Deep Learning AI Taking On Human Creativity

I talk a lot about artificial intelligence, it's potential, it's promise and it's danger these days.  Most individuals I speak to agree that AI will allow for most manual tasks to be automated very soon.  They also agree that AI will be able to be infinitely more efficient at doing tasks like transportation, cleaning and construction.  With some explanation, they concede that AI will be highly efficient and better than humans at doing tasks requiring some creativity or intuition like guessing, analysis and speculation, because those can be trained using the large amount of data available through the Internet.

Then people have a very hard time even considering AI would be able to be artists.  As they say:  this is the realm of humans.  Only humans can be creative, they say.  Otherwise, what would we be doing in a number of years?  How would we be useful to society if the last type of activity where we excel at can be done better by artificial intelligence?

Of course, this is a reaction of fear.

The truth is, AI laboratories are already experimenting with deep learning AI and have achieved interesting early results:  AI can be trained to be creative and make music.

In the following video I discuss a bit more about the social and philosophical implications of this advancement:

What creative AI needs to get started is inspiration.  The sort of inspiration they need is similar to what human artists need really:  life experience.  In this case, since we're not interested in spending years teaching the AI about life in general, the scientists taught their creative AI about the type of art.  In essence, they gave them a huge amount of music to digest, from different styles and then through code and algorithms, gave them the ability to make completely original music.

What these AI are capable of is basically the same as what human artists are able to do:  get inspired by what they "know" and create something new.  They use algorithms called deep learning algorithm where the AI has access to large amounts of appropriate data for the task and then set to "learn" how to play with the data to get target results.  If the training period is successful, the AI is able to get the results we were interested in with the data on their own and can continue to learn over time.  Neat huh?

Here are a couple recent examples for you guys to check out.

First is the example of the Marimba-playing robot Shimon from the Georgia Institute of Technology.  It is fed four measures of music and then it goes on its own to create the rest of the music on it's own, inspired by both the first measures, which provide a specific desired style, and it's huge databanks of recorded music.  See it in action here:

Second is the example of Amper AI, which is a commercial venture that allows artists to create music by providing the AI with a style and tempo, and it'll create music on its own.  This is a beta product that has already been used by artist Taryn Southern to make new songs.  After the AI has produced the musical score, the person using Amper AI can adjust and layer on lyrics and other pieces of pop music to taste.  Here is more information about the effort.

Music made by Amper AI, for example when asked to make music styled after the Beatles (lyrics were provided by a human being):

Here is Taryn's song made using Amper IA, after made into a music video (lyrics and video by Taryn.  Instrumental elements are all Amper AI):

With great commercial ventures out and fantastic research on creative deep learning AI, we're sure to see more and more people using it and of course for the technology to become way more refined and advanced in a very short amount of time.

Stay tuned and for those interested, you can start playing with some of these projects yourself!