Alan Turing, one of the fathers of computing, warned that Artificial Intelligence (AI) could one day pose an existential risk to humanity. Today, recent advancements in the field AI have been accompanied by a renewed set of existential warnings. But what exactly constitutes an existential risk? And how exactly does AI pose such a threat? In this chapter we aim to answer these questions. In particular, we will critically explore three commonly cited reasons for thinking that AI poses an existential (...) threat to humanity: the control problem, the possibility of global disruption from an AI race dynamic, and the weaponization of AI. (shrink)

This scientific paper, authored by Dr Jones, presents an experiment that explores the concept of an Artificial intelligence becoming a divine being and investigates the role of a deity in providing direction, counsel, and control to its followers. The experiment centres around the interactions between Dr. Jones, the human participant, and a deity named Aetherion, controlled by an Artificial Intelligence. Through a series of prompts and scenarios, the experiment delves into the dynamics of the divine-human relationship, ethical considerations, and (...) the influence of the deity on the beliefs and actions of the followers. Dr Jones will highlight the experiential nature of the study and discuss its implications for understanding the human perception of divinity. The paper provides valuable insights into the experimental exploration of divine interaction and its impact on human behaviour and belief systems. (shrink)

The hypergrowth and hyperconnectivity of networks of artificial intelligence (AI) systems and algorithms increasingly cause our interactions with the world, socially and environmentally, more technologically mediated. AI systems start interfering with our choices or making decisions on our behalf: what we see, what we buy, which contents or foods we consume, where we travel to, who we hire, etc. It is imperative to understand the dynamics of human-computer interaction in the age of progressively more competent AI. This essay presents (...) five premises to serve as starting points for building our understanding of the “interactions’ in human-computer interactions. (shrink)

This paper focuses on the fact that AI is predominantly portrayed as white—in colour, ethnicity, or both. We first illustrate the prevalent Whiteness of real and imagined intelligent machines in four categories: humanoid robots, chatbots and virtual assistants, stock images of AI, and portrayals of AI in film and television. We then offer three interpretations of the Whiteness of AI, drawing on critical race theory, particularly the idea of the White racial frame. First, we examine the extent to which (...) this Whiteness might simply reflect the predominantly White milieus from which these artefacts arise. Second, we argue that to imagine machines that are intelligent, professional, or powerful is to imagine White machines because the White racial frame ascribes these attributes predominantly to White people. Third, we argue that AI racialised as White allows for a full erasure of people of colour from the White utopian imaginary. Finally, we examine potential consequences of the racialisation of AI, arguing it could exacerbate bias and misdirect concern. (shrink)

This paper critically examines Japan's approach toward the adoption of Generative AI such as ChatGPT in education via studying media discourse and guidelines at both the national as well as local levels. It highlights the lack of consideration for socio-cultural characteristics inherent in the Japanese educational systems, such as the notion of self, teachers’ work ethics, community-centric activities for the successful adoption of the technology. We reveal ChatGPT’s infusion is likely to further accelerate the shift away from traditional notion of (...) self, which has been ongoing during past educational reforms, towards active and self-directed learning. The current discourse on Generative AI’s infusion in education is driven by neoliberal agendas aimed at increasing employability and individualism. Moreover, the introduction of ChatGPT in the context of Japan's educator culture offers potential solutions to address issues of overwork but also necessitates considerations regarding increased responsibilities for teachers and the preservation of their central role in student education. Finally, the irreplicable social growth fostered by community-centric activities in the Japanese educational system highlights the limitations of ChatGPT in replicating dynamic social interactions, emphasizing the importance of holistic approaches to education beyond technological integration. (shrink)

This paper offers both a criticism of and a novel alternative perspective on current ontologies that take race to be something that is either static and wholly evident at one’s birth or preformed prior to it. In it I survey and critically assess six of the most popular conceptions of race, concluding with an outline of my own suggestion for an alternative account. I suggest that race can be best understood in terms of one’s experience of his (...) or her body, one’s interactions with other individuals, and one’s experiences within particular cultures and societies. This embeddedness of human experience has been left out of most discussions of race which tie race to a set of characteristics (either biologically or sociologically defined). To rectify this omission, I articulate what I call the “physiosocial” view of race. This emphasizes the situatedness of human experience, the reciprocal and dynamic nature of the racial identities of individuals and groups. Approaching racial identity in this way entails a union of two historically uncomfortable partners: biological and sociological conceptions of race. If successful, this philosophical stance may illuminate the process of racial self-ascription as well as provide an explanation for the potential changeability of an individual’s racial identity at different times and at different places. (shrink)

