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Designing ecosystems of intelligence from first principles

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Friston_2-17z8pcyni05uz7.pdf
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Datum

2024

Autor:innen

Friston, Karl J.
Ramstead, Maxwell JD
Kiefer, Alex B.
Tschantz, Alexander
Buckley, Christopher L.
Albarracin, Mahault
Pitliya, Riddhi J.
Klein, Brennan
Sakthivadivel, Dalton AR
et al.

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Collective Intelligence. Sage. 2024, 3(1), pp. 1-19. eISSN 2633-9137. Available under: doi: 10.1177/26339137231222481

Zusammenfassung

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, that is, 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.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
570 Biowissenschaften, Biologie

Schlagwörter

Active inference, free energy principle, artificial intelligence, belief updating, belief propagation

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ISO 690FRISTON, Karl J., Maxwell JD RAMSTEAD, Alex B. KIEFER, Alexander TSCHANTZ, Christopher L. BUCKLEY, Mahault ALBARRACIN, Riddhi J. PITLIYA, Conor HEINS, Brennan KLEIN, Dalton AR SAKTHIVADIVEL, 2024. Designing ecosystems of intelligence from first principles. In: Collective Intelligence. Sage. 2024, 3(1), pp. 1-19. eISSN 2633-9137. Available under: doi: 10.1177/26339137231222481
BibTex
@article{Friston2024Desig-69915,
  year={2024},
  doi={10.1177/26339137231222481},
  title={Designing ecosystems of intelligence from first principles},
  number={1},
  volume={3},
  journal={Collective Intelligence},
  pages={1--19},
  author={Friston, Karl J. and Ramstead, Maxwell JD and Kiefer, Alex B. and Tschantz, Alexander and Buckley, Christopher L. and Albarracin, Mahault and Pitliya, Riddhi J. and Heins, Conor and Klein, Brennan and Sakthivadivel, Dalton AR}
}
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