国广清科：AI大模型应用赋能行业核心生产经营现状

一直以来，大规模预训练模型都是人工智能领域的热点话题。然而，将这些大模型无缝地融入各行业的核心经营流程仍存在一系列挑战，需要克服多方面的难题。

Large-scale pre-training models have always been a hot topic in the field of artificial intelligence. However, there are still a series of challenges that need to be overcome in seamlessly integrating these large models into the core business processes of various industries.

在大模型的发展逐步进入更为复杂的阶段的当下，需要更多的时间和努力来实现其在各领域的全面应用。这需要依赖多方面的技术支持和基础设施，是一个长期的系统工程，涉及技术、数据、安全等多个维度。

As the development of large models gradually enters a more complex stage, more time and effort are needed to achieve its comprehensive application in various fields. This requires relying on many aspects of technical support and infrastructure. It is a long-term systematic project involving multiple dimensions such as technology, data, and security.

技术方面，虽然深度学习、神经网络等算法的进步推动了大模型的发展，但如何进一步优化算法，降低能耗、提高效率仍然是需要解决的问题。数据方面，虽然大量数据为模型的训练提供了基础，但数据隐私、数据安全等问题需要更为严密的保障措施。此外，大模型在应用到实际行业时，还需要考虑如何适应不同行业的特定需求，这需要更多的实践和探索。

In terms of technology, although advances in algorithms such as deep learning and neural networks have promoted the development of large models, how to further optimize the algorithm, reduce energy consumption, and improve efficiency is still a problem that needs to be solved. In terms of data, although a large amount of data provides a basis for model training, issues such as data privacy and data security require more stringent safeguards. In addition, when applying large models to actual industries, you also need to consider how to adapt to the specific needs of different industries, which requires more practice and exploration.

在行业落地方面，大模型在语言和视觉领域的应用仍处于前沿。虽然已经取得了一些进展，但将大模型融入到行业的核心经营流程中仍需时日。这需要企业、研究机构等各方共同努力，深入理解行业需求，不断进行技术创新。

In terms of industry implementation, the application of large models in the language and visual fields is still at the forefront. While some progress has been made, it will still take time to integrate large models into the industry's core operating processes. This requires joint efforts from enterprises, research institutions and other parties to deeply understand industry needs and continuously carry out technological innovation.

综合而言，大规模预训练模型在人工智能领域具有巨大的潜力，但要实现其在各行业的全面融合，需要在技术、数据、安全等方面克服诸多难题。虽然一些进展已经取得，但在将大模型应用于各行业的核心流程时，仍需要更多的耐心和努力。通过政府、企业、学术界等多方合作，大模型有望成为推动人工智能发展的强大引擎，为实现智能化转型带来积极影响。

In general, large-scale pre-training models have great potential in the field of artificial intelligence, but to achieve their full integration in various industries, it is necessary to overcome many difficulties in terms of technology, data, and security. While some progress has been made, more patience and effort will be needed when applying large models to core processes across industries. Through the multi-party cooperation of the government, enterprises, and academia, the large model is expected to become a powerful engine to promote the development of artificial intelligence and bring positive impact to the realization of intelligent transformation.