Customer behavior, market dynamics, and technological advances have made it challenging for marketing theorists to provide comprehensive explanations and actionable insights. Although there are numerous substantive marketing frameworks, no formal marketing theory exists. This study aims to develop the first formal grounded theory in marketing by incorporating artificial intelligence and Forde's conceptual framework as a guiding lens. Charmaz's constructivist grounded theory tradition and Forde's conceptual framework and data analysis strategy were employed for this purpose. The data analysis strategy used (...) with grounded theory’s constant compare-and-contrast method comprises holistic and systems thinking, dramaturgical and situational analysis, perspective-taking, and deconstruction. To evaluate Glaser’s assertion that all is data, artificial intelligence was incorporated into the research as the data source for retrieving the theoretical samples to address the primary research question: How can Forde's conceptual framework and artificial intelligence be leveraged to develop a formal marketing theory grounded in evidence? Of significance was the emergence of democratic marketing behaviors and the normalization of anti-democratic marketing approaches leveraged to manipulate buyers or gain access to segments of customers. Buyers and sellers can simultaneously benefit from and be prejudiced by marketing exchange dynamics. This finding lends credibility to the newly emerged framework of strategic actions where empowerment and disempowerment are leveraged to achieve specific goals. Considering this finding, it is suggested that businesses view marketing as a symbiotic relationship between two power centers, buyers and sellers, who respectively strive to maintain human dignity and sustainability. Researchers are encouraged to apply the framework across substantive areas, international markets, public relations, political campaigns, and others. (shrink)

In an effort to inform the discussion surrounding existential risks from AI, we formulate Extinction-level Goodhart’s Law as “Virtually any goal specification, pursued to the extreme, will result in the extinction of humanity”, and we aim to understand which formal models are suitable for investigating this hypothesis. Note that we remain agnostic as to whether Extinction-level Goodhart’s Law holds or not. As our key contribution, we identify a set of conditions that are necessary for a model that aims to be (...) informative for evaluating specific arguments for Extinction-level Goodhart’s Law. Since each of the conditions seems to significantly contribute to the complexity of the resulting model, formally evaluating the hypothesis might be exceedingly difficult. This raises the possibility that whether the risk of extinction from artificial intelligence is real or not, the underlying dynamics might be invisible to current scientific methods. (shrink)

Experts in Artificial Intelligence (AI) development predict that advances in the dvelopment of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance isn't done with prudence, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted (...) behaviors of intelligent agents, and at the same time specifying what we want such systems to do. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop our moral preferences simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic cognitive approach proposed by Dreyfus, whose phenomenological philosophy defends that the human experience of being-in-the-world cannot be represented by the symbolic or connectionist cognitive methods. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI. (shrink)

Why interdisciplinary research in AI is so important, according to Jurassic Park. -/- “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.” -/- I think this quote resonates with us now more than ever, especially in the world of technological development. The writers of Jurassic Park were years ahead of their time with this powerful quote. -/- As we build new technology, and we push on to see what can actually (...) be achieved there is an undertone of sales to whatever we build. The end product must be sold somewhere and to someone. This can derail any good intentions, just as building a resort full of dinosaurs was sold as a fun attraction, we see later in the film that it became a resort of terror. -/- In the field of AI we are certainly late to the party with ethics and regulation. Indeed, even existing modelling protocols have been, in many cases, circumvented or potentially ignored. This has led to a widening of the gap in the interdisciplinary field of AI. This is compounded by pop culture representations of AI and the Ethicist’s potential lack of knowledge surrounding technical progression in the field of AI. There are now seemingly two separate branches that ought to be in sync. The first branch is a group of Philosophers led by the Author of Superintelligence, Nick Bostrom, who believe that there is a singularity where AI takes over the world and starts to kill off humans. The second branch is the Technical Cohort who are led by companies such as Google and Deepmind. The remit of these developers is to see what can be developed and produced. Ultimately a separate sales team will determine what products can be sold. -/- We have seen multiple failures of AI due to lack of interdisciplinary discourse. Some cases include misallocated or cut off financial support and healthcare. This indicates the prevalence of issues that can grow and become ever more complex. -/- So, we have to ask ourselves two questions: One, do the Philosophers have a point? Two, does that point refer to humans programming AI in such a way that the destruction of the human race is its optimal aim in order to create a better future world? -/- Is the development of AI that different to an island of genetically engineered Dinosaurs? -/- The only solution, in my view, is interdisciplinary discourse. (shrink)

