Narrow Artificial Intelligence Research Articles

Potential Safety Issues and Moral Hazard Posed by Artificial General Intelligence

Abstract. Artificial Intelligence (AI), a technology with a wide range of intelligence capabilities, has developed rapidly in recent years, bringing significant convenience and efficiency to society. However, most of the current artificial intelligence technologies belong to narrow artificial intelligence. Unlike Narrow AI, Artificial General Intelligence (AGI) possesses a more comprehensive understanding and problem-solving capability. AGI can learn in an unsupervised manner. General artificial intelligence can not only stand out in specific fields. It can also make effective decisions to a certain extent and operate in a wide range of environments. However, rapid progress has also raised widespread concerns about its potential risks. Therefore, the development of artificial intelligence requires standardization, which is urgent to ensure that it can make decisions that benefit humanity. Based on existing literature and data results, this paper explores the security issues and moral risks that general artificial intelligence may bring to humans. The research results indicate that these risks include user privacy breaches, system security issues, and social ethical conflicts. Dealing with these risks requires the joint efforts of all practitioners. This includes developing AGI in an ethical manner and ensuring that AI does not engage in activities that violate human interests.

From Pursuit of the Universal AGI Architecture to Systematic Approach to Heterogeneous AGI (SAGI): Addressing Alignment, Energy & AGI Grand Challenges

Artificial intelligence (AI) faces a trifecta of grand challenges: the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. Contemporary AI solutions consume unsustainable amounts of energy during model training and daily operations. Making things worse, the amount of computation required to train each new AI model has been doubling every 2 months since 2020, directly translating to unprecedented increases in energy consumption. The leap from AI to AGI requires multiple functional subsystems operating in a balanced manner, which requires a system architecture. However, the current approach to AI lacks system design; even though system characteristics play a key role in the human brain; from the way it processes information to how it makes decisions. In this paper, we posit that system design is the missing piece in overcoming current AI the grand challenges. We present a Systematic Approach to AGI (SAGI) that utilizes system design principles to overcome the energy wall and the alignment challenges. This paper asserts that artificial intelligence can be realized through a multiplicity of design-specific pathways, rather than a singular, overarching AGI architecture. AGI systems may exhibit diverse architectural configurations and capabilities, contingent upon their intended use cases. We argue that AI alignment, the most difficult among the grand challenges, is not attainable without a way to reflect the complexity of the human moral system and its subsystems in the AGI architectures. We claim that AGI approaches such as symbolicism, connectionism and others are not fundamental to AGI but emergent from the system design processes. Hence, we focus on employing system design principles as a guiding framework, rather than solely concentrating on a universal AGI architecture.

Understanding public perceptions of revolutionary technology: the role of political ideology, knowledge, and news consumption

This study investigates public perceptions of Artificial Intelligence (AI) in Taiwan, focusing on the roles of political ideology, science news consumption, and knowledge. Analyzing responses from 502 participants, the research reveals that political ideology has a limited impact on AI benefit perceptions but likely becomes more significant with increased AI application. The study suggests that, for narrow AI, science news consumption and content knowledge are more influential than political ideology. It emphasizes the need for tailored communication strategies and highlights the positive association between respect for science authority and favorable AI perceptions. The findings provide essential insights for policymakers navigating AI adoption.

Celiac Disease Deep Learning Image Classification Using Convolutional Neural Networks.

Celiac disease (CD) is a gluten-sensitive immune-mediated enteropathy. This proof-of-concept study used a convolutional neural network (CNN) to classify hematoxylin and eosin (H&E) CD histological images, normal small intestine control, and non-specified duodenal inflammation (7294, 11,642, and 5966 images, respectively). The trained network classified CD with high performance (accuracy 99.7%, precision 99.6%, recall 99.3%, F1-score 99.5%, and specificity 99.8%). Interestingly, when the same network (already trained for the 3 class images), analyzed duodenal adenocarcinoma (3723 images), the new images were classified as duodenal inflammation in 63.65%, small intestine control in 34.73%, and CD in 1.61% of the cases; and when the network was retrained using the 4 histological subtypes, the performance was above 99% for CD and 97% for adenocarcinoma. Finally, the model added 13,043 images of Crohn's disease to include other inflammatory bowel diseases; a comparison between different CNN architectures was performed, and the gradient-weighted class activation mapping (Grad-CAM) technique was used to understand why the deep learning network made its classification decisions. In conclusion, the CNN-based deep neural system classified 5 diagnoses with high performance. Narrow artificial intelligence (AI) is designed to perform tasks that typically require human intelligence, but it operates within limited constraints and is task-specific.

