Big Data Analytics: Articles, Movies, Songs Robo-written by Computer?

Computer-generated pop-music?

Let’s come back to the topic of computers writing hit songs. Is this possible? We already mentioned earlier experiments dating back to the 1960s of computer-generated music, although this music mostly lacked elegant variation or overall thematic sense. Of course there are now computer versions of Epagogix, such as TheHitEquation that can predict with 60% accuracy whether a given song will be a hit, looking purely at the contents of the music.

Some celebrities have criticized such software, saying it can never be as good as a human expert at finding hits because it cannot analyze important song elements such as lyrics. (And, of course, the all-important marketing budget is ignored because these algorithms since the point is to help industry executives decide which songs to invest marketing budget on.) But the software doesn’t have to be as good as a human expert on every aspect of analyzing a song. It just has to be ‘pretty good’ at predicting hits using some subset of criteria that it is expert on evaluating. The reason is simple: deciding what’s going to be the next hit isn’t typically made by a single executive, it’s made by a human team of executives combining their individual expertise to make a decision. If the algorithm is as good as any one of these team members — and being right 60% of the time is pretty good —- then you can replace one highly-paid executive on the team with the computer algorithm. The lyrics and such other important song aspects that the computer doesn’t know anything about will be analyzed by the remaining team members. And according to a Harvard Business Review article, that’s exactly how these algorithms are implemented in practice — by combining three human experts plus the computer expert together and voting on songs.

What if you combine the two technologies? Have the 1960s random music composer (updated to include the latest popular music phrases) with something like TheHitEquation, and keep generating random music until the algorithm says you have a hit. Would it work? To be efficient, it would require some feedback from the model being used to evaluate each song on how to best improve (otherwise you have the proverbial monkey writing Shakespeare by punching random typewriter keys, which will succeed but take a very long time). The algorithms would likely need considerable updating to understand thematic consistency as well, but in time something like is possible. If you throw in the 3 human executives doing a further evaluation of the song score by computer (and providing machine-readable feedback to the algorithm writing/editing the song), that it becomes very tractable. (Although who is the songwriter in this scenario? The computer algorithms, or the human music executives that provided the feedback to the algorithm? (We’re ignoring the issue of lyrics here; expert lyric writers might still be needed for a while, or this approach could be limited to instrumental music.)

Of course,  some might argue say computer-generated pop-music is nothing new. Song and voice-manipulation technologies like Autotune (not to mention ‘technologies’ such as lip-synching) have become widespread if controversial. Less-controversial everyday digital song software like GarageBand and its professional analogues can be installed for free on your iPhone.

Big data analytics governance and democracy?

We started this article off by light-heartedly alluding to some darker governmental uses for big data. What about replacing politicians by computer algorithms? Assuming this were desirable? Is it possible? To some extent, given the use of market-research like techniques in political science (government by opinion poll or campaign contributor profiling) it already has. (This is lamentable, as public opinion can be short-sighted and donors might be closely aligned with special interests.) Is it possible to go further than this using big data? Of course it is. There were articles a few year back about detecting mood on Twitter and using it to predict the stock market a few days in advance (which at least worked for a short period of time back when the article was written). Stock markets influence politicians; this system could be taken a step further by having politicians actually attempt to respond to Twitter moods, thereby creating a fast feedback loop (which may, by virtue of the stock market traders influencing the market via algorithms that analyze Twitter, and in turn having the stock market influence governments, already exist to some extent, albeit indirectly). This is ‘governance by Twitter’ or big data analytics, an interesting topic, but one we’ll leave for a possible future blog post. [Update: we did multiple articles on governance by computer (click here), as well as using AI for crisis mitigation (click here).]

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