Cognitive computing represents a groundbreaking paradigm in artificial intelligence (AI) systems, aiming to emulate and replicate the intricate processes of human intelligence. This article explores the fundamental principles, methodologies, and applications of cognitive computing, shedding light on how it transforms traditional AI approaches. By drawing inspiration from human cognition, cognitive computing systems leverage advanced algorithms, neural networks, and machine learning techniques to emulate complex cognitive functions such as perception, reasoning, and problem-solving.
{"title":"Cognitive Computing Emulating Human Intelligence in AI Systems","authors":"Amandeep Singla","doi":"10.60087/jaigs.v1i1.38","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.38","url":null,"abstract":"Cognitive computing represents a groundbreaking paradigm in artificial intelligence (AI) systems, aiming to emulate and replicate the intricate processes of human intelligence. This article explores the fundamental principles, methodologies, and applications of cognitive computing, shedding light on how it transforms traditional AI approaches. By drawing inspiration from human cognition, cognitive computing systems leverage advanced algorithms, neural networks, and machine learning techniques to emulate complex cognitive functions such as perception, reasoning, and problem-solving.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"130 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cognitive computing represents a groundbreaking paradigm in artificial intelligence (AI) systems, aiming to emulate and replicate the intricate processes of human intelligence. This article explores the fundamental principles, methodologies, and applications of cognitive computing, shedding light on how it transforms traditional AI approaches. By drawing inspiration from human cognition, cognitive computing systems leverage advanced algorithms, neural networks, and machine learning techniques to emulate complex cognitive functions such as perception, reasoning, and problem-solving.
{"title":"Cognitive Computing Emulating Human Intelligence in AI Systems","authors":"Amandeep Singla","doi":"10.60087/jaigs.v1i1.38","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.38","url":null,"abstract":"Cognitive computing represents a groundbreaking paradigm in artificial intelligence (AI) systems, aiming to emulate and replicate the intricate processes of human intelligence. This article explores the fundamental principles, methodologies, and applications of cognitive computing, shedding light on how it transforms traditional AI approaches. By drawing inspiration from human cognition, cognitive computing systems leverage advanced algorithms, neural networks, and machine learning techniques to emulate complex cognitive functions such as perception, reasoning, and problem-solving.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"43 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the profound impact of transfer learning on the performance of artificial intelligence (AI) models when applied across diverse domains. Transfer learning, a machine learning technique that leverages knowledge gained from one task to improve performance on a related task, has demonstrated remarkable success in various applications. The article explores the underlying principles of transfer learning, its mechanisms, and the ways in which it enhances AI performance. The findings highlight the potential of transfer learning to facilitate knowledge transfer between domains, reduce training data requirements, and accelerate model convergence, ultimately contributing to the broader adaptability and efficiency of AI systems
{"title":"The Impact of Transfer Learning on AI Performance Across Domains","authors":"Md.mafiqul Islam","doi":"10.60087/jaigs.v1i1.37","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.37","url":null,"abstract":"This study investigates the profound impact of transfer learning on the performance of artificial intelligence (AI) models when applied across diverse domains. Transfer learning, a machine learning technique that leverages knowledge gained from one task to improve performance on a related task, has demonstrated remarkable success in various applications. The article explores the underlying principles of transfer learning, its mechanisms, and the ways in which it enhances AI performance. The findings highlight the potential of transfer learning to facilitate knowledge transfer between domains, reduce training data requirements, and accelerate model convergence, ultimately contributing to the broader adaptability and efficiency of AI systems","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A significant amount of evidence has emerged to demonstrate that the so called gnostic gospels of the Roman period were written utilizing a cypher. In other words, they contain literal historical messages that can not be understood without applying a code to the text. The implications of this discovery are vast and significant. In order to demonstrate this cypher I will be using the texts from the Nag Hammed Library. A large collection of gnostic gospels was discovered in 1945 near the Egyptian town of Nag Hammadi, and I will be using these texts in this analysis. The Gnostic gospels contain dozens of parables and stories, most of which make little to no sense. This is because the documents were written in a way that