On singularity and the Stoics: why Stoicism offers a valuable approach to navigating the risks of AI (Artificial Intelligence)

AbstractThe potential benefits and risks of artificial intelligence technologies have sparked a wide-ranging debate in both academic and public circles. On one hand, there is an urgent call to address the immediate and avoidable challenges associated with these tools, such as accountability, privacy, bias, understandability, and transparency; on the other hand, prominent figures like Geoffrey Hinton and Elon Musk have voiced concerns over the potential rise of Super Artificial Intelligence, whose singularity could pose an existential threat to humanity. Coordinating the efforts of thousands of decentralized entities to prevent such a hypothetical event may seem insurmountable in our intricate and multipolar world. Thus, drawing from both perspectives, this work suggests employing the tools and framework of Stoic philosophy, particularly the concept of the dichotomy of control—focusing on what is within our power. This Stoic principle offers a practical and epistemological approach to managing the complexities of AI, and it encourages individuals to organize their efforts around what they can influence while adapting to the constraints of external factors. Within this framework, the essay found that Stoic wisdom is essential for assessing risks, courage is necessary to face contemporary challenges, and temperance and tranquility are indispensable; and these lessons can inform ongoing public and academic discourse, aiding in the development of more effective policy proposals for aligning Narrow AI and General AI with human values.

A Contextual Model for Visual Information Processing

Despite significant achievements in the artificial narrow intelligence sphere, the mechanisms of human-like (general) intelligence are still undeveloped. There is a theory stating that the human brain extracts the meaning of information rather than recognizes the features of a phenomenon. Extracting the meaning is finding a set of transformation rules (context) and applying them to the incoming information, producing an interpretation. Then, the interpretation is compared to something already seen and is stored in memory. Information can have different meanings in different contexts. A mathematical model of a context processor and a differential contextual space which can perform the interpretation is discussed and developed in this paper. This study examines whether the basic principles of differential contextual spaces work in practice. The model is developed with Rust programming language and trained on black and white images which are rotated and shifted both horizontally and vertically according to the saccades and torsion movements of a human eye. Then, a picture that has never been seen in the particular transformation, but has been seen in another one, is exposed to the model. The model considers the image in all known contexts and extracts the meaning. The results show that the program can successfully process black and white images which are transformed by shifts and rotations. This research prepares the grounding for further investigations of the contextual model principles with which general intelligence might operate.

ШТУЧНИЙ ІНТЕЛЕКТ У ВИЩІЙ ОСВІТІ: МОЖЛИВОСТІ ВИКОРИСТАННЯ

The integration of artificial intelligence into the system of higher education represents a turning point in the process of learning and teaching. The development of artificial intelligence has opened the way to personalized training, automation of administrative tasks and the introduction of innovative training methods. The purpose of the study was to analyze the practical aspects of using artificial intelligence in higher education institutions of Ukraine. It was determined that artificial intelligence is an organized set of information technologies, which makes it possible to perform complex complex tasks. There are three main categories of artificial intelligence: narrow-spectrum artificial intelligence, or Artificial Narrow Intelligence, general artificial intelligence, or Artificial General Intelligence, and artificial superintelligence, or Artificial Super Intelligence. The main educational services provided by artificial intelligence in institutions of higher education are the development and conduct of lectures, seminars and practical classes; teacher counseling; creation of educational programs and electronic courses; development of tasks and simulation of their solution; conducting various educational events; evaluation of the works of education seekers. Some examples of the use of artificial intelligence, in particular chatbots, in the higher education of Ukraine are analyzed and their potential for improving the educational process and forming professional skills is emphasized. An example of the use of GPT-3.5 in the Luhansk Educational and Scientific Institute for teaching foreign languages is presented. Such applications based on artificial intelligence as Thinkster and Duolingo and the main aspects of their use by students of higher education are characterized. Recommendations are provided for the successful implementation of artificial intelligence technologies in higher education.

