The following MBW blog comes from Taishi Fukuyama – who has written, arranged and produced some of Japan and Korea’s biggest pop stars including Juju, BoA, Tohoshinki and more. He has also represented leaders in music tech for the Japanese market, including Spotify, The Echo Nest, and CI (Consolidated Independent). Taishi (pictured) also co-founded Qrates, a groundbreaking vinyl-on-demand service. He is currently the co-founder and COO of music-making startup, Amadeus Code – whose core product is an ‘artificial intelligence-powered songwriting assistant’.
Would you buy an idea from a machine?
That’s a bizarre question at first glance; it reads like absurdist poetry. Yet we hold that our ideas are discrete entities with definable intrinsic value.
Is it possible a machine could generate such a thing? What’s the value of a computer-generated idea?
As AI slowly seeps into business, culture, everywhere, we will be forced to answer these questions.
If the results of AI’s data ingesting, pattern recognizing, and predictions have some validity, they may qualify as ideas worthy of the same consideration as human-created ideas.
After all, determining worthiness or value is a deeply human-inflected system, and so is AI.
Without human input, machine learning cannot happen. The machine is merely the surface layer of the ideation process.
Buying an idea from a machine, then, is buying the distillation of human experience and perspective, processed on a scale unheard of before our time, using methods that feel alien and opaque – for now.
Accepting one possible answer – yes, I’ll buy a machine’s idea – somehow upends some of our notions of creativity.
The machine is not a person, not conscious, has no awareness or context. It has nothing to say. It has merely generated something.
We are used to considering human artists as the driving force behind value.
In the traditional definitions of artistic merit, the value of an object, utterance, or performance depends on the artist’s unique abilities and perspective. A machine’s idea is perceived as less valuable.
After all, it didn’t really put anything into its creation. Or did it?
Our relationship with machines has so far been relatively one-directional. We’ve created technology to solve basic problems.
AI too is suited for this but, assistive AI, particularly in music and other art forms, introduces a new paradigm – because the problem it attempts to solve is our limited creative nature.
Many are conflicted, sometimes even offended, by the idea of paying for such technology, as if the transaction itself would be an admission of defeat.
Yet we are now being introduced to a new paradigm, a new relationship with machines.
Creative AI by design is a combination of both human intuition and machine intelligence.
This newly-shared control principle frees humans to imagine new creative processes introduced by the machine – something not possible independently, by either human or machine.
We’re still struggling to understand the relationship between human and machine, just as we’re struggling to think of all humans as equals.
A similar tension arose with the advent of photography. Was it really art if an image did not have to be manually produced and could be reproduced fairly easily?
It continued with film, which felt even more divorced from the “authenticity” and “aura” (to use terms critics kicked around in the early 20th century) of painting or sculpture.
When machines get involved in making art, it makes creativity more accessible. It lowers the time between intention and execution, and this democratization of creativity ultimately shifts the center of commercial value.
It moves from the prizing of a unique object, or the restricted access to a certain live performance, to the audience.
This is a very natural source of value: we love to give a number or value to objects and even more so to the people around us.
We yearn to be valued, and we also yearn to value.
In a modern post-internet society, content value is post-creation. Machines can take an inhuman number of human hours and produce novel ideas in a very non-human way.
That still doesn’t feel right to us: “You’re taking a Millennium of work and giving me a random idea!”
No matter how good that idea sounds, we struggle to say that it is worth purchasing.
Yet in a world where every piece of music is equally accessible, the worth of a piece of music isn’t associated with the music itself, but its ability to attract listeners’ attention; the amount of time that people listen, share, talk about it.
There are dozens of examples of mediocre artists who have large, passionate fan bases. Even if it’s a masterpiece, if people don’t share it or devote time to a track or album or video, it doesn’t have much value.
This dynamic is present, with or without machine learning.
AI is merely forcing us to reckon even more explicitly with the tension between originality and value, collaboration and consumption.
According to this theory, the hours put into consumption are more determinate of value compared to the endless hours put into production: I may have practiced longer but I may not be more skilled – or more able to produce something that attracts sufficient attention.
Advances in tech can allow people to skip those long production hours and start creating, as these hours are not really rewarded (though they can be truly rewarding to the creator).
The value of a piece of clothing or artwork is only quantifiable by the consumer; if they want to see it or take people to see it, if they want to wear it.
As AI becomes as normal to us as the drum machine and vocoder, as photos and film, we may change our answer.
We may be happy to spend a few bucks on a machine’s idea, if it might pay our bills for a month, when developed into a hit.
We have come to a new age of reckoning with machines in art. It’s a time that may completely reframe our understanding of artistry and value.