Creativity is the ability to produce novel, useful, and surprising ideas, and has been widely studied as a crucial aspect of human cognition. Machine creativity on the other hand has been a long-standing challenge. With the rise of advanced generative AI, there has been renewed interest and debate regarding AI's creative capabilities. Therefore, it is imperative to revisit the state of creativity in AI and identify key progresses and remaining challenges. In this work, we survey leading works studying the creative capabilities of AI systems, focusing on creative problem-solving, linguistic, artistic, and scientific creativity. Our review suggests that while the latest AI models are largely capable of producing linguistically and artistically creative outputs such as poems, images, and musical pieces, they struggle with tasks that require creative problem-solving, abstract thinking and compositionality and their generations suffer from a lack of diversity, originality, long-range incoherence and hallucinations. We also discuss key questions concerning copyright and authorship issues with generative models. Furthermore, we highlight the need for a comprehensive evaluation of creativity that is process-driven and considers several dimensions of creativity. Finally, we propose future research directions to improve the creativity of AI outputs, drawing inspiration from cognitive science and psychology.

翻译：创造力是产生新颖、有用且令人惊喜的想法的能力，作为人类认知的关键方面已被广泛研究。而机器创造力则是一个长期存在的挑战。随着先进生成式人工智能的兴起，关于人工智能创造能力的兴趣与争论再度兴起。因此，有必要重新审视人工智能中创造力的发展现状，并明确关键进展与尚存挑战。本文综述了研究人工智能系统创造能力的前沿工作，重点关注创造性问题解决、语言创造力、艺术创造力与科学创造力。我们的综述表明，尽管最新的人工智能模型已基本能够生成诗歌、图像和音乐等语言及艺术层面的创造性输出，但在需要创造性问题解决、抽象思维与组合性的任务上仍存在困难，且其生成内容普遍存在多样性不足、原创性欠缺、长程连贯性缺失以及幻觉问题。我们还探讨了与生成模型相关的版权与作者身份等关键问题。此外，我们强调需要建立一种过程驱动、涵盖创造力多个维度的全面评估体系。最后，借鉴认知科学与心理学的启示，我们提出了提升人工智能输出创造力的未来研究方向。