Creative Loss: Ambiguity, Uncertainty and Indeterminacy

Tom Holberton

REVIEW 4 major objections 3 minor 31 references

Creative Loss: Ambiguity, Uncertainty and Indeterminacy

T0 review · 4 major / 3 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Machine learning's most distinctive creative quality is indeterminacy, found in the half-trained spaces of trained models.

desk verdict Useful taxonomy wrapped around an unsupported central hypothesis; the essay is worth reading but needs to reframe its main claim as a research program. read the letter →

arxiv 2501.10369 v1 pith:LJO6MEPJ submitted 2024-12-12 cs.CY cs.AIcs.HCcs.LG

Tom Holberton This is my paper · ORCID

classification cs.CYcs.AIcs.HCcs.LG

keywords ambiguityuncertaintyindeterminacylatentspacecreativeAImachinelearninganddesigngenerativeadversarialnetworksarchitectural

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper distinguishes three ways creative machine learning can be open: ambiguity, uncertainty, and indeterminacy, and argues that they correspond to different ambitions for AI as a creative partner. Its central proposal is that the most distinctive creative quality of machine learning is indeterminacy: trained models are not uniformly complete, and the latent space is unevenly trained, so the most creative outcomes may appear in regions that are half-trained and half-determinate. The argument is illustrated with architectural design projects described in the paper, including a conditional GAN trained on traditional Japanese tea bowls, probabilistic stone forms, and a crossmodal drawing-music interface. If the paper is right, creative AI research should treat final loss minimization as one goal among others and instead learn to calibrate the degree of indeterminacy in the latent space.

What carries the argument

The central object is the latent space, the internal learned representation of a generative model in which each point maps to an output. The key move is to read that space as unevenly trained: an incremental, statistically learned whole with margins of indeterminacy, a concept the paper borrows from the philosophy of technical objects. This reading carries the argument because it turns indeterminacy from a property of an artwork into a property of the model itself that can in principle be measured, controlled, and calibrated against the structure of the training data.

What would settle it

A direct test would train a generative model on a fixed dataset, measure per-region training quality in the latent space using indicators such as reconstruction error, discriminator confidence, or interpolation smoothness, then generate outputs from the least-trained, half-trained, and fully trained regions and have independent evaluators judge creativity. The paper's claim is falsified if outputs from the best-trained regions are consistently rated as creative as or more creative than those from the half-trained regions, or if the measured training variation across the space is negligible.

Watch

Extended reading notes

Core claim

A neural network's latent space is usually treated as a uniform, fully trained instrument, but the paper claims this is false: because training is incremental and driven by an overall loss, different regions of the latent space are trained to different degrees and with different degrees of determination. Indeterminacy is precisely this incompleteness, and it is what gives machine learning its own creative potential, distinct from the ambiguity of hallucinated or dream-like outputs and the probabilistic uncertainty of transformer sampling. The paper therefore proposes that future creative machine learning should not only minimize loss but deliberately control and calibrate indeterminacy, working in the spaces of the half-trained and half-determinate, and may even construct datasets rather than only curate them in order to exploit this quality.

Load-bearing premise

The claim rests on the factual assumption that latent spaces are not uniformly trained but vary in degree of training and determination; if standard training actually produces effectively uniform latent spaces, or if that unevenness has no stable relationship to creative quality, the proposed direction loses its foundation.

