陈奕强：小语言模型：企业未来人工智能

在快速发展的人工智能世界中，焦点正在从大型、资源密集式的大语言模型 (LLM) 转向更小、更有效率的小语言模型 (SLM)。这一转变将使人工智能民主化，更广泛的企业和产业能够使用此先进科技。OpenAI最近推出的GPT-4o Mini就体现了这一趋势，为大型语言模型提供了更实惠、更有效率的替代方案。 

为何选择较小语言模型？ 

1.成本效益：较小的模型更容易培训和维护，使预算有限的新创公司和小型企业可以使用。由于功耗降低，这意味著营运成本降低，环境足迹也可减少。

2.专业精准度：与大型模型不同，SLM可以针对特定任务或产业进行微调，例如法律或医疗应用。与广义模型相比，这种专业化可以带来更相关、更准确的输出。 

3.更快的回应时间：较小的模型提供较低的延迟，使其成为聊天机器人和虚拟助理等即时应用程式的理想选择，从而增强客户服务体验。 

一些行业发现较小的语言模型非常适合他们需求： 

医疗保健：SLM可以根据医疗数据集进行微调，使医疗保健提供者能够获得准确患者回应以及有效记录管理。其理解专业术语的能力，将使其在这个领域非常宝贵。 

金融：金融机构利用SLM来分析合约并评估风险。这种针对性培训，能确保遵守法规，同时降低使用大型模型相关的成本。

客户支援：许多公司正在聊天机器人中部署SLM来处理客户询问。这些模型的快速反应时间和较低的延迟增强了客户体验，使企业能够提供高效的支援。 

教育：教育机构正在利用SLM创建个人化的学习体验，以满足个别学生的需求，提高参与度和成果。 

SLM最显著的优势之一是其自托管能力。此功能对于关注资料主权和隐私的组织至关重要。通过在自己的伺服器上部署SLM，企业可以确保敏感资料仍在其控制范围内，从而降低与第三方云端服务相关的风险。这对于医疗保健和金融等资料隐私法规非常严格的行业尤其重要。 

自托管还允许组织进一步客制化其模型，确保在不影响安全性的情况下根据特定的营运需求进行客制化。这种对资料的控制不仅增强了隐私，还与日益关注其资讯处理方式的客户建立了信任。 

SLM的自托管能力可与数码转型即服务 (DTaaS) 的成长趋势完美契合。在马来西亚，Agmo和SNS Network等公司是最早提供DTaaS解决方案的公司；本地企业可以整合这些模型，而无需进行大规模的基础设施变更，从而促进向先进技术的平稳过渡。 

透过利用SLM作为数码转型策略的一部分，公司可以提高营运效率、改善客户互动并推动创新。这种方法不仅可以提高敏捷性，还可以帮助企业快速适应不断变化的市场需求。企业可以增强营运能力，同时应对数码转型的复杂性，最终为迈向更创新、更安全的人工智能未来铺路。

陈奕强《小语言模型：企业未来人工智能》原文：Small Language Models: The Future of AI for Businesses

In the fast-evolving world of artificial intelligence, the focus is shifting from large, resource-intensive language models (LLMs) to smaller, more efficient language models (SLMs). This transition is set to democratise AI, making advanced capabilities accessible to a broader range of businesses and industries. OpenAI's recent launch of the GPT-4o Mini exemplifies this trend, providing a more affordable and efficient alternative to larger models. 

Why Choose Smaller Language Models?

1.Cost-Effectiveness: Smaller models are more affordable to train and maintain, making them accessible to startups and small businesses operating on limited budgets. This translates to lower operational costs and a reduced environmental footprint due to decreased power consumption.

2.Specialised Precision: Unlike larger models, SLMs can be fine-tuned for specific tasks or industries, such as legal or medical applications. This specialisation leads to more relevant and accurate outputs compared to generalised models.

3.Faster Response Times: Smaller models offer lower latency, making them ideal for real-time applications like chatbots and virtual assistants, enhancing customer service experiences.

Several industries are finding that smaller LLMs fit their needs perfectly:

Healthcare: SLMs can be fine-tuned on medical datasets, allowing healthcare providers to generate accurate patient responses and manage records efficiently. Their ability to understand specialised terminology makes them invaluable in this sector.

Finance: Financial institutions utilise SLMs to analyse contracts and assess risks. Their targeted training ensures compliance with regulations while reducing costs associated with larger models.

Customer Support: Many companies are deploying SLMs in chatbots to handle customer inquiries. The quick response times and lower latency of these models enhance the customer experience, allowing businesses to provide efficient support.

Education: Educational institutions are leveraging SLMs to create personalised learning experiences that cater to individual student needs, improving engagement and outcomes.

One of the most significant advantages of SLMs is their ability to be self-hosted. This capability is crucial for organisations concerned about data sovereignty and privacy. By deploying SLMs on their own servers, businesses can ensure that sensitive data remains within their control, mitigating risks associated with third-party cloud services. This is particularly important for industries like healthcare and finance, where data privacy regulations are stringent.

Self-hosting also allows organisations to customise their models further, ensuring that they are tailored to specific operational needs without compromising security. This control over data not only enhances privacy but also builds trust with customers who are increasingly concerned about how their information is handled.

The ability to self-host SLMs aligns perfectly with the growing trend of Digital Transformation as a Service (DTaaS). In Malaysia, companies like Agmo and SNS Network are the first in offering DTaaS solutions; local businesses can integrate these models without extensive infrastructure changes, facilitating a smoother transition to advanced technologies.

By utilising SLMs as part of their digital transformation strategy, companies can enhance their operational efficiency, improve customer interactions, and drive innovation. This approach not only fosters agility but also positions businesses to adapt quickly to changing market demands. Businesses can enhance their operations while navigating the complexities of digital transformation, ultimately paving the way for a more innovative and secure future in AI.