Computers and content generation

The advances are likely to increase, and generative design techniques are likely to come into the core curricula of data science, creative, and engineering professions globally. IBM Research

Al can not only boost our analytic and decision-making abilities but also heighten creativity. Harvard Business Review

Generative Al, which creates original artifacts or reconstructed content and data, is the next frontier. Gartner

Computational creativity

Computational creativity is a field that focuses on designing and building computational systems that are able to exhibit creative behavior. This can include generating original ideas, producing novel artifacts, and exhibiting creative problem-solving behavior. Researchers in this field use a variety of techniques, including artificial intelligence, machine learning, and cognitive modeling, to design and build creative systems. Some areas of focus within computational creativity include music and art generation, story and poem generation, and design.

Defining computational creativity:

According to the Computational Creativity Conference Steering Committee (the group behind many computational creativity research events):

Computational creativity is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy and the arts. The goal of computational creativity is to model, simulate or replicate creativity using a computer.

More on possible definitions of computational creativity: https://www.creativitypost.com/science/what_is_computational_creativity

Convergent vs divergent thinking

Convergent VS Divergent:

Convergent thinking refers to the ability to solve a problem by focusing on a single, correct solution. On the other hand, divergent thinking refers to the ability to generate multiple solutions to a problem and is often associated with creativity.

Machine learning, the previous wave of AI, relies heavily on convergent thinking. Its primary focus is on predicting outcomes, such as whether a stock will go up or down, if a user will click on an ad, or what movie someone is likely to watch. The goal of machine learning is to accurately determine the truth through prediction.

Generative AI allows for divergent machine thinking, expanding the range of potential outcomes. Its roots in the fields of Computational Creativity and Creative AI emphasize its potential for creative applications like music, art, dance choreography, and writing. Unlike traditional problem-solving approaches, there is no single correct answer in these fields; rather, there is a vast search space of endless possibilities to explore. Generative AI systems like Stable Diffusion and DALL-E in the realm of art and LyricStudio and MelodyStudio music, leverage the divergent capabilities of generative AI.

It is worth noting that not all #generativeai systems prioritize divergent thinking. ChatGPT, for example, is designed to generate answers that may vary to some degree, but ultimately adhere to a fairly narrow understanding of reality. While it may produce multiple options, ChatGPT exhibits a high level of self-consistency. This type of generative #AI could potentially be utilized in lucrative applications like revamping search engines, but it also raises concerns about misinformation and plagiarism.