Optimizing Robotic Interaction and Decision Making Using Advanced NLP and AI Technologies Alaa Salim Abdalrazzaq, Noor Kadhim Meftin, Doaa Mohammed Hussein Al Fawadi, Hanaa Hameed Merzah, Omar Baban, et al. Conference of Open Innovation Association Fruct, 2024 Background: Incorporating Natural Language Processing (NLP) and Artificial Intelligence (AI) algorithms into robotic assistants can further improve their capacity to independently execute more sophisticated tasks across various sectors, such as healthcare, manufacturing, or retail. However, there have been numerous roadblocks to making them practical from an end-user perspective and addressing ethical issues.Objective: The article delves into the role of NLP and AI assimilation in extending the functional capabilities of robotic assistants, specifically improving language comprehension, decision-making accuracy, and adaptive learning. Additionally, explores the ethical consequences of increased robot independence.Methods: The methodology uses two publicly available datasets that facilitated the training and testing of these language models: 1) Multi-Domain Dialogue Dataset (MDDD), which has half a million labeled conversations with tasks as complex as open-domain multi-turn dialogues, and 2) OpenAI Language Interaction Data set (OLID), an interaction data collected from over one million human-robot interactions. The robots’ performance was tested in simulated and real-world settings, including healthcare, manufacturing, and retail.Results: Healthcare exhibited an increase in language comprehension accuracy of 16% (84% post-integration), while autonomous decision-making and manufacturing improved by around 25% (83% post-integration), as well as customer services response rate and retail reaching around upto15% (80% post-integration) improvements. Adaptive learning effectiveness improved by 27% in manufacturing, indicating the robots obtained better performance as they worked.Conclusion: Combining NLP with AI allows robotic assistants to do more complicated, contextually aware activities, expanding their usefulness across many disciplines. While acknowledging the many advances, continual study and investigation are essential to navigate robotic helper technology and its many uses. Though these advancements show potential, upcoming studies need to concentrate on reducing ethical risks such as bias and privacy issues to guarantee the responsible implementation of AI-powered robots.
The Importance of Big Data in the Advancement of Robotic Capabilities Firas Mahmood Mustafa, Sabah M. Kallow, Noor Ali Thamer, Hanaa Hameed Merzah, Moroj Mohammed, et al. Conference of Open Innovation Association Fruct, 2024 Background: In recent years, Big Data has invaded many technical sectors, enabling extraordinary advances, notably in robotics. Large amounts of data from many areas may improve robotic system functionality and adaptability.Objective: This article examines Big Data’s effects on robotic learning, decision-making, and adaptability to help explain how it advances robotic capabilities and provide a thorough review of integrated data-driven robotic systems.Methods: A thorough evaluation and synthesis of the literature was undertaken to examine the integration and implementation of Big Data in robotics. Case studies, and theoretical frameworks on Big Data analytics and robotics were sought from databases and academic publications.Results: Big Data analytics in robots improves learning in predictive analytics and machine learning algorithms, improving decision-making. Industrial robots and autonomous vehicles boost operational efficiency and flexibility by improving data processing, anomaly detection, and real-time decision-making.Conclusion: Technology such as Big Data and robotics makes robots more adaptive and decision-making. Integration improves robotic capabilities and allows for innovative applications across domains, supporting data-driven robotic ability and improvement decisions. The findings propose using Big Data analytics to improve robotics and study future applications, contributing to the conversation on technological convergences and societal implications.
Variable modulation index PWM inverter using AGC fed induction motor based WSN International Journal of Innovative Technology and Exploring Engineering, 2019
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