Search Results (46)

Contemporary discussions of AI are often framed according to generally held assumptions which have largely escaped serious critical analysis. For instance, those who promote AI tout its predictive prowess: powerful algorithms fed massive amounts of data are able to discover knowable patterns that can accurately forecast the behaviors in anything from individual movie preferences to financial markets. Armed with this type of knowledge we can then use AI, the hope goes, to be more objective in our ethical practices. And most seriously, we must extend this to the way we develop AI, not only do we want AI to function ethically, but we caution ourselves that if Artificial General Intelligence (AGI), superintelligence, or anything like the “singularity” is ever developed, it should be positively aligned with human values. Reflecting on these positions from the perspective of classical Daoism gives us reason to pause. While Daoist texts also assume there are patterns in the world which one can successfully go along with, they are not enthusiastic about the rational or knowable nature of these patterns—rather, they encourage us to appreciate them as fundamentally complex and mysterious. In this article, some Daoist attitudes are also concretely applied to ethical considerations, which cannot easily be controlled or known, much less put into code. Inspired by Daoist texts, we might cultivate an attitude less filled with hubris than humility, where we are allowed more space from which we can reflect on how we think about AI. Many of the most pressing issues associated with AI could, in fact, be significantly alleviated simply by shifting the way we think about, use, and develop these technologies. Full article

As artificial intelligence advances toward unprecedented capabilities, society faces a choice between two trajectories. One continues scaling transformer-based architectures, such as state-of-the-art large language models (LLMs) like GPT-4, Claude, and Gemini, aiming for broad generalization and emergent capabilities. This approach has produced powerful tools but remains largely statistical, with unclear potential to achieve hypothetical “superintelligence”—a term used here as a conceptual reference to systems that might outperform humans across most cognitive domains, though no consensus on its definition or framework currently exists. The alternative explored here is the Mindful Machines paradigm—AI systems that could, in future, integrate intelligence with semantic grounding, embedded ethical constraints, and goal-directed self-regulation. This paper outlines the Mindful Machine architecture, grounded in Mark Burgin’s General Theory of Information (GTI), and proposes a post-Turing model of cognition that directly encodes memory, meaning, and teleological goals into the computational substrate. Two implementations are cited as proofs of concept. Full article

Our temptation to personify machine intelligence is not unexpected. As a child we named our dolls and took our Teddy Bear to bed with us. Today we ask death bots to comfort us with post-mortem conversation. All the while we know this to be pretend. Yet we must ask: if Artificial General Intelligence (AGI) or even Artificial Super-Intelligence (ASI) become available, will our game of pretend continue? Or will intelligent robots actually become selves deserving of dignity that hitherto could be ascribed only to human persons? If government-imposed guardrails shut the door on development of AGI and ASI in order to preserve human safety and even dignity, we might never learn whether AGI or ASI could develop selfhood, personhood, virtue, or religious sensibilities. As we approach the future, can we live without knowing whether AGI or ASI would be capable of developing selfhood and commanding dignity? Full article

Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures (e.g., Limited Memory and Theory of Mind). Based on capabilities, most AI systems today are categorized as Narrow AI, performing specific tasks such as medical image analysis and risk prediction with high accuracy. More advanced forms like General Artificial Intelligence (AGI) and Superintelligent AI remain theoretical but hold transformative potential. From a functional standpoint, Limited Memory AI dominates clinical applications by learning from historical patient data to inform decision-making. Reactive systems are used in rule-based alerts, while Theory of Mind (ToM) and Self-Aware AI remain conceptual stages for future development. This dual perspective provides a comprehensive framework to assess the maturity, impact, and future direction of AI in healthcare. It also highlights the need for ethical design, transparency, and regulation as AI systems grow more complex and autonomous, by incorporating cross-domain AI insights. Moreover, we evaluate the viability of developing AGI in regionally specific legal and regulatory frameworks, using South Korea as a case study to emphasize the limitations imposed by infrastructural preparedness and medical data governance regulations. Full article

