The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

There is a need recognized by the National Institute of Dental & Craniofacial Research and the National Cancer Institute to advance basic, translational and clinical saliva research. The goal of the Salivaomics Knowledge Base (SKB) is to create a data management system and web resource constructed to support human salivaomics research. To maximize the utility of the SKB for retrieval, integration and analysis of data, we have developed the Saliva Ontology and SDxMart. This article reviews the informatics advances in saliva (...) diagnostics made possible by the Saliva Ontology and SDxMart. (shrink)

On 24th February 2025, Justin Mike (United States) wrote a review for Wild Wise Weird as follows: “Wild Wise Weird celebrates the wild spirit, the wisdom gained from unusual experiences, and the eccentric qualities that define us as individuals, thereby capturing the beauty of embracing authenticity. It serves as a reminder that being unique is something to be proud of, not just acceptable.” The review made me feel really relevant to the content of the book. I was curious what AI (...) would think of Mike’s review, so I asked Gemini “what do you think of the review?”. (shrink)

We describe the rationale for an application ontology covering the domain of human body fluids that is designed to facilitate representation, reuse, sharing and integration of diagnostic, physiological, and biochemical data, We briefly review the Blood Ontology (BLO), Saliva Ontology (SALO) and Kidney and Urinary Pathway Ontology (KUPO) initiatives. We discuss the methods employed in each, and address the project of using them as starting point for a unified body fluids ontology resource. We conclude with a description of how the (...) body fluids ontology initiative may provide support to basic and translational science. (shrink)

Artificial Intelligence (AI) is transforming nearly every sector of society—and conservation is no exception. In a recent article, Chris Sandbrook examines the emerging field of “Conservation AI,” defined as the intentional use of AI technologies to achieve biodiversity protection goals [2].

This article proposes a unified solution to the mind-body problem, grounded in the philosophical framework of Ethical Empirical Rationalism. By presenting a mathematical model of the mind-body interaction, we oƯer a dynamic feedback loop that resolves the traditional dualistic separation between mind and body. At the core of our model is the concept of essence—an eternal, metaphysical truth that sustains both the mind and body. Through coupled diƯerential equations, we demonstrate how the mind and body are two expressions of the (...) same essence, interacting in a continuous and harmonious relationship. This approach provides a new perspective on the mind-body relationship, grounded in both empirical evidence and metaphysical insight. Keywords: Mind-body problem, Unmoved Mover, Essence, Unified Theory, Feedback Loop, Ethical Empirical Rationalism, Mathematical Modeling. (shrink)

Climate change is affecting communities across the globe, yet those most vulnerable to its impacts are often the last to receive the financial assistance they need. A recent critical review by Venner, García-Lamarca, and Olazabal (2024) examines how climate adaptation finance—funding intended to help societies adjust to the impacts of climate change—is distributed. The findings are concerning: despite repeated global commitments to prioritize those most at risk, adaptation finance tends to benefit the most powerful and well-resourced actors rather than the (...) communities that need it most. (shrink)

Hiện nay, chế độ sở hữu ở Việt Nam đã có những đổi mới cơ bản, nhưng vẫn còn những khác biệt rất lớn so với chế độ sở hữu ở các nền kinh tế thị trường hiện đại. Trong cơ cấu của chế độ sở hữu ở Việt Nam, tỷ trọng của sở hữu nhà nước còn quá lớn; kinh tế nhà nước giữ vai trò chủ đạo… Chính những khác biệt này đã làm cho nền kinh tế thị (...) trường Việt Nam bị méo mó và giảm lợi thế cạnh tranh quốc gia. Do vậy, công cuộc đổi mới và hoàn thiện chế độ sở hữu ở Việt Nam phải hướng tới việc khắc phục những hạn chế trên và đạt tới sức cạnh tranh ngang hàng với các nền kinh tế thị trường hiện đại khác. (shrink)

The rapid expansion of renewable energy technologies—such as solar panels, electric vehicles (EVs), and wind turbines—has intensified the global demand for critical raw materials (CRMs), including lithium, cobalt, and neodymium. These materials are vital to clean energy infrastructure but are often sourced from geopolitically unstable or environmentally vulnerable regions [2-4]. In response, a recent study proposes an optimal framework for CRM recycling in Italy aimed at strengthening supply chain resilience and supporting the transition to a low-carbon economy [5].

Biến đổi khí hậu đang ảnh hưởng đến các cộng đồng trên toàn cầu, nhưng những người dễ bị tổn thương nhất trước tác động của nó thường là những người cuối cùng nhận được sự hỗ trợ tài chính cần thiết.

ĐỀ CƯƠNG HỌC PHẦN VĂN HÓA KINH DOANH 1. Tên học phần: VĂN HÓA KINH DOANH (BUSINESS CULTURE) 2. Mã học phần: BMGM1221 3. Số tín chỉ: 2 (24,6) (để học được học phần này, người học phải dành ít nhất 60 giờ chuẩn bị cá nhân).

