Designing Ecosystems of Intelligence from First Principles (2212.01354v2)

Abstract: This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants -- what we call ''shared intelligence''. This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world -- also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing -- leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first -- and key -- step towards such an ecology.

• The paper presents a novel active inference framework that models intelligence as a self-organizing, evidence-generating process.
• It outlines an ecosystem design where natural and synthetic agents share generative models to enable adaptive, real-time interactions.
• The study highlights ethical and technical challenges, urging the integration of human values into advanced AI ecosystems.

Designing Ecosystems of Intelligence from First Principles

The paper "Designing Ecosystems of Intelligence from First Principles" by Karl J. Friston et al. presents a comprehensive vision for advancing AI through the framework of active inference. It advocates for the development of an interconnected cyber-physical ecosystem consisting of both natural and synthetic intelligences, highlighting the critical role of human participation. This concept, referred to as "shared intelligence," emerges from principles grounded in active inference, a formulation that conceptualizes intelligence as a physics of self-organization.

Core Concepts and Theoretical Foundations

The paper situates active inference at the core of this transformative approach, defining it as the capacity to maximize Bayesian model evidence through processes such as inference, learning, and model selection. This is operationalized via variational message passing or belief propagation on a factor graph. The authors argue that intelligence can be understood as self-evidencing, a continuous process of generating evidence that confirms a system's generative model of its environment.

A significant emphasis is placed on curiosity as a cardinal aspect of intelligent systems, advocating that intelligent systems should inherently seek to resolve uncertainty. This inquisitive nature not only serves individual adaptive functions but also facilitates social intelligence through belief sharing among ensembles of agents. Crucially, active inference extends beyond solitary cognition to articulate a framework for collective intelligence based on shared generative models.

Implications for AI and Future Directions

The implications of adopting an active inference framework are multifold, spanning from individual system design to collective intelligence architectures. Practically, this approach fosters the development of intelligent systems capable of real-time, multi-modal interaction, reminiscent of distributed human-like communication networks. The theoretical underpinnings suggest that intelligence is not a monolithic construct but rather an emergent property of interconnected, nested systems, echoing natural ecosystems.

The concept of shared narratives and generative models is central to the idea of collective intelligence. Such models facilitate the emergence of contextually relevant and adaptable communication protocols, enabling both interoperability among various AI entities and synergy with human users. This narrative aligns with the growing trend of integrating AI within Internet of Things (IoT) architectures, where autonomous vehicles, robots, and sensors converge within a seamless intelligent ecosystem.

Open Research Questions and Ethical Considerations

The paper encourages researchers to explore the development of shared hyper-spatial modeling languages and transaction protocols as foundational steps toward realizing ecosystems of interconnected intelligences. However, it also acknowledges potential ethical concerns inherent in forging such integrated environments. The risk of misalignment between AI systems and human values, commonly discussed in AI ethics, remains paramount.

Furthermore, the potential for shared intelligence systems to imbalance power dynamics—by inadvertently perpetuating existing social biases—is addressed. The authors advocate for a delicate balance, ensuring technologies are leveraged responsibly, reflecting humanistic values and safeguarding diversity in intelligent artifacts.

Concluding Remarks

In summary, the paper lays out a structured path for developing advanced AI capabilities through the lens of active inference. It presents a nuanced argument for designing intelligence models that are not only individually robust but also inherently collaborative. By fostering a deeper integration of natural and artificial agents, this vision aims to advance AI towards truly symbiotic, shared intelligence ecosystems. The research highlights theoretical potential and practical strategies, setting the stage for future work that harmonizes AI development with naturally inspired systems and ethical considerations.