随着人工智能技术的不断发展，大模型已经成为推动各行各业创新转型的重要力量。在法律、金融、生物医学等众多领域，大模型正在重塑工作流程，提高效率和准确性。然而，大模型在实际落地应用中仍面临诸多挑战，尤其是如何将这些强大的技术能力转化为实际的生产力。
为此，InfoQ《极客有约》特别邀请了蔚来汽车人工智能研发负责人高杰、智源研究院大模型行业应用总监周华、华院计算大模型算法负责人蔡华，共同探讨大模型落地的心得与干货。在AICon全球人工智能开发与应用大会上，他们分享了各自在大模型应用领域的实践和见解。
高杰在《大模型在智能座舱中的应用》的分享中提到，尽管大模型在提升人机交互的自然度和智能化方面取得了显著进步，但在将这些技术应用到实际的智能座舱中时，仍然面临着设备迭代周期长的挑战。他强调，如何让老系统和新系统能够无缝对接和升级，是当前的一大难题。
周华在《智源行业数据集及训练方法落地实践》的专题分享中指出，大模型在替代现有IT系统的人机交互方面已经取得了显著进展，但未来它们将逐步深入到系统内部，成为IT系统的核心部分。然而，人工智能技术在行业应用中也面临着“最后一公里”问题，尤其是在需要深度知识和高准确性的应用领域，行业模型还有很大的探索和发展空间。
蔡华在《大语言模型在法律领域的应用探索》的专题分享中，以法律行业为例，阐述了大模型在提升法律文档处理效率和准确性的作用。他表示，大型法律模型不仅能够推荐案例，还能分析案例之间的异同点，为法律专业人士提供了更深入的分析和帮助。
总的来说，大模型技术正在推动各行各业的发展，但企业在应用大模型时需要量力而行，选择合适的模型和算法。同时，大模型的推理能力需要加强，解决其幻觉问题，以实现真正的行业落地。随着技术的不断进步，大模型的应用前景将更加广阔。
英语如下：
News Title: “Large Models in Action: How Enterprises Overcome the ‘Last Mile’ Challenge?”
Keywords: Large Models, Industry Applications, Challenges in Implementation
News Content:
As artificial intelligence (AI) technology continues to evolve, large models have become a pivotal force driving innovation and transformation across various industries. In fields such as law, finance, and biomedicine, large models are reshaping workflows and enhancing efficiency and accuracy. However, there are still numerous challenges in the practical implementation and application of large models, especially how to translate these powerful technical capabilities into actual productivity.
To explore these challenges and best practices, InfoQ’s “Geek’s Rendezvous” invited Gao Jie, the AI R&D Director of NIO, Zhou Hua, the Industry Application Director of Zhiyuan Institute for large models, and Cai Hua, the Algorithmic Leader of Huayun Computing for large models, to discuss their insights and practical tips on implementing large models. At the AICon Global Artificial Intelligence Development and Application Conference, they shared their experiences and insights in the application of large models in their respective fields.
Gao Jie, in his presentation on “The Application of Large Models in Intelligent Cockpits,” mentioned that while large models have made significant progress in improving the naturalness and intelligence of human-machine interaction, there are still challenges in applying these technologies to actual intelligent cockpits. He emphasized the current challenge of making old systems seamlessly connect and upgrade with new ones.
Zhou Hua, in his topic on “Practical Application of Zhiyuan’s Industry Dataset and Training Methods,” pointed out that large models have already made significant progress in replacing human-computer interaction in existing IT systems. However, they will gradually delve into the internal systems and become the core components of IT systems. Nonetheless, there is still a “last mile” problem in the application of artificial intelligence technology, especially in areas requiring deep knowledge and high accuracy, where industry models have a lot of exploration and development space.
Cai Hua, in his topic on “Exploring the Application of Large Language Models in the Legal Field,” used the legal industry as an example to explain the role of large models in improving the efficiency and accuracy of legal document processing. He stated that large legal models not only recommend cases but also analyze the differences and similarities between cases, providing legal professionals with more in-depth analysis and assistance.
In summary, large model technology is driving the development of various industries, but enterprises need to proceed with caution when applying large models, selecting suitable models and algorithms. At the same time, the inference capabilities of large models need to be strengthened, and their hallucination issues need to be addressed to achieve true industry implementation. As technology continues to advance, the application prospects of large models will become even more promising.
【来源】https://mp.weixin.qq.com/s/RgEKUHkQV-tLgEN7ERpo0g
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