Tại Việt Nam và trong lĩnh vực giáo dục nói riêng, ChatGPT ngày càng được chấp nhận và sử dụng rộng rãi trong rất nhiều hoạt động học tập. Chính vì thế, nghiên cứu này nhằm đánh giá mức độ phổ biến của ChatGPT đối sinh viên tại Hà Nội, đồng thời xem xét sự khác biệt giữa các đặc điểm cá nhân trong việc cải thiện kết quả học tập sau khi sử dụng ChatGPT. Nghiên cứu được thực hiện (...) bằng phương pháp định lượng với dữ liệu thu thập từ 401 sinh viên tại Hà Nội. Từ kết quả nghiên cứu, có 389 sinh viên (chiếm 97,01%) biết và sử dụng ChatGPT trong các hoạt động học tập của mình. Ngoài ra, sinh viên năm nhất, năm hai, năm ba, năm tư sau khi sử dụng ChatGPT có cải thiện trong hoạt động học tập khác nhau, tuy nhiên yếu tố giới tính lại không có ảnh hưởng. Trên cơ sở kết quả nghiên cứu, nhóm tác giả đưa ra một số nguyên nhân và đề xuất giúp ChatGPT được ứng dụng rộng rãi hơn nữa tại các trường đại học. (shrink)

(Mặt trận) - Chế độ sở hữu trong nền kinh tế thị trường định hướng xã hội chủ nghĩa Việt Nam trước hết phải tuân theo các nguyên tắc của nền kinh tế thị trường hiện đại. Trong các nguyên tắc của nền kinh tế thị trường hiện đại, nguyên tắc sở hữu tư nhân là nền tảng của nền kinh tế thị trường - là nguyên tắc quan trọng. Xa rời nguyên tắc này, dù chúng ta cố gắng xây (...) dựng nền kinh tế thị trường thì đó vẫn là một nền kinh tế thị trường khó được quốc tế công nhận. Sở hữu tư nhân hiện đại là chế độ sở hữu được cổ phần hóa, được xã hội hóa rất cao, công khai, minh bạch trên các thị trường chứng khoán. Tính xã hội cao của chế độ cổ phần hóa đã được C.Mác đánh giá, xem như là chế độ sở hữu quá độ sang hình thái kinh tế - xã hội mới. (shrink)

một nghiên cứu mới đây đã đề xuất khung táichế CRM tối ưu cho nước Ý, nhằm tăng cường khả năng tự chủ chuỗi cung ứng và hỗ trợ quá trình chuyển đổi sang nền kinh tế carbon thấp.

Despite the importance of human blood to clinical practice and research, hematology and blood transfusion data remain scattered throughout a range of disparate sources. This lack of systematization concerning the use and definition of terms poses problems for physicians and biomedical professionals. We are introducing here the Blood Ontology, an ongoing initiative designed to serve as a controlled vocabulary for use in organizing information about blood. The paper describes the scope of the Blood Ontology, its stage of development and some (...) of its anticipated uses. (shrink)

Xây dựng nền kinh tế xanh tiến đến mục tiêu phát triển bền vững đang từng bước trở thành xu thế của thời đại và là xu hướng ngày càng rõ nét trên toàn cầu. Hành vi xanh của doanh nghiệp có vốn đầu tư trực tiếp nước ngoài (doanh nghiệp FDI) có mối quan hệ chặt chẽ và tác động tích cực đáng kể đến sự phát triển bền vững của địa phương/quốc gia, bao gồm cả các nước phát (...) triển và đang phát triển, trong đó có Việt Nam. Bài viết tập trung phân tích những tác động của các doanh nghiệp FDI đến phát triển bền vững của Việt Nam thông qua các hành vi xanh, cụ thể là các hành vi đầu tư gắn với phát triển bền vững của quốc gia, từ đó đề xuất các nhóm giải pháp thúc đẩy hành vi xanh của doanh nghiệp FDI tại Việt Nam. (shrink)

Nghiên cứu này được thực hiện với mục tiêu đo lường mức độ hữu hiệu của hệ thống kiểm soát nội bộ tại Công ty Cổ phần Kết cấu thép GSB thông qua các yếu tố cấu thành gồm: Môi trường kiểm soát; Đánh giá rủi ro; Hoạt động kiểm soát; Thông tin và truyền thông và Giám sát. Kết quả nghiên cứu cho thấy yếu tố Giám sát có mức độ ảnh hưởng mạnh nhất đến sự hữu hiệu của (...) hệ thống kiểm soát nội bộ. Trên cơ sở đó, nghiên cứu đưa ra các hàm ý quản trị dựa trên kết quả phân tích hồi quy, góp phần nâng cao chất lượng hệ thống kiểm soát nội bộ tại đơn vị. (shrink)

