Recently, the potential of large language models (LLMs) has been widely used in assisting programming. However, current research does not explore the artist potential of LLMs in creative coding within artist and AI collaboration. Our work probes the reflection type of artists in the creation process with such collaboration. We compare two common collaboration approaches: invoking the entire program and multiple subtasks. Our findings exhibit artists' different stimulated reflections in two different methods. Our finding also shows the correlation of reflection type with user performance, user satisfaction, and subjective experience in two collaborations through conducting two methods, including experimental data and qualitative interviews. In this sense, our work reveals the artistic potential of LLM in creative coding. Meanwhile, we provide a critical lens of human-AI collaboration from the artists' perspective and expound design suggestions for future work of AI-assisted creative tasks.

Intellectual property law has historically focused on protecting the fruit of human intellect,

A text-to-image AI Dall-E2 generated image with a prompt: Edward Hopper-style image of a man and a woman sitting at a cafe table, both absorbed in their smartphones without speaking. Video artist Jason Allen's Midjourney generated piece called "Théâtre D'opéra Spatial" won first place at the Colorado State Fair.

OpenAI – one of the world's leading artificial intelligence research laboratories – released the text generator ChatGPT and the image generator DALL-E 2. While both programmes represent monumental leaps in natural language processing and image generation, they've also been met with apprehension. Some critics have eulogised the college essay, while others have even proclaimed the death of art. But to what extent does this technology really interfere with creativity? After all, for the technology to generate an image or essay, a human still has to describe the task to be completed. The better that description – the more accurate, the more detailed – the better the results. After a result is generated, some further human tweaking and feedback may be needed – touching up the art, editing the text or asking the technology to create a new draft in response to revised specifications.

Can artificial intelligence be used to create compelling human art? That's a question which Google's Magenta team, a group researchers from the Silicon Valley giant's Brain Team, looks to answer, using Google's open-source TensorFlow AI engine. So far, the signs point to yes: The team has now released the first piece of music been written via machine learning, a simple tune that was composed without any human hands. Related: Take the Millennium Falcon for a spin: New'Star Wars' vinyl features embedded holograms The first bit of music released by the Magenta team is a 90-second clip of piano, which had a drum beat added by researchers to give context to the computer's harmonic rhythm. It's an interesting amalgamation of sounds: simple, but full of complex musical ideas like repeated phrasing, form, and feeling.