Art Frames the Intersection of AI, Machine Learning and Human Decision Making

Given agency by their human artists, AI creates art against a new backdrop of understanding.

Andrew White, Chief of Research and Content Lead for Data and Analytics at research firm Gartner Inc. tells us in his blog that the exponential increase of automation tasks is inevitable, as well as the discovery of new patterns in data that will help us solve the previously unsolvable problems. These are all understandable benefits of automation. But what happens when more cognitive work is given to AI and what can we do to understand the decision process? Forget human nature, this is AI nature.

At the World Science Festival this year, a panel of artists, musicians, neuroscientists and computer scientists came together to explore the future of artistry and imagination in the age of artificial intelligence. They sought to understand computational creativity in artificial intelligence through a new lens. For example, a human would probably never think to combine saffron, cocoa and almonds, but IBM's Watson did, and it's deliciously creative.

Nature Morte Gallery in New Delhi and economist Dr. Karthik Kalyanaraman came together this month to present “a group exhibition featuring works created entirely by artificial intelligence...Bringing together artists who address how contemporary art can create a dynamic human-machine relationship, this groundbreaking exhibition provides us with a vision of what art could be,” as well as broaden our understanding of AI.

Tom White, a lecturer in computational design at the University of Wellington, was one of the artists whose work was part of the AI exhibit. James Vincent, writer for The Verge, asks this about Tom White’s pieces using AI creativity: “And what is it saying? Well, as with any art, different people hear different things. Some see the imagery made by White and his peers as a bad omen, another sign that artificial intelligence is not only getting smarter but beginning to think creatively and take on roles reserved for humans.”

Kalyanaraman says, “Once so much of our labor (manual, mental, emotional, artistic) is replaced by machines, what is left for us to do? How will we define ourselves?” These are important questions to consider as we grow in understanding of AI’s part in our reality. According to Vincent, “Kalyanaraman suggests that art made with AI demonstrates that computers may deserve credit as creative actors. The type of machine learning used by White and his peers works by sifting through large amounts of data and then replicating the patterns it finds. Kalyanaraman suggests that this is similar to the process by which humans learn art, but that our ‘mysticism’ surrounding the notion of creativity stops us from seeing the parallels.” Kalyanaraman says, “If a machine can make humanly surprising, stylistically new kinds of art, I think it is foolish to say well it’s not really creative because it doesn’t have consciousness,”

There have already been and there will continue to be world-shaping advancements in science and every area of our lives that we could not possibly perceive or hope to understand because of AI. “Once the pattern has been discovered, humans will then decide how to employ those insights,” Andrew White says. “We are not seeking to let the algorithm find the insight then automate its application directly.” Of course, this is where human input comes in and why it is so important for us to find ways to inform our understanding of AI. White says his colleague Erick Brethenoux “wonders if this intersection point is where user and machine come together: humans to discover how to apply decisions and measure their impact and AI to automate part of the decision process.”

Reality Changing Observations:

Q1. What are some negative consequences of our implicit bias towards computational decision making?

Q2. Why is important for us to try to understand what AI is?

Q3. What are some examples of how automation has worked for Good in science?

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