内容来源：https://www.technologyreview.com/2026/04/16/1135216/making-ai-operational-in-constrained-public-sector-environments/

内容总结：

专为公共部门打造：小型语言模型如何破解政府AI应用困局

在全球各行业积极拥抱人工智能的浪潮下，公共部门机构同样面临加速应用AI的压力。然而，政府机构在安全性、治理和运营方面面临着独特且严格的约束，这使得许多在私营领域可行的AI部署方案在公共部门难以落地。

安全与可控是首要挑战
一项凯捷咨询的研究显示，全球79%的公共部门高管对AI的数据安全性心存疑虑。政府数据的极端敏感性及相关法律义务，使得数据控制成为不可妥协的核心需求。Elastic公司副总裁韩啸指出：“政府机构必须严格限制哪些数据可以发送至网络，这为其数据思维和管理方式设定了诸多边界。”

独特的运营环境构成障碍
与私营部门通常假设的持续云连接、集中式基础设施等条件不同，许多政府机构必须在网络连接受限、不稳定甚至完全离线的环境中运行系统。同时，它们还需确保数据完全自主可控、信息可核查、业务中断最小化。此外，公共部门在获取用于训练复杂AI模型的图形处理器（GPU）等专用硬件方面也面临瓶颈。这些因素导致许多有前景的AI试点项目难以超越实验阶段，实现规模化运营。

小型语言模型提供务实路径
面对这些刚性约束，参数规模庞大、通常依赖云端运行的大语言模型（LLM）往往不适用。而参数规模更小、专为特定任务构建的小型语言模型（SLM）展现出独特优势。SLM可以本地化部署，提供更强的安全性和控制力，且对计算资源的需求远低于大型模型。

实证研究表明，SLM的表现可与LLM媲美甚至更优。通过将AI工具“带至数据处”，而非将敏感数据发送至云端，SLM能在保障安全的前提下，有效利用信息。高德纳公司预测，到2027年，小型专业AI模型的使用量将达到LLM的三倍。

超越聊天：以智能搜索释放数据价值
对于公共部门而言，AI的价值远不止于聊天机器人。其最直接的机遇之一是彻底革新海量数据的搜索与管理方式。政府拥有大量非结构化数据，如技术报告、采购文档、会议纪要和票据。如今，由SLM驱动的系统能够索引混合媒体格式（如PDF、扫描件、图像、电子表格、录音）和多语言内容，提供精准的定制化响应，并协助起草符合法律规范的复杂文本。

此外，训练有素的SLM能帮助政府工作人员解读数据，例如阐释法律规范、从公众咨询中提取洞察、支持数据驱动的决策，并改善公众对服务和行政信息的获取，从而显著提升公共部门的运作效能。

聚焦效率与透明
采用SLM意味着将关注点从模型的“规模是否全面”转向“是否高效实用”。SLM在性能和计算成本上更具优势，所需专用硬件更少，总体成本更低，环境影响也更小。同时，其算法更易于实现透明化和审计合规，满足诸如欧盟《通用数据保护条例》（GDPR）等严格的隐私监管要求。

通过使用经过精心设计的提示词、智能检索、向量搜索及可验证信源等技术，SLM能够生成更具针对性、更准确的结果，有效减少错误、偏见和AI常见的“幻觉”问题。将数据保留在本地服务器或特定设备上，并非为了孤立，而是为了建立战略自主性，从而确保信任、韧性与实效。

专家建议，公共部门构建持久的AI能力，应从优先考虑为本地化数据处理环境设计的任务专用型模型开始，并持续监控其性能与影响。韩啸总结道：“不要从聊天机器人开始，而要从搜索开始。我们所认为的许多AI智能，其核心实质在于找到正确的信息。”

中文翻译：

赞助内容

在受限的公共部门环境中实现人工智能的落地应用

专为特定目的构建的小型语言模型，为政府组织提供了一种切实可行的解决方案，使其能够在满足所需的安全性、可信度和可控性的前提下，将人工智能投入实际运营。

与Elastic合作呈现

人工智能热潮席卷各行各业，公共部门组织正面临着加速采用的压力。与此同时，政府机构在安全、治理和运营方面面临着独特的限制，使其有别于商业机构。因此，专为特定目的构建的小型语言模型为在这些环境中实现人工智能落地应用提供了一条前景广阔的路径。