Editorial extensions

If this is right

Creative machine learning should deliberately search for outcomes in partially trained latent regions instead of treating final loss minimization as the sole objective.
Semi-supervised learning and synthetic data become tools for shaping how much of the latent space is determined, rather than merely ways to improve accuracy.
The three-term distinction gives designers a precise vocabulary: ambiguity aligns with combinatorial creativity, uncertainty with exploratory probability, and indeterminacy with transformative change.
Indeterminacy suggests a transductive potential between modalities, such as drawing and music, where the unevenly trained space itself becomes a site for creative translation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A concrete test follows from the paper's claim: train two generative models on the same data to different numbers of epochs, or with loss unevenly weighted across the latent space, then compare outputs from differently trained regions; the claim predicts that the half-trained regions will not merely be worse but will show a distinct kind of creative value. The paper does not run this test.
The argument implicitly points toward a broader research direction: treating neural network training as a developmental or annealing process in which final performance and creative affordance may pull in different directions, so that training schedules become creative parameters in their own right.
Read operationally, the claim suggests a new evaluation criterion for creative AI: instead of judging only the outputs, one could measure the heterogeneity of training across the latent space, for example through per-region reconstruction error or discriminator confidence, and ask whether that heterogeneity correlates with judged creativity. The paper gestures at this but does not formalize it.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Tom, this is a five-minute read that leaves you with a useful distinction and one big unanswered question. The paper maps ambiguity, uncertainty and indeterminacy onto phenomenology, epistemology and ontology, then onto Boden's combination, exploration and transformation. That triple mapping is genuinely neat—it gives practitioners a vocabulary for talking about what kind of 'openness' they are invoking, and I don't recall seeing it laid out that cleanly before. The three case studies from Unit 21 illustrate the categories well, especially the Wabi-sabi project for ambiguity and the Probobli Boboli for uncertainty.

The soft spot is the load-bearing claim in Section 4. The paper says latent space is 'not evenly or uniformly trained' and that the most creative outcomes will be found in 'half-trained and half-determinate' regions. As a metaphor, that's evocative; as a factual claim about how neural networks work, it's unsupported. Gradient descent optimizes shared weights globally; a latent coordinate is not an object that receives more or less training than its neighbors. What actually varies across latent space is data density, posterior mismatch, or conditioning, and the paper doesn't distinguish those from 'degree of training.' So the central future direction rests on a conflation, and without it the recommendation to chase half-trained regions is just a hunch.

I also think the conclusion is partly baked into the definitions. If indeterminacy is defined as testing the limits of ontology, then it's hardly a surprise that it comes out as the most transformative. And all the evidence is from the author's own studio, which is fine for a conceptual essay but doesn't provide any independent check.

That said, the paper is honest about its scope and is clearly written. The framework itself could be a real contribution to creative AI discourse, even if the half-trained hypothesis doesn't survive contact with the details. The author would do better to present it as a research question with testable predictions—e.g., comparing outputs from models trained to different checkpoints or with different data densities—rather than as a conclusion.

For the record: I'd bring this to a reading group in the creative AI / computational design community, and I'd cite it for the taxonomy. It deserves a serious referee; a good referee could push the author to sharpen the claim and specify what evidence would count. No formal proofs, no code, no data, so the evidentiary bar is just the quality of the argument, which is decent but not yet solid.

Referee Report

4 major / 3 minor

Summary. The paper proposes a conceptual framework for evaluating creative uses of machine learning, distinguishing ambiguity, uncertainty, and indeterminacy. It maps these onto Boden's combination, exploration, and transformation, and argues that indeterminacy—understood as a property of latent-space formation—is the most distinctive creative quality of machine learning. The argument is illustrated with three projects from the author's Unit 21 at the Bartlett: AI Wabi-sabi (ambiguity), Probobli Boboli and related works (uncertainty), and a programmatic call for 'half-trained' latent spaces (indeterminacy). The paper concludes with ethical remarks and suggestions for future research.

Significance. If the central premise about uneven training of latent spaces were established, the paper would give the creative-AI community a novel research direction beyond minimizing final loss and would offer a useful vocabulary for discussing degrees of creativity. The practice-based illustrations and the ethical emphasis on dataset authorship are strengths. However, as it stands, the key claim is a speculation with no supporting evidence, mechanism, or empirical comparison, which limits the paper's current contribution to that of a provocative essay rather than a substantiated research direction.

major comments (4)

