Not long ago, the wider sentiment in the AI industry was that "AI can't be creative." Even today, some people hold to that view, though AI is being used to compose music, poems, sculptures, and draw paintings, like the one below: You can create your own art using deep learning with tools like . To listen to AI-generated music, you can follow YouTube channels like , which has almost a million video views to date according to . Individual AI-composed hits like have reached millions of views. deepart Aiva SocialBlade Daddy's Car You can create your own AI-generated poetry with Google's project. Check out this poem : PoemPortraits written by an AI the sun is a beautiful thing in silence is drawn between the trees only the beginning of light Google actually runs a number of arts and culture projects, many of which are AI-related. You can check them out at . Another AI-driven experiment is called " ," an interactive public sculpture driven by machine learning: Experiments with Google Please Feed the Lions You might ask, how does AI do this? For most AI-generated creative work, whether it's art, poetry, sculptures, or music, it's using what's known as a "generative adversarial network," or for short. GAN To give an explanation, a GAN is based on unsupervised machine learning, which means that it works on data without labels, like images, audio, and video, and it's implemented via two competing neural networks. ELI5 For example, let's say you want to build an AI that can draw. Of the two competing neural networks, one neural network might start with a random static image, and calculate the error between this image and an inputted painting. Of course, the error is extremely high, so it makes adjustments to the algorithm in an attempt to decrease the error, until it successfully descends the gradient and makes an image with high likeness to the inputted paintings. In short, it can draw! If you want to go very in-depth into how GANs work, check out . Ultimately, the evolution of AI into creative fields is incredible to witness, and has grabbed the attention of even the most vocal AI naysayers. this Towards Data Science article
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