AI Adoption in Islamic Tourism Stakeholders’ Business Orientation

Despite the expanding worldwide Islamic tourism market, the industry is still largely untapped. As Islamic tourism market comprises of the technology-driven market dominated by the millennial, followed by gen Z and gen Alpha as the next successors, Islamic tourism stakeholders are urged to capitalize cutting-edge technology in their business orientation to gain competitive advantage and business sustainability. As such, this content-analysis paper aims to determine Islamic tourism stakeholders’ adoption of Artificial Intelligence (AI) in their business orientation. Findings indicated that Narrow AI is the most common AI types used by most of the Islamic tourism stakeholders. It was also found that both internal and external factors strongly influenced the AI adoption and it types among the stakeholders. Nevertheless, the organization’s financial health, the business size and scope, and the target market’s needs profoundly affect AI implementation by the business operators. As for the customers, the expected delivery performance, the service facilitating conditions and the expenses they have to pay were found to be significant in influencing them in their AI acceptance. AI adoption positively influences the Islamic tourism stakeholders’ business orientation and the organizations’ sustainability. Capitalizing AI in Islamic tourism business orientation helps to redress Islamic muamalat misconception and other religious related issues and practices, promote understanding about divine religions, allows for greater flexibility in the differences in Fiqh implementation, pooling stronger and sound Muslim experts manpower capitalization and empowerment in various fields as technology and business, ensure Syariah compliance and conformance which results to greater efficiency, increase performance, competitive advantage and Islamic tourism business sanctity and sustainability. As AI is evolving, it is projected that the industry will fully capitalize this technology and establishes legal measures to cope with the industry standard and business sustainability while at the same time ensuring experience and value-driven service to the customers.

Prospective research in the field of teaching creative skills to artificial intelligence

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

A journey from AI to Gen-AI

The history of artificial intelligence (AI), from its conception to the creation of general AI (Gen-AI), is a fascinating story of human inventiveness, technical growth, and philosophical research. This article examines the historical milestones, major inventions, and transformational concepts that have influenced AI's trajectory. Beginning with early symbolic AI and rule-based systems, it investigates the shift to machine learning, highlighting discoveries in neural networks and deep learning that transformed disciplines such as computer vision and natural language processing. The introduction of generative models, such as GANs and VAEs, resulted in a considerable increase in AI capabilities, paving the path for Gen-AI. Unlike narrow AI, Gen-AI strives to imitate human-like intelligence and adaptability across a wide range of jobs, bringing serious ethical and philosophical concerns. This essay also looks at the current state of Gen-AI, its problems, and possible applications in healthcare, education, finance, and other areas. It finishes by picturing a future in which human-machine collaboration and ethical AI development are prioritized, highlighting the need of continual learning and responsible innovation.

Foundations of ANNs: Tolstoy’s Genius Explored using Transformer Architecture

Artificial Narrow Intelligence is in the phase of moving towards the AGN, which will attempt to decide as a human being. We are getting closer to it by each day, but AI actually is indefinite to many, although it is no different than any other set of mathematically defined computer operations in its core. Generating new data from a pre-trained model introduces new challenges to science & technology. In this work, the design of such an architecture from scratch, solving problems, and introducing alternative approaches are what has been conducted. Using a deep thinker, Tolstoy, as an object of study is a source of motivation for the entire research.

A holistic approach to implementing artificial intelligence in radiology

ObjectiveDespite the widespread recognition of the importance of artificial intelligence (AI) in healthcare, its implementation is often limited. This article aims to address this implementation gap by presenting insights from an in-depth case study of an organisation that approached AI implementation with a holistic approach.Materials and methodsWe conducted a longitudinal, qualitative case study of the implementation of AI in radiology at a large academic medical centre in the Netherlands for three years. Collected data consists of 43 days of work observations, 30 meeting observations, 18 interviews and 41 relevant documents. Abductive reasoning was used for systematic data analysis, which revealed three change initiative themes responding to specific AI implementation challenges.ResultsThis study identifies challenges of implementing AI in radiology at different levels and proposes a holistic approach to tackle those challenges. At the technology level, there is the issue of multiple narrow AI applications with no standard use interface; at the workflow level, AI results allow limited interaction with radiologists; at the people and organisational level, there are divergent expectations and limited experience with AI. The case of Southern illustrates that organisations can reap more benefits from AI implementation by investing in long-term initiatives that holistically align both social and technological aspects of clinical practice.ConclusionThis study highlights the importance of a holistic approach to AI implementation that addresses challenges spanning technology, workflow, and organisational levels. Aligning change initiatives between these different levels has proven to be important to facilitate wide-scale implementation of AI in clinical practice.Critical relevance statementAdoption of artificial intelligence is crucial for future-ready radiological care. This case study highlights the importance of a holistic approach that addresses technological, workflow, and organisational aspects, offering practical insights and solutions to facilitate successful AI adoption in clinical practice.Key points1. Practical and actionable insights into successful AI implementation in radiology are lacking.2. Aligning technology, workflow, organisational aspects is crucial for a successful AI implementation3. Holistic approach aids organisations to create sustainable value through AI implementation.Graphical

