Augmented Humanity

To monitor and manage the dilemmas of digitalization, augmented agents must self-regulate in a collaborative fashion. Artificial agents are advancing rapidly in these respects and some are fully self-generative. They are increasingly capable of complex, fast, sensitive self-regulation. In consequence, augmented agents will have the potential for effective self-regulation and self-supervision. However, human self-regulation is often simplified and sluggish and lacks sensitivity. People rely on habit, routine, and docility, and often with good reasons. Yet, as noted, artificial agents are increasingly hyperactive and hypersensitive, compared to humans. When both agents combine, therefore, artificial self-regulatory functioning could be complex, fast, and precise, while human functioning is relatively simple, sluggish, and heuristic. The result could be self-regulatory divergence or convergence and possibly dysfunction. Further implications then follow for self-efficacy, task engagemen...

Artificial intelligence has experienced remarkable advancements, particularly with the emergence of genera However, these frameworks are inherently limited by their specialized functions, predefined objectives, and This paper introduces Elara Syn, a groundbreaking intelligence system that transcends the current paradig physics, biology, and consciousness. Designed as a dynamic, self-referential, and multidimensional ecosys unified adaptive intelligence capable of addressing real-world complexity and fostering long-term scalability architecture, core functionalities, and transformative potential of Elara Syn, situating it as a future-ready so

To supervise and enjoy the benefits of digitalization, humanity must develop the science of augmented agency. This future science will be multidisciplinary, joining the human and computer sciences, as well as neuroscience and related fields of human science and engineering, but also going further. In fact, the novelties of digital augmentation transcend the conceptual architecture of modernity and its problematics. Digitalized humanity is truly exceptional. To describe these phenomena, science needs fresh concepts, categories, and constructs. This chapter offers proposals in this regard. It maps the emerging field of augmented agency, its core categories, mechanisms, and dilemmas, and thereby looks forward to a new science of digitally augmented agency.

CyberMinds Technology & Research (CMT&R) is a leading research institute focused on advancing our understanding of consciousness, intelligence, and thought processes, with a unique emphasis on developing super-intelligent AI. CMT&R integrates principles from cognitive science, information theory, quantum mechanics, and ethics to create groundbreaking AI systems that can potentially exceed human intelligence.

To supervise and enjoy the benefits of digitalization, humanity must develop the science of augmented agency. This future science will be multidisciplinary, joining the human and computer sciences, as well as neuroscience and related fields of human science and engineering, but also going further. In fact, the novelties of digital augmentation transcend the conceptual architecture of modernity and its problematics. Digitalized humanity is truly exceptional. To describe these phenomena, science needs fresh concepts, categories, and constructs. This chapter offers proposals in this regard. It maps the emerging field of augmented agency, its core categories, mechanisms, and dilemmas, and thereby looks forward to a new science of digitally augmented agency.

This multidisciplinary work analyzes the impact of digitalization on civilized humanity, conceived in terms of purposive, goal-directed agency. More particularly, it examines the close collaboration of human and artificial agents as augmented agents, viewing them both as complex, open adaptive systems, which vary dynamically in context. This first chapter explains general metamodels of agentic form and function, where metamodels are defined as families or related sets of models. From this perspective, augmented agency presents a new class of agentic metamodel, for individuals, groups, and collectives. At the same time, however, new risks and dilemmas emerge, which reflect the challenge of combining and supervising different human and artificial capabilities and potentialities. Problems of this kind are a recurrent topic throughout the book.

Human and artificial agents are both committed to learning. Evaluation of performance is a key driver. This is the case for adaptive feedback, which is generated from the evaluation of performance outcomes. It is also true for feedforward guidance, which results from real-time monitoring of ongoing processes. Augmented agents will learn in both ways. However, these developments signal a shift from historic patterns of learning from performance, which mainly focus on slower, inter-cyclical feedback cycles. Indeed, much human learning occurs in simple increments and takes years to complete. By comparison, artificial agents learn complex lessons with extraordinary speed and precision. Therefore, if collaborative supervision is poor, artificial learning will be fast and complex, while human learning is relatively sluggish and incremental. Such learning will be distorted, often ambiguous, ambivalent, and potentially dysfunctional. This chapter examines these dilemmas.

Three organizing agentic modalities consistently occur, namely, individuals, relational groups, and collectives of various kinds. Digital augmentation transforms these modalities. Particularly, the infusion of artificial agency compresses traditional hierarchies and layers. It also strengthens organizing capabilities, enabling new agentic forms and functions. Yet human agency is likely to remain layered and relatively uncompressed. Therefore, augmented agentic modality could be either more fluid and flattened, or internally conflicted. This chapter examines these developments and highlights the changing nature of procedural habit and routine, as key mediators of agentic modality, mindful that habit forms the scaffold for individual personality, and collective routine for collectivity. When these patterns of action are digitalized, they become more dynamic and generative. Although here, too, risks and dilemmas emerge, regarding the potential divergence and convergence of agentic moda...

