I propose that meta-learned models, and in particular the situation-aware deployment of “learning-to-infer” modules can be advantageously extended to domains commonly thought to lie outside the cognitive, such as motivations and preferences on one hand, and the effectuation of micro- and coping-type behaviors.

I argue for a more complicated but nonetheless computationally feasible and algorithmically intelligible interplay between exploration and exploitation and for admitting into our conceptual toolkit regimes of exploitative exploration and exploratory exploitation that can enhance the novelty and usefulness of the results of either problemistic or serendipitous search.

I consider the dynamics of regulator–agent interactions in situations in which there are significant mismatches in their abilities to discern and register information and calculate and act upon successful inferences.

Depending on what we mean by “explanation,” challenges to the explanatory depth and reach of deep neural network models of visual and other forms of intelligent behavior may need revisions to both the elementary building blocks of neural nets (the explananda) and to the ways in which experimental environments and training protocols are engineered (the explanantia). The two paths assume and imply sharply different conceptions of how an explanation explains and of the explanatory function of models.

Can we explain the advantage natural languages enjoy over ideographies in a way that enables us to attempt the design of an ideography that “works”? I deploy an adapted version of Shannon's source- and channel-coding partitioning of a communication system to explain the communicative dynamics and shortfalls of ideographies, and reveal ways in which entrenchable, generalist ideographies could be designed.

Frames for interpreting situations are necessary in the face of time constraints for action and indeterminacy of the “right or optimal thing to do” given multiple objectives but not all frames are equally useful. We need a way of modeling representational frames according to the informational gain of using them and the computational cost of synthesizing a decisive reason for acting from them.