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Many physical systems retain traces of their past, but living systems differ in that they use memory for anticipation. This paper develops the thesis that anticipation depends on memory. In living systems, stored information about past states enables the prediction and modulation of future behavior. Drawing on Robert Rosen's theory of anticipatory systems, Friston's free-energy principle, and recent examples from microbiology, immunology, and cognition, I argue that Rosen's “model” is the organized memory of a system. Memory—whether mechanical, chemical, genetic, epigenetic, bioelectric, neural, or cultural—provides the substrate for anticipation, projecting possible futures and constraining present behavior. Examples across biological scales illustrate how this works in practice. Bacteria record viral encounters and use these genomic memories to defend against reinfection. In E. coli, biochemical traces of past interactions direct chemotaxis. Yeast cells store epigenetic stress memories that accelerate adaptation. With the advent of nervous systems, anticipation becomes centralized in internal neural models, enabling flexible simulations of organism–environment interactions. In all cases, anticipatory memory underlies teleonomy as goal-directedness that emerges from evolutionary and developmental processes. Top-down causation plays a central role in shaping the constraints that give rise to purposive, self-maintaining behavior. System-level goals emerge from past-informed constraints, giving living systems their distinctive autonomy, adaptivity, and creativity. 

Cognition and intelligence are central concepts in cognitive science, biology, philosophy of mind, and artificial intelligence, yet these disciplines offer conflicting accounts of what each of them means and how the two notions are related. In many accounts the two notions are used interchangeably, while in others intelligence is defined independently of cognitive processes. Dominant human-centered traditions identify cognition with mental processes associated with brains, whereas life-centered perspectives attribute cognitive capacities to all living systems. This article proposes a relational, life-centered, info-computational framework in which cognition is the ongoing autopoietic and sense-making organization of living systems, while intelligence is the degree of competence with which such organization achieves goal-directed problem solving under novelty, perturbation, and uncertainty. Cognition exists in degrees across living systems, from basal cellular sensing and regulation to increasingly complex cognitive organizations, while intelligence correspondingly appears in degrees in the ability to solve cognitive problems. Current artificial systems can exhibit engineered or derivative intelligence and may implement cognition-like functions, but they are not cognitive in the biological sense. The resulting framework clarifies how human-centered, life-centered, computational, and artificial intelligence can be related.

Cognition, sentience, intelligence, awareness, and mind are often treated as distinct phenomena that emerge only at higher levels of biological organization, typically associated with nervous systems or human cognition. However, empirical research increasingly demonstrates learning, memory, adaptive behavior, and goal-directed regulation across a wide range of living systems, including single cells, tissues, and organisms without brains. This paper proposes a unifying framework in which cognition is understood as an organizational property of living systems, grounded in information embodied in their physical structures and in their ongoing interactions with the environment. Within this info-computational (ICON) perspective, living systems engage in behavior, learning, and anticipation by dynamically transforming embodied information through distributed, physically realized processes that support viability and self-maintenance. These processes are present from the onset of life and become progressively more integrated and temporally extended with increasing biological organization. The framework provides explanatory continuity across biological scales and clarifies how complex forms of cognition, awareness, and mind arise as elaborations of basic life-regulatory dynamics. It generates empirically grounded, testable implications for basal cognition, developmental biology, and embodied artificial systems, in the domains such as morphogenetic regulation, bioelectric control, and embodied physical architectures where its implications can be tested.

One of the most transformative developments today is the integration of generative artificial intelligence into the development of critical software-intensive cyber-physical systems. From autonomous vehicles to industrial robotics, these systems are entering a new era shaped by artificial intelligence-driven development and automation. In this paper, we consider software engineering, artificial intelligence, artificial intelligence ethics, and social aspects, to explore how such technologies can be harnessed safely, transparently, and with human values at the center. Our contributions include a vision for software engineering, guiding the engineering of future trustworthy safety-critical cyber-physical systems under the influence of generative artificial intelligence. We critically analyze how three established certification principles can be leveraged to cope with the societal and technical tensions introduced by generative artificial intelligence adoption, and propose a research and practice agenda to ensure that future cyber-physical systems development and operations cycle remain trustworthy, both from a system (hardware and software) and from a societal perspective. 