Human and artificial agents are both intelligent problem solvers. Therefore, problem-solving will be central to their collaboration. Among notable developments in this domain is the capability for artificial agents to sample and search in a very farsighted fashion, or to be hyperopic, which is the technical term for farsighted vision, the opposite of myopia. This inverts the dominant concern of prior theory, which focuses on limited, bounded capabilities in problem-solving and decision-making. This shift poses significant opportunities and risks for augmented agents. Human processing will likely remain naturally myopic and limited, while artificial processing is increasingly hyperopic and powerful. Given these differences, digitally augmented problem-solving could be extremely divergent and dysfunctional, for example, by sampling and searching in a hyperopic fashion, while guided by persistent human myopia. Alternatively, one agent might dominate the other, leading to extreme conver...

Each major period of civilized humanity exhibits dominant metamodels of agentic form and function, which vary in terms of capability and supportive technology. The chapter identifies three such periods. The first is premodernity, which predates the European Enlightenment and industrialization. It was characterized by stable social systems, in which agentic form and functioning were replicative. Whereas in modernity, capabilities and technologies greatly advance, and the dominant agentic metamodel is one of adaptation and developmental learning. In the contemporary period, a new digitalized agentic metamodel is emerging, reflecting the close collaboration of human and artificial agents as augmented agents. It will be dynamic, generative, and transformative. It also poses new questions and problematics, especially about the supervision of digitalized capabilities and potentialities.

Autonomous agents are self-generative, meaning they can manage their own development and reproduction without external direction. For artificial agents, self-generation entails the composition of algorithmic systems and metamodels. For humans, it involves the composition of life narratives and identities. That said, many people have few self-generative options because they lack the required resources, opportunities, and capabilities. Digitalization supports remedial efforts in this regard. Digital augmentation strengthens capabilities and expands potentialities. Many people already enjoy these benefits. Yet there is also evidence of the opposite. Some individuals and groups are excluded, while others resist digital augmentation or feel blocked and incapable of absorbing these opportunities. Instead, they either persist with singular, traditional life paths, or flounder in the face of complexity, or surrender choice and submit to digital determination. As this chapter explains, such ...

Human beings try to interpret and read other minds. This is the process of cognitive empathizing, which can be implicit and intuitive, or explicit and deliberate. The process also qualifies as a form of complex problem-solving, where the focal problem is another person’s mental states. Hence, cognitive empathizing by digitally augmented agents will exhibit the characteristics discussed in the preceding chapter, regarding digitalized problem-solving. It follows, therefore, that augmented agents might combine human myopia and bias, with overly farsighted, artificial sampling and search of other minds. Augmented agents will then misread other minds, often viewing them as unrealistic, irrational, or deviant. This chapter examines the origins and implications of these effects, especially for interpersonal trust and cooperation.

Agents evaluate their performances to assess progress, learn, and improve. In doing so, they refer to criteria of various kinds. Some criteria are deeply encoded in mental models, organizational procedures, or cultural norms and logics, while other evaluative criteria are adaptive and may upregulate or downregulate, depending on the agent’s goals, expectations, and context. Here, too, digitalization is transformative. Artificial agents bring unprecedented power to the evaluation of performance, including the rapid intra-cyclical evaluation of ongoing processes. These mechanisms support feedforward guidance in real time. Therefore, when human and artificial agents combine in the evaluation of augmented performance, they face additional risks. Artificial evaluative processing could be fast and precise, while at the same time, human evaluation may be relatively sluggish and imprecise. Overall evaluations of performance could be distorted and dysfunctional.

Post-modernism's rejection of the "soul-less machine" or view of the world in clockwork terms opened the door for alternative logics (Fleener, 2002) as ways of understanding and navigating our world (Doll, 1993) and understanding schooling (Cunningham, 2014). Multidimensional, multi-modal, and multi-perspectival, post-modernism extends our epistemological and ontological frames (Hatch, 2013) to accommodate emergence and complexity as important and relevant aspects of complex adaptive learning systems (Harvey & Reed, 1996; Stamovlasis & Koopmans, 2016). Organizational theory has followed a similar path as early foci on efficiency and productivity have yielded more complex understandings of organizations as learning systems

Post-modernism's rejection of the "soul-less machine" or view of the world in clockwork terms opened the door for alternative logics (Fleener, 2002) as ways of understanding and navigating our world (Doll, 1993) and understanding schooling (Cunningham, 2014). Multidimensional, multi-modal, and multi-perspectival, post-modernism extends our epistemological and ontological frames (Hatch, 2013) to accommodate emergence and complexity as important and relevant aspects of complex adaptive learning systems (Harvey & Reed, 1996; Stamovlasis & Koopmans, 2016). Organizational theory has followed a similar path as early foci on efficiency and productivity have yielded more complex understandings of organizations as learning systems

Collaboration is a key ingredient for innovation, especially in the creative domain. As artificial intelligence (AI) becomes more prevalent and powerful, it offers new opportunities for collaboration between humans and machines. However, to harness the full potential of this partnership, we need to foster a culture of collaboration that respects and values the contributions of both parties.