obscures the messages they contain. The nomadic tribal nations surrounding the Roman Empire had devised a way of communicating so that the leadership of the empires they were targeting would not be able to decipher their plans. The Gnostic gospels relinquish their hidden messages with the application of a simple code. Throughout the Gnostic gospels the authors use a variety of opposite paired terms like Heaven and Earth, Above and Below, Light and Dark, or Immortal and Mortal. The key to deciphering the Gnostic code is to recognize that all of the opposites referred to the same nomadic tribal nation/Roman Empire divide. Any reference to Heaven, God, Father, Light, referred to the nomadic tribal nations while references to the Earth, World, Abyss, Chaos, or Mortal realm referred to the Roman Empire or agriculturalists in general. Along with these terms, concepts with natural dichotomies such as Summer and Winter, Right and Left, Above and Below, and Mother and Father all represented the nomadic/agricultural divide. Below is the bulk of the Gnostic code
{"title":"The Gnostic Code","authors":"David Klinkenberg","doi":"10.60087/jaigs.v1i1.32","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.32","url":null,"abstract":"A significant amount of evidence has emerged to demonstrate that the so called gnostic gospels of the Roman period were written utilizing a cypher. In other words, they contain literal historical messages that can not be understood without applying a code to the text. The implications of this discovery are vast and significant. In order to demonstrate this cypher I will be using the texts from the Nag Hammed Library. A large collection of gnostic gospels was discovered in 1945 near the Egyptian town of Nag Hammadi, and I will be using these texts in this analysis. The Gnostic gospels contain dozens of parables and stories, most of which make little to no sense. This is because the documents were written in a way that obscures the messages they contain. The nomadic tribal nations surrounding the Roman Empire had devised a way of communicating so that the leadership of the empires they were targeting would not be able to decipher their plans. The Gnostic gospels relinquish their hidden messages with the application of a simple code. Throughout the Gnostic gospels the authors use a variety of opposite paired terms like Heaven and Earth, Above and Below, Light and Dark, or Immortal and Mortal. The key to deciphering the Gnostic code is to recognize that all of the opposites referred to the same nomadic tribal nation/Roman Empire divide. Any reference to Heaven, God, Father, Light, referred to the nomadic tribal nations while references to the Earth, World, Abyss, Chaos, or Mortal realm referred to the Roman Empire or agriculturalists in general. Along with these terms, concepts with natural dichotomies such as Summer and Winter, Right and Left, Above and Below, and Mother and Father all represented the nomadic/agricultural divide. Below is the bulk of the Gnostic code","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"47 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A significant amount of evidence has emerged to demonstrate that the so called gnostic gospels of the Roman period were written utilizing a cypher. In other words, they contain literal historical messages that can not be understood without applying a code to the text. The implications of this discovery are vast and significant. In order to demonstrate this cypher I will be using the texts from the Nag Hammed Library. A large collection of gnostic gospels was discovered in 1945 near the Egyptian town of Nag Hammadi, and I will be using these texts in this analysis. The Gnostic gospels contain dozens of parables and stories, most of which make little to no sense. This is because the documents were written in a way that obscures the messages they contain. The nomadic tribal nations surrounding the Roman Empire had devised a way of communicating so that the leadership of the empires they were targeting would not be able to decipher their plans. The Gnostic gospels relinquish their hidden messages with the application of a simple code. Throughout the Gnostic gospels the authors use a variety of opposite paired terms like Heaven and Earth, Above and Below, Light and Dark, or Immortal and Mortal. The key to deciphering the Gnostic code is to recognize that all of the opposites referred to the same nomadic tribal nation/Roman Empire divide. Any reference to Heaven, God, Father, Light, referred to the nomadic tribal nations while references to the Earth, World, Abyss, Chaos, or Mortal realm referred to the Roman Empire or agriculturalists in general. Along with these terms, concepts with natural dichotomies such as Summer and Winter, Right and Left, Above and Below, and Mother and Father all represented the nomadic/agricultural divide. Below is the bulk of the Gnostic code
