  With AI systems becoming more powerful and pervasive, there is increasing
debate about keeping their actions aligned with the broader goals and needs of
humanity. This multi-disciplinary and multi-stakeholder debate must resolve
many issues, here we examine three of them. The first issue is to clarify what
demands stakeholders might usefully make on the designers of AI systems, useful
because the technology exists to implement them. We make this technical topic
more accessible by using the framing of cognitive architectures. The second
issue is to move beyond an analytical framing that treats useful intelligence
as being reward maximization only. To support this move, we define several AI
cognitive architectures that combine reward maximization with other technical
elements designed to improve alignment. The third issue is how stakeholders
should calibrate their interactions with modern machine learning researchers.
We consider how current fashions in machine learning create a narrative pull
that participants in technical and policy discussions should be aware of, so
that they can compensate for it. We identify several technically tractable but
currently unfashionable options for improving AI alignment.