[Section 4, second paragraph] The claim 'Space is not evenly or uniformly trained, but instead achieves its variation and hybridity due to differing degrees of training and determination' is load-bearing for the recommendation to seek creative outcomes in 'half-trained' spaces, yet it is asserted without evidence or argument. In standard GAN and VAE training, gradient descent updates the shared weights globally; a given latent coordinate is not a 'trained' object in any local sense. Non-uniformities that arise in latent space are typically attributable to data density, prior mismatch, or conditioning, not to variation in the amount of training applied to different regions. Unless one of these mechanisms is intended, the premise does not follow from the cited models. Please provide a mechanism or reference that supports the claim, or substantially soften the claim into an explicit speculation.
[Section 4, last sentence] The phrase 'the spaces of the half-trained and half-determinate' conflates the temporal training trajectory with spatial structure. A model early in training has an under-fit latent space, but that is a global property of the checkpoint, not a region that remains 'half-trained' after convergence. If the intended meaning is early-stopped checkpoints, intermediate training epochs, or regions of low data density, that should be stated explicitly; the current phrasing suggests a spatial localization that standard training dynamics do not produce.
[Section 5, first paragraph] The alignment of ambiguity, uncertainty, and indeterminacy with Boden's combination, exploration, and transformation is asserted in a single sentence ('It could be argued...') without supporting argument. This mapping is the paper's main conceptual contribution and should be defended. In particular, Boden's transformation requires changing the defining dimensions of the conceptual space, which is not obviously what is meant by 'indeterminacy' in latent spaces. The paper should explain how the mapping works rather than leaving it as a suggestion.
[Sections 2 and 4] The conclusion that indeterminacy is the most distinctive quality is partly predetermined by the definitions. Ambiguity is characterized from the start as 'imitation' that 'reinforces the corpus' (Section 2), whereas indeterminacy is characterized as 'testing the limits of ontology' (Section 4). Given these choices, the ranking is a foregone conclusion. The paper should either justify those characterizations against plausible alternatives or acknowledge that the ranking is a definitional framing rather than an empirical finding.

minor comments (3)

[References, [31]] Reference [31] contains a typographical error: 'F ormalized music' should read 'Formalized music'.
[Section 2 and Figure captions] The project name is rendered inconsistently as 'AI Wabi-sabi' and 'AI wabi-sabi' in the text and in the figure captions; please standardize the capitalization.
[Section 4, first paragraph] The section introduces two senses of indeterminacy (the meta-communicative frame from Bateson and Hertzmann, and the measurable 'constituent part' sense). The transition between these senses is abrupt; a sentence explicitly distinguishing them would improve clarity.

Circularity Check

1 steps flagged · score 4.0 of 10

Indeterminacy's unique status is baked into its definition; the latent-space claim is unsupported but not circular.

self definitional [Section 4 and Section 5 (Conclusion)]
"To create with indeterminacy is to not just suspend meaning, but test the limits of ontology. ... It could be argued ambiguity, uncertainty and indeterminacy respectively align with these approaches. ... Indeterminacy emerges as far more unique quality within machine learning."

The evaluative conclusion that indeterminacy is the most transformative or unique quality is already contained in the stipulative definition of indeterminacy as testing the limits of ontology. Ambiguity is characterized as corpus-reinforcing imitation and uncertainty as probabilism within an overall system of certainties, so the ranking among the three terms is fixed by the definitions rather than by an independent argument. The conclusion then restates this definition ('moving beyond the assumption that training and data must solely emulate established environmental patterns') instead of deriving it from evidence. This is a persuasive-definition ordering: the conclusion follows by construction from the way the terms are introduced.

full rationale

This is a conceptual essay rather than a formal derivation, so most of the claimed progression is rhetorical rather than mathematical. The one genuinely circular move is the pre-ordering of the three concepts: ambiguity is defined as imitation that reinforces the corpus, uncertainty as operating inside an established system of probabilities, and indeterminacy as testing the limits of ontology. The conclusion that indeterminacy aligns with Boden's 'transformation' and is 'far more unique' therefore follows by stipulation, not by independent evidence. The separate load-bearing claim in Section 4 that latent space is 'not evenly or uniformly trained' and that 'half-trained' regions are the most creative is not derived from anything; it is an unsupported empirical/interpretive premise. That is a correctness or evidence problem, not a circularity, because the claim is not an input to itself. The self-references to the author's own studio projects are illustrative rather than evidential, so they do not constitute load-bearing self-citation. No equations or fitted parameters are involved. Overall, one self-definitional step partially determines the central evaluative conclusion, but the latent-space proposal retains independent content, supporting a moderate score of 4.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no free parameters and no invented entities. Its load-bearing assumptions are conceptual: the triadic mapping, the uneven-training claim, and the Boden alignment. These are plausible but unverified, so the ledger is thin but honest.

