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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Computational creativity | 4/8 | https://en.wikipedia.org/wiki/Computational_creativity | reference | science, encyclopedia | 2026-05-05T16:31:23.728483+00:00 | kb-cron |
=== Innovation === Computational creativity is increasingly being discussed in the innovation and management literature as the recent development in AI may disrupt entire innovation processes and fundamentally change how innovations will be created. Philip Hutchinson highlights the relevance of computational creativity for creating innovation and introduced the concept of "self-innovating artificial intelligence" (SAI) to describe how companies make use of AI in innovation processes to enhance their innovative offerings. SAI is defined as the organizational utilization of AI with the aim of incrementally advancing existing or developing new products, based on insights from continuously combining and analyzing multiple data sources. As AI becomes a general-purpose technology, the spectrum of products to be developed with SAI will broaden from simple to increasingly complex. This implies that computational creativity leads to a shift of creativity-related skills for humans. Veale and Pérez y Pérez consider "optimal innovation" proposed by Giora et al. a useful foundation for developing computational creativity. Giora et al.'s experiment asks participants to do pleasure and familiarity ratings of verbal stimuli (e.g., body and soul vs. body and sole) and non-verbal stimuli (e.g., a peace dove vs. a peace dove vertically posed that looks like a waving hand). It reveals that pleasing stimuli need to be innovative while preserving the salient meaning of the literal form. Veale and Pérez y Pérez highlight the need to develop computational systems that capture how meaning changes due to formal changes.
=== Combinatorial creativity === A great deal, perhaps all, of human creativity can be understood as a novel combination of pre-existing ideas or objects. Common strategies for combinatorial creativity include:
Placing a familiar object in an unfamiliar setting (e.g., Marcel Duchamp's Fountain) or an unfamiliar object in a familiar setting (e.g., a fish-out-of-water story such as The Beverly Hillbillies) Blending two superficially different objects or genres (e.g., a sci-fi story set in the Wild West, with robot cowboys, as in Westworld, or the reverse, as in Firefly; Japanese haiku poems, etc.) Comparing a familiar object to a superficially unrelated and semantically distant concept (e.g., "Makeup is the Western burka"; "A zoo is a gallery with living exhibits") Adding a new and unexpected feature to an existing concept (e.g., adding a scalpel to a Swiss Army knife; adding a camera to a mobile phone) Compressing two incongruous scenarios into the same narrative to get a joke (e.g., the Emo Philips joke "Women are always using men to advance their careers. Damned anthropologists!") Using an iconic image from one domain in a domain for an unrelated or incongruous idea or product (e.g., using the Marlboro Man image to sell cars, or to advertise the dangers of smoking-related impotence). The combinatorial perspective allows us to model creativity as a search process through the space of possible combinations. The combinations can arise from composition or concatenation of different representations, or through a rule-based or stochastic transformation of initial and intermediate representations. Genetic algorithms and neural networks can be used to generate blended or crossover representations that capture a combination of different inputs.
==== Conceptual blending ====
Mark Turner and Gilles Fauconnier propose a model called Conceptual Integration Networks that elaborates upon Arthur Koestler's ideas about creativity as well as work by Lakoff and Johnson, by synthesizing ideas from Cognitive Linguistic research into mental spaces and conceptual metaphors. Their basic model defines an integration network as four connected spaces:
A first input space (contains one conceptual structure or mental space) A second input space (to be blended with the first input) A generic space of stock conventions and image-schemas that allow the input spaces to be understood from an integrated perspective A blend space in which a selected projection of elements from both input spaces are combined; inferences arising from this combination also reside here, sometimes leading to emergent structures that conflict with the inputs. Fauconnier and Turner describe a collection of optimality principles that are claimed to guide the construction of a well-formed integration network. In essence, they see blending as a compression mechanism in which two or more input structures are compressed into a single blend structure. This compression operates on the level of conceptual relations. For example, a series of similarity relations between the input spaces can be compressed into a single identity relationship in the blend. Some computational success has been achieved with the blending model by extending pre-existing computational models of analogical mapping that are compatible by virtue of their emphasis on connected semantic structures. In 2006, Francisco Câmara Pereira presented an implementation of blending theory that employs ideas both from symbolic AI and genetic algorithms to realize some aspects of blending theory in a practical form; his example domains range from the linguistic to the visual, and the latter most notably includes the creation of mythical monsters by combining 3-D graphical models.
=== AI-assisted writing as curation === One of the first attempts to provide a literary-theoretical framework for AI-assisted writing was undertaken by Luciano Floridi in 2025. In his model of 'Distant Writing', the author functions as a designer and curator who develops narrative structures rather than formulating text manually. Through iterative selection and 'Socratic maieutics' (prompting), the human directs the machine, thereby assuming full intellectual responsibility for the design of the resulting work. Floridi's framework has a pre-LLM antecedent in the visual arts: Nicolas Bourriaud's Postproduction (2002) had argued that artists increasingly function as programmers and navigators of pre-existing cultural material rather than as original creators — a logic that Floridi transfers, with substantial theoretical elaboration, to the context of AI-assisted literary production. Floridi’s term 'distant writing' itself is coined in explicit analogy to Franco Moretti's 'distant reading' — understood in its later, computationally inflected sense — which had reframed literary analysis as the large-scale, algorithm-assisted study of textual corpora.