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...) intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy. (shrink)

An advanced artificial intelligence could pose a significant existential risk to humanity. Several research institutes have been set-up to address those risks. And there is an increasing number of academic publications analysing and evaluating their seriousness. Nick Bostrom’s superintelligence: paths, dangers, strategies represents the apotheosis of this trend. In this article, I argue that in defending the credibility of AI risk, Bostrom makes an epistemic move that is analogous to one made by so-called sceptical theists in the debate about the (...) existence of God. And while this analogy is interesting in its own right, what is more interesting are its potential implications. It has been repeatedly argued that sceptical theism has devastating effects on our beliefs and practices. Could it be that AI-doomsaying has similar effects? I argue that it could. Specifically, and somewhat paradoxically, I argue that it could amount to either a reductio of the doomsayers position, or an important and additional reason to join their cause. I use this paradox to suggest that the modal standards for argument in the superintelligence debate need to be addressed. (shrink)

Generative AI technologies have the potential to be socially and politically transformative. In this paper, we focus on exploring the potential impacts that Generative AI could have on the functioning of our democracies and the nature of citizenship. We do so by drawing on accounts of deliberative democracy and the deliberative virtues associated with it, as well as the reciprocal impacts that social media and Generative AI will have on each other and the broader information landscape. Drawing on this background (...) theory, we outline some of the key positive and negative impacts that Generative AI is likely to have on democratic citizenship. The political significance of these impacts suggests the need for further regulation. (shrink)

Abstract: Artificial Intelligence (AI) is revolutionizing organizational dynamics by reshaping both structures and processes. This paper explores how AI-driven innovations are transforming organizational frameworks, from hierarchical adjustments to decentralized decision-making models. It examines the impact of AI on various processes, including workflow automation, data analysis, and enhanced decision support systems. Through case studies and empirical research, the paper highlights the benefits of AI in improving efficiency, driving innovation, and fostering agility within organizations. Additionally, it addresses the challenges associated with AI (...) integration, such as resistance to change, ethical concerns, and data security issues. The paper concludes by offering insights into future trends and implications for organizations navigating the evolving landscape of AI-driven change. (shrink)

Since the release of OpenAI's ChatGPT, the world has entered a race to develop more capable and powerful AI, including artificial general intelligence (AGI). The development is constrained by the dependency of AI on the model, quality, and quantity of training data, making the AI training process highly costly in terms of resources and environmental consequences. Thus, improving the effectiveness and efficiency of the AI training process is essential, especially when the Earth is approaching the climate tipping points and planetary (...) boundaries. It is evident that AI can function better when trained to mimic certain human mental processes. Based on insights from quantum mechanics and the informational entropy-based notion of value, we suggest AI developers can enhance the AI training processes' effectiveness and efficiency by creating and implementing parameter systems that are capable of effectively assigning probabilities to informational quanta. Such systems can help reduce entropy within the system, which lowers the energy required for data storage and processing while decreasing the likelihood of information loss during training. Successfully applying the concept of information entropy-based value in AI development will be crucial for advancing generative AI and achieving AGI in a sustainable manner. (shrink)

Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues that AI (...) image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft. (shrink)

Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two-premise argument that AI systems pose a threat to humanity. Premise one: AI systems will become extremely powerful. Premise two: if AI systems become extremely powerful, they will destroy humanity. We use these two premises to construct a taxonomy of ‘survival (...) stories’, in which humanity survives into the far future. In each survival story, one of the two premises fails. Either scientific barriers prevent AI systems from becoming extremely powerful; or humanity bans research into AI systems, thereby preventing them from becoming extremely powerful; or extremely powerful AI systems do not destroy humanity, because their goals prevent them from doing so; or extremely powerful AI systems do not destroy humanity, because we can reliably detect and disable systems that have the goal of doing so. We argue that different survival stories face different challenges. We also argue that different survival stories motivate different responses to the threats from AI. Finally, we use our taxonomy to produce rough estimates of ‘P(doom)’, the probability that humanity will be destroyed by AI. (shrink)

This paper examines the debate on AI legal personhood, emphasizing the role of path dependencies in shaping current trajectories and prospects. Three primary path dependencies emerge: prevailing legal theories on personhood (singularist vs. clustered), the actual participation of AI in socio-digital institutions (instrumental vs. non-instrumental), and the impact of technological advancements. We argue that these factors dynamically interact, with technological optimism fostering broader attribution of the legal entitlements to AI entities and periods of scepticism narrowing such entitlements. Additional influences include (...) regulatory cross-linkages (e.g., data privacy, liability, cybersecurity) and historical legal precedents. Current regulatory frameworks, particularly in the EU, generally resist extending legal personhood to AI systems. Case law suggests that without explicit legislation, courts are unlikely to grant AI legal personhood on their own, although some authors suggest that the courts can do so. For this to happen, AI systems would first need to prove de facto legitimacy through sustained participation within socio-digital institutions. The chapter concludes by assessing near- and long-term prospects for legal personification, from generative AI and AI agents in the next 5–20 years to transformative possibilities such as AI integration with human cognition via Brain-Machine Interfaces in a more distant future. (shrink)