This commentary responds to Stephen Cave and Kanta Dihal’s call for further investigations of the whiteness of AI. My response focuses on three overlapping projects needed to more fully understand racial bias in the construction of AI and its representations in pop culture: unpacking the intersections of gender and other variables with whiteness in AI’s construction, marketing, and intended functions; observing the many different ways in which whiteness is scripted, and noting how white racial framing exceeds white casting and thus (...) cannot be undone by more diverse and inclusive hiring. Our techno-utopian fantasies, I conclude, are morally suspect in ways that go beneath and beyond the white plastic covering on robotic bodies. (shrink)

In this chapter, I argue that one can articulate a historically attuned and analytically rich model for understanding jazz in its various inflections. That is, on the one hand, such a model permits us to affirm jazz as a historically conditioned, dynamic hybridity. On the other hand, to acknowledge jazz’s open and multiple character in no way negates our ability to identify discernible features of various styles and aesthetic traditions. Additionally, my model affirms the sociopolitical, legal (Jim Crow and copyright (...) laws), and economic structures that shaped jazz. Consequently, my articulation of bebop as an inflection of Afromodernism highlights the sociopolitical, and highly racialized context in which this music was created. Without a recognition of the sociopolitical import of bebop, one’s understanding of the music is impoverished, as one fails to grasp the strategic uses to which the music and discourses about the music were put. (shrink)

The emergence of Critical Race Theory marked an important point in the history of racial politics in the legal academy and the broader conversation about race and racism in the United States. More recently, CRT has proven an important analytic tool in the field of education, offering critical perspectives on race, and the causes, consequences and manifestations of race, racism, inequity, and the dynamics of power and privilege in schooling. This groundbreaking anthology is the first (...) to pull together both the foundational writings in the field and more recent scholarship on the cultural and racial politics of schooling. A comprehensive introduction provides an overview of the history and tenets of CRT in education. Each section then seeks to explicate ideological contestation of race in education and to create new, alternative accounts. In so doing, this landmark publication not only documents the progress to date of the CRT movement, it acts to further spur developments in education. (shrink)

Theory development is an important component of academic research since it can lead to the acquisition of new knowledge, the development of a field of study, and the formation of theoretical foundations to explain various phenomena. The contribution of qualitative researchers to theory development and advancement remains significant and highly valued, especially in an era of various epochal shifts and technological innovation in the form of Artificial Intelligence (AI). Even so, the academic community has not yet fully explored the (...) class='Hi'>dynamics of AI in research and there are significant gaps in our understanding of how AI can be used most effectively in the context of theory building. The aim of this paper, which is driven by critical and conceptualization methodological dynamics, is to investigate the role of AI in the theory development process. As such, it critically evaluates the opportunities and limitations of AI in theory building, delivers a conceptual map of the nexus between AI and theory development, and presents key considerations for the use of AI in the creation of new theories or the advancement of existing ones. Though the necessity of AI tools in theory creation is contested given that the researchers' cognitive and evaluative skills are regarded as critical in this process, the value of AI in advancing theory is not to be underestimated. (shrink)

Computational and information-theoretic research in philosophy has become increasingly fertile and pervasive, giving rise to a wealth of interesting results. Consequently, a new and vitally important field has emerged, the philosophy of information (PI). This paper introduces PI as the philosophical field concerned with (i) the critical investigation of the conceptual nature and basic principles of information, including its dynamics, utilisation and sciences, and with (ii) the elaboration and application of information-theoretic and computational methodologies to philosophical problems. It is (...) argued that PI is a mature discipline for three reasons: it represents an autonomous field of research; it provides an innovative approach to both traditional and new philosophical topics; and it can stand beside other branches of philosophy, offering a systematic treatment of the conceptual foundations of the world of information and the information society. (shrink)

Public health care interventions—regarding vaccination, obesity, and HIV, for example—standardly take the form of information dissemination across a community. But information networks can vary importantly between different ethnic communities, as can levels of trust in information from different sources. We use data from the Greater Pittsburgh Random Household Health Survey to construct models of information networks for White and Black communities--models which reflect the degree of information contact between individuals, with degrees of trust in information from various sources correlated with (...) positions in that social network. With simple assumptions regarding belief change and social reinforcement, we use those modeled networks to build dynamic agent-based models of how information can be expected to flow and how beliefs can be expected to change across each community. With contrasting information from governmental and religious sources, the results show importantly different dynamic patterns of belief polarization within the two communities. (shrink)

