Topics

Long-form articles and reports, grouped by topic.

Modelling the Self

Speculative, blue-skies work applying object-oriented cognitive modelling reflexively to the self, checked against real theories of consciousness and their ethical stakes. A weak-AI claim throughout: an analogue, not an instance.

Research Papers

Papers and technical manuscripts related to AI systems and geometric algebra.

PatLang

PatLang programming language, self-hosted compiler, and interactive portfolio.

The Half-Life of Knowledge

How knowledge in computing and AI actually decays, consolidates, and gets reused: the evidence behind claims that skills expire fast, the cognitive-science mechanisms behind durable learning, what happens — for an individual or a whole research field — to the knowledge that doesn't consolidate, and how a technology's publicity can diverge from its actual utility along the way.

Just for Fun

Lighthearted crossovers between computing and things that have nothing to do with it. Playful, not peer-reviewed.

Philosophy of AI

A sustained argument about intelligence, knowledge, self-reference, consciousness, embodiment, feeling, and the ethical consequences of building minds organised differently from our own. The series moves from what outside observers attribute to artificial systems, inward through representation, knowledge, self-reference, architecture, embodiment, and feeling, to moral consequence -- later pages rely on distinctions developed earlier, though each page is written to stand on its own. No single test or theory settles whether a system thinks, knows, experiences, or suffers: different questions call for behavioural evidence, architectural evidence, formal argument, or philosophical interpretation, and each page is explicit about which kind it is offering.

Organisations as Cognitive Systems

An exploration of organisations, teams, communities, and research fields as knowledge-processing systems rather than administrative structures -- not management theory, but cognitive science applied at a scale larger than one mind. The central question is the organisational version of a question this site's other series ask about individual and artificial minds: how do groups know things that no individual member fully knows, and what does it cost them to remember, forget, and adapt as a collective rather than as a crowd of separately competent people? The series moves from the basic mechanics of collective knowing, through how that knowledge persists and decays over time, to how trust, delegation, and duration change once a team is temporary, agentic, or spread across an entire discipline rather than a stable group.