Computational creativityComputational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts (e.g., computational art as part of computational culture). The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends: To construct a program or computer capable of human-level creativity.
NarrativeA narrative, story, or tale is any account of a series of related events or experiences, whether nonfictional (memoir, biography, news report, documentary, travelogue, etc.) or fictional (fairy tale, fable, legend, thriller, novel, etc.). Narratives can be presented through a sequence of written or spoken words, through still or moving images, or through any combination of these. The word derives from the Latin verb narrare (to tell), which is derived from the adjective gnarus (knowing or skilled). Narration (i.
Visual novelA visual novel (VN) is a form of digital interactive fiction. Visual novels are often associated with the medium of video games, but are not always labeled as such themselves. They combine a textual narrative with static or animated illustrations and a varying degree of interactivity. The format is more rarely referred to as novel game, a retranscription of the wasei-eigo term noberu gēmu, which is more often used in Japanese. Visual novels originated in and are especially prevalent in Japan, where they made up nearly 70% of the PC game titles released in 2006.
Generative artificial intelligenceGenerative artificial intelligence (AI) is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics. In the early 2020s, advances in transformer-based deep neural networks enabled a number of generative AI systems notable for accepting natural language prompts as input.
Self-conceptIn the psychology of self, one's self-concept (also called self-construction, self-identity, self-perspective or self-structure) is a collection of beliefs about oneself. Generally, self-concept embodies the answer to the question "Who am I?". The self-concept is distinguishable from self-awareness, which is the extent to which self-knowledge is defined, consistent, and currently applicable to one's attitudes and dispositions. Self-concept also differs from self-esteem: self-concept is a cognitive or descriptive component of one's self (e.
Implicit stereotypeAn implicit bias or implicit stereotype is the pre-reflective attribution of particular qualities by an individual to a member of some social out group. Implicit stereotypes are thought to be shaped by experience and based on learned associations between particular qualities and social categories, including race and/or gender. Individuals' perceptions and behaviors can be influenced by the implicit stereotypes they hold, even if they are sometimes unaware they hold such stereotypes.
Self-imageSelf-image is the mental picture, generally of a kind that is quite resistant to change, that depicts not only details that are potentially available to an objective investigation by others (height, weight, hair color, etc.), but also items that have been learned by persons about themselves, either from personal experiences or by internalizing the judgments of others. Self-image may consist of six types: Self-image resulting from how an individual sees oneself. Self-image resulting from how others see the individual.
Implicit-association testThe implicit-association test (IAT) is an assessment intended to detect subconscious associations between mental representations of objects (concepts) in memory. Its best-known application is the assessment of implicit stereotypes held by test subjects, such as associations between particular racial categories and stereotypes about those groups. The test has been applied to a variety of belief associations, such as those involving racial groups, gender, sexuality, age, and religion but also the self-esteem, political views, and predictions of the test taker.
Synthetic mediaSynthetic media (also known as AI-generated media, media produced by generative AI, personalized media, and colloquially as deepfakes) is a catch-all term for the artificial production, manipulation, and modification of data and media by automated means, especially through the use of artificial intelligence algorithms, such as for the purpose of misleading people or changing an original meaning.
Artificial intelligence artArtificial intelligence art is any visual artwork created through the use of artificial intelligence (AI) programs. Artists began to create AI art in the mid- to late-20th century, when the discipline was founded. In the early 21st century, the availability of AI art tools to the general public increased, providing opportunities for use outside of academia and professional artists. Throughout its history, artificial intelligence art has raised many philosophical concerns, including related to copyright, deception, and impact on more traditional artists.
StorytellingStorytelling is the social and cultural activity of sharing stories, sometimes with improvisation, theatrics or embellishment. Every culture has its own stories or narratives, which are shared as a means of entertainment, education, cultural preservation or instilling moral values. Crucial elements of stories and storytelling include plot, characters and narrative point of view. The term "storytelling" can refer specifically to oral storytelling but also broadly to techniques used in other media to unfold or disclose the narrative of a story.
Input methodAn input method (or input method editor, commonly abbreviated IME) is an operating system component or program that enables users to generate characters not natively available on their input devices by using sequences of characters (or mouse operations) that are available to them. Using an input method is usually necessary for languages that have more graphemes than there are keys on the keyboard. For instance, on the computer, this allows the user of Latin keyboards to input Chinese, Japanese, Korean and Indic characters.
Knowledge representation and reasoningKnowledge representation and reasoning (KRR, KR&R, KR2) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build.
Knowledge managementKnowledge management (KM) is the collection of methods relating to creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieve organizational objectives by making the best use of knowledge. An established discipline since 1991, KM includes courses taught in the fields of business administration, information systems, management, library, and information science. Other fields may contribute to KM research, including information and media, computer science, public health and public policy.
Interactive fictionInteractive fiction, often abbreviated IF, is software simulating environments in which players use text commands to control characters and influence the environment. Works in this form can be understood as literary narratives, either in the form of interactive narratives or interactive narrations. These works can also be understood as a form of video game, either in the form of an adventure game or role-playing game.
Foundation modelsA foundation model (also called base model) is a large machine learning (ML) model trained on a vast quantity of data at scale (often by self-supervised learning or semi-supervised learning) such that it can be adapted to a wide range of downstream tasks. Foundation models have helped bring about a major transformation in how artificial intelligence (AI) systems are built, such as by powering prominent chatbots and other user-facing AI.
EvaluationIn common usage, evaluation is a systematic determination and assessment of a subject's merit, worth and significance, using criteria governed by a set of standards. It can assist an organization, program, design, project or any other intervention or initiative to assess any aim, realisable concept/proposal, or any alternative, to help in decision-making; or to ascertain the degree of achievement or value in regard to the aim and objectives and results of any such action that has been completed.
AnnotationAn annotation is extra information associated with a particular point in a document or other piece of information. It can be a note that includes a comment or explanation. Annotations are sometimes presented in the margin of book pages. For annotations of different digital media, see web annotation and text annotation. Annotation Practices are highlighting a phrase or sentence and including a comment, circling a word that needs defining, posing a question when something is not fully understood and writing a short summary of a key section.
Large language modelA large language model (LLM) is a language model characterized by its large size. Their size is enabled by AI accelerators, which are able to process vast amounts of text data, mostly scraped from the Internet. The artificial neural networks which are built can contain from tens of millions and up to billions of weights and are (pre-)trained using self-supervised learning and semi-supervised learning. Transformer architecture contributed to faster training.
Generative adversarial networkA generative adversarial network (GAN) is a class of machine learning framework and a prominent framework for approaching generative AI. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set.
