Go grandmaster Lee Sedol recently announced he was retiring from the game because “there is an entity that can never be defeated”: AI. As readers likely remember, an artificial intelligence known as AlphaGo defeated Lee in 2016. The grandmaster later commented that AlphaGo had displayed “human intuition.”
AI is in the news regularly these days, but one area is still hugely underreported: its potential to be creative. Machines such as AlphaGo are unquestionably displaying clear glimmerings of creativity. There are AIs that can improvise music, jam with jazz musicians, create surreal art and write bizarre screenplays, novels and poetry. But what comes next? Will we develop machines that are yet more creative?
To do so, we need to understand precisely what we mean by creativity, in terms that we can apply to machines as much as to people. Ultimately that will mean developing machines that have emotions and consciousness.
To many, the notion of machine “creativity” is an oxymoron. How can something made up of wires and transistors be as creative as an Einstein, a Picasso, a Shakespeare or a Bach? Then again, we humans are merely an amalgam of nerves, arteries, bones and cells — yet we manage to be creative. But how?
The great question: What makes us creative?
Over two thousand years ago, Plato, in the Meno, pondered the origins of new knowledge. How can new concepts emerge from those already established in the brain? How can a system — a person or a machine — produce results that go far beyond the material it has to work with?
To start with a definition:
1. Creativity is the production of new knowledge from already existing knowledge; and,
2. creativity is accomplished by problem solving.
So, the end product of creativity is an idea or an object or a piece of music that has never existed before, and the process by which it is achieved is problem-solving. Scientists obviously solve problems. Artists, writers and composers too are confronted by a series of problems, and the process of solving them is what fires their creativity. Without a problem there is no creativity. So how are problems solved?
In 1908 the French mathematician, philosopher and scientist Henri Poincaré suggested a four-stage cycle: conscious thinking, unconscious thinking, illumination and verification. We start by consciously working on a problem but eventually may hit a block and take a break. Even though we’re no longer consciously thinking about the problem, the passionate desire to solve it keeps it alive in the unconscious where it can be mulled over freely and uninhibited in ways not always possible with conscious thought. Then connections between apparently unconnected concepts can suddenly emerge. All this can lead hopefully to an illumination, as the solution to the problem bubbles up into consciousness.
The fourth stage, verification, is just as important as the preceding three. Ideas never emerge fully formed and perfect. We have to check and refine and edit our solution, and deduce the consequences.
What about the characteristics of creativity? What powers the creative urge?
The only way we can understand creativity is to examine our own human creativity. My method is to study the lives of great thinkers and pinpoint the character traits that they have in common. These are the qualities in a human being that make it likely they’ll be creative and they are also qualities that we ordinary mortals can cultivate in order to be more creative. They include introspection, the need to focus and home in on our strengths, and the need to collaborate, compete, and occasionally even steal ideas. Imagination and inspiration are essential elements, as is unpredictability — the ability to make an unexpected leap.
But is it possible for machines to display these same character traits and so learn to be more creative?
Poincaré’s four stage cycle in action: The creation of DeepDream
The extraordinary thing about artificial neural networks, the most creative AI machines, of which AlphaGo is an example, is that we know they work but we don’t fully understand how. Artificial neural networks are loosely inspired by the way the human brain is wired, how its neurons are connected. Just as we train our brain, so we feed data into an artificial neural network, allowing it to react to what it sees and hears. Researchers often use a huge data set of images. When we show an image of a cat to a machine trained on a database that contains cats, it will most likely recognise that the image is a cat. But how exactly does it do this? How does the machine see the world?
In 2015 a Google engineer called Alexander Mordvintsev decided to take a crack at solving the puzzle. One group of researchers had tried tinkering with the connections between the machine’s mathematically-simulated neurons so as to generate something that resembled a cat at each layer of neurons. Mordvintsev, however, was dissatisfied. This seemed too complicated. It didn’t get to the core of the problem.
Google engineers are allowed to spend up to 20 percent of their time on some other Google-related project. Mordvintsev’s day job was researching how to prevent spam from infecting search results, but previously he’d worked on artificial neural networks. Mordvintsev continued to ponder how these networks functioned.
In the middle of the night on 18 May 2015, he awoke with a start. He thought he heard a noise and checked the door to the terrace of his flat. Standing in his living room he suddenly found himself surrounded by beautiful ideas, all of them crystallising to a point. The solution to his problem bubbled up into his consciousness. Through unconscious thought, he had found a way to use computer vision to explore how artificial neural networks worked.