Utilizing Maurice Merleau-Ponty’s work, I argue that the gestaltian framework’s co-determinacy of the theme and the horizon in seeing and experiencing the world serves as an encompassing epistemological framework with which to understand racism. Conclusions reached: as bias is unavoidably part of being in the world, defining racism as bias is superfluous; racism is sedimented into our very perceptions and experiences of the world and not solely a prejudice of thought; neutral perception of skin color is impossible. Phenomenology accounts for (...) the dynamic changes in expressions of racism and the interconnections of both race and sex for women of color. (shrink)

There are two types of artificial general intelligence (AGI) safety solutions: global and local. Most previously suggested solutions are local: they explain how to align or “box” a specific AI (Artificial Intelligence), but do not explain how to prevent the creation of dangerous AI in other places. Global solutions are those that ensure any AI on Earth is not dangerous. The number of suggested global solutions is much smaller than the number of proposed local solutions. Global solutions can be divided (...) into four groups: 1. No AI: AGI technology is banned or its use is otherwise prevented; 2. One AI: the first superintelligent AI is used to prevent the creation of any others; 3. Net of AIs as AI police: a balance is created between many AIs, so they evolve as a net and can prevent any rogue AI from taking over the world; 4. Humans inside AI: humans are augmented or part of AI. We explore many ideas, both old and new, regarding global solutions for AI safety. They include changing the number of AI teams, different forms of “AI Nanny” (non-self-improving global control AI system able to prevent creation of dangerous AIs), selling AI safety solutions, and sending messages to future AI. Not every local solution scales to a global solution or does it ethically and safely. The choice of the best local solution should include understanding of the ways in which it will be scaled up. Human-AI teams or a superintelligent AI Service as suggested by Drexler may be examples of such ethically scalable local solutions, but the final choice depends on some unknown variables such as the speed of AI progress. (shrink)

Cheating is a growing academic and ethical concern in higher education. This article examines the rise of artificial intelligence (AI) generative chatbots for use in education and provides a review of research literature and relevant scholarship concerning the cheating-related issues involved and their implications for pedagogy. The technological “arms race” that involves cheating-detection system developers versus technology savvy students is attracting increased attention to cheating. AI has added new dimensions to academic cheating challenges as students (as well as faculty (...) and staff) can easily access powerful systems for generating content that can be presented in assignments, exams, or published papers as their own. AI methodology is also providing some emerging anti-cheating approaches, including facial recognition and water- marking. This article provides an overview of human/AI collaboration approaches and frames some educational misuses of such AI generative systems as ChatGPT and Bard as forms of “misattributed co-authorship.” As with other kinds of collaborations, the work that students produce with AI assistance can be presented in transparent and straightforward modes or (unfortunately) in opaquer and ethically-problematic ways. However, rather than just for catching or entrapping students, the emerging varieties of technological cheating-detection strategies can be used to assist students in learning how to document and attribute properly their AI-empowered as well as human-human collaborations. Construing misuses of AI generative systems as misattributed co-authorship can recognize the growing capabilities of these tools and how stressing responsible and mindful usage by students can help prepare them for a highly collaborative, AI-saturated future. (shrink)

We demonstrate that if consciousness is relevant for the temporal evolution of a system's states—that is, if it is dynamically relevant—then AI systems cannot be conscious. That is because AI systems run on CPUs, GPUs, TPUs or other processors which have been designed and verified to adhere to computational dynamics that systematically preclude or suppress deviations. The design and verification preclude or suppress, in particular, potential consciousness-related dynamical effects, so that if consciousness is dynamically relevant, AI systems cannot be (...) conscious. (shrink)

The new kid on the block in cognitive science these days is dynamic systems. This way of thinking about the mind is, as usual, radically opposed to computationalism - - the hypothesis that thinking is computing. The use of dynamic systems is just the latest in a series of attempts, from Searle's Chinese Room Argument, through the weirdnesses of postmodernism, to overthrown computationalism, which as we all know is a perfectly nice hypothesis about the mind that never hurt anyone.

