All the AI Song Contest songs were evaluated both by the audience and by a panel of AI experts. The panel with Vincent Koops (NL), Anna Huang (US) and Ed Newton-Rex (UK), looked at the use of AI and creative application of AI and was impressed by the entries.

'We were amazed by the teams’ wide range of innovative approaches to using AI in their creative process in creating AI Song Contest songs. Every song felt very personal in different ways, and this reflects how the artistic vision of each of the teams drove how they collaborated with AI.'

'Composing a song with AI is hard, because not only do you have all the creative challenges that come with writing a song, but you also have to juggle with getting the machine learning right. Working with machine learning is this constant push and pull, where you try your best to direct it in a certain direction with the data you have, priming it with your own tune, and then it steers you in another direction. We see teams embracing this unpredictableness, listening up close and finding inspiration that in some cases fueled the whole story of the song. We see teams leveraging the firehose of ideas that machine learning generates, which often requires you to listen to a massive number of samples before you find your gem. Some teams approached this problem by creating another model that evaluates and ranks the machine learning output for them, while also being sensitive to the ethical implications of using AI in songwriting.'

'We are encouraged to see teams who might not have as much musical experience before find their musical voice through the use of AI. Overall, we were delighted by the diversity and collaboration within teams, whose members not only pushed the boundaries of their personal creativity, but also gave the audience a look into the exciting future of human-AI co-creativity in music.'