Summary of Creativity in Ai: Progresses and Challenges, by Mete Ismayilzada et al.
Creativity in AI: Progresses and Challenges
by Mete Ismayilzada, Debjit Paul, Antoine Bosselut, Lonneke van der Plas
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper surveys leading works on machine creativity, focusing on creative problem-solving, linguistic, artistic, and scientific creativity in AI systems. It highlights that while recent models can generate linguistically and artistically creative outputs like poems, images, and music, they struggle with tasks requiring abstract thinking, compositionality, and creative problem-solving. The review also notes that generated content often lacks diversity, originality, long-range coherence, and is prone to hallucinations. Additionally, the paper discusses copyright and authorship issues with generative models and emphasizes the need for a comprehensive evaluation of creativity considering multiple dimensions. Finally, it proposes future research directions inspired by cognitive science and psychology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well AI systems can be creative. It talks about different types of creativity, like coming up with new ideas or making art. The researchers found that some AI models are really good at generating creative content, but they struggle when it comes to solving problems creatively. They also point out that this generated content often doesn’t feel very original or coherent. Another important topic is how we should handle the rights to creative work made by machines. Overall, the paper suggests that we need a better way to measure creativity and thinks about what we can learn from human psychology to make AI more creative. |
