The Illusion Engine: The Quest for Machine Consciousness

This weeks post is by Kristina Šekrst, a researcher and engineer working at the crossroads of logic, artificial intelligence, and cognitive science. She is sharing an introduction to her new book The Illusion Engine: The Quest for Machine Consciousness (Springer Nature, 2025).

Kristina Šekrst

The Illusion Engine began with a simple question: how could machines ever think? It quickly met a less simple one: how could I, with a foot in both philosophy and software engineering, make either side intelligible to the other? Engineers glaze over at metaphysics; philosophers glaze over at code. Somewhere between the two, confusion turned into fascination.

The book grew out of that mismatch, moving between deep technical dives – attention mechanisms, backpropagation, transformers – and philosophical puzzles about consciousness, intentionality, and meaning. It asks whether a machine that hallucinates might, in doing so, come closer to something like experience.

This question continues threads from my recent work: “Do Large Language Models Dream of Electric Fata Morganas?” (forthcoming in the Journal of Consciousness Studies), “Unjustified Untrue Beliefs: AI Hallucinations and Justification Logics,” and “The Chinese Chatroom: AI Hallucinations, Epistemology, and Cognition”. Each explores a different corner of the same problem – what it means for a system to appear as if it has a mind. The Fata Morganas paper argues that a sophisticated hallucination can be phenomenologically indistinguishable from a genuine mental state. The chatroom paper re-examines Searle’s argument with a new empirical context, and the epistemological piece asks whether our confidence in “understanding” these systems might itself be an illusion.

Interpretability and explainability should have helped. They have not. The field of explainable AI still lingers somewhere between aspiration and metaphor. We can trace some attention heads, label a few neurons, and visualize activation patterns that correlate with linguistic categories, but the causal picture remains opaque. Recent interpretability work has provided us with networks and attribution graphs whose inner logic we can partially decode – yet the general phenomenon remains mysterious. Why do models hallucinate at all? Why do they sometimes reason correctly and sometimes invent? We have patterns and partial answers, but no comprehensive theory. 

The Illusion Engine

 

That is where AI hallucinations – an old misnomer dating back two decades in computer vision – become philosophically interesting. We tend to treat hallucinations as errors, yet they might be the first glimpse of synthetic imagination: a system’s attempt to reconcile incomplete internal states with the demand for coherent output. If an AI model claims to be sentient, we are confident it is hallucinating today. But will we still be so sure when version 125 insists on its own awareness? In humans, that dynamic is consciousness – an ongoing negotiation between prediction and reality, following the footsteps of Anil Seth. If an artificial model exhibits the same pattern, are we observing failure or emergence?

The Illusion Engine does not claim to answer that question. It tries instead to map the terrain where equations about gradient descent run into arguments about qualia. It suggests that the border between mind and mechanism may not be where we thought it was, and that the effort to explain away hallucinations might one day explain consciousness itself.