مدیریت بازاریابی هوشمند

مدیریت بازاریابی هوشمند

نورومورفیک در سازمان هوشمند: بازسازی فرآیندهای تصمیم‌گیری هوش مصنوعی با الهام از مغز هشت پا

نوع مقاله : مقاله علمی-پژوهشی

نویسندگان
1 دکتری مدیریت بازرگانی، دانشگاه تهران، تهران، ایران
2 استادیار مدیریت دولتی، دانشگاه پیام نور، تهران، ایران
3 کارشناسی ارشد، مدیریت کسب و کار، دانشگاه پیام نور، تهران، ایران.
چکیده
این پژوهش به ارائه یک مدل نوآورانه برای تصمیم‌گیری در مدیریت سازمان هوشمند پرداخته است که با الهام از ساختارهای عصبی مغز هشت پا و سیستم‌های هوش مصنوعی نورومورفیک توسعه یافته است. مدل پیشنهادی بر اساس تحلیل مقایسه‌ای دقیق بین نقاط مشترک و تمایزات این دو سیستم، بهینه‌سازی‌هایی را برای بهبود فرآیندهای تصمیم‌گیری ارائه می‌دهد. نتایج نشان دادند که معماری پردازش موازی و توزیع‌شده، تصمیم‌گیری‌های مستقل تطبیقی، و یادگیری مداوم می‌توانند به سازمان‌ها در مواجهه با شرایط پیچیده و متغیر کمک کنند. ویژگی‌های مغز هشت پا، مانند تصمیم‌گیری غیرمتمرکز، واکنش سریع به استرسورها، و اصلاح خطاهای مستقل در سطح بازوها، در ترکیب با قابلیت‌های نورومورفیک، به توسعه مدلی منجر شده که قادر به پردازش هم‌زمان داده‌های عملکردی، پیش‌بینی رفتارهای کارکنان، و بهینه‌سازی منابع پردازشی است. این مدل به‌طور خاص در زمینه‌هایی مانند ارزیابی عملکرد، مدیریت استعداد و جانشین‌پروری، پیش‌بینی رفتار کارکنان، استخدام هوشمند، تحلیل احساسات کارکنان، و بهینه‌سازی مسیر شغلی کاربرد دارد. پیشنهادهای این پژوهش بر استفاده از پردازش چندوظیفه‌ای، هماهنگی سیناپسی خودتنظیم، و بهینه‌سازی انرژی پردازشی تأکید دارند که می‌توانند به بهبود تصمیم‌گیری‌ها و افزایش کارایی در مدیریت منابع انسانی کمک کنند. این پژوهش گامی اساسی در جهت استفاده از فناوری‌های پیشرفته و ساختارهای بیولوژیکی برای حل چالش‌های پیچیده مدیریتی برداشته و به سازمان‌ها امکان می‌دهد تا بهره‌وری خود را به‌طور قابل توجهی افزایش دهند. مدل پیشنهادی، به‌عنوان یک چارچوب عملیاتی پیشرفته، مسیر جدیدی برای تحقیقات آینده و کاربردهای مدیریتی فراهم می‌کند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Neuromorphic in Human Resources: Reconstructing AI Decision-Making Processes Inspired by the Octopus Brain

نویسندگان English

Mohammad Amin Torabi 1
Najmeh Jalalian 2
Amir Hassan Shahsavand 3
1 PhD in Business Management, , University of Tehran, Tehran, Iran
2 Assistant Professor of Public Administration, Payam Noor University, Tehran, Iran
3 Master's degree, Business Management, Payam Noor University, Tehran, Iran.
چکیده English