Integrating ethics in AI development: a qualitative study

BackgroundWhile the theoretical benefits and harms of Artificial Intelligence (AI) have been widely discussed in academic literature, empirical evidence remains elusive regarding the practical ethical challenges of developing AI for healthcare. Bridging the gap between theory and practice is an essential step in understanding how to ethically align AI for healthcare. Therefore, this research examines the concerns and challenges perceived by experts in developing ethical AI that addresses the healthcare context and needs.MethodsWe conducted semi-structured interviews with 41 AI experts and analyzed the data using reflective thematic analysis.ResultsWe developed three themes that expressed the considerations perceived by experts as essential for ensuring AI aligns with ethical practices within healthcare. The first theme explores the ethical significance of introducing AI with a clear and purposeful objective. The second theme focuses on how experts are concerned about the tension that exists between economic incentives and the importance of prioritizing the interests of doctors and patients. The third theme illustrates the need to develop context-sensitive AI for healthcare that is informed by its underlying theoretical foundations.ConclusionsThe three themes collectively emphasized that beyond being innovative, AI must genuinely benefit healthcare and its stakeholders, meaning AI also aligns with intricate and context-specific healthcare practices. Our findings signal that instead of narrow product-specific AI guidance, ethical AI development may need a systemic, proactive perspective that includes the ethical considerations (objectives, actors, and context) and focuses on healthcare applications. Ethically developing AI involves a complex interplay between AI, ethics, healthcare, and multiple stakeholders.

Buddhist Transformation in the Digital Age: AI (Artificial Intelligence) and Humanistic Buddhism

Humanistic Buddhism is one of the mainstreams of modern Buddhism, with special emphasis on the humanistic dimension. With the development of artificial intelligence (AI) technology, Humanistic Buddhism is also at an important stage of modernization and transformation, thus facing a continuous negotiation between religious values and technological innovations. This paper first argues that AI is technically beneficial to the propagation of Buddhism by citing several cases in which AI technology has been used in Buddhism. Then, by comparing Master Hsing Yun’s Buddhist ethics to “Posthuman” ethics, it points out that the theories of Humanistic Buddhism share similarities with AI and Posthuman ethics. Among them, Master Hsing Yun’s theory of “the nature of insentient beings” provides an important theoretical reference for the question of “whether AI can become a Buddha”. From the technical and ethical dimensions, it points out that the interaction between Humanistic Buddhism and AI can promote original uses or implementations of AI technology. However, it should also be noted that compared to the cases of “Artificial Narrow Intelligence”discussed in the paper, the “Strong AI” could lead to much more ethical crises. It is also likely to cause the cult of science and technology, and thus subvert the humanistic tradition of Buddhism with a new instrumental rationality. In addition, there are some potential pitfalls that Humanistic Buddhism may encounter when using AI. Hence, while it is necessary to encourage the use of technologies such as AI in contemporary Buddhism, it is also important for Buddhism to keep a critical distance from digital technologies.

Artificial Intelligence: The Future

Artificial intelligence is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is also the field of study in computer science that develops and studies intelligent machines. "AI" may also refer to the machines themselves. AI is not a new for the scientist, it was introduce in 1943 with artificial neurons model and get popular in 1950 due to “Turting test” the test was done to get answer that machine can think?, purposed by Alan Turing. Basically AI is categorized into three types, Artificial Narrow Intelligence, Artificial General Intelligence and Artificial Super Intelligence. Deep learning and machine learning is major subfield of Artificial intelligence. DL as a subset of ML, which is also another subset of AI. Therefore, AI is the all-encompassing concept that initially erupted. Application of AI fields are Healthcare, Business, Education, Agriculture, Finance, Law, Entertainment and media, Software coding and IT processes, Security, Manufacturing, Banking and Transportation. In reference to Job creation or distraction, Artificial Intelligence is not job killer but a job category killer”. In the latest report (May 2023) on The Future of Jobs, the World Economic Forum (WEF) predicts the creation of 69 million jobs by 2027 thanks to AI, but also the destruction of 89 million jobs. In context to intelligence level, Jan. 2022, Age of IQ level of AI was as 7 year Children and in Dec. 2022, Age of IQ level of AI was as 9 year Children. India is emerging market in global and it has around 12 % of work could be automated by AI. In India more than 2000 startup are related to AI and 90000 plus AI Professional work in India.The economic impact of AI, for select G20 countries and estimates AI to boost India’s annual growth rate by 1.3 percentage points by 2035. AI has potential to add 1 trillion to India’s economy in 2035. We are going to enter into new technological world, it’s may be our fortune or misfortune. Key Words: - Artificial Intelligence, Machine learning, Job, India

