Building AI Music Composition Products

 Today's talk is the Yin to the Yang associated with yesterday's AI Death Metal talk by CJ Carr of the dadabots group ( or maybe i have that yin-yang labeling backwards).  They are seemingly two very different and contrary viewpoints on how to approach deep learning and apply it to automatic music composition.  But they are interconnected and perhaps even complimentary in our ever more interconnected world.  And it's wonderful they were both presented at the same technical conference on AI and Musical Creativity.

Ed Newton-Rex is a product director at TikTok Europe.  He is working there now because TicTok acquired ByteDance, which acquired Jukedeck (an award winning AI music composition company he founded).

This talk has a very heavy 'business' zeitgeist to it.  We could tag it with terms like serious, market analysis, profit-loss analysis, practical, etc. So it sits at on end of latent space associated with automated music composition and the databots presentation lies in some opposite corner of the latent pace.  

And we'd all like to twist a few knobs and explore the uncharted space in the latent space between these to viewpoints.  Preferably using an automatic AI based system that will do all of the work for us, perhaps even generating the Github posts with all of the code.  

A GAN algorithm to automatically generate different automatic music composition systems for us.

All kidding aside, it's a very interesting talk.  He breaks down the AI automatic music composition marketplace down for us. Various user scenarios, different approaches to monetization, different degrees of user adjustability (or lack of such).

He makes a special point of acknowledging the work of David Cope, who was an early pioneer it he area of Ai based music compositional systems.

The talk also makes a big point of emphasizing choosing one thing to focus on and then trying to do that one thing really well. Hope it was the right one thing.  Doing a few things pretty well might better equip you to adapt to whatever your final potential marketplace and/or product really are, since oftentimes they change once you start getting into really developing a products ad getting feedback from users.

He also points out how in the grand scale of 'where the money is' this is a small potential marketplace (a small sandbox to be playing in).  At least in terms of how we currently think about it.

You may or may not agree with the end results of how he constructed his random forest decision tree for taxonifying potential uses and users of automated compositional systems.  That 'random forest decision tree reference is a nerdy joke, he's not literally using random forest algorithms.  But he is cleaving the world apart in a binary classifier tree like way.

Observation about some people's obsession on potential market size for idea evaluation

Speaking from personal experience, you can build very successful ground breaking software companies in smaller (sometimes very small) potential marketplaces.  Not everyone needs to be the next Google, or the next Tesla.  

It's perfectly fine to create small boutique software (or hardware) companies that focus on doing great work for smaller marketplaces. 

Oftentimes the customers associated with these smaller marketplaces will be super devoted to their niche interest or niche professions.  If you make a good product for these niche users and niche customers, they will love you for it.  Both in terms of buying your software, and then repeat buying it again later via upgrade purchases.  But also by being incredibly thankful that you are devoting energy to listening to them, understanding their unique niche needs, and then trying to address those needs in software you are writing for them. 


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