Artificial Intelligence Creativity: Fundamentals and Applications

By Fouad Sabry

What Is Artificial Intelligence Creativity

A interdisciplinary endeavor that may be found at the crossroads of the sciences of artificial intelligence, cognitive psychology, philosophy, and the arts, computational creativity is an area of study that combines all of these subjects.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Computational Creativity

Chapter 2: Natural Language Processing

Chapter 3: Machine Learning

Chapter 4: Artificial Consciousness

Chapter 5: Algorithmic Composition

Chapter 6: Neural Network

Chapter 7: Outline of Artificial Intelligence

Chapter 8: Deep Learning

Chapter 9: DeepDream

Chapter 10: Artificial Intelligence Art

(II) Answering the public top questions about artificial intelligence creativity.

(III) Real world examples for the usage of artificial intelligence creativity in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of artificial intelligence creativity' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of artificial intelligence creativity.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 4, 2023

Book preview

Chapter 1: Computational creativity

A multidisciplinary endeavor that can also be referred to as artificial creativity, mechanical creativity, creative computing, or creative computation, computational creativity is situated at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts. Other names for computational creativity include creative computing and creative computation (e.g., computational art as part of computational culture).

Modeling, simulating, or reproducing creativity through the use of a computer is the objective of the field of computational creativity. This can serve a number of purposes:

To build into a computer or program creative capabilities comparable to those of humans.

in order to gain a deeper understanding of human creativity and to develop an algorithmic approach to studying creative behavior in humans.

To design computer programs that, without necessarily being creative themselves, can boost the creative capacity of humans.

The study of creativity, both theoretically and practically, is the focus of the discipline of computational creativity. Work on the nature and proper definition of creativity is carried out in parallel with work on the implementation of systems that exhibit creativity, with one line of work informing the other. Theoretical work on the nature of creativity also informs the practical work.

Media synthesis is the name given to the practical application of computational creativity.

The theoretical approaches focus on creativity in its most fundamental form. In particular, under what conditions would it be appropriate to refer to the model as creative, given that exceptional creativity involves flouting established norms or rejecting accepted practices? This is an alternate version of Ada Lovelace's argument against artificial intelligence, which has been summarized by contemporary theorists such as Teresa Amabile. How can the actions of a machine be considered creative if it can only carry out the instructions it was given by its creators?

It is true that not all computer theorists would agree with the premise that computers can only do what they are programmed to do; this is an important argument in favor of computational creativity.

The AI researchers Newell, Shaw, and Simon developed the combination of novelty and usefulness into the cornerstone of a multi-pronged view of creativity, one that uses the following four criteria to categorize a given answer or solution as creative. Because no single perspective or definition seems to offer a complete picture of creativity, the AI researchers Newell, Shaw, and Simon developed the combination of novelty and usefulness into the cornerstone of a multi-pronged view of creativity:

The response is original as well as helpful (either for the individual or for society)

The answer requires us to dismiss concepts that we had previously recognized as valid.

The solution is found by maintaining a high level of motivation and perseverance.

The solution can be found by elaborating on aspects of the issue that were not clear at first.

An alternative line of thought has emerged among bottom-up computational psychologists who are engaged in research on artificial neural networks in contrast to what has been discussed above, which reflects a top-down approach to computational creativity. These types of generative neural systems, for example, were driven by genetic algorithms in the late 1980s and early 1990s. were able to successfully hybridize simple musical melodies while also successfully predicting listener expectations.

Traditional computational approaches to creativity rely on the explicit formulation of prescriptions by developers and a certain degree of randomness in computer programs. Machine learning methods, on the other hand, allow computer programs to learn on heuristics from input data, which enables creative capacities to exist within the computer programs themselves. In the recently developed strategy, there are two neural networks, and one of them is responsible for providing training patterns to the other. In later work by Todd, a composer would select a set of melodies that define the melody space, place them on a two-dimensional plane using a graphic interface that was controlled by a mouse, train a connectionist network to produce those melodies, and then listen to the new melodies that were interpolated by the network and corresponded to intermediate points in the two-dimensional plane. This is an example of how later work by Todd was accomplished.

The study of computational creativity is characterized by the recurrence of a number of high-level and philosophical themes.

