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Experimentation and Measurement (E&M) capabilities allow organizations to accurately assess the impact of new propositions and to experiment with many variants of existing products. However, until now, the question of measuring the measurer, or valuing the contribution of an E&M capability to organizational success has not been addressed. We tackle this problem by analyzing how, by decreasing estimation uncertainty, E&M platforms allow for better prioritization. We quantify this benefit in terms of expected relative improvement in the performance of all new propositions and provide guidance for how much an E&M capability is worth and when organizations should invest in one. [ABSTRACT FROM AUTHOR]

How to represent image information more effectively is the key to the task of image caption. In the existing research, a large number of image caption methods are proposed. Most of them use the global information of the image, and the information in the image that is not related to the caption generation also participates in the calculation, caused a certain amount of waste of resources. In order to solve this problem, a method of generating image caption based on object detection is proposed in this paper. Firstly, the object detection algorithm is used to extract image feature, only the features of meaningful regions in the image are used, and then image caption is generated by combining the spatial attention mechanism with the caption generation network. Experiments show that the image feature of the object region and the salient region are sufficient to represent the information of the entire image in the image caption task. For better convergence of the model, this paper also uses a new strategy for model training. The experimental results show that the proposed model in this paper work well on the test dataset of image caption, and it has created a precedent for new technology to a large extent. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during the generation of data, the development of algorithms, and the evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and researches. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors’ collaborative research. [ABSTRACT FROM AUTHOR]