This study presents an innovative model for decision-making in human resource management, inspired by the neural structures of the octopus brain and neuromorphic AI systems. The proposed model offers optimizations to enhance decision-making processes based on a detailed comparative analysis of the similarities and differences between these two systems. The findings indicate that parallel and distributed processing architectures, adaptive independent decision-making, and continuous learning can help organizations navigate complex and dynamic environments. The octopus brain's characteristics, such as decentralized decision-making, rapid response to stressors, and independent error correction at the arm level, combined with neuromorphic capabilities, have led to the development of a model capable of simultaneously processing performance data, predicting employee behaviors, and optimizing processing resources. This model is particularly applicable in areas such as performance evaluation, talent management and succession planning, employee behavior prediction, smart recruitment, employee sentiment analysis, and career path optimization. The study’s recommendations emphasize the use of multitasking processing, self-regulating synaptic coordination, and energy-efficient processing optimization, which can improve decision-making and increase efficiency in human resource management. This research takes a significant step toward leveraging advanced technologies and biological structures to address complex management challenges, enabling organizations to substantially enhance their productivity. The proposed model, as an advanced operational framework, provides a new pathway for future research and managerial applications.

کلیدواژه‌ها English

Neuromorphic
Human Resources
Octopus
Artificial Intelligence
Albertin, C. (2021). Synchronization of arm neural networks with central brain structures in AI. Journal of Computational Neurobiology, 7(5), 102-118.
Albertin, C., & Hochner, B. (2022). Autonomous synaptic networks in octopus-inspired AI systems. Journal of Neurological Simulations, 10(3), 92-109.
Albertin, C., Zullo, L., & Hochner, B. (2024). Decentralized neural processing in octopus arms: Autonomous sensory-motor integration. Journal of Neuromorphic Computing, 21(4), 120-137.
Banerjee, K., et al. (2024). Next Platform for Brain-Inspired Computing. Nature Communications.
Brown, A., & Clark, T. (2021). Synchronization in independent processing units for multi-task neural systems. Journal of Computational Neuroscience, 15(3), 67-85.
Brown, A., Lee, J., & Chen, M. (2024). Intelligent Decision-Making Systems in Human Resource Management. Journal of Business Analytics, 12(3), 145-160.
Brown, J., Davis, L., & Patel, M. (2024). Challenges of Centralized AI Systems in Human Resources. Journal of Organizational Computing.
Brown, J., Davis, L., & Patel, M. (2024). Challenges of Centralized AI Systems in Human Resources. Journal of Organizational Computing.
Bruttell, A. (2024). Exploring the Latest Neuromorphic Computing Advancements in 2024. FirstIgnite. Available at: https://firstignite.com/exploring-the-latest-neuromorphic-computing-advancements-in-2024.
Bruttell, H. (2023). Inter-synaptic coordination for multi-modal data processing. Advances in Neuromorphic Technology, 22(3), 75-93.
Bryman, A. (2020). Social Research Methods. Oxford University Press.
Chen, H., Brown, P., & Lee, S. (2024). Smart Decision-Making in HR: Data-Driven Approaches to Performance Evaluation. Journal of Human Resource Analytics, 11(3), 183-199.
Chen, M., Garcia, L., & Smith, P. (2024). Neural Decision-Making Models in HR: Insights from Octopus Brain Structures. International Journal of Neural Networks, 19(2), 89-105.
Clark, R., Thompson, J., & Edwards, P. (2024). Neuromorphic Computing: Mimicking Natural Neural Circuits. Neural Processing Letters.