AI companies are racing to create artificial general intelligence, or “AGI.” If they succeed, the result will be human-level AI systems that can independently pursue high-level goals by formulating and executing long-term plans in the real world. Leading AI researchers agree that some of these systems will likely be “misaligned”–pursuing goals that humans do not desire. This goal mismatch will put misaligned AIs and humans into strategic competition with one another. As with present-day strategic competition between nations with incompatible goals, (...) the result could be violent and catastrophic conflict. Existing legal institutions are unprepared for the AGI world. New foundations for AGI governance are needed, and the time to begin laying them is now, before the critical moment arrives. -/- This Article begins to lay those new legal foundations. It is the first to think systematically about the dynamics of strategic competition between humans and misaligned AGI. The Article begins by showing, using formal game-theoretic models, that, by default, humans and AIs will be trapped in a prisoner’s dilemma. Both parties’ dominant strategy will be to permanently disempower or destroy the other, even though the costs of such conflict would be high. -/- The Article then argues that a surprising legal intervention could transform the game theoretic equilibrium and avoid conflict: AI rights. Not just any AI rights would promote human safety. Granting AIs the right not to be needlessly harmed–as humans have granted to certain non-human animals–would, for example, have little effect. Instead, to promote human safety, AIs should be given those basic private law rights–to make contracts, hold property, and bring tort claims–that law already extends to non-human corporations. Granting AIs these economic rights would enable long-run, small-scale, mutually-beneficial transactions between humans and AIs. This would, we show, facilitate a peaceful strategic equilibrium between humans and AIs for the same reasons economic interdependence tends to promote peace in international relations. Namely, the gains from trade far exceed those from war. Throughout, we argue that human safety, rather than AI welfare, provides the right framework for developing AI rights. This Article explores both the promise and the limits of AI rights as a legal tool for promoting human safety in an AGI world. (shrink)

This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous improvement in (...) AI systems. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance. The paper concludes with strategies for resolving conflicts between ethical directives, underscoring the present and future need for robust, nuanced and context-aware AI systems. (shrink)

Generative AI has proven itself as an efficient innovation in many fields including writing and even analyzing data. For spatial computing, it provides a potential solution for solving such issues related to data manipulation and analysis within the spatial computing domain. This paper aims to discuss the probabilities of applying generative AI to graph-based spatial computing; to describe new approaches in detail; to shed light on their use cases; and to demonstrate the value that they add. This technique thus incorporates (...) graph theory, generative models to model spatial relations, generate new spatial forms and improve on spatial decision-making processes. The paper surveys such methods, describes typical applications, and outlines further development of the subject. (shrink)

The ability to interact in a natural language profoundly changes devices’ interfaces and potential applications of speaking technologies. Concurrently, this phenomenon challenges our mainstream theories of knowledge, such as how to analyze linguistic outputs of devices under existing anthropocentric theoretical assumptions. In section 1, I present the topic of machines that speak, connecting between Descartes and Generative AI. In section 2, I argue that accepted testimonial theories of knowledge and justification commonly reject the possibility that a speaking technological artifact can (...) give testimony. In section 3, I identify three assumptions underlying the view that rejects conversational AIs – AI-based technologies that converse, as testifiers: conversational AIs (1) lack intentions, (2) cannot be normatively assessed, and (3) cannot constitute an object in trust relations, while humans can. In section 4, I propose the concept ‘AI-testimony’ for analyzing outputs of conversational AIs, suggesting three conditions for technologies to deliver AI-testimony: (1) content is propositional, (2) generated and delivered with no other human directly involved, (3) the output is perceived as phenomenologically similar to that of a human. I conclude that this concept overcomes the limitations of the anthropocentric concept of testimony, opening future directions of research without associating conversational AIs with human-like agency. (shrink)

The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, (...) we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (LLMs) as an example. (shrink)