{"title":"The Gnostic Code","authors":"David Klinkenberg","doi":"10.60087/jaigs.v1i1.32","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.32","url":null,"abstract":"A significant amount of evidence has emerged to demonstrate that the so called gnostic gospels of the Roman period were written utilizing a cypher. In other words, they contain literal historical messages that can not be understood without applying a code to the text. The implications of this discovery are vast and significant. In order to demonstrate this cypher I will be using the texts from the Nag Hammed Library. A large collection of gnostic gospels was discovered in 1945 near the Egyptian town of Nag Hammadi, and I will be using these texts in this analysis. The Gnostic gospels contain dozens of parables and stories, most of which make little to no sense. This is because the documents were written in a way that obscures the messages they contain. The nomadic tribal nations surrounding the Roman Empire had devised a way of communicating so that the leadership of the empires they were targeting would not be able to decipher their plans. The Gnostic gospels relinquish their hidden messages with the application of a simple code. Throughout the Gnostic gospels the authors use a variety of opposite paired terms like Heaven and Earth, Above and Below, Light and Dark, or Immortal and Mortal. The key to deciphering the Gnostic code is to recognize that all of the opposites referred to the same nomadic tribal nation/Roman Empire divide. Any reference to Heaven, God, Father, Light, referred to the nomadic tribal nations while references to the Earth, World, Abyss, Chaos, or Mortal realm referred to the Roman Empire or agriculturalists in general. Along with these terms, concepts with natural dichotomies such as Summer and Winter, Right and Left, Above and Below, and Mother and Father all represented the nomadic/agricultural divide. Below is the bulk of the Gnostic code","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"17 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cybercriminals continually develop innovative strategies to confound and frustrate their victims, necessitating constant vigilance to protect the availability, confidentiality, and integrity of digital systems. Machine learning (ML) has emerged as a powerful technique for intelligent cyber analysis, enabling proactive defenses by studying recurring patterns of successful attacks. However, two significant drawbacks hinder the widespread adoption of ML in security analysis: high computing overheads and the need for specialized frameworks. This study aims to quantify the extent to which a hub can enhance ecosystem safety. Typical cyberattacks were executed on an Internet of Things (IoT) network within a smart house to validate the hub's efficacy. Furthermore, the resistance of the intrusion detection system (IDS) to adversarial machine learning (AML) attacks was investigated, where models are targeted with adversarial samples exploiting weaknesses in the pre-trained detector.
网络犯罪分子不断开发创新策略来迷惑和挫败受害者，因此必须时刻保持警惕，以保护数字系统的可用性、保密性和完整性。机器学习（ML）已成为一种强大的智能网络分析技术，通过研究成功攻击的重复模式，实现主动防御。然而，在安全分析中广泛采用 ML 有两个重大缺陷：高计算开销和需要专门的框架。本研究旨在量化集线器能在多大程度上提高生态系统的安全性。在智能屋内的物联网（IoT）网络上实施了典型的网络攻击，以验证集线器的功效。此外，还研究了入侵检测系统（IDS）抵御对抗性机器学习（AML）攻击的能力，即利用预先训练的检测器的弱点，用对抗性样本对模型进行攻击。
{"title":"Cybersecurity Threat Detection using Machine Learning and Network Analysis","authors":"Amaresh Kumar","doi":"10.60087/jaigs.v1i1.p46","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.p46","url":null,"abstract":"Cybercriminals continually develop innovative strategies to confound and frustrate their victims, necessitating constant vigilance to protect the availability, confidentiality, and integrity of digital systems. Machine learning (ML) has emerged as a powerful technique for intelligent cyber analysis, enabling proactive defenses by studying recurring patterns of successful attacks. However, two significant drawbacks hinder the widespread adoption of ML in security analysis: high computing overheads and the need for specialized frameworks. This study aims to quantify the extent to which a hub can enhance ecosystem safety. Typical cyberattacks were executed on an Internet of Things (IoT) network within a smart house to validate the hub's efficacy. Furthermore, the resistance of the intrusion detection system (IDS) to adversarial machine learning (AML) attacks was investigated, where models are targeted with adversarial samples exploiting weaknesses in the pre-trained detector.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"259 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140500166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article delves into the transformative collaboration between Artificial Intelligence (AI) and robotics within the context of Industry 4.0 applications. Industry 4.0 represents a paradigm shift in manufacturing, characterized by the integration of advanced technologies. The synergy between AI and robotics plays a pivotal role in reshaping industrial processes, leading to increased automation, predictive maintenance strategies, collaborative robotics (cobots), enhanced quality control, and optimized supply chain operations. AI algorithms empower machines to learn, adapt, and make intelligent decisions, fostering adaptability and efficiency in manufacturing. The seamless integration of AI and robotics not only improves operational processes but also introduces novel approaches to human-robot collaboration, quality assurance, and supply chain management. The article also addresses challenges associated with this integration, such as workforce displacement concerns and the need for standardized communication protocols. As the field continues to evolve, navigating these challenges and capitalizing on the ongoing advancements in AI and robotics will be instrumental in unlocking the full potential of their collaborative synergy, ultimately defining the future landscape of Industry 4.0.
