Digital entrepreneurship ecosystem : How digital technologies and collective intelligence are reshaping the entrepreneurial process
اکوسیستم کارآفرینی دیجیتال: چگونه فناوری های دیجیتال و هوش جمعی در حال شکل گیری مجدد روند کارآفرینی هستند-2020
Digital technologies have nowadays a significant impact on how new business ventures are imagined and created. The arising technology paradigm is leveraging the potential of collaboration and collective intelligence to design and launch more robust and sustainable entrepreneurial initiatives. However, although the topic of digital entrepreneurship is relevant and timely, there is a limited literature discussion on the real impact of digital technologies and collaboration on the entrepreneurial process. Further research is needed to describe the nature and characteristics of the entrepreneurial ecosystem enabled by the new socio-technical paradigm. Based on extant literature, this article proposes a definition of digital entrepreneurship ecosystem by highlighting the integrated digital-output and digital-environment perspectives. A collective intelligence approach is then adopted to define a descriptive framework and identify the distinguishing genes of a digital entrepreneurship ecosystem. Four dimensions associated to digital actors (who), digital activities (what), digital motivations (why), and digital organization (how) are defined and discussed. The framework was also applied to describe 9 real cases of companies and initiatives, which are analyzed as digital entrepreneurship ecosystems along the four key dimensions presented. The article ends with a discussion about the results and a research agenda for future studies.
Keywords: Collective intelligence | Digital entrepreneurship | Digital technologies | Entrepreneurial process | Ecosystem | Framework
Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach
طبقه بندی یافته های پاتولوژیک گلومرولی با استفاده از یادگیری عمیق و رویکرد هوش جمعی نفرولوژیست-هوش مصنوعی-2020
Background: Automated classification of glomerular pathological findings is potentially beneficial in establishing an efficient and objective diagnosis in renal pathology. While previous studies have verified the artificial intelligence (AI) models for the classification of global sclerosis and glomerular cell proliferation, there are several other glomerular pathological findings required for diagnosis, and the comprehensive models for the classification of these major findings have not yet been reported. Whether the cooperation between these AI models and clinicians improves diagnostic performance also remains unknown. Here, we developed AI models to classify glomerular images for major findings required for pathological diagnosis and investigated whether those models could improve the diagnostic performance of nephrologists. Methods: We used a dataset of 283 kidney biopsy cases comprising 15,888 glomerular images that were annotated by a total of 25 nephrologists. AI models to classify seven pathological findings: global sclerosis, segmental sclerosis, endocapillary proliferation, mesangial matrix accumulation, mesangial cell proliferation, crescent, and basement membrane structural changes, were constructed using deep learning by fine-tuning of InceptionV3 convolutional neural network. Subsequently, we compared the agreement to truth labels between majority decision among nephrologists with or without the AI model as a voter. Results: Our model for global sclerosis showed high performance (area under the curve: periodic acid-Schiff, 0.986; periodic acid methenamine silver, 0.983); the models for the other findings also showed performance close to those of nephrologists. By adding the AI model output to majority decision among nephrologists, out of the 14 constructed models, the results of the majority decision showed improvement in sensitivity for 10 models (four of them were statistically significant) and specificity for eight models (five significant). Conclusion: Our study showed a proof-of-concept for the classification of multiple glomerular findings in a comprehensive method of deep learning and suggested its potential effectiveness in improving diagnostic accuracy of clinicians.
Keywords: Renal pathology | Artificial intelligence | Deep learning | Collective intelligence
Computational Collective Intelligence with Big Data: Challenges and Opportunities
هوش جمعی محاسباتی با بزرگ داده ها: چالش ها و فرصت ها-2017
Collective intelligence has been an important research topic in many AI communities. With The big data phenomenon, we have been facing on many research problems on how to integrate the big data with collective intelligence. This special issue has selected 9 high quality papers covering various research issues.
Keywords:Computational collective intelligence|Computer-supported collaboration|Big data