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  1. MIT Technology Review · AI

    Connecting AI agents to enterprise knowledge

    中文摘要

    尽管人工智能系统不断积累和分析大量数据，企业人工智能代理通常存在一个奇怪的缺陷：缺乏知识。比起数据，知识是指对数据在单个组织背景中意义的理解。人工智能代理需要这种理解，才能对情况做出推理、做出决策，最终……

    英文原文

    For all the data that AI systems continually amass and analyze, enterprise AI agents often suffer from a curious shortcoming: a lack of knowledge. More than data, knowledge is the understanding of what the data means in the context of individual organizations. AI agents need this understanding to reason about situations, make decisions, and ultimately…

  2. MIT Technology Review · AI

    Bringing predictive analytics to the agentic AI era

    中文摘要

    2026年，企业人工智能的问题不再是预测模型是否能优于统计预测——这个争论已经解决了。现在最大的问题是，如何让预测系统根据自己的结论自主行动，而不会偏离业务意图。前沿已经从预测转向了自主决策，而……之间的差距……

    英文原文

    In 2026, the question for enterprise AI is no longer whether predictive models can outperform statistical forecasts—that argument is settled. The big question now is how to enable predictive systems to act on their own conclusions without drifting from business intent. The frontier has moved from prediction to autonomous decision making, and the gap between…

  1. MIT Technology Review · AI

    Redefining enterprise intelligence with autonomous AI

    中文摘要

    企业人工智能已不再是未来的愿景，而是正处于全面运行状态。模型能力的提升速度远超大多数组织的吸收能力，而性能成本却持续下降。全球人工智能投资预计到2026年将达到2.5万亿美元，较前一年增长44%。对许多企业而言，这项投资已经……

    英文原文

    Enterprise AI is no longer a future ambition. It is in full operational flight. Model capabilities are advancing faster than most organizations can absorb, while the cost of performance continues to fall. Globally, AI investment is set to reach $2.5 trillion in 2026, up 44% from the previous year. For many enterprises, this investment has…

  1. MIT Technology Review · AI

    Making AI an asset, not an expense

    中文摘要

    当客户谈论AI成本时，对话通常从令牌价格开始，最终会谈到对云中最新、最强大模型的访问权限。他们真的始终需要这种级别的能力吗？不一定。但通常对话都会走向这里。随着AI从实验阶段转向生产阶段，模型选择仅仅是……

    英文原文

    When customers talk about AI costs, the conversation usually starts with token prices and ends with access to the latest, most capable model in the cloud. Do they always need that level of capability? Not necessarily. But that is often where the conversation goes. As AI moves from experimentation to production, model choice is only…

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