" Go " is a game of territorial disputes, and the rules are simple. But there is no distinction between white stones and black stones, and the location information is not clear. It is difficult to create an evaluation function. Even around 2015, AI would take another 10 years to catch up with humans. However, with the advent of deep learning, instead of catching up, humans have far outstripped us. This series of processes cannot be simply explained by the increase in computer processing power and the development of AI but should be academically and historically recorded. Observing and considering how AI influences human beings gives us great suggestions for thinking about the relationship between humans and AI.

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The purpose of this research is to detect the location of foreign material in foods on factory line by the technique of object detection using deep learning. In a real factory line, both detection speed and detection performance are important. The proposed system is based on SSD (Single Shot Multibox Detector) which is known to be a relatively high-speed and high-performance. The proposed model improves the location detection performance for small foreign materials by increasing the size of the input image and modifying the SSD network structure. In order to evaluate the proposed model, the authors compared it with existing models. The experimental results show that the proposed model achieved 0.972 with F-value and its processing time was less than 0.35 seconds.

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We refer to Boyd’s original works and confirm that OODA “loop” is the model of management activities under competitive circumstances. This clarifies that information used in OODA “loop” is mainly intended/perceived (i.e. intangible) one, and has strong relationships to SECI model of Nonaka & Takeuchi (1996). Based on the fact that developing purposeful domain-specific AI systems by deep learning, formalized information, i.e. data can only be applied but not intended/perceived one, a practical limitation of such AI systems is stated and then some properties of human resources necessary for so-called AI society.

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As digital services such as online shopping or mobile apps driven by digital artifacts become a crucial part of our lives, people often feel annoyed, discomfort, and irritable by these new designs or services. On the one hand, cordiality and paternalistic digital nudges are helpful and acceptable, while malicious sites called dark patterns which pretending to nudge, mislead and intimidate website users, on the other hand. This study examines the evaluation of both the digital nudge and dark pattern sites focusing on their acceptability, desirability, and ethicality. It is revealed that the users think digital nudging sites are desirable and ethical, basically. However surprisingly, though users evaluate dark pattern sites, confirmshaming, visual interference, trick question, and roach motel, are undesirable and unethical, they accept the proposals against their will. This result suggests a future warning to digital service designers and providers about the users’ evaluation and acceptance of reluctant websites. In addition, the significance of the ethical requirements for nudges should be discussed.

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The purpose of this paper is to clarify how consumers feel about the collection and commercial use of personal data (specifically, personal behavioral history data such as Web search and browsing, purchase history, and smartphone location data) by companies through a questionnaire survey. For this aim, we focus on the following two points. (1) consumers' precautionary attitudes toward personal information, and (2) consumers' attitudes toward control of personal information. The results revealed the following: consumers who use both their real names and anonymity when using social networking services are more concerned about their own and others' privacy; the desired control over one's own information differs in emphasis depending on age; and those with low privacy awareness are more likely to emphasize " limiting the length of time information " is retained.

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This paper focuses on CONWIP, one of the production methods used when a shortage is not acceptable in case of fluctuating demands. CONWIP is used when priority is given to stabilizing the inventory quantity rather than the utilization rate of the production system. However, when using this method, inventory costs tend to increase due to the automatic progress to downstream processes. In response to this issue, this paper describes the effectiveness of “the prototypical lean production system” which is different with Toyota Production System, as well as the concept of choice between CONWIP and " the prototypical lean production system ".

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