Artificial Intelligence is present in the generation and distribution of culture. How do artists exploit neural networks? What impact do these algorithms have on artistic practice? Through a practice-based research methodology, this paper explores the potentials and limits of current AI technology, more precisely deep neural networks, in the context of image, text, form and translation of semiotic spaces. In a relatively short time, the generation of high-resolution images and 3D objects has been achieved. There are models, like CLIP and text2mesh, that do not need the same kind of media input as the output; we call them translation models. Such a twist contributes toward creativity arousal, which manifests itself in art practice and feeds back to the developers' pipeline. Yet again, we see how artworks act as catalysts for technology development. Those creative scenarios and processes are enabled not solely by AI models, but by the hard work behind implementing these new technologies. AI does not create a 'push-a-button' masterpiece but requires a deep understanding of the technology behind it, and a creative and critical mindset. Thus, AI opens new avenues for inspiration and offers novel tool sets, and yet again the question of authorship is asked.

翻译：人工智能已然渗透文化的生成与传播领域。艺术家如何运用神经网络？这些算法对艺术实践会产生何种影响？本文采用基于实践的研究方法，探讨当前人工智能技术（更确切地说是深度神经网络）在图像、文本、形态及符号空间转译语境中的潜力与局限。在相对短暂的时间内，高分辨率图像与三维物体的生成已得以实现。诸如CLIP和text2mesh等模型，其输入媒介无需与输出格式对应，我们称之为转译模型。这种转化机制促进了创造力的激发，既体现在艺术实践中，又反哺开发者的技术迭代。我们再次见证了艺术作品如何成为技术发展的催化剂。这些创意场景与过程的实现，不仅依赖于AI模型，更依托于应用新技术背后的艰辛工作。人工智能并非'一键生成'的杰作创造者，它需要我们深刻理解技术本质，并兼具创造性思维与批判性眼光。因此，人工智能为灵感启发开辟了新维度，提供了新颖的工具集，同时也再次叩问着作者权的问题。