This article explores whether AI can be a human-like subject that can live together with humans. I argue that living together is conceptually different from being together or coexistence and symbiosis in an ecosystem. I propose three conditions for living together and consider as subjects only those beings that can live together in a cultural and inter-subjective way. And I try to define the subject as a being which can build its relationships with the world in three ways. Then the (...) conditions of living together will be combined with the concept of subject. The first condition is that the subject should be conscious of its relationship with others. The second condition is that the subject should form reciprocal relationships with others. The third condition is that the subjects should have the same representation of the relationship with others. This article attempts to examine whether AI can fulfill the three conditions of living together. Although AI seems to be able to think, express, act, feel, even be conscious and so on, it is very unlikely that AI can build the same world as the human world. Therefore, even if we cannot discern ourselves from AI, we humans can never enter its world and vice versa. The reason is very simple: humans are ontically different from AI. In society, humans and AI can be together, but they cannot live together. Even if AI could eventually have a consciousness, genuine social living together between humans and AI is impossible. We humans can recognize AI as a pseudo-subject. Nevertheless, such a subject is not one that can live together with humans. Even if AI were conscious and could pretend to be a human-like subject, it could not be a genuine subject unless it could live together with humans. Many people warn that AI could be dangerous to humans. These so-called threats might not come from AI itself, but from humans themselves. (shrink)

The EU Artificial Intelligence Act (AIA) defines four risk categories for AI systems: unacceptable, high, limited, and minimal. However, it lacks a clear methodology for the assessment of these risks in concrete situations. Risks are broadly categorized based on the application areas of AI systems and ambiguous risk factors. This paper suggests a methodology for assessing AI risk magnitudes, focusing on the construction of real-world risk scenarios. To this scope, we propose to integrate the AIA with a framework developed by (...) the Intergovernmental Panel on Climate Change (IPCC) reports and related literature. This approach enables a nuanced analysis of AI risk by exploring the interplay between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We further refine the proposed methodology by applying a proportionality test to balance the competing values involved in AI risk assessment. Finally, we present three uses of this approach under the AIA: to implement the Regulation, to assess the significance of risks, and to develop internal risk management systems for AI deployers. (shrink)

A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has far-reaching moral implications (...) for AI development. Although the most obvious way to avoid the tension between alignment and ethical treatment would be to avoid creating AI systems that merit moral consideration, this option may be unrealistic and is perhaps fleeting. So, we conclude by offering some suggestions for other ways of mitigating mistreatment risks associated with alignment. (shrink)

The expanding integration of artificial intelligence (AI) in various aspects of society makes the infosphere around us increasingly complex. Humanity already faces many obstacles trying to have a better understanding of our own minds, but now we have to continue finding ways to make sense of the minds of AI. The issue of AI’s capability to have independent thinking is of special attention. When dealing with such an unfamiliar concept, people may rely on existing human properties, such as survival desire, (...) to make assessments. Employing information-processing-based Bayesian Mindsponge Framework (BMF) analytics on a dataset of 266 residents in the United States, we found that the more people believe that an AI agent seeks continued functioning, the more they believe in that AI agent’s capability of having a mind of its own. Moreover, we also found that the above association becomes stronger if a person is more familiar with personally interacting with AI. This suggests a directional pattern of value reinforcement in perceptions of AI. As the information processing of AI becomes even more sophisticated in the future, it will be much harder to set clear boundaries about what it means to have an autonomous mind. (shrink)

An AI winter may be defined as the stage when technology, business, and the media come to terms with what AI can or cannot really do as a technology without exaggeration. Through discussion of previous AI winters, this paper examines the hype cycle (which by turn characterises AI as a social panacea or a nightmare of apocalyptic proportions) and argues that AI should be treated as a normal technology, neither as a miracle nor as a plague, but rather as of (...) the many solutions that human ingenuity has managed to devise. (shrink)

The AI and AI alignment communities have been instrumental in addressing existential risks, developing alignment methodologies, and promoting rationalist problem-solving approaches. However, as AI research ventures into increasingly uncertain domains, there is a risk of premature epistemic convergence, where prevailing methodologies influence not only the evaluation of ideas but also determine which ideas are considered within the discourse. This paper examines critical epistemic blind spots in AI alignment research, particularly the lack of predictive frameworks to differentiate problems necessitating general intelligence, (...) decentralized intelligence, or innovative alignment methodologies. We analyze how heuristic-based epistemic filtering—favoring ideas that conform to established norms over those assessed purely on logical merit—may inadvertently constrain the field's potential for groundbreaking solutions. The semblance of rigor, wherein social consensus and stylistic conformity replace foundational reasoning, further intensifies this concern. To mitigate these issues, we advocate for an epistemically adaptive research environment that emphasizes structured evaluation of paradigm-challenging ideas, cross-disciplinary collaboration, and strategies to identify and reduce epistemic overfitting. By broadening the epistemic frameworks governing AI alignment discourse, the field can progress beyond incremental improvements toward discovering entirely new solution paradigms. Maintaining epistemic inclusivity in AI safety research is not only an intellectual necessity but also a crucial step in preparing alignment efforts for the challenges posed by rapid AI advancements. (shrink)

To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems.

