Research Roadmap

This is contemporary state-of-the-art AI; namely, universal function approximation—mapping from input or sensory states to outputs or action states— that optimizes some well-defined value function or cost of (systemic) states. Examples include deep learning, Bayesian reinforcement learning, etc.

Sentient behavior or active inference based on belief updating and propagation (i.e., optimizing beliefs about states as opposed to states per se); where “sentient” means behavior that looks as if it is driven by beliefs about the sensory consequences of action. This entails planning as inference; namely, inferring courses of action that maximize expected information gain and expected value, where value is part of a generative (i.e., world) model; namely, prior preferences. This kind of intelligence is both information-seeking and preference-seeking. It is quintessentially curious, in virtue of being driven by uncertainty minimization, as opposed to reward maximization.

Sentient behavior—as defined under S1—in which plans are predicated on the consequences of action for beliefs about states of the world, as opposed to states per se. i.e., a move from “what will happen if I do this?” to “what will I believe or know if I do this?”. This kind of inference generally uses generative models with discrete states that “carve nature at its joints”; namely, inference over coarse-grained representations and ensuing world models. This kind of intelligence is amenable to formulation in terms of modal logic, quantum computation, and category theory. This stage corresponds to “artificial general intelligence” in the popular narrative about the progress of AI.

The deployment of sophisticated AI to recognize the nature and dispositions of users and other AI and—in consequence—recognize (and instantiate) attentional and dispositional states of self; namely, a kind of minimal selfhood (which entails generative models equipped with the capacity for Theory of Mind). This kind of intelligence is able to take the perspective of its users and interaction partners—it is perspectival, in the robust sense of being able to engage in dyadic and shared perspective taking.

The kind of collective that emerges from the coordination of Sympathetic Intelligences (as defined in S3) and their interaction partners or users—which may include naturally occurring intelligences such as ourselves, but also other sapient artifacts. This stage corresponds, roughly speaking, to “artificial super-intelligence” in the popular narrative about the progress of AI—with the important distinction that we believe that such intelligence will emerge from dense interactions between agents networked into a hyper-spatial web. We believe that the approach that we have outlined here is the most likely route toward this kind of hypothetical, planetary-scale, distributed super-intelligence.