This review paper provides a comprehensive review and analysis of the research and practice literature relating to data models and frameworks pertaining to urban and other AI-rich environments, extending to the planetary environment. Elements of focus include the very definition, along with the nature and stability, of the concept of AI itself; consideration of the notion of “open” in an AI context; data sharing, exchange, access, control, and use; and associated challenges and opportunities. Current gaps and problems in the literature on these data models are identified, giving rise to opportunities for research and practice going forward. One of the key gaps associated with AI models and frameworks lies in meeting the needs of the public, with the current top-down approach to AI design, development, and use emerging as a key problem. Such gaps set the stage for a number of recommendations, including human–AI collaboration; extending understanding of human–AI interactions; risk mitigation associated with artificial superintelligence and agentic approaches; and rethinking current AI models and the very definition of AI. This review paper is significant in that it integrates a SWOT (strengths, weaknesses, opportunities, threats) analysis to synthesize challenges, opportunities, gaps, and problems, offering a roadmap for human–AI interactions and collaborations in urban development. Full article

In today’s high-stakes arenas—from healthcare to defense—algorithms are advancing at an unprecedented pace, yet they still lack a crucial element of human decision-making: an instinctive caution that helps prevent harm. Inspired by both the protective reflexes seen in military robotics and the human amygdala’s role in threat detection, we introduce a novel idea: an integrated module that acts as an internal “caution system”. This module does not experience emotion in the human sense; rather, it serves as an embedded safeguard that continuously assesses uncertainty and triggers protective measures whenever potential dangers arise. Our proposed framework combines several established techniques. It uses Bayesian methods to continuously estimate the likelihood of adverse outcomes, applies reinforcement learning strategies with penalties for choices that might lead to harmful results, and incorporates layers of human oversight to review decisions when needed. The result is a system that mirrors the prudence and measured judgment of experienced clinicians—hesitating and recalibrating its actions when the data are ambiguous, much like a doctor would rely on both intuition and expertise to prevent errors. We call on computer scientists, healthcare professionals, and policymakers to collaborate in refining and testing this approach. Through joint research, pilot projects, and robust regulatory guidelines, we aim to ensure that advanced computational systems can combine speed and precision with an inherent predisposition toward protecting human life. Ultimately, by embedding this cautionary module, the framework is expected to significantly reduce AI-induced risks and enhance patient safety and trust in medical AI systems. It seems inevitable for future superintelligent AI systems in medicine to possess emotion-like processes. Full article

Deciding what to make of secular, religious, and spiritual speculations about AI and digital technologies can be overwhelming, and focusing on the extreme utopic or dystopic outcomes may be obscuring the larger facts. Is this technology a beautiful blessing or a damning curse? What can paying close attention to these technologies and the discourse surrounding them show? How founded are our anxieties? By following the apocalyptic throughline in this rhetoric across fields in recent years, this essay seeks to consider the effect of apocalyptic thought on recent developments in tech, and consider how this worldview orients our future. The deterministically utopic, dystopic, and apocalyptic rhetoric surrounding these technologies obscures their function and efficacy, giving agency to what is functionally still just a tool, the use for which depends on its designers and users. Full article

To generate a stable and effective air–liquid discharge in an open atmosphere, we investigated the effect of the dielectric barrier on the discharge between the pin electrode and liquid surface in an atmospheric-pressure plasma reactor. The atmospheric-pressure plasma reactor used in this study was based on a pin–plate discharge structure, and a metal wire was used as a pin-type power electrode. A plate-type ground electrode was placed above and below the vessel to compare the pin–liquid discharge and pin–liquid barrier discharge (PLBD). The results indicated that the PLBD configuration utilizing the bottom of the vessel as a dielectric barrier outperformed the pin–liquid setup in terms of the discharge stability and that the concentration of reactive species was different in the two plasma modes. PLBD can be used as a digestion technique for determining the phosphorus concentration in natural water sources. The method for decomposing phosphorus compounds by employing PLBD exhibited excellent decomposition performance, similar to the performance of thermochemical digestion—an established conventional method for phosphorus detection in water. The PLBD structure can replace the conventional chemical-agent-based digestion method for determining the total dissolved phosphorus concentration using the ascorbic acid reduction method. Full article