Editorial: The Artificial Intelligence in Translational Medicine and Biomedical Research, How Future Can be Shaped?!!

The new era of translational medicine is facing a special and unique transition as the Artificial Intelligence (AI) and Machine Learning (ML) advance. The latest findings in many laboratories around the globes are promising and paved the way toward extraordinary innovative development in the field of medicine and medical research. In medical research, gradual and consistent advancement, from rudimentary origins, were initial key steps toward medical discoveries and innovation. However, with the AI inclusion into these research approaches, the traditional translational medicine is pacing up. Creating a great hope for filling many sophisticated gaps in knowledge that has been a challenge by the traditional methodology. Such advancements in AI and its involvement in biomedicine and healthcare are forging the framework for a flourishing and visionary translational medicine and biomedical research. Lately, following the discoveries made by a collaborative team of scientists from the University of Vermont and Tufts University (The xenobots), enormous research and investigation started to glance in the field. Attempts toward drug discovery, wound recoveries and self-proliferating stem cells were made. Most recently, scientists at Tufts University and Harvard University’s Wyss Institute have designed and created living small robots from cells of human trachea. In an in vitro world, these created cells could induce tissue damage recovery. The AI based medical diagnosis is also a revolutionary step toward proper diagnosis and consequently, medical intervention. The AI and ML are becoming so intelligent to an extent not to only diagnose the disease but also predict disease prognosis in future. The narrow AI (WatsonX) that was introduced by the IBM company can play a major role in assisting clinical decisions and image analysis. The challenge that faced the globe after the COVID-19 outbreak with multiple false negativity and false positivity outcomes in the diagnose necessitated the support of the AI in this field. Additionally, the Neuralink that is introduced by Neuralink Corp. is taking a historical step after gaining the approval from FDA to start human trials. Such dramatical change in the brain interface using AI can be a solution for many diseases that human couldn’t treat. Restoring brain activity and autonomy through integration of an AI to human nervous system is a mind-boggling promise that AI can give to medicine and medical science. Finally, these new approaches and advancement in AI and ML are prone to proper design and supervision, as well as, performing randomized clinical trials prior to the use in real-life medical interventions. Nonetheless, research and experimentations for AI inclusion in medicine are still an ongoing process, yet the opportunities are realistic that AI can be of great benefit in supporting medicine and biomedical research, specifically, where there is gap in knowledge and inability of human capacity to resolve them. There is no doubt that AI will be a significant party of the healthcare system in the future and a core medical support.

Can AI be Evil: The Criminal Capacities of ANI

Artificial Narrow Intelligence (ANI) represents a captivating domain within technological advancement, bearing the potential for profound societal transformations. While ANI holds the promise of enhancing various facets of human existence, it concurrently engenders inquiries into its “darker aspects.” This study delves into the challenges associated with ANI’s conceivable manifestation of harm and injustice, a phenomenon devoid of consciousness, intention, or responsibility akin to that of human entities. A pivotal dimension of ANI’s “dark side” pertains to its susceptibility to malevolent utilization. Despite its lack of awareness, ANI serves as a tool for malicious endeavors, encompassing the propagation of disinformation, compromise of security systems, and consequential decision-making. This prompts contemplation on strategies to mitigate these “precise manifestations of malevolence” arising from ANI’s technological progression. Additionally, ANI’s development introduces profound ethical quandaries. Ensuring ANI’s alignment with moral principles while averting scenarios in which it generates decisions conflicting with human morality becomes a pressing concern. This research underscores the imperative for rigorous regulatory frameworks and ethical directives to curtail potential hazards and unscrupulous utilization of ANI. The fundamental objective of this investigation is to advocate for the responsible deployment of ANI in society. A comprehensive understanding of potential risks, complemented by meticulous consideration of ethical dimensions, emerges as an indispensable prerequisite to harmonizing technological advancement with safeguarding societal and individual interests.

