中小企业的人工智能应用：Tadviser 峰会报告
1 分•作者: anong1•4 个月前
以下是两家公司在全企业范围内整合人工智能的不同方法：一种是从下往上，另一种是从上往下。
第一家公司（tom-tailor.ru）采用“数字生态系统”方法来防止“过度自动化”。他们使用许多小型服务来赋能员工。一个通用数据总线将这些服务集成在一起，但主要侧重于独立性和人。他们与市场紧密合作，并监控趋势和竞争对手。这就是他们实现灵活性的一种方式。他们需要1.5个月来检查一个流程是否成功。
第二家公司（政府机构unirusgroup.ru）使用的座右铭是：“能力中心”不是“开发中心”。能力中心充当专业知识和培训的枢纽，并被用作采用人工智能的主要场所。该中心汇集了来自整个企业的专家，并将他们的知识整合到中心。该中心拥有一个强大的沙盒，用于开发和使用人工智能模块。这使他们能够扩展和共享人工智能知识，并避免技术创新中的孤岛。
这些公司平衡了“拼凑式自动化”与“过度自动化”。
其他观察：
* 仪表板无助于解决问题，反而会使人不堪重负；与此同时，当人创建仪表板时，他们会分享自己对数据的高度理解。
* 人工智能用于预测风险增加的领域，这些领域需要关注以防止关键故障；其他领域将受到较少的关注。这使得公司能够在资源有限的情况下运作。
* 自下而上的方法需要更少的时间来检查新模块是否成功，因为它们在这里更小。
* 架构是人与人工智能之间的桥梁；其作用类似于认知距离中的工件。
提到的图表：
* 创新扩散中的营销鸿沟（“mure-abyss”）显示了在项目扩展期间，13%的活跃用户和70%的被动用户之间的差距。
* 边际效益微笑曲线：研发阶段高，生产阶段低，销售阶段高。
* 在利益相关者之间存在一个“认知距离”三角：业务、管理和开发人员。如果没有平衡和尊重，项目很可能会失败。人工智能或工件可以在它们之间进行调解。
斯科尔科沃研究亮点：
* 记忆和决策是分布式的；所有组件都具有本地内存和一定的自主性（分布式认知，Noosphere）。
* 决策类型：专家驱动、流程驱动、数据驱动。每种类型都有其特定的应用领域；数据驱动并非完美无缺。专家驱动类似于一个紧凑的黑盒决策者，如大型语言模型。
* 数据驱动方法作为一种物质过程，促进人工智能应用。
* 原型测试需要90天。
企业人工智能开发的步骤（斯科尔科沃，SberService）：
1. 找到流程足够成熟的领域
2. 定义指标并建立“数据故事”
3. 将原型开发为模块
4. 扩展和监控
建议使用TRL和MRL指标（技术和制造工艺就绪度等级）来寻找创新机会。
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There were two companies with different approaches to integrating AI enterprise-wide: from bottom to
top and from top to bottom. The first company (tom-tailor.ru) uses a "digital ecosystem" approach
to prevent "hyper-automation." Many small services are used to empower people. A common data bus
integrates them, but the main focus is on independence and people. They work closely with
marketplaces and monitor trends and competitors. This is how they achieve flexibility. They take
1.5 months to check if a process is successful.<p>Second (government corporation unirusgroup.ru) uses the motto: “Competence center” is not a
“development center.”
A competence center serves as a hub for expertise and training, and is used as the primary place to
adopt AI. This center gathers experts from across the enterprise and integrates their knowledge in
the center.
The center has a robust sandbox for developing and using AI modules.
This allows them to scale and share AI knowledge and avoid silos in technical innovation.<p>These companies balance “patchwork automation” versus “hyperautomation”.
Other observations:
- Dashboards do not help but instead overload humans; meanwhile, when humans create dashboards they
share their own high understanding of data.
- AI is used to predict fields with increasing risk that require attention to prevent critical
failures; other fields will receive less attention. This allows the company to function with
limited resources.
- The bottom-up approach uses significantly less time to check if new modules are successful because
they are smaller here.
- Architecture is a bridge between humans and AI; its role is similar to artifacts in cognitive
distance.<p>Diagrams mentioned:
- The marketing chasm (“mure-abyss”) in diffusion of innovations shows a gap between 13% active and
70% passive users during project scaling.
- The Smiling Curve of Marginality: high in R&D, low in production, high in selling.
- There is a “Cognitive Distance” triangle between stakeholders: business, management, and
developers. Project failure is likely without balance and respect. AI or artifacts can mediate
between them.<p>Skolkovo Research Highlights:
- Memory and decision-making are distributed; all components have local memory and some autonomy
(Distributed Cognition, Noosphere).
- Decision-making types: expert-driven, process-driven, data-driven. Each has its niche; data-driven
is not flawless. Expert-driven is akin to a LLM as a compact black-box decision maker.
- Data-driven approaches as a matere process, facilitating AI applications.
- 90 days for prototype testing.<p>Steps for Enterprise AI Development (Skolkovo, SberService):
1) Locate areas with sufficiently mature processes
2) Define metrics and establish the “Data Story”
3) Develop prototypes as modules
4) Scale and monitor<p>TRL and MRL metrics (Technology and Manufacturing Process Readiness Levels) was suggested to find
opportunities for innovation.