Summary

Computational creativity is a specialty within artificial intelligence that focuses on endowing computers with the ability to produce things that are considered creative, such as artwork, music, or unique problem-solving approaches.

ELI5

Imagine if your computer could paint a beautiful picture, write a funny joke, or come up with a cool new video game idea all on its own. It’s like the computer is using its own ‘imagination’. That’s what computational creativity is all about!

In-depth explanation

Computational creativity is a field of artificial intelligence that seeks to design computer systems capable of tasks traditionally deemed creative. The idea is not just to model or replicate human creativity, but to achieve ‘autonomous creativity’, where computational systems can generate original and valuable outputs independently.

This involves two main sub-tasks: creative generation and creative evaluation. The generation aspect focuses on producing novel patterns, ideas, or artifacts using techniques like generative modeling, which can include everything from statistical models to deep learning techniques such as Generative Adversarial Networks (GANs). The evaluation aspect involves assessing the quality or creativity of the computer-generated outputs, often involving techniques from machine learning and preference learning.

An important aspect of computational creativity is the requirement for the output to not just be novel, but also valuable or meaningful in some way. This typically involves having some understanding or model of the domain in which the creativity is being applied, which leads to considerations of knowledge representation and reasoning.

While the goal of standalone creative systems is ambitious, there are many practical applications of computational creativity even today. Examples include automated design systems, tools for aiding human creativity, game-level generators, and even computational humor and joke generation.

Generative Modeling, Artificial Creativity, Machine Learning (ML),, Deep Learning, Generative Adversarial Networks (GANs),, Preference Learning, Autonomous Systems, Creativity Support Tools