Born from a week long design sprint exploring the shift towards a more human AI, Solo combines facial feature recognition with music valence to read the nuance of expression and match tracks to your current mood. Sensing movement, Solo draws you in with its playful antenna. As you approach it takes your photo. It then sends that photo to a Microsoft API that analyses features and sends that information back again as an emotional breakdown with values measuring your happiness, sadness and anger. Solo translates these figures into a valence rating that corresponds with Spotify’s track valence ratings. Solo then plays the track it thinks you want to hear most. Kind of like a mix CD from a friend with great taste.
One AI issue Solo highlights - and one we predict will improve as algorithms increase in sophistication - is atypical feature recognition. AI has a lot to learn about our aesthetic individuality, and Solo helps that conversation, because when it gets it wrong, it’s through the highly forgivable medium of music.
Through Solo, we’ve learned that the technological possibilities for designing AI through human characteristics (such as emotion) are endless. Solo instills empathy in AI, and in turn predicts the future of designing AI as one that embraces uses beyond the data-driven or servile, towards the human. Solo opens up the conversation on how technologies can be broken down and mean something to people, not just the tech industry.
AI is coming. As it becomes entrenched deeper and deeper into our everyday lives, isn’t it right that it should work to understand us on an emotional level, and use that understanding in playful and engaging ways? We think so. While the idea of a mood dependent radio may seem simple, we knew the challenge of incorporating human characteristics (here, the fallibility of musical selection based on emotion) would be significant. The solution or marker of success was never meant to be the most mood accurate song, but AI designed through a forgiving, emotional, human framework.
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