凯捷咨询的一项研究发现，全球79%的公共部门高管对人工智能的数据安全性持谨慎态度。考虑到政府数据的高度敏感性及其使用所涉及的法律义务，这个数字是可以理解的。正如Elastic公司人工智能副总裁韩笑所言："政府机构必须对发送到网络上的数据类型有非常严格的限制。这为他们思考和管理数据的方式设定了很多边界。"

对敏感信息进行控制的基本需求，是使人工智能部署复杂化的众多因素之一，尤其是在与私营部门的标准运营假设相比时。

独特的运营挑战

当私营部门实体扩展人工智能应用时，他们通常假设某些条件已经具备，包括与云的持续连接、对集中式基础设施的依赖、对模型透明度不完全的接受，以及对数据流动的限制较少。然而，对于许多国家机构而言，接受这些条件可能从危险到根本不可能。

政府机构必须确保其数据处于自身控制之下，信息可以被检查和验证，并且运营中断要保持在绝对最低限度。与此同时，他们常常不得不在互联网连接有限、不可靠或无法使用的环境中运行其系统。这些复杂性使得许多前景广阔的公共部门人工智能试点项目难以走出实验阶段。"许多人低估了人工智能的运营挑战，"韩笑说。"公共部门需要人工智能能够在各类数据上可靠地运行，并且能够在不中断的情况下扩展。运营的连续性常常被低估。" Elastic对公共部门领导人的一项调查发现，65%的受访者在持续、实时、大规模地使用数据方面存在困难。

基础设施的限制加剧了这个问题。政府组织可能也难以获得用于训练和访问复杂人工智能模型的图形处理单元。正如韩笑指出的："与私营部门不同，政府不常购买GPU——他们不习惯管理GPU基础设施。因此，获取GPU来运行模型对许多公共部门来说是一个瓶颈。"

更小、更实用的模型

公共部门中许多不容妥协的要求使得大型语言模型难以维系。但小型语言模型可以本地部署，提供更高的安全性和可控性。小型语言模型是专门化的人工智能模型，通常使用数十亿而非数千亿的参数，这使得它们对计算能力的要求远低于最大的大型语言模型。

公共部门无需构建越来越庞大、部署在异地集中式位置的模型。一项实证研究发现，小型语言模型的表现与大型语言模型相当甚至更好。小型语言模型允许敏感信息被有效且高效地利用，同时避免了维护大型模型的运营复杂性。韩笑这样描述："使用ChatGPT进行校对很容易。但在一个没有网络访问的环境中，要同样顺畅地运行自己的大型语言模型则非常困难。"

小型语言模型是为使用它们的部门或机构的特定需求量身定制的。数据安全地存储在模型外部，仅在查询时被访问。精心设计的提示词确保只检索最相关的信息，从而提供更准确的响应。通过使用智能检索、向量搜索和可验证来源追溯等方法，可以构建出满足公共部门需求的人工智能系统。

因此，公共部门采用人工智能的下一阶段，可能是将人工智能工具带到数据所在之处，而不是将数据发送到云端。高德纳预测，到2027年，小型、专业化的人工智能模型的使用量将是大型语言模型的三倍。

卓越的搜索能力

"当公共部门的人听到人工智能时，他们可能想到的是ChatGPT。但我们可以有更大的抱负，"韩笑说。"人工智能可以彻底改变政府搜索和管理其海量数据的方式。"

超越聊天机器人，我们会发现人工智能最直接的机遇之一：显著改善的搜索能力。与许多组织一样，公共部门拥有堆积如山的非结构化数据——包括技术报告、采购文件、会议纪要和发票。然而，当今的人工智能可以提供来自混合媒体（如可读的PDF、扫描件、图像、电子表格和录音）并以多种语言呈现的结果。所有这些都可以由小型语言模型驱动的系统进行索引，以提供量身定制的响应，并用任何语言起草复杂的文本，同时确保输出内容符合法律规定。"公共部门有很多数据，他们并不总是知道如何使用这些数据。他们不知道有哪些可能性，"韩笑说。