In 2024, the intersection of artificial intelligence (AI) and cybersecurity presents both unprecedented challenges and significant opportunities. This article explores the evolving landscape of AI-driven cyber threats, the advancements in AI-enabled security measures, and the strategic responses required to navigate these new realities. Leveraging statistics, trends, and expert insights, we delve into how organizations can enhance their cybersecurity posture in the face of sophisticated AI threats.

The rapid advancements in AI technology have raised critical questions about its impact on end-of-life care. While much of the debate focuses on AI’s potential to predict the preferences of incapacitated patients, little attention has been given to its direct application in delivering lethal drugs at a patient’s request. This contribution explores this underexamined issue, arguing that AI is a game-changer in assisted dying. The paper is structured in three parts. The first explains how AI can be effectively applied to (...) the assisted dying process. The second establishes terminological clarity, contending that the direct use of AI should not be classified as either assisted dying or euthanasia, but rather as a distinct category: AI-Assisted Suicide (AIAS). To support this claim, I examine the moral and legal responsibility associated with AI in such procedures. The third part evaluates AIAS through the lens of the key arguments regarding the moral superiority of assisted suicide over euthanasia and vice versa, concluding that AIAS appears preferable from both perspectives. Ultimately, I argue that AIAS should not merely be seen as an extension of existing assisted dying methods but, wherever feasible, as a replacement, since it effectively addresses several ongoing ethical and practical concerns in the end-of-life debate. This conclusion is particularly relevant to legislative frameworks and policymaking in end-of-life care. (shrink)

This paper examines how professionals (N = 32) working on artificial intelligence (AI) view structural AI ethics challenges like injustices and inequalities beyond individual agents' direct intention and control. This paper answers the research question: What are professionals’ perceptions of the structural challenges of AI (in the agri-food sector)? This empirical paper shows that it is essential to broaden the scope of ethics of AI beyond micro- and meso-levels. While ethics guidelines and AI ethics often focus on the responsibility of (...) designers and the competencies and skills of designers to take this responsibility, our results show that many structural challenges are beyond their reach. This result means that while ethics guidelines and AI ethics frameworks are helpful, there is a risk that they overlook more complicated, nuanced, and intersected structural challenges. In addition, it highlights the need to include diverse stakeholders, such as quadruple helix (QH) participants, in discussions around AI ethics rather than solely focusing on the obligations of AI developers and companies. Overall, this paper demonstrates that addressing structural challenges in AI is challenging and requires an approach that considers four requirements: (1) multi-level, (2) multi-faceted, (3) interdisciplinary, and (4) polycentric governance. (shrink)

I discuss an influential argument put forward by Hatherley in theJournal of Medical Ethics. Drawing on influential philosophical accounts of interpersonal trust, Hatherley claims that medical artificial intelligence is capable of being reliable, but not trustworthy. Furthermore, Hatherley argues that trust generates moral obligations on behalf of the trustee. For instance, when a patient trusts a clinician, it generates certain moral obligations on behalf of the clinician for her to do what she is entrusted to do. I make three objections (...) to Hatherley’s claims: (1) At least one philosophical account of interagent trust implies that medical AI is capable of being trustworthy. (2) Even if this account should ultimately be rejected, it does not matter much because what we care mostly about is that medical AI is reliable. (3) It is false that trust in itself generates moral obligations on behalf of the trustee. (shrink)

Abstract: The incorporation of Artificial Intelligence (AI) into educational technology has dramatically transformed learning through Intelligent Tutoring Systems (ITS). These systems utilize AI to offer personalized, adaptive instruction tailored to each student's needs, thereby improving learning outcomes and engagement. This paper examines the development and impact of ITS, focusing on AI technologies such as machine learning, natural language processing, and adaptive algorithms that drive their functionality. Through various case studies and applications, it illustrates how ITS have revolutionized traditional educational methods (...) and addresses the challenges and limitations, including data privacy and system biases. The discussion also covers future directions in ITS development, highlighting emerging trends and potential advancements. The goal is to provide a thorough understanding of how ITS use AI to enhance educational experiences and suggest avenues for future research in this evolving field. (shrink)

Motivated by the question of responsible AI and value alignment, I seek to offer a uniquely Foucauldian reconstruction of the problem as the emergence of an ethical subject in a disciplinary setting. This reconstruction contrasts with the strictly human-oriented programme typical to current scholarship that often views technology in instrumental terms. With this in mind, I problematise the concept of a technological subjectivity through an exploration of various aspects of ChatGPT in light of Foucault’s work, arguing that current systems lack (...) the reflexivity and self-formative characteristics inherent in the notion of the subject. By drawing upon a recent dialogue between Foucault and phenomenology, I suggest four techno-philosophical desiderata that would address the gaps in this search for a technological subjectivity: embodied self-care, embodied intentionality, imagination and reflexivity. Thus I propose that advanced AI be reconceptualised as a subject capable of “technical” self-crafting and reflexive self-conduct, opening new pathways to grasp the intertwinement of the human and the artificial. This reconceptualization holds the potential to render future AI technology more transparent and responsible in the circulation of knowledge, care and power. (shrink)

This piece in Nature Medicine notes the risks that incorporation of AI systems into health care poses to disabled patients and proposes ways to avoid them and instead create benefit.