Margaret Boden

Boden also makes a distinction between the creativity that results from exploring a preexisting conceptual space and the creativity that results from consciously altering or transcending the boundaries of this space. The first type, which she refers to as exploratory creativity, is contrasted with the second type, which she refers to as transformational creativity. She views the second type as a form of creativity that is significantly more revolutionary, difficult, and uncommon than the first type. Following the criteria from Newell and Simon that have been elaborated above, we can see that both types of creativity should produce results that are appreciably novel and useful (criterion 1), but exploratory creativity is more likely to arise from a thorough and persistent search of a well-understood space (criterion 3), whereas transformational creativity should involve the rejection of some of the constraints that define this space (criterion 2) or some of the assumptions that defy this space (criterion 4). (criterion 4). The work that has been done in computational creativity has been guided by Boden's insights on a very general level. These insights have provided more of an inspirational touchstone for development work rather than a technical framework of algorithmic substance. However, Boden's observations have also been the focus of formalization efforts, most notably in the research conducted by Geraint Wiggins.

In order to meet the requirement that creative products should be original and applicable, creative computational systems are typically divided into two stages: the generation phase and the evaluation phase. In the first stage, novel (to the system itself, and therefore P-Creative) constructs are generated, and at this point, the system filters out any unoriginal constructs that it already is familiar with. The aforementioned pool of potentially inventive constructions is then put through some sort of examination in order to figure out which of them are meaningful and helpful, and which are not. The Geneplore model developed by Finke, Ward, and Smith is a psychological model of creative generation that is based on empirical observation of human creativity. This two-phase structure is consistent with the Geneplore model.

Although a significant portion of research into computational creativity is concentrated on the autonomous and algorithmically-driven generation of machine-based creativity, many researchers lean toward an approach that emphasizes collaboration.

Because recent advancements in AI have the potential to disrupt entire innovation processes and fundamentally alter how innovations will be created, the concept of computational creativity is receiving a growing amount of attention in the academic literature on innovation and management.

highlights the relevance of computational creativity for creating innovation and introduced the concept of self-innovating artificial intelligence (SAI) to describe how companies make us of AI in innovation processes to enhance their innovative offerings.

The application of AI within an organization with the purpose of making small but steady improvements to existing products or developing brand-new ones is known as strategic AI (SAI), on the basis of discoveries made through the ongoing combination and analysis of multiple sources of data.

With the advent of AI as a technology for general use, The range of products that can be developed with SAI will expand from the straightforward to the ever-more-complicated.

This suggests that the creative abilities required of humans will change as a result of the rise of computational creativity.

A large portion of human creativity, if not all of it, can be understood as the novel combination of concepts or things that already exist in the world. Combinatorial creativity often makes use of common strategies such as:

Putting a well-known object in an unusual place (like Marcel Duchamp's Fountain), or the other way around, putting a not-so-well-known object in a well-known place (e.g., a fish-out-of-water story such as The Beverly Hillbillies)

Combining two things or styles that are only superficially distinct from one another (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.)

The practice of comparing a well-known thing to a concept that is only superficially connected to it and is conceptually removed from it (e.g., Makeup is the Western burka; A zoo is a gallery with living exhibits)

incorporating a fresh and unforeseen element into an already established idea (e.g., adding a scalpel to a Swiss Army knife; adding a camera to a mobile phone)

Combining two situations that have nothing in common in order to create a humorous situation (for example, the Emo Philips joke) Women almost always seek the assistance of men in order to advance their careers. Anthropologists are to blame! )

Utilization of a well-known image from one industry in another industry for the promotion of a concept or product that is irrelevant to either industry (e.g., using the Marlboro Man image to sell cars, or to advertise the dangers of smoking-related impotence).

The combinatorial viewpoint enables us to model creative problem-solving as a process that involves searching through the space of all possible combinational permutations. The combinations may be the result of the composition or concatenation of various representations, or they may be the result of a transformation that is rule-based or stochastic, and it may be either of these that produce the combinations. It is possible to generate blended or crossover representations using genetic algorithms and neural networks. These representations capture a combination of a variety of inputs.

Mark Turner and Gilles Fauconnier created mental spaces and conceptual metaphors by synthesizing ideas gleaned from cognitive linguistics research. According to their fundamental model, an integration network is composed of four connected spaces:

A primary area for incoming data (contains one conceptual structure or mental space)

A second area for taking input (to be blended with the first input)

A generic space consisting of stock conventions and image-schemas that facilitates an integrated comprehension of the input spaces.

A blend space is a space in which a selected projection of elements from both input spaces are combined; inferences arising from this combination also reside in this space, and they can sometimes lead to emergent structures that are in conflict with the inputs.

Fauconnier and Turner provide an overview of a set of optimality principles that, according to the authors, should serve as a roadmap for the development of a coherent integration network. Blending, in their view, is essentially a compression mechanism that involves the compression of two or more input structures into a single blend structure. On the level of conceptual relations is where this compression is taking place. For instance, in the blend, a number of relationships based on similarity that exist between the input spaces can be condensed into a single relationship based on identity.

Because they place an emphasis on connected semantic structures, the previously existing computational models of analogical mapping have been extended so that they can