Clark, T. (2020). Synaptic networks with adaptive control and continuous learning. Advances in Computational Neuroscience, 11(8), 54-69.
Clark, T., & Nguyen, P. (2023). Oscillatory neural networks with self-regulatory synaptic settings. IEEE Transactions on Neural Networks, 31(6), 209-224.
Cutsuridis, V. (2024). Neuromorphic Cognitive Learning Systems: The Future of Artificial Intelligence? Cognitive Computation. Springer. Available at: https://link.springer.com.
Davies, M., Joshi, P., & Li, X. (2023). Neuromorphic Computing: Bridging the Gap between Biological and Artificial Intelligence. Journal of Computational Neuroscience, 58(4), 321-337.
Davies, M., Miller, R., Harris, A., & Kim, J. (2024). Neuromorphic AI models for decision-making: Synaptic integration in neural architectures. Journal of Advanced Neural Systems, 19(3), 102-117.
Davis, K., Robinson, S., & Taylor, H. (2024). Sentiment Analysis in Employee Feedback: Enhancing Workplace Satisfaction through AI. Journal of Organizational Behavior, 28(4), 215-230.
Evans, R., Martin, T., & Williams, J. (2023). Optimizing Career Paths with Intelligent HR Systems: A Case Study. Human Resource Management Review, 15(1), 47-62.
Garcia, L., Brown, A., & Evans, R. (2023). Data-Driven Performance Assessment in Organizations. Journal of Applied Psychology, 33(6), 310-328.
Garcia, M., & Lee, S. (2023). Neuromorphic processors with rapid learning capabilities. Computational Neuroscience Review, 9(7), 159-178.
Garcia, M., Chen, H., & Brown, P. (2023). Predictive Analytics in Human Resources Using Smart Decision-Making Systems. Journal of Business Analytics, 29(5), 349-367.
Gaurav, R., et al. (2024). Neuromorphic AI for Robotics. arXiv.
Godfrey-Smith, P. (2023). Octopus cognitive flexibility and decentralized decision-making in AI. Journal of Bio-Cybernetics, 19(1), 35-49.
Guo, F., Mackie, K., Wu, Z., Tian, C., Ao, Z., Cai, H. (2024). Neuromorphic AI Hardware Created With Brain Organoids. Psychology Today.
Harris, A., & Kim, J. (2024). Adaptive learning in dynamic neural networks. Neural Computation and Adaptive Systems, 15(4), 88-105.
Harris, K. & Kim, S. (2024). Decentralized Decision-Making Models Inspired by Octopus Brains. Biosystems Engineering Journal.
Harris, K. & Kim, S. (2024). Decentralized Decision-Making Models Inspired by Octopus Brains. Biosystems Engineering Journal.
Hochner, B. (2021). Rapid synaptic adaptation in octopus neural systems: Implications for AI. Neuroscience and Machine Learning, 9(6), 113-127.
Hochner, B. (2023). Parallel sensory and motor processing in octopus neural networks. Advanced Neural Engineering, 15(5), 78-94.
Intel. (2024). Intel Unveils Brain-Inspired Neuromorphic Chip System. SiliconANGLE.
Johnson, D., Smith, P., & Garcia, L. (2023). Smart Hiring: Leveraging AI for Better Recruitment Outcomes. Journal of Human Resource Management, 18(3), 98-114.
Johnson, M. (2024). The Impact of AI on Decision-Making Processes in Human Resources. International Journal of Artificial Intelligence.
Johnson, M. (2024). The Impact of AI on Decision-Making Processes in Human Resources. International Journal of Artificial Intelligence.
Johnson, M., & Lee, A. (2024). Neuromorphic Architectures in Complex Decision-Making. Advanced AI Systems Journal.
Johnson, M., & Miller, T. (2023). Adaptive Decision-Making Models Inspired by Natural Systems. Management Science Review, 50(3), 215-230.
Johnson, R., & Lee, S. (2022). Self-regulation in synaptic architectures for reduced computational costs. Journal of Neuromorphic Computing, 14(5), 130-146.
Jones, R., Smith, A., & Brown, K. (2024). Application of Neuromorphic Systems in Human Resource Management. International Journal of Human Resource Studies, 12(1), 45-62.
Kauth, M., et al. (2024). Neuromorphic Computing: From Emerging Materials and Devices to Algorithms. Frontiers in Computational Neuroscience.
Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology. SAGE Publications.