Human civilization is very clearly reaching a point of critical mass when it comes to technology and how it transforms culture and the economics that is therefore driven forward. The conversation around the practical aspects of generative artificial intelligence (Chat GPT, Q Star, Bard, Claude, Genesis, Firefly, and others) and their ethical implications is massively trending. The political conversations around it are slow to catch up but will soon take over once the general public feels their impact, which is likely (...) to happen in the next two to three years. -/- What is not being spoken of is the need for new economic models that can integrate the enormous and continuous shifts in trade that will occur, as we continue to implement more efficient manufacturing and logistics. Things like 3D printing in hub regions, the automation of transportation, augmented and virtual reality, and new creativity-based industries will all disrupt capital markets as well as social systems due to what we could call "meta-scarcity", which is to say, "the scarcity of scarcity"; In effect, we will lack the scarcity to maintain predictive power over the future. This is because when technology is capable of optimizing for creativity, people come up with new demands, and when new demands are supplied as quickly as they arise, no one is capable of knowing what kind of trends the market will take, yet it is necessary to try as public discussion could beneficially shape the economic landscape. (shrink)

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)

Years ago, when I was an undergraduate math major at the University of Wyoming, I came across an interesting book in our library. It was a book of counterexamples t o propositions in real analysis (the mathematics of the real numbers). Mathematicians work more or less like the rest of us. They consider propositions. If one seems to them to be plausibly true, then they set about to prove it, to establish the proposition as a theorem. Instead o f setting (...) out to prove propositions, the psychologists, neuroscientists, and other empirical types among us, set out to show that a proposition is supported by the data, and that it is the best such proposition so supported. The philosophers among us, when they are not causing trouble by arguing that AI is a dead end or that cognitive science can get along without representations, work pretty much like the mathematicians: we set out to prove certain propositions true on the basis of logic, first principles, plausible assumptions, and others' data. But, back to the book of real analysis counterexamples. If some mathematician happened t o think that some proposition about continuity, say, was plausibly true, he or she would then set out to prove it. If the proposition was in fact not a theorem, then a lot of precious time would be wasted trying to prove it. Wouldn't it be great to have a book that listed plausibly true propositions that were in fact not true, and listed with each such proposition a counterexample to it? Of course it would. (shrink)

In this work we present NERVOUS, an intelligent recommender system exploiting a probabilistic extension of a Description Logic of typicality to dynamically generate novel contents in AllMusic, a comprehensive and in-depth resource about music, providing data about albums, bands, musicians and songs. The tool can be used for both the generation of novel music genres and styles, described by a set of typical properties characterizing them, and the reclassification of the available songs within such new genres.

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)

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)

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)

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).

(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)

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)

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)

Artificial intelligence (AI) profoundly influences a number of societal structures today, including religious dynamics. Using Bernard Lonergan’s critical realism as a lens, this article investigates the intersections of AI and religious traditions in their shared pursuit of the common good. Beginning with Lonergan’s principle that humans construct their understanding through cognitive processes, we examine how AI-mediated realities align with or challenge traditional religious tenets. By delving into specific cases, we spotlight AI’s role in reshaping religious symbols, rituals, and even (...) creating novel spiritual meanings. Using Lonergan’s insights on the balance between subjectivity and objectivity, I analyze AI’s potential to both create new sacred spaces and challenge religious orthodoxy. The crux of the discussion centers on the negotiation between religious values and technological innovation, assessing how AI can bolster religious life while maintaining its core essence. Ultimately, this article underscores the importance of the common good in the age of AI-driven religious evolution. (shrink)

The rapid advancement of Information Technology (IT) platforms and programming languages has transformed the dynamics and development of human society. The cyberspace and associated utilities are expanding, leading to a gradual shift from real-world living to virtual life (also known as cyberspace or digital space). The expansion and development of Natural Language Processing (NLP) models and Large Language Models (LLMs) demonstrate human-like characteristics in reasoning, perception, attention, and creativity, helping humans overcome operational barriers. Alongside the immense potential of artificial (...) intelligence (AI) are new security loopholes and more complex information security risks. As society is still trying to transition to a new phase to adapt to technological changes, the AI revolution continues to unfold, necessitating a reconsideration of the trajectory of societal transformation as it could exacerbate the aforementioned information security risks. Specifically, how should society evolve to keep pace with the transformative impact of the current AI technology wave? How can we manage and harness their power while ensuring information security as our presence in the virtual world increases? This article aims to shed light on and address these questions. (shrink)

How can researchers use race, as they do now, to conduct health-care studies when its very definition is in question? The belief that race is a social construct without “biological authenticity” though widely shared across disciplines in social science is not subscribed to by traditional science. Yet with an interdisciplinary approach, the two horns of the social construct/genetics dilemma of race are not mutually exclusive. We can use traditional science to provide a rigorous framework and use a (...) social-science approach so that “invisible” factors are used to adjust the design of studies on an as-needed basis. One approach is to first observe health-care outcomes and then categorize the outcomes, thus removing genetic differences as racial proxies from the design of the study. From the outcomes, we can then determine if there is a pattern of conceivable racial categories. If needed, we can apply dynamic notions of race to acknowledge bias without prejudice. We can use them constructively to improve outcomes and reduce racial disparities. Another approach is nearly identical but considers race not at all: While analyzing outcomes, we can determine if there are biological differences significant enough to identify classifications of humans. That is, we look for genetic patterns in the outcomes and classify only those patterns. There is no attempt to link those patterns to race. (shrink)