更强大的是，人工智能可以帮助政府雇员解读他们访问的数据。"今天的人工智能可以为你提供一个关于如何利用这些数据的全新视角，"韩笑说。一个训练有素的小型语言模型可以解读法律规范，从公众咨询中提取见解，支持数据驱动的行政决策，并改善公众获取服务和行政信息的途径。这可以极大地改善公共部门的运作方式。

小型语言的承诺

专注于小型语言模型，将讨论的焦点从模型的全面性转向其效率。大型语言模型会产生显著的性能和计算成本，并且需要许多公共实体无法负担的专用硬件。尽管小型语言模型也需要一些资本支出，但其资源密集程度低于大型语言模型，因此往往更便宜，并能减少对环境的影响。

公共部门机构通常面临严格的审计要求，而小型语言模型的算法可以被记录和认证为透明的。一些国家，特别是欧洲国家，还有诸如GDPR之类的隐私法规，小型语言模型可以被设计来满足这些要求。

量身定制的训练数据能产生更有针对性的结果，减少人工智能容易产生的错误、偏见和"幻觉"。正如韩笑所说："大型语言模型根据其训练数据生成文本，因此存在一个训练截止日期。如果你询问那之后的任何事情，它就会产生幻觉。我们可以通过强制模型从经过验证的来源获取信息来解决这个问题。"

将数据保存在本地服务器，甚至特定设备上，也能将风险降至最低。这并非孤立，而是关于实现可信度、韧性和相关性的战略自主权。

通过优先考虑为本地处理数据环境设计的、针对特定任务的模型，并持续监控性能和影响，公共部门组织可以建立持久的人工智能能力，以支持现实世界的决策。"不要从聊天机器人开始；从搜索开始，"韩笑建议道。"我们认为的人工智能智能，很大程度上其实是关于找到正确的信息。"

深度探索

人工智能

OpenAI正全力构建一个全自动研究员
与OpenAI首席科学家雅库布·帕乔基的独家对话，探讨其公司的新宏伟挑战和人工智能的未来。

《精灵宝可梦Go》如何为送货机器人提供厘米级精度的世界视图
独家报道：Niantic的人工智能衍生公司正在利用玩家众包的300亿张城市地标图像，训练一个新的世界模型。