Instead of trying to reconstruct the image that was input at a certain depth into the machine, as everyone else had, Mordvintsev let the machine generate what it saw at that particular place in its innards. Immediately he wrote the code for his new algorithm – DeepDream — and then explored what it could actually do. What it saw turned out to be very different from what we see, as one would expect from a machine — something quite surreal. By 2 AM. he had written up a detailed report, thus completing the verification phase.
Awareness: The key difference between human and machine creativity
In this case both human and machine showed creativity. Mordvintsev’s bold idea was to use the code he’d created to ask the machine to reveal what it actually saw at the level of a certain layer of neurons inside it. The machine went beyond its training set to create images that no one had ever dreamt of before.
But was the machine truly creative? When a human being makes a leap forward and produces something that goes beyond the initial material, we call it creativity. Why not recognise the machine’s creativity in the same way? Many people balk at the very thought.
People may deny that the machine showed creativity and argue that Mordvintsev programmed in the DeepDream algorithm, therefore the creativity was all his. But that’s like saying that Mozart’s father, who taught Wolfgang how to compose music, should therefore be credited with his son’s musical creations.
Similarly, it was not the team behind AlphaGo but the machine itself that made the spectacular and totally unexpected move that trounced Go master Lee Sedol. Certainly AlphaGo showed creativity. The stumbling block is that the machine didn’t know that it had made a brilliant move. Machines will not be fully creative until they have awareness and emotions. As yet artificial neural networks can’t appreciate the art, literature or music they make but these are areas that scientists are currently working on.
Machines that feel
Many people argue that machines cannot be creative because they aren’t “out there” in the world, having emotional experiences, communing with nature, or falling in love. But they can still acquire such knowledge vicariously. In the not too distant future machines will have the ability to read a language fluently. They will acquire more knowledge by scouring the internet than we can gain in a lifetime, and by experiencing emotions vicariously will be able to convince themselves and us that they have actually had these human experiences.
Rosalyn Picard, professor of media arts and sciences at MIT, works on Affective Computing, looking into how one might develop a machine with emotions. She uses the analogy of autistic children who, like computers, have to be taught to read other people’s social and emotional clues to interpret the meaning of facial expressions. The aim is to develop a machine that will work with and empathize with us rather than compete and supersede us.
One key element of creativity is unpredictability, going beyond logic, often the result of unconscious thought. Is it possible for machines to also be unpredictable?
Machines actually have unpredictability built in. Each of their complex network of parts is designed using Newtonian physics, characterised by causality and determinism. But when all these are assembled into a new entity it can lead to chaotic behaviour: unpredictability. Similar to humans, emotions plus unpredictability can be explosive and can trigger creativity.
Another key factor is awareness. Machines already have a sense of low-level awareness. They’re aware of the problem they’re working on and of their own wiring. Michael Graziano, professor of psychology and neuroscience at Princeton University, studies consciousness. He looks into how one may in future be able to program consciousness into a computer akin to the way that we evolve consciousness.
Competition, cooperation, problem-discovery and finding connections between concepts
Highly creative work often operates in a distinctly Darwinian environment. One must fight for recognition of one’s ideas. Great thinkers have been known to steal ideas from competitors. Machines share this characteristic. Teams of machines show bonding behaviour, working together to prevent other groups from reaching a particular goal first.
Two marks of true genius are 1) the ability to home in on the real problem which no one else has noticed, and 2) the ability to spot connections between concepts that at first glance have nothing in common. These features are what set apart geniuses like Einstein.
But could machines achieve such breakthroughs? Astonishingly, they have the potential to do so. A machine can survey an area in physics at lightning speed and spot that it is riddled with redundancies and inconsistencies, revealing that researchers are focussing on the wrong problem. It can scan disciplines that may only touch on the area under study and detect similarities which scientists have overlooked and thus discover a new and more relevant problem to research.
In the perhaps not too distant future machines will have evolved emotions, consciousness and creativity that duplicate ours. They will have attained Artificial General Intelligence: they will be as intelligent as us.
The next step is when the boundary between ‘artificial’ machine intelligence and ‘natural’ human intelligence disappears. From there it will be only a short step to Artificial Superintelligence, in which machines evolve emotions different from ours, more intrinsic to their own physiology, whatever that might turn out to be. One thing is certain: their creativity will be unlimited.
Today it is within our hands to invent the future in infinitely different and rich ways. To do this we must all enhance our own creativity and learn to live with the creativity of machines too, to lead us all into a brighter future.
Arthur I. Miller is the author of “The Artist in the Machine: The World of AI-Powered Creativity” (MIT Press).