To supervise and enjoy the benefits of digitalization, humanity must develop the science of augmented agency. This future science will be multidisciplinary, joining the human and computer sciences, as well as neuroscience and related fields of human science and engineering, but also going further. In fact, the novelties of digital augmentation transcend the conceptual architecture of modernity and its problematics. Digitalized humanity is truly exceptional. To describe these phenomena, science needs fresh concepts, categories, and constructs. This chapter offers proposals in this regard. It maps the emerging field of augmented agency, its core categories, mechanisms, and dilemmas, and thereby looks forward to a new science of digitally augmented agency.

Existing theoretical universal algorithmic intelligence models are not practically realizable. More pragmatic approach to artificial general intelligence is based on cognitive architectures, which are, however, non-universal in sense that they can construct and use models of the environment only from Turingincomplete model spaces. We believe that the way to the real AGI consists in bridging the gap between these two approaches. This is possible if one considers cognitive functions as a "cognitive bias" (priors and search heuristics) that should be incorporated into the models of universal algorithmic intelligence without violating their universality. Earlier reported results suiting this approach and its overall feasibility are discussed on the example of perception, planning, knowledge representation, attention, theory of mind, language, and some others.

The recurrent action patterns of individual persons (defined as habits) combine into organizational recurrent action patterns (defined as collective performances including routines, administrative procedures and genres of action). Among their other functions, habits and collective performances underpin patterns of stability and novelty at both individual and organizational levels of analysis. In order to understand the interaction between these two levels of action, scholars research their microfoundations. This paper does so as well. However, in contrast to much prior theorizing on this subject, I adopt a social cognitive theory of human personality and individual psychology. From this perspective, social context and situational contingency become central factors in the explanation of personality. Complex intra-psychic processes also play a central role. The resulting theory proposes that both individual habits and collective performances evolve from patterns of situated behavior t...

To supervise and enjoy the benefits of digitalization, humanity must develop the science of augmented agency. This future science will be multidisciplinary, joining the human and computer sciences, as well as neuroscience and related fields of human science and engineering, but also going further. In fact, the novelties of digital augmentation transcend the conceptual architecture of modernity and its problematics. Digitalized humanity is truly exceptional. To describe these phenomena, science needs fresh concepts, categories, and constructs. This chapter offers proposals in this regard. It maps the emerging field of augmented agency, its core categories, mechanisms, and dilemmas, and thereby looks forward to a new science of digitally augmented agency.

This multidisciplinary work analyzes the impact of digitalization on civilized humanity, conceived in terms of purposive, goal-directed agency. More particularly, it examines the close collaboration of human and artificial agents as augmented agents, viewing them both as complex, open adaptive systems, which vary dynamically in context. This first chapter explains general metamodels of agentic form and function, where metamodels are defined as families or related sets of models. From this perspective, augmented agency presents a new class of agentic metamodel, for individuals, groups, and collectives. At the same time, however, new risks and dilemmas emerge, which reflect the challenge of combining and supervising different human and artificial capabilities and potentialities. Problems of this kind are a recurrent topic throughout the book.

Human and artificial agents are both committed to learning. Evaluation of performance is a key driver. This is the case for adaptive feedback, which is generated from the evaluation of performance outcomes. It is also true for feedforward guidance, which results from real-time monitoring of ongoing processes. Augmented agents will learn in both ways. However, these developments signal a shift from historic patterns of learning from performance, which mainly focus on slower, inter-cyclical feedback cycles. Indeed, much human learning occurs in simple increments and takes years to complete. By comparison, artificial agents learn complex lessons with extraordinary speed and precision. Therefore, if collaborative supervision is poor, artificial learning will be fast and complex, while human learning is relatively sluggish and incremental. Such learning will be distorted, often ambiguous, ambivalent, and potentially dysfunctional. This chapter examines these dilemmas.