这家初创公司想要改变数学家做数学的方式
Axiom Math正在免费提供一款强大的新人工智能工具。但它是否能像公司希望的那样加速研究，仍有待观察。

想了解人工智能的现状？看看这些图表。
根据斯坦福大学2026年人工智能指数报告，人工智能正在飞速发展，而我们正努力跟上。

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英文来源：

Sponsored
Making AI operational in constrained public sector environments
Purpose-built small language models provide a practical solution for government organizations to operationalize AI with the security, trust, and control they require.
In partnership withElastic
The AI boom has hit across industries, and public sector organizations are facing pressure to accelerate adoption. At the same time, government institutions face distinct constraints around security, governance, and operations that set them apart from their business counterparts. For this reason, purpose-built small language models (SLMs) offer a promising path to operationalize AI in these environments.
A Capgemini study found that 79 percent of public sector executives globally are wary about AI’s data security, an understandable figure given the heightened sensitivity of government data and the legal obligations surrounding its use. As Han Xiao, vice president of AI at Elastic, says, “Government agencies must be very restricted about what kind of data they send to the network. This sets a lot of boundaries on how they think about and manage their data.”
The fundamental need for control over sensitive information is one of many factors complicating AI deployment, particularly when compared against the private sector’s standard operational assumptions.
Unique operational challenges
When private-sector entities expand AI, they typically assume certain conditions will be in place, including continuous connectivity to the cloud, reliance on centralized infrastructure, acceptance of incomplete model transparency, and limited restrictions on data movement. For many state institutions, however, accepting these conditions could be anything from dangerous to impossible.
Government agencies must ensure that their data stays under their control, that information can be checked and verified, and that operational disruptions are kept to an absolute minimum. At the same time, they often have to run their systems in environments where internet connectivity is limited, unreliable, or unavailable. These complexities prevent many promising public sector AI pilots from moving beyond experimentation. “Many people undervalue the operating challenge of AI,” Xiao says. “The public sector needs AI to perform reliably on all kinds of data, and then to be able to grow without breaking. Continuity of operations is often underestimated.” An Elastic survey of public sector leaders found that 65 percent struggle to use data continuously in real time and at scale.
Infrastructure constraints compound the problem. Government organizations may also struggle to obtain the graphics processing units (GPUs) used to train and access complex AI models. As Xiao points out, “Government doesn’t often purchase GPUs, unlike the private sector—they're not used to managing GPU infrastructure. So accessing a GPU to run the model is a bottleneck for much of the public sector.”
A smaller, more practical model
The many nonnegotiable requirements in the public sector make large language models (LLMs) untenable. But SLMs can be housed locally, offering greater security and control. SLMs are specialized AI models that typically use billions rather than hundreds of billions of parameters, making them far less computationally demanding than the largest LLMs.
The public sector does not need to build ever-larger models housed in offsite, centralized locations. An empirical study found that SLMs performed as well or better than LLMs. SLMs allow sensitive information to be used effectively and efficiently while avoiding the operational complexity of maintaining large models. Xiao puts it this way: “It is easy to use ChatGPT to do proofreading. It's very difficult to run your own large language models just as smoothly in an environment with no network access.”
SLMs are purpose-built for the needs of the department or agency that will use them. The data is stored securely outside the model, and is only accessed when queried. Carefully engineered prompts ensure that only the most relevant information is retrieved, providing more accurate responses. Using methods such as smart retrieval, vector search, and verifiable source grounding, AI systems can be built that cater to public sector needs.
Thus, the next phase of AI adoption in the public sector may be to bring the AI tool to the data, rather than sending the data out into the cloud. Gartner predicts that by 2027, small, specialized AI models will be used three times more than LLMs.
Superior search capabilities
“When people in the public sector hear AI, they probably think about ChatGPT. But we can be much more ambitious,” says Xiao. “AI can revolutionize how the government searches and manages the large amounts of data they have.”
Looking beyond chatbots reveals one of AI’s most immediate opportunities: dramatically improved search. Like many organizations, the public sector has mountains of unstructured data—including technical reports, procurement documents, minutes, and invoices. Today’s AI, however, can deliver results sourced from mixed media, like readable PDFs, scans, images, spreadsheets, and recordings, and in multiple languages. All of this can be indexed by SLM-powered systems to provide tailored responses and to draft complex texts in any language, while ensuring outputs are legally compliant. “The public sector has a lot of data, and they don't always know how to use this data. They don't know what the possibilities are,” says Xiao.
Even more powerful, AI can help government employees interpret the data they access. “Today's AI can provide you with a completely new view of how to harness that data,” says Xiao. A well-trained SLM can interpret legal norms, extract insights from public consultations, support data-driven executive decision-making, and improve public access to services and administrative information. This can contribute to dramatic improvements in how the public sector conducts its operations.
The small-language promise
Focusing on SLMs shifts the conversation from how comprehensive the model can be to how efficient it is. LLMs incur significant performance and computational costs and require specialized hardware that many public entities cannot afford. Despite requiring some capital expenses, SLMs are less resource-intensive than LLMs, so they tend to be cheaper and reduce environmental impact.
Public sector agencies often face stringent audit requirements, and SLM algorithms can be documented and certified as transparent. Some countries, particularly in Europe, also have privacy regulations such as GDPR that SLMs can be designed to meet.
Tailored training data produces more targeted results, reducing errors, bias, and hallucinations that AI is prone to. As Xiao puts it, “Large language models generate text based on what they were trained on, so there is a cut-off date when they were trained. If you ask about anything after that, it will hallucinate. We can solve this by forcing the model to work from verified sources.”
Risks are also minimized by keeping data on local servers, or even on a specific device. This isn’t about isolation but about strategic autonomy to enable trust, resilience, and relevance.
By prioritizing task-specific models designed for environments that process data locally, and by continuously monitoring performance and impact, public sector organizations can build lasting AI capabilities that support real-world decisions. “Do not start with a chatbot; start with search,” Xiao advises. “Much of what we think of as AI intelligence is really about finding the right information.”
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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