AI systems generate ethical tensions that cannot be addressed through principle-based guidance alone. This paper brings forward an Integrated Axiology-MCDA Framework for AI ethics that distinguishes intrinsic, instrumental, and relational values and uses multi-criteria analysis to operationalize value pluralism in practice. The framework structures ethical evaluation by making value commitments explicit, enabling transparent examination of trade-offs, and supporting context-sensitive judgment. A healthcare hyper-scenario with sensitivity analysis shows how different weight configurations influence the relative acceptability of diagnostic systems and clarifies the thresholds at which accuracy considerations outweigh privacy or fairness. Cross-domain applications in education, criminal justice, and finance further illustrate how domain-specific value tensions require distinct criteria sets and weighting structures. The analysis shows that ethical challenges in AI arise from genuine value pluralism. Explicit value classification enables more accountable decision making across the AI lifecycle.

As intelligent autonomous systems (IAS) continue to assume increasingly central roles in safety-and mission-critical domains such as transportation, healthcare, finance, and infrastructure management, humans are becoming unable to monitor or intervene in real time. This shift is driven by the speed, data-processing capacity, and adaptivity of IAS. To manage this complexity, a new paradigm is emerging: IAS controlling and monitoring other IAS, a development that introduces at the same time practical efficiency and profound practical and ethical challenges. This article explores the multi-layered delegation of responsibilities within IAS ecosystems, where decisions influencing human lives and well-being are made with minimal human intervention. One often-overlooked consequence of this delegation is the capacity of AI systems to shape and create new human habits, whether through personalized persuasion, behavioral feedback loops, or autonomous decision enforcement. As humans increasingly adapt their behaviors to machine-optimized environments, questions arise about autonomy, agency, and responsibility for resulting behavior changes. Drawing on insights from recent research on responsibility delegation in IAS and on AI-driven habit formation, the article critically examines how responsibility should be distributed across human actors, autonomous systems, and institutions. Framed within the principles of Digital Humanism, I argue for a value-sensitive governance model that ensures transparency, explainability and human oversight even in complex IAS-to-IAS control scenarios. I propose a normative framework for responsibility attribution that accounts for both the technical architecture of IAS networks and the behavioral effects these systems have on human users. The article concludes by addressing the ethical risks of diminished human agency, manipulation through behavioral design, and the need for institutional mechanisms that align IAS operations with fundamental human values. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

The fast advancement of artificial intelligence presents ethical challenges that exceed the scope of traditional moral theories. This paper proposes a value-centered framework for AI ethics grounded in axiology, which distinguishes intrinsic values like dignity and fairness from instrumental ones such as accuracy and efficiency. This distinction supports ethical pluralism and contextual sensitivity. Using Multi-Criteria Decision Analysis (MCDA), the framework translates values into structured evaluations, enabling transparent trade-offs. A healthcare case study illustrates how ethical outcomes vary across physician, patient, and public health perspectives. The results highlight the limitations of single-theory approaches and emphasize the need for adaptable models that reflect diverse stakeholder values. By linking philosophical inquiry with governance initiatives like Responsible Artificial Intelligence (AI) and Digital Humanism, the framework offers actionable design criteria for inclusive and context-aware AI development.

As AI systems increasingly operate with autonomy and adaptability, the traditional boundaries of moral responsibility in techno-social systems are being challenged. This paper explores the evolving discourse on the delegation of responsibilities to intelligent autonomous agents and the ethical implications of such practices. Synthesizing recent developments in AI ethics, including concepts of distributed responsibility and ethical AI by design, the paper proposes a functionalist perspective as a framework. This perspective views moral responsibility not as an individual trait but as a role within a socio-technical system, distributed among human and artificial agents. As an example of "AI ethical by design," we present Basti and Vitiello's implementation. They suggest that AI can act as artificial moral agents by learning ethical guidelines and using Deontic Higher-Order Logic to assess decisions ethically. Motivated by the possible speed and scale beyond human supervision and ethical implications, the paper argues for "AI ethical by design," while acknowledging the distributed, shared, and dynamic nature of responsibility. This functionalist approach offers a practical framework for navigating the complexities of AI ethics in a rapidly evolving technological landscape.