assumptions (3)

domain assumption The three terms map cleanly to phenomenology, epistemology, and ontology.
Section 1 asserts the mapping ('comparable to the mapped, the navigable and the uncharted') without argument; the rest of the paper builds on it.
domain assumption Latent space is not evenly or uniformly trained, and variation arises from differing degrees of training and determination.
Section 4 states this as a fact about VAEs and GANs; it is load-bearing for the half-trained-space proposal but is not demonstrated.
ad hoc to paper Ambiguity, uncertainty, and indeterminacy align respectively with Boden's combination, exploration, and transformation.
Section 5 introduces the alignment with 'It could be argued', but no derivation or external reference connects the two triads.

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Cite this review

Pith. "Pith review of Creative Loss: Ambiguity, Uncertainty and Indeterminacy." pith.science (2026). https://pith.science/paper/LJO6MEPJ

@misc{pith2026250110369,
  author       = {Pith},
  title        = {Pith review of: Creative Loss: Ambiguity, Uncertainty and Indeterminacy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LJO6MEPJ}},
  note         = {Machine review of arXiv:2501.10369}
}

read the original abstract

This article evaluates how creative uses of machine learning can address three adjacent terms: ambiguity, uncertainty and indeterminacy. Through the progression of these concepts it reflects on increasing ambitions for machine learning as a creative partner, illustrated with research from Unit 21 at the Bartlett School of Architecture, UCL. Through indeterminacy are potential future approaches to machine learning and design.

Figures

Figures reproduced from arXiv: 2501.10369 by the authors.

**Figure 1.** AI wabi-sabi : Four physical bowls generated from the ’digital clay’ of satellite images and trained from a craft of imperfection [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** AI wabi-sabi : Acrylic and hand lacquered bowl generated from lunar site. 2 [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Probobli Boboli: a stone training set used to create a model that generates ’natural’ forms with precise probabilities. Probobli Boboli [24] reimagines the context of grotto creation in Florence through machine learning. These are spaces where artifice and nature are blended into a hyper simulated version. A 3D generative model [30] creates ‘naturalistic’ stone forms indexed to a precise probability distribution of … view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Probobli Boboli: Alternative architectural forms are autogenerated according to a recipe of probable stones - analysed, positioned and connected to form surfaces with continuous water drainage paths. See films at [24]. In Towards a Non-Universal Architecture [8] a data…

**Figure 5.** Figure 5: Towards a Non-Universal Architecture: autogenerated combinations of gestures that have been generated according to different community recipes, combining VR and GAN interpolation. 4 [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]

**Figure 6.** Figure 6: Towards a Non-Universal Architecture: This allows an infinite variety of architectural designs to be automatically created that each respond to different locations in Barcelona and different local combinations of community co-authorship. See films at [8] [PITH_FULL_IM…

**Figure 7.** Figure 7: Crossmodal Compositions: creating a simultaneous design and musical composition [25]. Crossmodal Compositions [25] ( [PITH_FULL_IMAGE:figures/full_fig_p005_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

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Gregory Bateson. Steps to an ecology of mind: collected essays in anthropology, psychiatry, evolution and epistemology. J. Aronson, Northwale (N.J.) London, 1987

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Mario Carpo. The alphabet and the algorithm . MIT Press, Cambridge, Mass, 2011

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Mario Carpo. Imitation Games. Artforum, 61(10), 2023. URL https://www.artforum.com/ features/mario-carpo-on-the-new-humanism-252735/

work page 2023

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Jose V . Ciprut, editor.Indeterminacy: The Mapped, the Navigable, and the Uncharted . The MIT Press, March 2009. doi: 10.7551/mitpress/8011.001.0001

work page doi:10.7551/mitpress/8011.001.0001 2009

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