Bach lives again, in AI form
Trying to emulate human skills and talents isn't easy, even for humans, but increasingly AI is muscling in on what we once considered to be unique human traits.
Key takeaways
- Artificial intelligence (AI) is increasing parking its caravan on humanity's lawn.
- Deep learning relies on sifting through large amounts of structured data to identify and synthesise waveforms, and when it comes to applying these methods to music, Bach seems to be a perfect fit.
- Not only was he incredibly prolific, but his music is consistently structured, following various patterns and formal intricacies that defined Baroque music.
Cite or link to this article
Griffin, M. (2017) 'Bach lives again, in AI form', 311 Institute, 11 January. Available at: https://www.311institute.com/bach-lives-again-in-ai-form/ (Accessed: 25 October 2026).
Artificial intelligence (AI) is increasing parking its caravan on humanity's lawn. Traits that we once considered as uniquely human are now increasingly being found in new AI systems, and creativy is one of them. Now, hot on the heels of Google's first attempt to create a musical AI, named Magenta, that can make it in the American billboard charts now, another AI, called DeepBach appears to have imitated Bach, the genius himself, so well that most people can't tell the difference. And if you don't believe me then why not try it for yourself and see if you fall into the fooled bucket. Boom! I guess you fell in. Join the club.
DeepBach is a deep learning program created by Gaetan Hadjeres and François Pachet of the Sony Computer Science Laboratories in Paris, and while the team have been busy in the past, trying to emulate pop songs, it now looks like their FlowMachines product has conquered Bach, and its success says a lot about AI's current capabilities, not just for "creativity" but also for it's ability to create and manage waveforms so that they sound natural, something that even Google's stunning DeepMind platform has struggled with.
Deep learning relies on sifting through large amounts of structured data to identify and synthesise waveforms, and when it comes to applying these methods to music, Bach seems to be a perfect fit. Not only was he incredibly prolific, but his music is consistently structured, following various patterns and formal intricacies that defined Baroque music.
For DeepBach, Hadjeres and Pachet concentrated on Bach’s chorales, the pieces of music that set traditional hymns to stately, four part vocal melodies, Bach wrote 389 of these in his lifetime, giving DeepBach plenty of material to study, and each chorale has recurring patterns.
However, while DeepBach is indeed an accomplishment it’s not clear just how many of the melodies in the test at the top of the page were solely the work of AI. One of the key features of DeepBach is that it can, in AI parlance, be “constrained,” in other words its developers can help to direct the music it creates by imposing certain melodies, bass lines, rhythms, and cadences. Similarly, the music that DeepBach creates is limited to only a single key and time signature, accommodations made to make analysis of the source material simpler.
But even accounting for these limitations, DeepBach is a serious tool, and it's an early footnote in our journey towards realising true AI creativity.
What this means for your organisation
Structure makes style learnable.
Bach's chorales are consistently structured with recurring patterns, which is why the imitation works. Highly patterned work in any field is easiest to copy.
Volume of work matters.
389 chorales gave the system plenty of material. Anyone with a large, consistent body of work is more exposed to imitation.
Value shifts to the new.
If imitation is cheap, the premium moves to original work and to provenance. Firms should decide what they are actually paying creators for.
Three questions for your next leadership meeting
- Which of our outputs are patterned enough to be copied?
- How much of our own work could train an imitator?
- What do we pay creators for if style can be copied?
The trends and technologies behind this story
From the 311 Starburst, the 311 Strategic Intelligence Unit’s map of the technologies and trends shaping the next 50 years.
Analysis by the 311 Strategic Intelligence Unit, from Matthew Griffin’s article and 311 data. First published on Fanatical Futurist.
FAQ
Why does this matter?
Trying to emulate human skills and talents isn't easy, even for humans, but increasingly AI is muscling in on what we once considered to be unique human traits.

About the author
Matthew Griffin Founder, President and Futurist, 311 Institute
Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath."
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Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath." 17-time best selling author of the "Codex of the Future" series, Matthew is the Founder, President and Futurist of the 311 Institute, a global Futures and Deep Futures advisory firm working with royal households, world leaders, G7, G20, and G77 governments, NGOs, and multi-national mid and mega cap firms to help them explore, shape, and lead the next 50 years of business and society.
An award-winning YouTube creator with over a million followers, with an unrivalled global reach and impact, Matthew is a highly sought-after international keynote speaker, lecturer, and mentor who collaborates with global leaders through the United Nations Alliance of Civilizations (UNAOC) and United Nations General Assembly (UNGA) to shape pivotal initiatives such as the UN’s AI for Humanity programme, the United Nations Conference of the Parties (UN COP), and the World Economic Forum in Davos.
As the former Global Head of Cloud, National Security, and Enterprise Sales for companies including Atos, Dell-EMC, and IBM, Matthew has a proven track record of building multi-billion dollar business units and turning failing divisions into market leaders. His ability to identify, analyse, and communicate the implications of hundreds of emerging technologies and trends is unparalleled, and his insights are trusted by many of the world’s most respected organisations, including ABB, Accenture, Adidas, AON, ARM, BCG, Centrica, Citi, Coca-Cola, Dentons, Deloitte, Dow Jones, EY, Google, KPMG, Lego, Legal & General, LinkedIn, Microsoft, PepsiCo, Qualcomm, RWE, Samsung, Siemens AG and Siemens Energy, T-Mobile, UBS, VISA, Walmart, Workday, Worldpay and many others.
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Sources and further reading
- try it for yourself flow-machines.com
- 1612.01010 arxiv.org
- Sony Computer Science Laboratories csl.sony.fr