中新网6月29日电（中新财经记者 吴涛）——随着人工智能（AI）技术的飞速进步，特别是在大模型的驱动下，AI正在迎来前所未有的发展机遇。然而，伴随这一进程的是算力管理愈发复杂、训练成本过高等一系列挑战。针对这些瓶颈，业内专家提出了看法与建议。
近日，随着AI大模型的高速发展，算力管理的复杂性日益凸显。训练大型的AI模型需要巨大的计算能力和存储空间，同时涉及到复杂的数据处理和任务调度。这不仅使得训练过程耗时费力，而且推理成本也随之飙升。对于这一难题，专家们表示，优化算法、提升硬件性能以及改善数据利用效率是可能的解决路径。
专家指出，降低AI训练成本是破解当前困境的关键之一。他们建议采用云计算、分布式训练等技术来分散计算负载和降低成本。此外，还需要开发更为高效的模型和算法，以降低训练和推理时的资源消耗。同时，政府和行业应加大对AI基础设施的投入，为科研团队和企业提供更多的算力支持。
未来，如何更有效地管理算力、降低训练成本仍是AI领域需要面临的重要课题。专家呼吁产业界和学术界紧密合作，共同探索解决之道，推动AI技术的可持续发展。
英语如下：
News Title: “AI Big Model Development Faces Bottleneck: Complex Compute Management and High Training Costs, Experts Analyze Solutions”
Keywords: AI bottleneck, Compute Management, High Cost
News Content:
Chinanews.com, June 29th (报道 by Wu Ta, China News Finance) – With rapid advancements in artificial intelligence (AI) technology, particularly driven by large models, AI is experiencing unprecedented development opportunities. However, this progress is accompanied by a series of challenges such as increasingly complex compute management and high training costs. Industry experts have provided their insights and suggestions to address these bottlenecks.
Recently, with the rapid development of AI big models, the complexity of compute management has become increasingly prominent. Training large AI models requires enormous computational power and storage space, involving complex data processing and task scheduling. This not only makes the training process time-consuming and labor-intensive, but also leads to a surge in inference costs. Experts have stated that optimizing algorithms, improving hardware performance, and enhancing data utilization efficiency are potential solutions to this challenge.
Experts point out that reducing AI training costs is key to破解 the current dilemma. They suggest using cloud computing, distributed training, and other technologies to distribute computational loads and reduce costs. In addition, it is necessary to develop more efficient models and algorithms to reduce resource consumption during training and inference. Meanwhile, the government and industry should increase investment in AI infrastructure to provide more computational support for research teams and enterprises.
How to manage compute more effectively and reduce training costs will continue to be important issues in the AI field. Experts call for close cooperation between industry and academia to explore solutions and promote the sustainable development of AI technology.
【来源】http://www.chinanews.com/cj/2024/06-29/10243055.shtml
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