Three organizing agentic modalities consistently occur, namely, individuals, relational groups, and collectives of various kinds. Digital augmentation transforms these modalities. Particularly, the infusion of artificial agency compresses traditional hierarchies and layers. It also strengthens organizing capabilities, enabling new agentic forms and functions. Yet human agency is likely to remain layered and relatively uncompressed. Therefore, augmented agentic modality could be either more fluid and flattened, or internally conflicted. This chapter examines these developments and highlights the changing nature of procedural habit and routine, as key mediators of agentic modality, mindful that habit forms the scaffold for individual personality, and collective routine for collectivity. When these patterns of action are digitalized, they become more dynamic and generative. Although here, too, risks and dilemmas emerge, regarding the potential divergence and convergence of agentic moda...

To monitor and manage the dilemmas of digitalization, augmented agents must self-regulate in a collaborative fashion. Artificial agents are advancing rapidly in these respects and some are fully self-generative. They are increasingly capable of complex, fast, sensitive self-regulation. In consequence, augmented agents will have the potential for effective self-regulation and self-supervision. However, human self-regulation is often simplified and sluggish and lacks sensitivity. People rely on habit, routine, and docility, and often with good reasons. Yet, as noted, artificial agents are increasingly hyperactive and hypersensitive, compared to humans. When both agents combine, therefore, artificial self-regulatory functioning could be complex, fast, and precise, while human functioning is relatively simple, sluggish, and heuristic. The result could be self-regulatory divergence or convergence and possibly dysfunction. Further implications then follow for self-efficacy, task engagemen...

Human and artificial agents are both intelligent problem solvers. Therefore, problem-solving will be central to their collaboration. Among notable developments in this domain is the capability for artificial agents to sample and search in a very farsighted fashion, or to be hyperopic, which is the technical term for farsighted vision, the opposite of myopia. This inverts the dominant concern of prior theory, which focuses on limited, bounded capabilities in problem-solving and decision-making. This shift poses significant opportunities and risks for augmented agents. Human processing will likely remain naturally myopic and limited, while artificial processing is increasingly hyperopic and powerful. Given these differences, digitally augmented problem-solving could be extremely divergent and dysfunctional, for example, by sampling and searching in a hyperopic fashion, while guided by persistent human myopia. Alternatively, one agent might dominate the other, leading to extreme conver...

Each major period of civilized humanity exhibits dominant metamodels of agentic form and function, which vary in terms of capability and supportive technology. The chapter identifies three such periods. The first is premodernity, which predates the European Enlightenment and industrialization. It was characterized by stable social systems, in which agentic form and functioning were replicative. Whereas in modernity, capabilities and technologies greatly advance, and the dominant agentic metamodel is one of adaptation and developmental learning. In the contemporary period, a new digitalized agentic metamodel is emerging, reflecting the close collaboration of human and artificial agents as augmented agents. It will be dynamic, generative, and transformative. It also poses new questions and problematics, especially about the supervision of digitalized capabilities and potentialities.

Autonomous agents are self-generative, meaning they can manage their own development and reproduction without external direction. For artificial agents, self-generation entails the composition of algorithmic systems and metamodels. For humans, it involves the composition of life narratives and identities. That said, many people have few self-generative options because they lack the required resources, opportunities, and capabilities. Digitalization supports remedial efforts in this regard. Digital augmentation strengthens capabilities and expands potentialities. Many people already enjoy these benefits. Yet there is also evidence of the opposite. Some individuals and groups are excluded, while others resist digital augmentation or feel blocked and incapable of absorbing these opportunities. Instead, they either persist with singular, traditional life paths, or flounder in the face of complexity, or surrender choice and submit to digital determination. As this chapter explains, such ...

Human beings try to interpret and read other minds. This is the process of cognitive empathizing, which can be implicit and intuitive, or explicit and deliberate. The process also qualifies as a form of complex problem-solving, where the focal problem is another person’s mental states. Hence, cognitive empathizing by digitally augmented agents will exhibit the characteristics discussed in the preceding chapter, regarding digitalized problem-solving. It follows, therefore, that augmented agents might combine human myopia and bias, with overly farsighted, artificial sampling and search of other minds. Augmented agents will then misread other minds, often viewing them as unrealistic, irrational, or deviant. This chapter examines the origins and implications of these effects, especially for interpersonal trust and cooperation.

Agents evaluate their performances to assess progress, learn, and improve. In doing so, they refer to criteria of various kinds. Some criteria are deeply encoded in mental models, organizational procedures, or cultural norms and logics, while other evaluative criteria are adaptive and may upregulate or downregulate, depending on the agent’s goals, expectations, and context. Here, too, digitalization is transformative. Artificial agents bring unprecedented power to the evaluation of performance, including the rapid intra-cyclical evaluation of ongoing processes. These mechanisms support feedforward guidance in real time. Therefore, when human and artificial agents combine in the evaluation of augmented performance, they face additional risks. Artificial evaluative processing could be fast and precise, while at the same time, human evaluation may be relatively sluggish and imprecise. Overall evaluations of performance could be distorted and dysfunctional.