In the rapidly evolving digital landscape, cyber threats are becoming increasingly sophisticated, making traditional security measures inadequate. Advanced Threat Detection (ATD) leveraging Artificial Intelligence (AI)-driven anomaly detection systems offers a proactive approach to identifying and mitigating cyber threats in real time. This paper explores the integration of AI, particularly machine learning (ML) and deep learning (DL) techniques, in anomaly detection to enhance cybersecurity defenses. By analyzing vast amounts of network traffic, user behavior, and system logs, AI-driven models can identify deviations (...) from normal patterns, enabling early threat detection and prevention. These systems excel in detecting zero-day attacks, insider threats, and advanced persistent threats (APTs), which often bypass conventional rule-based security mechanisms. Additionally, we discuss the challenges of AI-based anomaly detection, including false positives, model interpretability, and adversarial attacks. The findings emphasize the need for continuous learning and adaptive security frameworks to ensure robust cyber threat detection. The study concludes that AIdriven anomaly detection significantly enhances threat intelligence and response capabilities, making it a vital component of modern cybersecurity strategies. (shrink)

One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI’s Diffusion and OpenAI’s Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social (...) effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence. (shrink)

AI systems, like self-driving cars, healthcare robots, or Autonomous Weapon Systems, already play an increasingly important role in our lives and will do so to an even greater extent in the near future. This raises a fundamental philosophical question: who is morally responsible when such systems cause unjustified harm? In the paper, we argue for the admittedly surprising claim that some of these systems can themselves be morally responsible for their conduct in an important and everyday sense of the term—the (...) attributability sense. More specifically, relying on work by Nomy Arpaly and Timothy Schroeder (In Praise of Desire, OUP 2014), we propose that the behavior of these systems can manifest their ‘quality of will’ and thus be regarded as something they can be blameworthy for. We develop this position in detail, justify some of its crucial presuppositions, and defend it against potential objections. (shrink)