In the era of rapid technological advancement, understanding and confronting the challenges posed by AI systems are imperative. The concept of Superintelligence denotes the potential for AI to surpass the intellectual capacities of even the most brilliant human minds. As AI capabilities outpace human intellect and continually evolve, achieving such Superintelligence could lead to a point of no return—technological singularity—with uncontrollable repercussions, risking humanity’s existence. The proposed Waning Intellect theory suggests placing a finite lifespan on AI models to prevent unchecked evolution. Waning Intellect anticipates potential diminishing AI capabilities due to increased neural network complexity, posing risks to reliability, safety, and ethical concerns. Upholding ethical standards, human–AI collaboration, and robust regulatory frameworks are pivotal in leveraging AI’s potential while ensuring responsible deployment and mitigating risks. Full article

Artificial intelligence covers a variety of methods and disciplines including vision, perception, speech and dialogue, decision making and planning, problem solving, robotics and other applications in which self-learning is possible. The aim of this work was to study the possibilities of using AI algorithms at various stages of construction to ensure the safety of the process. The objects of this research were scientific publications about the use of artificial intelligence in construction and ways to optimize this process. To search for information, Scopus and Web of Science databases were used for the period from the early 1990s (the appearance of the first publication on the topic) until the end of 2022. Generalization was the main method. It has been established that artificial intelligence is a set of technologies and methods used to complement traditional human qualities, such as intelligence as well as analytical and other abilities. The use of 3D modeling for the design of buildings, machine learning for the conceptualization of design in 3D, computer vision, planning for the effective use of construction equipment, artificial intelligence and artificial superintelligence have been studied. It is proven that automatic programming for natural language processing, knowledge-based systems, robots, building maintenance, adaptive strategies, adaptive programming, genetic algorithms and the use of unmanned aircraft systems allow an evaluation of the use of artificial intelligence in construction. The prospects of using AI in construction are shown. Full article

Skin has a dynamic surface and offers essential information through biological signals originating from internal organs, blood vessels, and muscles. Soft and stretchable bioelectronics can be used in wearable machines for long-term stability and to continuously obtain distinct bio-signals in conjunction with repeated expansion and contraction with physical activities. While monitoring bio-signals, the electrode and skin must be firmly attached for high signal quality. Furthermore, the signal-to-noise ratio (SNR) should be high enough, and accordingly, the ionic conductivity of an adhesive hydrogel needs to be improved. Here, we used a chitosan-alginate-chitosan (CAC) triple hydrogel layer as an interface between the electrodes and the skin to enhance ionic conductivity and skin adhesiveness and to minimize the mechanical mismatch. For development, thermoplastic elastomer Styrene-Ethylene-Butylene-Styrene (SEBS) dissolved in toluene was used as a substrate, and gold nanomembranes were thermally evaporated on SEBS. Subsequently, CAC triple layers were drop-casted onto the gold surface one by one and dried successively. Lastly, to demonstrate the performance of our electrodes, a human electrocardiogram signal was monitored. The electrodes coupled with our CAC triple hydrogel layer showed high SNR with clear PQRST peaks. Full article

In wearable bioelectronics, various studies have focused on enhancing prosthetic control accuracy by improving the quality of physiological signals. The fabrication of conductive composites through the addition of metal fillers is one way to achieve stretchability, conductivity, and biocompatibility. However, it is difficult to measure stable biological signals using these soft electronics during physical activities because of the slipping issues of the devices, which results in the inaccurate placement of the device at the target part of the body. To address these limitations, it is necessary to reduce the stiffness of the conductive materials and enhance the adhesion between the device and the skin. In this study, we measured the electromyography (EMG) signals by applying a three-layered hydrogel structure composed of chitosan–alginate–chitosan (CAC) to a stretchable electrode fabricated using a composite of styrene–ethylene–butylene–styrene and eutectic gallium-indium. We observed stable adhesion of the CAC hydrogel to the skin, which aided in keeping the electrode attached to the skin during the subject movement. Finally, we fabricated a multichannel array of CAC-coated composite electrodes (CACCE) to demonstrate the accurate classification of the EMG signals based on hand movements and channel placement, which was followed by the movement of the robot arm. Full article

