Sunday, May 03, 2015

How to make a lot of money as an artist

An artist friend asked me, "How can I make a lot of money?"

"Make something people will give you money for," I said.

"It can't be that easy," my friend snorted.

"Who said it was easy?" I snorted back.

My friend is a musician, who follows his heart, without much regard for what other people want. He's an artist. Almost by definition that makes him an independent individual, unmoved by the suggestions of friends and family to follow a more lucrative career. Like many artists I have come to know he is rebellious, self-centered, stubborn, and driven by a vision other people cannot see. It's what makes his contribution potentially so valuable.

As a businessman and economist, what I find most fascinating about the profession of an artist is that she makes a career out of communicating her vision. Like an inventor, who tries to make money from an idea, artists try to make money from a perspective. And if the perspective is valuable and easy to reproduce, the supply will outstrip the demand, lowering the price towards zero, moving most of the benefit away from the artist and to the consumer. So, just as in the case of inventors in a competitive economy, the number of artists is only limited by the lower profits (poverty) the artists will tolerate. Those that want to make more money will have to find other occupations to supplement their income.

Now, just as with any other business, if one can create monopoly power, making a product so unique that it is not reproduced, and one's product is something that consumers are willing to pay for, then the sky is the limit. However, in order to be such an artist may require two compromises: (1) sharing the wealth with others who can help create monopoly power (such as distributors who control access to sales channels), and (2) producing art that consumers are willing to pay for, perhaps compromising the artist's original vision.

With the increasing role of the internet, it will be interesting to see what happens to the companies that have traditionally controlled the distribution channels. They are struggling at defining their value to consumers in the face of a plethora of talent flooding the market through internet channels such as YouTube. Will artists benefit from the more open marketplace? It's not clear. What is clearer to me is that the number of artists reaching consumers is likely to increase, swelling the ranks of "starving" artists, probably bad news for the artists, perhaps good news for the art consumer.

How to change the world? Vote for the future with every dollar you spend!

I recently saw an article about social capitalism (The Opinion Pages - Op-Ed Columnist David Brooks - How to Leave a Mark The comments reminded me of something I tell myself when I want to change the world:
If I want to change the world, a world driven and shaped by economic forces, then communicate using the language of the world: vote for the future with every dollar I spend.
What does that mean? Well, for starters, I have to remember that what I buy is telling manufacturers what I want them to make. So being more conscious of what I buy, conscious of the consequences of my purchase, helps me choose the world that I want to be leave for my grandchildren.

This is really good news. In other words, every dollar I spend is a dollar I am voting for the future. I get to vote on the future every single day. I don't have to wait for an election. Every purchase is election day.

This is really bad news, too, since most of my training, the way I was brought up, was to consider purchases as a reward for my hard work. Ever since I saved my paper route money for my new bicycle, I have learned that if I do something for someone else, I can get paid. And the money I get can turn my hard work into something I want. But what I did not learn was how to manage my responsibility for the consequences of my buying. At no time did I consider what kind of world I was encouraging when I bought my shiny new bike. I didn't think of how the people were treated who made the bike. I didn't think of how the resources were acquired to make the bike, or what would need to happen to dispose of the bike. I didn't think of any of the consequences of my purchase, other than the smile on my face as I rode it down the street.

So it's ironic when I point my finger at the "big bad companies" that are tearing the world apart to compete, or who are treating people as if they were just another commodity resource, and the environment as if it was a garbage can. It's especially ironic since I know that companies in a competitive economy will respond to purchasers much faster than politicians respond to voters.

When, and how, will I begin to vote "responsibly"? How do I find out if a product has been manufactured in a way that I want? How do I know if the product will encourage a future I want to leave for my grandchildren?

In today's internet-connected society, is there a way I can get ratings on products to know how well they match my own preferences for the future? Will enough people "vote" responsibly to make a difference?

And I can't just say that it's too complicated and stick my head in the sand. As a good friend and mentor reminded me, I'm already voting for the future every time I buy something. The only question is which future I am voting for.

How Humans are Limiting Machine Learning

I've started refreshing my knowledge of predictive modelling and am reminded of something that has always struck me with the way we teach students about machine learning: we, not the machines, do most of the thinking.

How can this be so? Don't machines learn by themselves? 

I'm reading about "supervised learning"(1), where the model is based on observing variables and looking for a relationship between the variables and some known outcome. The goal is to find a way to predict the outcome based on the observed variables. Well, I guess the machine is learning, but my experience with learning has a lot more to do with figuring out HOW to look at something -- how to aggregate, weight, and disregard in the presence of an enormous quantity of variation, finding patterns buried in noise.

But in most cases, we limit the machine in what it can learn because we limit what the machine can observe. WE choose what the machine pays attention to, and then tell it to do the best it can do. It is like putting blindfolds over my eyes, then telling me to learn to predict where a ball will land based on wind-speed, sound, and air temperature instead of giving me eyes to see the ball.

For the first time since machines have been invented, we are finally taking off the blindfolds and giving machines more to look at by using the internet as an input. The internet has the requisite variety(2) to become a sense organ. And hence the mad dash to make sense of all this internet data. "Data-miners" are pounding larger and larger data sets through machine learning algorithms.

But who is writing the algorithms to specify the models? How are we teaching machines to learn on their own? What are we asking them to learn? Are we giving them the eyes to see? Or are we limiting their learning?

And what happens when the machines learn faster and better than we do?