{"title":"Exploring the Synergy of Artificial Intelligence and Robotics in Industry 4.0 Applications","authors":"Md. Rashel Mia, Jeff Shuford","doi":"10.60087/jaigs.v1i1.31","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.31","url":null,"abstract":"This article delves into the transformative collaboration between Artificial Intelligence (AI) and robotics within the context of Industry 4.0 applications. Industry 4.0 represents a paradigm shift in manufacturing, characterized by the integration of advanced technologies. The synergy between AI and robotics plays a pivotal role in reshaping industrial processes, leading to increased automation, predictive maintenance strategies, collaborative robotics (cobots), enhanced quality control, and optimized supply chain operations. AI algorithms empower machines to learn, adapt, and make intelligent decisions, fostering adaptability and efficiency in manufacturing. The seamless integration of AI and robotics not only improves operational processes but also introduces novel approaches to human-robot collaboration, quality assurance, and supply chain management. The article also addresses challenges associated with this integration, such as workforce displacement concerns and the need for standardized communication protocols. As the field continues to evolve, navigating these challenges and capitalizing on the ongoing advancements in AI and robotics will be instrumental in unlocking the full potential of their collaborative synergy, ultimately defining the future landscape of Industry 4.0.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"53 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140499829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As organizations increasingly rely on cloud computing for storage, processing, and deployment of sensitive data, ensuring robust security measures becomes paramount. This paper explores the intersection of artificial intelligence (AI) and cloud security, presenting AI-driven solutions as the future of safeguarding sensitive data in the digital age. Leveraging AI algorithms and machine learning techniques, cloud security can adapt and evolve to counter emerging threats in real-time, enhancing detection, prevention, and response capabilities. This paper discusses various AI-driven approaches to cloud security, including anomaly detection, threat intelligence analysis, and behavior analytics, highlighting their effectiveness in mitigating risks and ensuring compliance with regulatory standards. Additionally, it addresses the challenges and ethical considerations associated with AI-driven cloud security, emphasizing the importance of transparency, accountability, and ethical AI principles. By embracing AI-driven solutions, organizations can fortify their defenses against cyber threats and maintain the integrity and confidentiality of their sensitive data in the evolving digital landscape.
{"title":"AI-Driven Cloud Security: The Future of Safeguarding Sensitive Data in the Digital Age","authors":"Hassan Rehan","doi":"10.60087/jaigs.v1i1.p66","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.p66","url":null,"abstract":"As organizations increasingly rely on cloud computing for storage, processing, and deployment of sensitive data, ensuring robust security measures becomes paramount. This paper explores the intersection of artificial intelligence (AI) and cloud security, presenting AI-driven solutions as the future of safeguarding sensitive data in the digital age. Leveraging AI algorithms and machine learning techniques, cloud security can adapt and evolve to counter emerging threats in real-time, enhancing detection, prevention, and response capabilities. This paper discusses various AI-driven approaches to cloud security, including anomaly detection, threat intelligence analysis, and behavior analytics, highlighting their effectiveness in mitigating risks and ensuring compliance with regulatory standards. Additionally, it addresses the challenges and ethical considerations associated with AI-driven cloud security, emphasizing the importance of transparency, accountability, and ethical AI principles. By embracing AI-driven solutions, organizations can fortify their defenses against cyber threats and maintain the integrity and confidentiality of their sensitive data in the evolving digital landscape.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"51 9-10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140499474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imaging enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real-time insights based on patient data and clinical guidelines, facilitating evidence-based decision-making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming patient care but also shaping the future of healthcare delivery.
{"title":"AI in Healthcare: Transforming Patient Care through Predictive Analytics and Decision Support Systems","authors":"Md. Shohel Rana, Jeff Shuford","doi":"10.60087/jaigs.v1i1.30","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.30","url":null,"abstract":"This article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imaging enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real-time insights based on patient data and clinical guidelines, facilitating evidence-based decision-making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming patient care but also shaping the future of healthcare delivery.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"31 5-6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140499579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial Intelligence (AI) has emerged as a transformative force across numerous domains, from healthcare to finance and beyond. However, as AI systems become increasingly integrated into daily life, the ethical implications of their development and deployment are garnering significant attention. This article conducts a comprehensive survey of the ethical considerations in AI, with a specific focus on navigating the complex landscape of bias and fairness.
{"title":"A Survey of Ethical Considerations in AI: Navigating the Landscape of Bias and Fairness","authors":"Md.mafiqul Islam, Jeff Shuford","doi":"10.60087/jaigs.v1i1.27","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.27","url":null,"abstract":"Artificial Intelligence (AI) has emerged as a transformative force across numerous domains, from healthcare to finance and beyond. However, as AI systems become increasingly integrated into daily life, the ethical implications of their development and deployment are garnering significant attention. This article conducts a comprehensive survey of the ethical considerations in AI, with a specific focus on navigating the complex landscape of bias and fairness.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140499665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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