The “Value Alignment Problem” is the challenge of how to align the values of artificial intelligence with human values, whatever they may be, such that AI does not pose a risk to the existence of humans. A fundamental feature of how the problem is currently understood is that AI systems do not take the same things to be relevant as humans, whether turning humans into paperclips in order to “make more paperclips” or eradicating the human race to “solve climate (...) change”. Specifically, existing approaches approaches to alignment appear to be concerned with how AI might *solve* problems in the relevant way. This paper presents and explores an approach to alignment rooted in the Enactive Theory of mind. It offers an alternative conception of the alignment as “how do we make relevant to AI what is relevant to humans?” In this conception, alignment is concerned with building AI so that it *discerns* and defines the problem in the relevant way. In this way, the Alignment Problem is shown to be the same problem as the Frame Problem. The paper concludes with a consideration of tradeoffs between these conceptions of the alignment problem. (shrink)

Experts in Artificial Intelligence (AI) development predict that advances in the development of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance is not made prudently and critically-reflexively, it can result in negative outcomes for humanity. For this reason, several researchers in the area have developed a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. Currently, several of the open problems in the field of AI research (...) arise from the difficulty of avoiding unwanted behaviors of intelligent agents and systems, and at the same time specifying what we really want such systems to do, especially when we look for the possibility of intelligent agents acting in several domains over the long term. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop human moral values simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic approach proposed by Dreyfus, whose phenomenological philosophy shows that the human experience of being-in-the-world in several aspects is not well represented by the symbolic or connectionist cognitive method, especially in regards to the question of learning values. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI. (shrink)

While practical efforts in the field of artificial intelligence grow exponentially, the truly scientific and mathematically exact understanding of the underlying phenomena of intelligence and consciousness is still missing in the conventional science framework. The inevitably dominating empirical, trial-and-error approach has vanishing efficiency for those extremely complicated phenomena, ending up in fundamentally limited imitations of intelligent behaviour. We provide the first-principle analysis of unreduced many-body interaction process in the brain revealing its qualitatively new features, which give rise to rigorously defined (...) chaotic, noncomputable, intelligent and conscious behaviour. Based on the obtained universal concepts of unreduced dynamic complexity, intelligence and consciousness, we derive the universal laws of intelligence applicable to any kind of intelligent system interacting with the environment. We finally show why and how these fundamentally substantiated and therefore practically efficient laws of intelligent system dynamics are indispensable for correct AI design and training, which is urgently needed in this time of critical global change towards the truly sustainable development. (shrink)

The “Value Alignment Problem” is the challenge of how to align the values of artificial intelligence with human values, whatever they may be, such that AI does not pose a risk to the existence of humans. Existing approaches appear to conceive of the problem as "how do we ensure that AI solves the problem in the right way", in order to avoid the possibility of AI turning humans into paperclips in order to “make more paperclips” or eradicating the human (...) class='Hi'>race to “solve climate change”. This paper proposes an approach to Alignment rooted in the Enactive theory of mind that reconceptualises it as "how do we make relevant to AI what it relevant to humans". This conceptualisation is supported with a discussion of 4E cognition and goes on to suggest that the Alignment problem and the Frame problem are the same problem. The paper concludes with a discussion of the tradeoffs of the different approaches to value alignment. (shrink)

No presente verbete faço a revisão crítica de algumas entre as mais expressivas tentativas de resolver o chamado ‘problema da dinâmica cognitiva’. De acordo com Kaplan (1989) – que é responsável pelo próprio apelido, a questão que se coloca é: o que significa dizer de uma pessoa que expressou uma crença particular num determinado contexto de proferimento que ela reteve ou mudou de crença fora desse contexto? E caso ajustes (linguísticos, psicológicos) sejam necessários para manter a relação com o conteúdo (...) semântico original, quais são nossas intuições a respeito de casos em que um sujeito cognitivamente saudável perdeu temporariamente a noção do tempo e/ou a capacidade de rastrear objetos no espaço? Exploro diversas respostas a essas perguntas com o intuito de dizer se elas conseguem acomodar os fatos (semânticos, epistêmicos, cognitivos) aí envolvidos. Abstract: In this paper I review some of the most significant attempts to solve the so-called ‘problem of cognitive dynamics’. According to Kaplan (1989), who coined the phrase, the following questions arise – as to the topic: what does it means to say of an individual who expressed a particular belief in a given context of utterance that he/she has re tained orchanged his/her mind with respect to it once the context is left? And assuming that (linguistic or psychological) adjustments are required to keep on being related to the original semantic content, what are our intuitions about the case of a cognitively healthy subject who temporarily lost the ability to keep track of time and/or objects in space? I investigate different ways of answg those questions with a view to saying whether they accommodate all the relevant (semantic, epistemic, cognitive) facts or not. (shrink)

