美国国家工程院外籍院士沈向洋在近期的发言中，阐述了他对人工智能大模型发展的深刻洞察。他认为，通用大模型正以前所未有的态势席卷各个垂直行业，其性能的提升将依赖于规模庞大的参数量，甚至可能达到万卡、上万亿参数的级别。这一发展趋势标志着人工智能技术的新里程碑，同时也对人机交互模式提出了全新的挑战和机遇。
沈向洋院士特别指出，未来的焦点可能在于个性化的个人大模型。这些模型将根据每个用户的特性和需求进行定制，从而提供更为精准的服务。他提出，将个性化参数与云计算和端设备的融合，将可能开创出一个价值巨大的新领域。这一设想不仅能够提升用户体验，也将极大地推动人工智能在日常生活和工作中的应用深度。
沈向洋的见解为人工智能行业的发展提供了新的思考方向，预示着在大模型时代，人机关系将不再局限于传统的命令与执行模式，而是将朝着更加个性化、智能化的方向演进。这一变化不仅将影响科技领域，也将深远地改变社会的各行各业。来源：财联社。
英语如下：
Title: Academician Shen Xiangyang Envisions a New Era for Human-Machine Relationships with General-Purpose Large Models
Keywords: Shen Xiangyang, Large Models, Personalization
News Content:
Academician Shen Xiangyang, a foreign member of the US National Academy of Engineering, recently shared his profound insights into the development of artificial intelligence (AI) large models. He believes that these models are poised to revolutionize various vertical industries with unprecedented force, and their performance enhancements will rely on enormous parameter counts, potentially reaching the scale of tens of thousands or even trillions of parameters. This development signifies a new milestone in AI technology and presents both challenges and opportunities for human-computer interaction.
Specifically, Shen Xiangyang highlighted the future focus on personalized individual large models. These models would be tailored to each user’s characteristics and needs, providing more precise services. He proposed that integrating personalized parameters with cloud computing and edge devices could potentially create a valuable new domain. This vision not only enhances user experience but also significantly advances the depth of AI application in daily life and work.
Shen Xiangyang’s perspective offers a new dimension for the AI industry’s development, suggesting that in the era of large models, human-machine relationships will evolve beyond the conventional command-and-execute paradigm, moving towards a more personalized and intelligent direction. This transformation will not only impact the tech sector but also have far-reaching consequences across various industries. Source: Sina Finance.
【来源】https://www.cls.cn/detail/1628008
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