Ultra-widefield fundus image (UFI) has become a crucial tool for ophthalmologists in diagnosing ocular diseases because of its ability to capture a wide field of the retina. Nevertheless, detecting and classifying multiple diseases within this imaging modality continues to pose a significant challenge for ophthalmologists. An automated disease classification system for UFI can support ophthalmologists in making faster and more precise diagnoses. However, existing works for UFI classification often focus on a single disease or assume each image only contains one disease when tackling multi-disease issues. Furthermore, the distinctive characteristics of each disease are typically not utilized to improve the performance of the classification systems. To address these limitations, we propose a novel approach that leverages disease-specific regions of interest for the multi-label classification of UFI. Our method uses three regions, including the optic disc area, the macula area, and the entire UFI, which serve as the most informative regions for diagnosing one or multiple ocular diseases. Experimental results on a dataset comprising 5930 UFIs with six common ocular diseases showcase that our proposed approach attains exceptional performance, with the area under the receiver operating characteristic curve scores for each class spanning from 95.07% to 99.14%. These results not only surpass existing state-of-the-art methods but also exhibit significant enhancements, with improvements of up to 5.29%. These results demonstrate the potential of our method to provide ophthalmologists with valuable information for early and accurate diagnosis of ocular diseases, ultimately leading to improved patient outcomes. Full article

Polymers for implantable devices are desirable for biomedical engineering applications. This study introduces a water-resistant, self-healing fluoroelastomer (SHFE) as an encapsulation material for antennas. The SHFE exhibits a tissue-like modulus (approximately 0.4 MPa), stretchability (at least 450%, even after self-healing in an underwater environment), self-healability, and water resistance (WVTR result: 17.8610 g m⁻² day⁻¹). Further, the SHFE is self-healing in underwater environments via dipole–dipole interactions, such that devices can be protected from the penetration of biofluids and withstand external damage. With the combination of the SHFE and antennas designed to operate inside the body, we fabricated implantable, wireless antennas that can transmit information from inside the body to a reader coil that is outside. For antennas designed considering the dielectric constant, the uniformity of the encapsulation layer is crucial. A uniform and homogeneous interface is formed by simply overlapping two films. This study demonstrated the possibility of wireless communication in vivo through experiments on rodents for 4 weeks, maintaining the maximum communication distance (15 mm) without chemical or physical deformation in the SHFE layer. This study illustrates the applicability of fluoroelastomers in vivo and is expected to contribute to realizing the stable operation of high-performance implantable devices. Full article

Conductive hydrogels are promising materials in bioelectronics that ensure a tissue-like soft modulus and re-enact the electrophysiological function of damaged tissues. However, recent approaches to fabricating conductive hydrogels have proved difficult: fixing of the conductive hydrogels on the target tissues hydrogels requires the aids from other medical glues because of their weak tissue-adhesiveness. In this study, an intrinsically conductive and tissue-adhesive granular hydrogel consisting of a PEDOT:PSS conducting polymer and an adhesive catechol-conjugated alginate polymer was fabricated via an electrohydrodynamic spraying method. Because alginate-based polymers can be crosslinked by calcium ions, alginate-catechol polymers mixed with PEDOT:PSS granular hydrogels (ACP) were easily fabricated. The fabricated ACP exhibited not only adhesive and shear-thinning properties but also conductivity similar to that of muscle tissue. Additionally, the granular structure makes the hydrogel injectable through a syringe, enabling on-tissue printing. This multifunctional granular hydrogel can be applied to soft and flexible electronics to connect humans and machines. Full article