The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of applications, then explores questions around AI value alignment, well-being, safety and malicious uses, and considers the deployment of advanced assistants at a societal scale.
It is argued that traditional technocratic standards are failing to integrate normative considerations into biomedical translation, and a translational domain that moves beyond safety and efficacy toward anticipating how proposed technologies will be effective in society as it exists is needed.
It is argued that, while not infallible, CoT monitoring offers a substantial layer of defense that requires active protection and continued stress-testing, and introduces a conceptual framework distinguishing CoT-as-rationalization from CoT-as-computation.
Questions about human centrality and agency in the research process are raised, and about the multiple philosophical and practical challenges the authors are facing now and ones they will face in the future are raised.
The paper explores how bias, lack of transparency, and challenges in maintaining patient trust can undermine the effectiveness and fairness of AI applications in healthcare, and outlines pathways for achieving more responsible and inclusive AI implementation in healthcare.
Attempts to label as ‘pseudoscientific’ a theory distinguished by decades of conceptual, mathematical, and empirical developments expose a crisis in the dominant computational-functionalist paradigm, which is challenged by IIT’s consciousness-first paradigm.
It is argued that whether AI is conscious is less of a concern than the fact that AI can be considered conscious by users during human-AI interaction, because this ascription of consciousness can lead to carry-over effects on human-human interaction.
The socio-technical nature of RAI limitations and the resulting necessity of producing socio-technical solutions are considered, bridging the gap between the theoretical considerations of RAI and on-the-ground processes that currently shape how AI systems are built.
This work proposes a post-training framework called Reinforcement Learning via Self-Play (RLSP), and proposes a theory as to why RLSP search strategy is more suitable for LLMs inspired by a remarkable result that says CoT provably increases computational power of LLMs.
The transformation of educational environments from hierarchical instructionism to constructionist models that emphasize learner autonomy and interactive, creative engagement is discussed, providing insights for educators and policymakers seeking to harness digital innovations to foster adaptive, student-centered learning experiences.
It is concluded that biology’s advantage over engineering arises from better integration of subsystems, and four fundamental obstacles that roboticists must overcome are identified.
This Essay proposes a new hierarchical model linking genes to vital rates, enabling us to critically reevaluate the DST and DTA in terms of their relationship to evolutionary genetic theories of aging (mutation accumulation and antagonistic pleiotropy).
A strategic questionnaire for assessing artificial intelligence–based mental health applications emphasizes the importance of ensuring that GenAI advancements are not only technologically sound but also ethically grounded and patient-centered.
This paper delves into the ethical implications of AI in the Metaverse through the analysis of real-world case studies, including Horizon Worlds, Decentraland, Roblox, Sansar, and Rec Room, revealing recurring concerns related to content moderation.
A typology is proposed that distinguishes the different stages of the AI life-cycle, the high-level ethical principles that should govern their implementation, and the tools with the potential to foster compliance with these principles, encompassing both technical and conceptual resources.
It is argued that in order to enable emergent behaviors, digital twins should be designed to reconstruct the behavior of a physical twin by “dynamically assembling” multiple digital “components”.
This study examines recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration, and proposes practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases.
Future research should explore ethical ways of promoting placebo effects, aligning with broader goals in medical practice, as argued in this review.
This perspective article focuses on a recent application of Artificial Intelligence (generative AI models, such as ChatGPT), which are based on machine learning and Natural Language Processing and are applied to Natural Language Processing.
For LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific process should be pursued in collaboration and alignment with human scientific goals, with clear evaluation metrics.
The crucial role of medical educators in adapting to changes in ethical challenges introduced by AI is explored, ensuring that ethical education remains a central and adaptable component of medical curricula.
This article proposes adequacy conditions for a representation in an LLM to count as belief-like and establishes four criteria that balance theoretical considerations with practical constraints, which together help lay the groundwork for a comprehensive understanding of belief representation in LLMs.
This article identifies and reconcile the ethical issues imposed on CME by climate change including the dispersion of related causes and effects, the transdisciplinary and transhuman nature of climate change, and the historic divorce of CME from the environment.
An abductive-deductive framework named Logic-Explainer is presented, which integrates LLMs with an external backward-chaining solver to refine step-wise natural language explanations and jointly verify their correctness, reduce incompleteness and minimise redundancy.
From a response to the call for concerted efforts to safeguard the authenticity of information in the age of AI, the necessity of implementing effective detection, verification, and explainability mechanisms to counteract the potential harms arising from the proliferation of AI-generated inauthentic content and science is shared.
The stability of universal nomenclatural systems is threatened by recent discussions asking for a fairer nomenclature, raising the possibility of bulk revision processes for “inappropriate” names.
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