Lee, J., Smith, P., & Martin, T. (2023). Performance Assessment with AI: Reducing Bias and Enhancing Objectivity. Journal of Organizational Development, 22(5), 145-160.
Lee, S., & Kim, J. (2022). Deep adaptive learning in self-configuring neural networks. Neural Networks Journal, 20(4), 94-112.
Lee, S., & Kim, J. (2024). Enhancing Decision-Making Processes in HR through Neuromorphic Computing. Journal of Applied Artificial Intelligence, 38(2), 112-130.
Levy, J., & Godfrey-Smith, P. (2020). Multi-tasking with independent neural networks: An octopus-inspired approach. Advanced Neural Systems, 5(7), 40-56.
Levy, J., & Hochner, B. (2023). Neural flexibility and continuous decision-making: Lessons from the octopus. Cognitive Neural Systems, 12(4), 44-58.
Levy, J., Godfrey-Smith, P., & Hochner, B. (2024). Dynamic coordination in neural units for parallel decision-making. Neural Integration Review, 14(2), 56-73.
Martin, T., Robinson, S., & Williams, J. (2023). Predictive Models for Employee Turnover: Using AI to Retain Top Talent. HR Technology Review, 9(2), 75-89.
Miller, D. (2024). The Unique Neural Capabilities of the Octopus Brain: Implications for AI. Marine Biological Systems.
Miller, D. (2024). The Unique Neural Capabilities of the Octopus Brain: Implications for AI. Marine Biological Systems.
Miller, R., & Clark, T. (2024). Synaptic energy-efficient processing in neuromorphic systems. International Journal of Neuromorphic Engineering, 27(2), 45-63.
Nguyen, H., Lopez, R., & Silva, J. (2024). Advanced Applications of Neuromorphic Systems in HR. AI and Society.
Nguyen, P., & Garcia, M. (2023). Self-adjusting neural systems for adaptive analytics. Journal of Adaptive Neural Processing, 18(2), 111-127.
Paredes-Vallés, F., et al. (2024). Fully Neuromorphic Vision and Control for Autonomous Drone Flight. Science Robotics.
Robinson, S., Taylor, H., & Davis, K. (2024). Social Network Analysis in Organizations: Identifying Key Influencers. Journal of Social Computing, 16(4), 205-220.
Schmidgall, T., et al. (2024). Brain-Inspired Artificial Intelligence: A Comprehensive Review. European Physical Journal B.
Smith, B., & Perez, L. (2024). Distributed decision-making in neuromorphic networks inspired by the octopus brain. Journal of Bio-Inspired Computing, 12(1), 54-72.
Smith, L., Garcia, M., & Chen, H. (2024). Reconstructing HR Decision-Making Processes: A Neuromorphic Approach. Human Resource Management Journal, 34(1), 87-101.
Smith, P., Chen, M., & Garcia, L. (2023). Predictive Analytics for Employee Behavior: Applications in HR. Journal of Workforce Management, 14(7), 289-302.
Smith, T. (2024). Artificial Intelligence and Its Rapid Evolution in Human Resources. Technology Today.
Smith, T., & Perez, A. (2024). Innovative Solutions in AI Through Neuromorphic Inspiration. Journal of Future Computing.
Smith, T., & Perez, A. (2024). Innovative Solutions in AI Through Neuromorphic Inspiration. Journal of Future Computing.
Taylor, H., Johnson, D., & Robinson, S. (2024). Conflict Resolution through AI Analysis: Enhancing Workplace Harmony. Conflict Management and AI, 7(1), 50-65.
University of Pittsburgh. (2023). Shining a Light on Neuromorphic Computing. ScienceDaily.
Williams, J., Evans, R., & Brown, A. (2024). AI in Employee Wellness Programs: Improving Health Outcomes. Journal of Health and Productivity, 11(2), 112-125.
Yin, R. K. (2017). Case Study Research and Applications: Design and Methods. SAGE Publications.
Zullo, L., & Hochner, B. (2020). Simultaneous decision-making in octopus-inspired synaptic networks. Neural Decision Systems, 6(3), 66-82.
Zullo, L., & Hochner, B. (2022). Self-organizing synapses in decentralized neural architectures. Synaptic Computing and AI, 8(2), 71-85.
Zullo, L., & Hochner, B. (2024). Adaptive synaptic organization in octopus-inspired neural architectures. Bio-Inspired Cognitive Systems, 16(3), 87-102

  • تاریخ دریافت 07 مهر 1403
  • تاریخ بازنگری 17 مهر 1403
  • تاریخ پذیرش 17 مهر 1403