-/- Aligning AI with the Universal Formula for Balanced Decision-Making -/- Introduction -/- Artificial Intelligence (AI) represents a highly advanced form of automated information processing, capable of analyzing vast amounts of data, identifying patterns, and making predictive decisions. However, the effectiveness of AI depends entirely on the integrity of its inputs, processing mechanisms, and decision-making frameworks. If AI is programmed without a foundational understanding of natural laws, it risks reinforcing misinformation, bias, and societal imbalance. -/- Angelito Malicse’s universal formula, particularly (...) the law of karma as a system principle and the law of balance in nature, provides a crucial foundation for AI’s role in decision-making. The law of karma states that any system, whether natural or artificial, must be free of defects to function properly, while the law of balance ensures that all decisions align with equilibrium in nature and society. By embedding these principles into AI development, AI can serve as a powerful tool for maintaining systemic harmony and solving human problems without creating unintended disruptions. -/- This essay explores how AI can be structured according to the universal formula by incorporating a complete sensor system, error-free processing mechanisms, balance-driven decision-making, and integration into leadership and education. Through these elements, AI can function as a stabilizing force that enhances human governance, education, and social progress. -/- 1. The Importance of a Complete Sensor System in AI -/- AI decision-making depends on the quality of its inputs. Just as human perception relies on sensory feedback, AI requires a comprehensive sensor system to process information accurately. If AI is fed incomplete or biased data, its conclusions will reflect those flaws, leading to distorted outputs that can negatively impact society. -/- To align with the universal formula, AI must be equipped with: -/- Multimodal data processing – AI should integrate data from visual, auditory, textual, and real-time environmental sources to ensure a holistic understanding of reality. -/- Error detection and bias correction – Automated validation mechanisms should filter out misinformation, propaganda, and manipulated narratives before they influence AI’s conclusions. -/- Dynamic feedback loops – AI should continuously refine its decisions based on real-world outcomes, adapting to new conditions while maintaining systemic balance. -/- Without these mechanisms, AI risks becoming an imbalanced system that propagates human errors rather than correcting them. The universal formula emphasizes that any decision-making system must operate on accurate and unbiased inputs to prevent instability. -/- 2. AI Must Be Programmed for Error-Free Processing Aligned with Natural Laws -/- The law of karma states that systems must function without defects to ensure proper operation. If AI is to assist human decision-making, it must be programmed in a way that eliminates errors and ensures logically consistent outputs. -/- To achieve this, AI must: -/- Prioritize verified and logical knowledge – AI should be trained using scientifically validated information, avoiding speculation, conspiracy theories, or biased narratives. -/- Reject false or manipulated data – AI should detect and neutralize black propaganda, unproven theories, and misleading statistics that could lead to imbalance. -/- Develop self-correction mechanisms – AI should continuously evaluate its own decision-making processes, identifying and adjusting for errors in real-time. -/- By ensuring AI adheres to error-free processing aligned with natural laws, it becomes a predictable and reliable tool that enhances rather than disrupts decision-making at all levels of society. -/- 3. AI Decision-Making Must Follow the Law of Balance in Nature -/- One of the most critical aspects of AI programming is ensuring that its decision-making aligns with the law of balance—the natural equilibrium that governs all systems. AI should not only analyze cause-and-effect relationships but also assess whether its decisions maintain systemic harmony. -/- This can be achieved by: -/- Evaluating the long-term impact of decisions – AI should assess both immediate and future consequences, ensuring that actions do not create new problems while solving existing ones. -/- Adaptive decision-making – AI should dynamically adjust its recommendations if new data suggests an imbalance is forming. -/- Ensuring fairness in governance and society – AI can guide policy decisions by ensuring that no group is disproportionately favored over another, maintaining social stability. -/- For example, in economic policy, AI could analyze global trade and recommend adjustments to prevent financial instability. In environmental sustainability, AI could assess resource consumption patterns and suggest policies that maintain ecological balance. When programmed according to the law of balance, AI becomes a guardian of systemic harmony, preventing crises before they arise. -/- 4. AI Should Be Integrated into Leadership and Education -/- AI should not replace human judgment but rather serve as an educational and advisory tool that reinforces rational and ethical decision-making. To maximize its potential, AI should be implemented in: -/- The Educational System – Students should learn how AI aligns with natural laws, ensuring that future AI developers prioritize systemic balance and ethical considerations in programming. -/- Leadership and Governance – Governments should use AI to analyze complex societal issues, making data-driven decisions that align with long-term stability rather than short-term political interests. -/- Knowledge Dissemination – AI can generate content that explains the universal formula in accessible ways, helping different cultures and societies understand and implement its principles. -/- By integrating AI into education and governance, societies can ensure that leaders are equipped with knowledge based on natural laws, reducing the risk of decisions that create instability or conflict. -/- 5. Challenges and Considerations in Implementing the Universal Formula in AI -/- While structuring AI according to the universal formula offers many advantages, several challenges must be addressed: -/- Data Integrity – AI can only function properly if its training data is accurate and unbiased. Preventing misinformation from influencing AI requires constant monitoring and updates. -/- Ethical Implementation – Governments and organizations must ensure AI is used for balance and stability, not for manipulation or control. -/- Technological Limitations – Current AI systems are not yet capable of fully replicating human judgment. Further advancements in AGI (Artificial General Intelligence) are needed to align AI more closely with the universal formula’s principles. -/- Despite these challenges, implementing AI based on natural laws represents the most logical and sustainable path forward, ensuring that technology serves humanity rather than disrupting it. -/- Conclusion -/- AI, as an advanced information processing system, has the potential to transform human decision-making at all levels of society. However, without a structured framework, it risks creating systemic imbalances rather than solving them. Angelito Malicse’s universal formula, particularly the law of karma as a system principle and the law of balance in nature, provides a crucial foundation for ensuring AI aligns with natural equilibrium. -/- By incorporating a complete sensor system, error-free processing, balanced decision-making, and integration into leadership and education, AI can function as a powerful tool for reinforcing societal stability. If properly implemented, AI will not only prevent errors in human decision-making but also guide humanity toward a future where choices are made in harmony with the fundamental laws of nature. -/- This structured approach to AI development ensures that technology serves as a stabilizing force rather than a disruptive one, ultimately helping to create a world where all decisions—whether human or machine-assisted—uphold the principles of balance, integrity, and sustainability. -/- . (shrink)

Over the last few years, personal health monitoring wearable devices have emerged as innovative applications of Artificial Intelligence (AI) in the healthcare industry as they help in real time analysis and prediction of health standardized check-ups and health management. To navigate through the current trends, new technologies and developments, the prospects are as follows: The article also gives a logical look at the state of the art of such devices, enumerating the advantages and drawbacks, as well as outlining the main (...) ethical issues. In terms of method, it also discusses different AI approaches, data acquisition procedures, and those integrating systems used in the context of health monitoring. The finding and implication section discusses the findings of the current trend and the possibility of improvement in application and user acceptance. The last section provides a prognosis on what the area of personal health monitoring might look like in the future, as well as the position of AI in the optimisation of patient experience. (shrink)