南方财经 21世纪经济报道记者胡慧茵 马来西亚雪兰莪州报道 《21世纪》：马来西亚政府方面多次表示，人工智能不仅仅是一项技术，而且是马来西亚未来经济的一 个必需品。你怎么看待这个观点？发展人工智能行业对马来西亚的发展来说有着怎样的重要性? 人工智能正成为推动东盟经济增长和产业升级的新引擎。近期，东盟与东亚经济研究所的一项研究预 测，到2030年，人工智能产业有望带动东盟地区生产总值增长10%至18%，创造逾1万亿美元的潜在增 值。 与此同时，马来西亚在AI产业发展方面也大步迈进，从2021年开始陆续通过《数字经济蓝图》及"国家 人工智能路线图"促进产业加速升级。 近日，马来西亚中华总商会人工智能组主任、马来西亚人工智能研发公司WISE AI创始人林道钦在马来 西亚雪兰莪州接受21世纪经济报道记者专访时表示，马来西亚作为发展中国家，将人工智能视为实现国 家现代化的核心路径和必然选择，AI能够提升民众生活品质，帮助企业降低成本并提高生产效率。谈 到AI对新兴行业和传统行业的影响，林道钦表示，在推动新兴产业发展过程中，传统行业并不会被简 单淘汰，而是有望通过AI技术融合实现行业的重塑与升级。 频繁往来于中国和马来西 ...

Core Viewpoint - Small Language Models (SLMs) are considered the future of AI agents, as they are more efficient and cost-effective compared to large language models (LLMs) [1][3]. Group 1: Advantages of SLMs - SLMs are powerful enough to handle most repetitive and specialized tasks within AI agents [3]. - They are inherently better suited for the architecture of agent systems, being flexible and easy to integrate [3]. - Economically, SLMs significantly reduce operational costs, making them a more efficient choice for AI applications [3]. Group 2: Market Potential - The AI agent market is projected to grow from $5.2 billion in 2024 to $200 billion by 2034, with over half of enterprises already utilizing AI agents [5]. - Current AI agent tasks are often repetitive, such as "checking emails" and "generating reports," making the use of LLMs inefficient [5]. Group 3: SLM Characteristics - SLMs can be deployed on standard consumer devices, such as smartphones and laptops, and have fast inference speeds [9]. - Models with fewer than 1 billion parameters are classified as SLMs, while larger models typically require cloud support [9]. - SLMs are likened to a "portable brain," balancing efficiency and ease of iteration, unlike LLMs which are compared to "universe-level supercomputers" with high latency and costs [9]. Group 4: Performance Comparison - Cutting-edge small models like Phi-3 and Hymba can perform tasks comparable to 30B to 70B large models while reducing computational load by 10-30 times [11]. - Real-world tests showed that 60% of tasks in MetaGPT, 40% in Open Operator, and 70% in Cradle could be replaced by SLMs [11]. Group 5: Barriers to Adoption - The primary reason for the limited use of SLMs is path dependency, with significant investments (up to $57 billion) in centralized large model infrastructure [12]. - There is a strong industry bias towards the belief that "bigger is better," which has hindered the exploration of small models [12]. - SLMs lack the marketing hype that large models like GPT-4 have received, leading to fewer attempts to explore more cost-effective options [13].