• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Cross-Cultural Studies
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• v.31
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• pp.371-394
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• 2013

This study aims to know word forms and usages according to personal relationships of 'Kureru Kudasaru' by analyzing dialogues of female speakers. Novels of the Meiji period when there were attempts of a language revolution were mainly used for this study as well as novels of the Edo Period and the Taisho Period. Firstly, the number of examples according to gender differences in the novels was as follows. In case of 'Kureru', female speakers showed a high usage rate in the novels of the Edo period. 'Kureru' was mostly connected with female languages such as 'Naharu', 'Namasu', 'Nansu'. These expressions were not used in the novels of the Meiji Period and the Taisho Period. Although 'Okureru' and 'Okurenasaru' were used in the novels of the Meiji Period, the number of examples of 'Kureru' by female speakers was decreased in the novels of the Meiji Period and the Taisho Period. 'Kudasaru' was predominantly used by female speakers. Especially, female speakers used clearly to show vertical relationships in the novels of the Edo Period and"Doseishoseikatagi"of Meiji 10s. After"Ukigumo", the usage rate of female speakers was decreased but the usage rate of male speakers was increased. Gender differences became gradually smaller. Female speakers in the novels were increased from geisha and relatives such as wife, sister, mother and children to young women, teacher and student. Aspects of benefactive verbs' usages could be summarized as follows. Female speakers at licensed quarters used clearer and more typical expressions according to vertical relationships and gender differences in the novels of The Edo Period than the novels of The Meiji Period and the Taisho Period. In the novels of the Meiji Period, female speakers in a sophisticated social group used benefactive verbs to show strong respect and concern for the other person. In the novels of the Taisho Period, female speakers used benefactive verbs to show respect and concern for the other person according to their areas of outside activities. In the novels of the Meiji Period, female speakers used 'Okureru' when the other person was younger than them and was socially and psychologically close to them. Also, 'O~Nasaru' which was one of respect expressions was used by female speakers. Female speakers used it to older people in the Edo period but they also used it to younger people in the Meiji Period. Examples were not shown in the novels of the Taisho Period. Usages of 'Kureru' 'Kudasaru' according to vertical relationships were as follows. If 'a giver' was an older person, 'Kureru' with respect expressions 'Nasaru' 'Nansu' 'Namasu' was used more than 'Kudasaru' in the novels of the Edo Period. However, many examples of 'Kudasaru' were shown on the novels of the Meiji Period and the Taisho period. In the novels of the Meiji Period, 'Okureru' and 'Okurenasaru' which were expressions included in 'Kureru' were shown. Female speakers used them to older people who were socially and psychologically close to them like family. There were not many examples of 'a giver' and 'a receiver' around the same age. However, 'Kureru' and 'Okureru' were used in a younger group and 'Kudasaru' was used in an older group in the novels of the Meiji Period. If 'a giver' was an younger person, 'Kureru' was mainly used in the novels of the Edo period and "Doseishoseikatagi"in Meiji 10s. However, 'Kudasaru' was used many times in the novels of the latter Meiji Period and the Taisho Period.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Korean Philosophical Society
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• v.145
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• pp.91-117
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• 2018

This paper aims to clarify the similarities and differences between the Zhouyi and artificial intelligence. The divination of the Zhouyi is rooted in the oldest system of human knowledge, while artificial intelligence stands at the cutting edge of modern scientific revolution. At first sight, there does not appear to be any association that links the one to the another. However, they share the same ground as seen from a semiotic standpoint because both of them depend on the semiotic system as a means of obtaining knowledge. At least four aspects can be pointed out in terms of similarities. First, artificial intelligence and the Zhouyi use artificial language that consists of semiotic signs. Secondly, the principle that enables divination and artificial intelligence lies in imitation and representation. Thirdly, artificial intelligence and the Zhouyi carry out inferences based on mathematical algorithms that adopt the binary system. Fourth, artificial intelligence and the Zhouyi use analogy as a means of obtaining knowledge. However, those similarities do not guarantee that the Zhouyi could arrive at the scientific certainty. Nevertheless, it can give us important insight into the essence of our civilization. The Zhouyi uses intellect in order to get new information about the unknown world. However, it is hard to know what kind of intellect is involved in the process of divination. Likewise, we do not know the fundamental character of artificial intelligence. The intellect hidden in the unknown subject is a mystic and fearful existence to us. Just as the divination of the Zhouyi inspires the sense of reverence toward the supernatural subject, we could not but have fear in front of the invisible subject hidden in artificial intelligence. In the past, traditional philosophy acknowledged the existence of intellect only in conscious beings. Nonetheless, it becomes evident that human civilization ushers into a new epoch. As Ray Kurzweil mentioned, the moment of singularity comes when artificial intelligence surpasses human intelligence. In my viewpoint, the term of singularity can be used for denoting the critical point in which the human species enters into the new phase of civilization. To borrow the term of Shao Yong(邵雍) in the Northern Song Dynasty, the past civilization belongs to the Earlier Heaven(先天), the future civilization belongs to the Later Heaven(後天). Once our civilization passes over the critical point, it is impossible to go back into the past. The opening of the Later Heaven foretold by the religious thinkers in the late period of Joseon Dynasty was a prophecy in its own age, but it is becoming a reality in the present.

• Sijohaknonchong
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• v.42
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• pp.213-238
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• 2015

Jeongun(丁芸) Lee, Young-do (李永道), who is deemed a representative female poet of Korea, began her literary career in May, 1946 when she published in a publication called "Bamboo Sprout, (죽순)". Her Korean identity, which was formed through her Confucius upbringing as well as traditional value system of her family, had a strong presence in her work, and she remained a quintessential figure in Korea's female sijo poet circle for 30 years until her passing in 1976. Despite the highly acclaimed talent and her noble aspirations, it is undeniable that her works did not receive fair assessment due to her private life. Against this backdrop, it is necessary to deeply inquire the literary values and beauty of Young-do Lee's sijo. As mentioned, Young-do Lee is a solidly established figure in Korea's modern poetry. The following illustrates the spirit and the world of her poetry. First, Young-do Lee lived through turbulent times and it was her country that served as the source of her sijo work. Assessing the multitude of dramatic historical events such as Japanese colonization, 8.15 Liberation of Korea, division of the nation, 6.25 Korean war, 4.19 Revolution, 5.16 military coup, it is natural that patriotism was strongly present in her work who was one of the intellectuals at the time. Second, Young-do Lee is a poet who had experienced more pain than others in terms of the turbulence of the time. Her Korean identity, which was formed through her Confucius upbringing as well as traditional value system of her family, had a strong presence in her work. Third, Jeongun Lee, Young-do is a poet of longing. The abundance and richness of her emotions were fortified through the relationship with another poet, Chihwan Yu. Fourth, Young-do Lee is a poet opened up new horizons for the modennization. The transparency of image reflected in her work and the elaborate nature of her language are outstanding. In summary, Young-do Lee was a true artist, who has a strong presence in Korea's modern poetry society, and who was a poet of patriotism, poet who suffered the turbulence of the times, and a poet of longing.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.