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$\unicode{x2014}$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$\unicode{x2014}$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$\unicode{x2014}$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$\unicode{x2014}$and key$\unicode{x2014}$step towards such an ecology.

我们开发了一种主动推论中的策略选择方法，该方法使我们能够通过将每个策略映射到矢量空间中的嵌入来有效地搜索大型策略空间。我们采样了空间中代表点的预期自由能，然后在此初始样本中最有希望的点进行更彻底的策略搜索。我们考虑了创建策略嵌入空间的各种方法，并建议使用K-均值聚类选择代表点。我们将技术应用于面向目标的图形 - 传播问题，对于中等大型图，天真的策略选择也很棘手。

现象学是对有意识经验的严格描述性研究。最近对侯赛利亚现象学形式化的尝试为我们提供了一种数学模型，这是先验知识和期望的函数。在本文中，我们通过主动推理的镜头重新检查了侯赛利亚现象学的元素。在这样做的过程中，我们旨在推进计算现象学的项目，正如主动推理的支持者最近概述的那样。我们建议，可以将胡塞尔对意识描述的关键方面映射到与主动推理方法相关的生成模型的各个方面。我们首先简要审查主动推论。然后，我们讨论了胡塞尔的现象学，重点是时间意识。最后，我们介绍了从侯赛利亚现象学到主动推断的映射。