One Thing, stated that AI technology, represented by big model technology, will reshape the productivity landscape of various industries and disrupt the existing organizational structure and scale of enterprises. However, the full potential of the big model capability has not been fully unleashed in To B applications at present. Only by entering the core business system and deploying quickly, lightweight, and in large quantities can it maximize cost reduction and efficiency improvement for enterprises. In the field of big models today, most To B projects are focused on privatizing customized models. How to truly integrate big models into customer core business scenarios and form a standardized and replicable application product empowered by big models is a major challenge for To B in the future Qi Ruifeng, co-founder of Zero One Thing, said. The benefits are gradually expanding as the application and practice of AI have penetrated into multiple industries, and the specific scale and participation are the common focus of attention in the industry. Kusto, Executive Vice President of Jianwei Software Product Line, which will release new AI products in 2024, stated that AI technology is continuously learning through long-term accumulation of industry data and operational parameters in the large-scale refining, petrochemical, mechanical manufacturing and other industrial fields it is involved in, assisting manual improvement of equipment detection and operation efficiency. She believes that the data accumulation of language interactive AI has unique advantages and is developing faster than industrial applications. But in 2024, known as the year of AI application, the direction of accelerated and large-scale application development has gradually emerged. In the future, with the release of data dividends and learning achievements, the proportion of AI participation in production will significantly increase. Kusto stated that Jianwei has been involved in the research and application of AI for a long time, and in recent years, its application in segmented industries is gradually deepening. The key to future applications in the industrial field is to adapt to local conditions, create value for customers based on different enterprise and business solutions, and achieve their desired goals. This year, we released a new AI product, demonstrating our determination to empower customers with AI on a large scale. In addition, Exel Energy, which sources more than 20% of its energy supply from wind energy, has reduced wind prediction errors by more than one-third through the deployment of PI System, a data-driven tool, and achieved over $45 million in operational cost reduction and efficiency improvement over six years Industry insiders have stated that the challenges in energy efficiency and data accumulation applications in the future will limit the application of AI in the industrial sector, and require all parties to plan ahead and solve them. Faced with diverse manufacturing scenarios, AI solution providers find it difficult to develop a unique solution. After the addition of large-scale models, the application of industrial AI has deepened, but currently there is still a long way to go before making a universal industrial large-scale model, and it is difficult to obtain sufficient data. Sun Yat Sen Chunshi, a commentator on the Nihon Keizai Shimbun, wrote an analysis that personal data only accounted for about 10% of the world's data, while about 90% of the industrial data, or BtoB (inter enterprise) data, included orders between enterprises and factory operations, "IoT" (Internet of Things) and other information, in addition to renewable energy and vehicle driving related data. The battle for industrial data will intensify in the future. These data are currently mostly lying quietly on the own systems or clouds of various enterprises, and no platform operator has processed and assigned various added values to these data. If companies can deepen their cooperation in data, it will be easier to achieve improved operational efficiency and technological innovation, and an "industrial data giant" may also emerge. Protecting data privacy and maintaining economic security are certainly important, but as companies realize the value of information generation and begin to promote data-driven operations, the arrival of such a day is even more anticipated. Recently, consulting firm Gartner issued a warning in its latest survey report, predicting that by 2027, 40% of existing AI data centers will face operational difficulties due to insufficient power supply. The report states that AI requires unprecedented levels of computing resources and energy consumption in processing and training large models. In order to meet the huge energy demand of AI data centers, the industry has shown measures to strengthen the development of clean energy. Tech giants such as Google, Microsoft, Amazon, and Meta are actively investing in nuclear power generation facilities and have announced plans to promote the construction of small modular nuclear reactors (SMRs). In addition, a recent report on the website of Nature magazine shows that geothermal technology is on the brink of widespread commercial success, with several companies including Meta and Google investing in this field. (New Society)