On Creativity, Metaphor, and What AI Still Needs to Learn

A Theory of Creativity First

Before building any system, I needed to understand what creativity actually is.

Here is the formulation I arrived at around 2005, working as a researcher in Québec City:

Humans cannot create something entirely new from nothing.

We can only perceive reality through our senses. You cannot imagine a color that does not exist. You cannot conceive a sound you have never heard. The entire domain available to human creativity is bounded by accumulated sensory experience.

Creative thinking is therefore combinatorial rather than generative ex nihilo. The only way to produce something genuinely new within a bounded experiential space is to discover a structural correspondence between two distant domains that are already known.

When that correspondence holds, it is a metaphor.

But novelty alone is insufficient. A creative idea must also be useful, and usefulness is not intrinsic to the idea itself. It is evaluated by a human observer with a specific problem and a specific set of objectives. The same association can be brilliant in one context and irrelevant in another.

This leads to a precise formulation:

A creative idea is an association between two distant known domains whose relevance is judged by a human evaluator as both novel and useful within a specific context.

Novelty is a function of semantic distance between domains.

Utility is a function of the match with the problem and with the evaluator’s own criteria.

From this theory, an architecture follows naturally.

The Architecture (2005–2010)

If creativity is the identification of structural correspondences across distant domains, then a system designed to assist creativity should not search for what is similar. It should search for what is structurally equivalent across distance.

Between 2005 and 2010, working at a private imaging company in Québec City, I designed and implemented such an architecture.

Each object in the system (initially images, but the framework was modality-independent) was described through three structured vectors:

  • A composition vector capturing spatial organization, perspective, and luminosity
  • An object-property vector encoding detected geometries, shapes, and physical attributes
  • A semantic vector containing qualitative keyword descriptors

These vectors were computed automatically using classical computer vision techniques: color histograms, wavelet decompositions, geometric segmentation, texture analysis, spatial frequency distributions. Each image received a multidimensional signature, which we called a “genetic code,” stored in a database and queryable in real time across a collection of 100,000 images.

In today’s terminology: a hand-crafted feature-embedding pipeline feeding a structured vector database, with sub-second query response time. Built in 2008, before those words existed in this context.

But the architectural intent was quite different from similarity search.

The system did not ask: What is nearest?

It asked:

  • Which objects share equivalent structural overlaps with a reference? (alternative objects: substitutable, not combinable)
  • Which objects contribute complementary, non-overlapping components of a reference? (complementary objects: combinable to form a stronger metaphor)
  • Which combinations increase analogical coherence rather than surface resemblance? (linked objects: internal reinforcement as a tiebreaker)

The metaphorical strength of a combination was computed as the aggregate overlap across structured dimensions. Combinations were ranked by analogical coverage, not by proximity.

A red apple and a white tablecloth, taken separately, are only partial metaphors for the Japanese flag. Combined, they cover it completely: one contributes the red circle, the other the white rectangle. Their combination scores higher not because they resemble the flag, but because together they account for its structure. That is analogy, not similarity.

Similar questions were being explored across content-based retrieval and computational creativity research during that period. The problem was recognized; what was missing were the representational tools to address it at scale.

Why This Distinction Still Matters

Modern embedding systems, including the best multimodal foundation models, are extraordinarily capable. They organize high-dimensional spaces where related concepts cluster together. Distance minimization in embedding space has proven to be a remarkably powerful paradigm.

But the analogical operation itself remains largely latent in these systems.

Current architectures optimize for distance, retrieve nearest neighbors, and interpolate within learned manifolds. They do not explicitly evaluate which pairs or sets of representations together form a structurally stronger mapping to a reference than any single element alone.

Foundation models can produce metaphor-like outputs through statistical association, sometimes impressively so. But the mechanism is emergent rather than structured, which means it cannot be controlled, composed, or inspected.

Making analogy a first-class, explicit, steerable operation in AI systems remains an open architectural problem.

What AI Still Needs

Revisiting this research today, three limitations of current AI systems stand out, each rooted in the same theory of creativity I was working from in 2005.

1. Implicit context inference

Human creativity does not operate in a vacuum. The relevance of any idea depends heavily on the context in which the human is operating: their constraints, objectives, mental state, environment. A skilled collaborator reads this context without being explicitly told.

Current AI systems depend almost entirely on what the user explicitly provides. They do not perceive the context in which the human evolves. For AI to become a genuine creative partner, it needs access to richer contextual signals, along with the capacity to infer meaning from them without requiring the human to articulate everything. That is a problem of situated cognition rather than retrieval.

2. Memory as a knowledge reservoir

In my theory of creativity, the quality of associative reasoning is directly proportional to the richness of available memory. More diverse experience means more potential connections across distant domains.

Current AI systems have impressive parametric knowledge, but their memory architecture is largely static between training runs. The strategic management of episodic, contextual, and long-term memory, treated as a dynamic, queryable reservoir that evolves with use, remains underdeveloped. Memory, in this view, is the substrate of association and not just storage.

3. Emotional memory

This is perhaps the most significant gap, and the one most likely to trigger an objection from researchers in reinforcement learning or affective computing.

The objection is predictable: AI systems do incorporate affective signals, through reward modeling, reinforcement learning from human feedback, and preference learning. This is true, and the work is serious.

But it addresses a different problem. These approaches treat affect as an external training signal: feedback injected during learning to shape behavior. Human emotional memory is structurally different. Rather than a signal applied to knowledge from the outside, it is woven into knowledge itself.

The neuroscientist Antonio Damasio demonstrated this precisely. Emotion is not separate from rational cognition; it is constitutive of it. Patients with damage to emotional memory centers do not become more rational. They lose the capacity to make decisions at all, because they can no longer assign differential weight to recalled experiences.

Every human memory carries affective encoding (fear, joy, discomfort, wonder) that influences which associations surface, which combinations feel meaningful, and which ideas resonate. This weighting is embedded in the retrieval structure itself rather than applied post-hoc.

Current AI systems process semantic relationships without this integrated affective architecture. The gap may therefore be architectural rather than merely incremental.

Building systems where affective structure is embedded in memory, instead of applied to it from outside, may be one of the most important open problems in AI research on creativity and reasoning.

Why I Am Writing This Now

The research described here was conducted between 2005 and 2010 and was not publicly discussed at the time due to contractual obligations.

Revisiting it now is less an exercise in retrospection than a recognition that the underlying questions remain open, and that the technological conditions to investigate them meaningfully finally exist.

I am not writing to claim priority. Parallel discovery is the norm in research, and I have no interest in relitigating timing. I am writing because the problem is still in front of us.

If you are working on reasoning architectures, compositional intelligence, contextual AI systems, or human–AI collaboration, I would like to talk.

The research described above is documented in an unpublished patent specification and a research poster produced in 2008.

Gabriel Mailhot
Software Architect and Software Engineering Quality Advisor
Series: Organizational Engineering for the AI Era