Designing Ecosystems of Intelligence from First Principles
Exploring Ecosystems of Intelligence
The story draws inspiration from the concept of shared intelligence, where humans and AI collaborate within an interconnected ecosystem of natural and synthetic sense-making. This vision of intelligence transcends individual systems, resting on belief-sharing and collective intelligence. It's a space where curiosity drives innovation, where we engage in an endless, playful dialogue with the universe.
Designing Ecosystems of Intelligence from First Principles
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.
This white paper presents active inference as an approach to research and development in the field of artificial intelligence (AI), with the aim of developing ecosystems of natural and artificial intelligences.The path forward in AI is often presented as progressing from sys- tems that are able to solve problems within one narrow domain—so-called “artificial narrow intelligence” (ANI)—to systems that are able to solve problems in a domain general-manner, at or beyond human levels: what are known as “artificial general intelligence” (AGI) and “ar- tificial super-intelligence” (ASI), respectively [1].
We believe that approaching ASI (or, for reasons outlined below, even AGI) likely requires an understanding of networked or collective intelligence. Given the growing ubiquity of things like autonomous vehicles, robots, and arrays of edge computing devices and sensors (collectively, the internet of things), the zenith of the AI age may end up being a distributed network of intelligent systems, which interact frictionlessly in real time, and compose into emergent forms of intelligence at superordinate scales.
The nodes of such a distributed, interconnected ecosystem may then be human users as well as human-designed artifacts that embody or implement forms of intelligence.
In order to enable such ecosystems, we must learn from nature.While acknowledging neuroscience as a key inspiration for AI research, we argue that we must move beyond brains, and embrace the active and nested characteristics of natural intelligence, as it occurs in living organisms and as it might be implemented in physical systems more generally.
In our view, this entails asking not only “How does intelligence present to us, as researchers?” but also, crucially, the complementary question “What is it that intelligence must be, given that intelligent systems exist in a universe like ours?” To address this challenge, we aim to deduce fundamental properties of intelligence from foundational considerations about the nature of persisting physical systems (i.e., “first principles”).”” ]]
So like that paper said, resolution of uncertainity, but also we are the creator of that uncertainity too; So we are creating the problem, that wondering about it’s solution.
This is, a form of play in my opinion that we created between us, and the universe, life, existence.
And also this is something so lovely for me, because wondering and playing so purely human, each of us wonder, each of us play.
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