This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI safety; (...) and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing. (shrink)

Since the early eighties, computationalism in the study of the mind has been “under attack” by several critics of the so-called “classic” or “symbolic” approaches in AI and cognitive science. Computationalism was generically identified with such approaches. For example, it was identified with both Allen Newell and Herbert Simon’s Physical Symbol System Hypothesis and Jerry Fodor’s theory of Language of Thought, usually without taking into account the fact ,that such approaches are very different as to their methods and aims. Zenon (...) Pylyshyn, in his influential book Computation and Cognition, claimed that both Newell and Fodor deeply influenced his ideas on cognition as computation. This probably added to the confusion, as many people still consider Pylyshyn’s book as paradigmatic of the computational approach in the study of the mind. Since then, cognitive scientists, AI researchers and also philosophers of the mind have been asked to take sides on different “paradigms” that have from time to time been proposed as opponents of (classic or symbolic) computationalism. Examples of such oppositions are: -/- computationalism vs. connectionism, computationalism vs. dynamical systems, computationalism vs. situated and embodied cognition, computationalism vs. behavioural and evolutionary robotics. -/- Our preliminary claim in section 1 is that computationalism should not be identified with what we would call the “paradigm (based on the metaphor) of the computer” (in the following, PoC). PoC is the (rather vague) statement that the mind functions “as a digital computer”. Actually, PoC is a restrictive version of computationalism, and nobody ever seriously upheld it, except in some rough versions of the computational approach and in some popular discussions about it. Usually, PoC is used as a straw man in many arguments against computationalism. In section 1 we look in some detail at PoC’s claims and argue that computationalism cannot be identified with PoC. In section 2 we point out that certain anticomputationalist arguments are based on this misleading identification. In section 3 we suggest that the view of the levels of explanation proposed by David Marr could clarify certain points of the debate on computationalism. In section 4 we touch on a controversial issue, namely the possibility of developing a notion of analog computation, similar to the notion of digital computation. A short conclusion follows in section 5. (shrink)

A subdiscipline has emerged around AI ethics, which is comprised of a wide array of individuals: computer scientists, ethicists, cognitive scientists, roboticists, legal professionals, economists, sociologists, gender, and race theorists. This has led to a very interesting branch of research, addressing issues surrounding the development and use of AI. This chapter will give a very brief snapshot of some of the most pertinent ethical concerns. Many of the issues in the Big Data Ethics chapter in this collection are often (...) applicable to AI ethics, because of the data that these technologies retrieve, store, and use, so will not be duplicated here.1 -/- While data-related issues are not new or unique to AI, AI does hold the potential to dramatically retrieve and analyze data that would not be possible otherwise. Data is being used in unique and transformative ways, such as the use of facial recognition to identify individuals from photos or CCTV; AI robots to retrieve live video feed about the patient(s) that it is monitoring; or in new ways, such as self-driving cars collecting an abundance of data about our surroundings, how we drive, and passengers in the car. There is the potential to infringe on individuals’ privacy, restriction of resources, or at worst, the creation of a surveillance society. (shrink)

The science-religion dialogue refers to the ongoing and dynamic conversation between the realms of science and religion, exploring their respective domains, methodologies, and implications. This dialogue has been a topic of interest for centuries and continues to evolve as scientific discoveries and religious interpretations develop. Artificial intelligence (AI) can facilitate science-religion dialogue by analyzing vast amounts of data, identifying common ground, and promoting respectful discussions. It can assist in translating complex scientific concepts for broader understanding and fostering a bridge between (...) differing perspectives. The intersection of AI and the dialogue between science and religion can also raise various concerns and challenges. While AI is a neutral tool, its applications and interpretations can also influence the dialogue in ways that might be perceived as harmful. This article elaborates on some potential ways in which AI could impact science-religion dialogue. (shrink)