Penyuluhan Mengenai Artificial Intelligence Untuk Siswa-Siswi SMP dan SMA Sekolah Lentera Harapan Medan

Artificial Intelligence (Artificial Intelligence or AI) is a field that is growing rapidly and has great potential in influencing various aspects of human life. AI education is important for the younger generation in facing future challenges. This study aims to teach the basics of AI to junior high school (SMP) students at Sekolah Lentera Harapan (SLH). Learning sessions are carried out using interactive methods and actively involve students in discussions. The material includes an introduction to AI, the history and purpose of AI, how it works, the types of AI, and the advantages and disadvantages of AI. AI needs data to come up with appropriate answers, and with time, it will learn and improve. There are three types of AI, namely Artificial Narrow Intelligence (limited AI), Artificial General Intelligence (general AI), and Artificial Superintelligence (super AI). The advantages of AI include fast data processing, job efficiency and handling of dangerous tasks. However, there are also drawbacks such as reliance on big data, limitations to the specific capabilities of AI, and security concerns. This research provides insight into AI and promotes optimal use of AI in society.

Artificial Intelligence (AI) in Nursing

Artificial Intelligence (AI) is a broad field of computer science focused on creating systems that can perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, language understanding, and speech recognition. AI can be categorized into two main types: Narrow AI (or Weak AI) and General AI (or Strong AI).While the integration of AI in nursing brings numerous benefits, it is essential to address ethical considerations, data privacy, and the need for ongoing education and training for healthcare professionals. The collaboration between AI systems and human expertise is crucial for ensuring the highest standards of patient care.

Personal Data Privacy in the Age of Artificial Intelligence

The article discusses the relationship between Artificial Intelligence (AI) and personal data privacy. Artificial Intelligence which emerged in the 1950s with Alan Turing's question "Can Machines Think?" has achieved significant successes in recent years, particularly in the development of human-like machine designs. The advancement of AI is closely related to the collection of big data, which in turn involves gathering more personal data. In today's context, marked by increased philosophical discussions about freedom and heightened emphasis on the individual, the issue of personal data privacy has become considerably important, accompanied by numerous challenges. Despite the existence of many national and international legal regulations regarding data privacy, there lacks a regulation overseeing the development processes of related technologies. Artificial Intelligence is often classified into categories based on its technical capabilities, namely "Narrow AI," "General AI," and "Superintelligence." AI has attracted significant attention in philosophy and has brought about interdisciplinary research in fields ranging from logic and aesthetics to law, ethics, medicine, and even art. In the face of emerging technologies, it seems inevitable that philosophy will initiate new research trajectories. The paper examines these three concepts related to AI, their connection with the concept of privacy, the issues arising and potentially arising in the realm of personal privacy, and the measures to mitigate these risks. The methodology employed includes a literature review and qualitative data analysis. The research reveals that the notion of privacy is mainly linked to ensuring the security of personal data related to technological advancements and the changing perception of privacy in individuals. However, it's observed that these studies often do not encompass current developments. The paper endeavors to answer questions such as: What do the terms "AI, Narrow AI, General AI, Superintelligence, and Personal Data Privacy" refer to? Have studies been conducted on the relationship between AI concepts and personal data privacy? What are the risks associated with personal data privacy, and how can these risks be mitigated? The study aims to highlight the risks in the development processes of AI as increasingly vocalized by scientists working in this field. It delves into issues such as the collection and storage of personal data, often unbeknownst to individuals, through cloud technologies and the potential problems arising from the use of this data in AI training. The article also discusses potential solutions to these issues. Drawing attention to recent developments in this scope is of paramount importance for social scientists, and disseminating information about these advancements is crucial. Based on the examinations and analyses conducted, it's determined that AI, now possessing new competencies, requires an interdisciplinary approach to address the areas it influences. It's found that leaving this field solely to engineers would result in numerous problems in the near future. Safeguarding privacy in the face of intelligent machines that encompass every aspect of our lives is a formidable challenge. Reducing potential problems necessitates individual awareness, legal regulations concerning AI development processes, and recognizing that AI technologies causing issues can also be part of the solution.