In recent years, Artificial intelligence products and services have been
offered potential users as pilots. The acceptance intention towards artificial
intelligence is greatly influenced by the experience with current AI products
and services, expectations for AI, and past experiences with ICT technology.
This study aims to explore the factors that impact AI acceptance intention and
understand the process of its formation. The analysis results of this study
reveal that AI experience and past ICT experience affect AI acceptance
intention in two ways. Through the direct path, higher AI experience and ICT
experience are associated with a greater intention to accept AI. Additionally,
there is an indirect path where AI experience and ICT experience contribute to
increased expectations for AI, and these expectations, in turn, elevate
acceptance intention. Based on the findings, several recommendations are
suggested for companies and public organizations planning to implement
artificial intelligence in the future. It is crucial to manage the user
experience of ICT services and pilot AI products and services to deliver
positive experiences. It is essential to provide potential AI users with
specific information about the features and benefits of AI products and
services. This will enable them to develop realistic expectations regarding AI
technology.

本研究探讨影响人工智能接受意向的因素及其形成过程，结果发现人工智能和 ICT 经验对 AI 接受意向有直接和间接影响，期望是提高了接受意向的关键。因此，有必要管理用户体验并提供详细的信息以帮助潜在用户了解 AI 技术优势并制定现实的期望。

AI 技术被接受的驱动因素：期望和经验的作用

What drives the acceptance of AI technology?: the role of expectations  and experiences

We give a general framework for inference in spanning tree models. We propose
unified algorithms for the important cases of first-order expectations and
second-order expectations in edge-factored, non-projective spanning-tree
models. Our algorithms exploit a fundamental connection between gradients and
expectations, which allows us to derive efficient algorithms. These algorithms
are easy to implement with or without automatic differentiation software. We
motivate the development of our framework with several \emph{cautionary tales}
of previous research, which has developed numerous inefficient algorithms for
computing expectations and their gradients. We demonstrate how our framework
efficiently computes several quantities with known algorithms, including the
expected attachment score, entropy, and generalized expectation criteria. As a
bonus, we give algorithms for quantities that are missing in the literature,
including the KL divergence. In all cases, our approach matches the efficiency
of existing algorithms and, in several cases, reduces the runtime complexity by
a factor of the sentence length. We validate the implementation of our
framework through runtime experiments. We find our algorithms are up to 15 and
9 times faster than previous algorithms for computing the Shannon entropy and
the gradient of the generalized expectation objective, respectively.

提出了一种用于跨度树模型推断的普适框架，该框架的算法包括第一和第二领域期望。这些算法易于使用，并且利用梯度与期望之间的基本关系，可以有效地计算多种量值，包括期望附加分数，熵和广义期望准则等。