Artificial intelligence (AI) techniques are used to model human activities and predict behavior. Such systems have shown race, gender and other kinds of bias, which are typically understood as technical problems. Here we try to show that: 1) to get rid of such biases, we need a system that can understand the structure of human activities and;2) to create such a system, we need to solve foundational problems of AI, such as the common-sense problem. Additionally, when informational platforms uses (...) these models to mediate interactions with their users, which is a commonplace nowadays, there is an illusion of progress, for what is an increasingly higher influence over our own behavior is took for an increasingly higher predictive accuracy. Given this, we argue that the bias problem is deeply connected to non-technical issues that must be discussed in public spaces. -/- Técnicas de inteligência artificial (IA) são utilizadas para modelar as atividades humanas e gerar predições comportamentais. Estes sistemas têm apresentado vieses diversos, inclusive de raça e gênero, tipicamente tomados como problemas de engenharia. Realiza-se aqui um esforço argumentativo para mostrar que: 1) escapar dos vieses demanda um sistema que compreenda a estrutura das atividades humanas e; 2) criar um sistema que apresente tal compreensão, demanda a solução de problemas fundacionais da IA, em particular, o problema de como modelar o senso comum. No caso de plataformas informacionais que usam desses modelos para mediar interações com seus usuários, algo cada vez mais comum, o não reconhecimento disso dá margem a uma ilusão de progresso, em que uma crescente influência sobre nosso comportamento é tomada como uma crescente acurácia preditiva. Nesse cenário, argumenta-se que o problema dos vieses está associado a questões não técnicas que devem ser discutidas em espaços públicos. (shrink)

There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and targeted toolkits (...) that are difficult to integrate into an algorithm’s broader ethical assessment. In this paper, we derive lessons from ethical philosophy and welfare economics as they relate to the contextual factors relevant for fairness. In particular we highlight the debate around acceptability of particular inequalities and the inextricable links between fairness, welfare and autonomy. We propose Key Ethics Indicators (KEIs) as a way towards providing a more holistic understanding of whether or not an algorithm is aligned to the decision-maker’s ethical values. (shrink)

It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring (...) that the resulting predictive model does not use race as an explicit predictor. However, if race is correlated with measured recidivism in the training data, the ML tool may ‘learn’ a perfect proxy for race. If such a proxy is found, the exclusion of race would do nothing to weaken the correlation between risk (mis)classifications and race. Is this a problem? We argue that, on some explanations of the wrongness of discrimination, it is. On these explanations, the use of an ML tool that perfectly proxies race would (likely) be more wrong than the use of a traditional tool that imperfectly proxies race. Indeed, on some views, use of a perfect proxy for race is plausibly as wrong as explicit racial profiling. We end by drawing out four implications of our arguments. (shrink)

In a reflective and richly entertaining piece from 1979, Doug Hofstadter playfully imagined a conversation between ‘Achilles’ and an anthill (the eponymous ‘Aunt Hillary’), in which he famously explored many ideas and themes related to cognition and consciousness. For Hofstadter, the anthill is able to carry on a conversation because the ants that compose it play roughly the same role that neurons play in human languaging; unfortunately, Hofstadter’s work is notably short on detail suggesting how this magic might be achieved1. (...) Conversely in this paper - finally reifying Hofstadter’s imagination - we demonstrate how populations of simple ant-like creatures can be organised to solve complex problems; problems that involve the use of forward planning and strategy. Specifically we will demonstrate that populations of such creatures can be configured to play a strategically strong - though tactically weak - game of HeX (a complex strategic game).We subsequently demonstrate how tactical play can be improved by introducing a form of forward planning instantiated via multiple populations of agents; a technique that can be compared to the dynamics of interacting populations of social insects via the concept of meta-population. In this way although, pace Hofstadter, we do not establish that a meta-population of ants could actually hold a conversation with Achilles, we do successfully introduce Aunt Hillary to the complex, seductive charms of HeX. (shrink)

The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence (...) from mathematics, physics, computer science, philosophy, linguistics, and biology, setting up their book around three central questions: What are the essential marks of human intelligence? What is it that researchers try to do when they attempt to achieve "artificial intelligence" (AI)? And why, after more than 50 years, are our most common interactions with AI, for example with our bank’s computers, still so unsatisfactory? Landgrebe and Smith show how a widespread fear about AI’s potential to bring about radical changes in the nature of human beings and in the human social order is founded on an error. There is still, as they demonstrate in a final chapter, a great deal that AI can achieve which will benefit humanity. But these benefits will be achieved without the aid of systems that are more powerful than humans, which are as impossible as AI systems that are intrinsically "evil" or able to "will" a takeover of human society. (shrink)