Artificial Intelligence Won’t be Replacing Artists Any Time Soon
Is there something special about being human? Or could an artificial intelligence system imitate our behaviours with such fidelity that we would never know the difference?
That is the question that is the basis of the Turing Test. In 1950, Alan Turing described a test he believed would tell us whether a computer is capable of thinking. An interrogator would be in a separate room from a machine, and another human being, and the objective in this “imitation game” is for the machine to fool the interrogator into thinking it’s a human [1]. The assumption is that if this machine has the capability to navigate the complexities of a conversation, then we can reasonably assume that it has some capacity for thought.
But there’s a problem with that assumption. It’s possible the machine in question doesn’t really understand the conversation at all. Rather, it has sufficient computing power to control the inputs and outputs, such that we can’t tell the difference between it and a human.
In 1980, John Searle presented his criticism of what he viewed as the “Strong AI” view, which argues that an “appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.” He proposed his “Chinese Room” thought experiment, where a computer would receive an input in the form of a Chinese character, and it would use a rulebook to select the appropriate output [2]. To the observer on the outside, it appears as if the machine understands the language as well as a human does. But Searle argues that this does not show that the computer understands Chinese, but rather it’s simulating an understanding.
The Turing Test was called an “Imitation Game” for a reason. Our computers have come a long way since 1950, but the principle of imitation remains the same. We now have artificial intelligence systems that can beat any human at chess, create their own paintings, drive cars on their own, and diagnose certain types of cancer almost as well as a human doctor [3]. But this complex behaviour, no matter how impressive it is, still could just be an advanced form of imitation. It is possible we are merely giving birth to problem-solving machines that are simulating the behaviours of humans, rather than possessing understanding and thought.
The reason artificial intelligence systems can complete increasingly complex tasks is the emergence of “Deep Learning.” Instead of possessing a series of computational circuits and inflexible algorithms, we’ve designed AI systems with a series of “nodes” that imitate the structure of the human brain. A neuron can be seen as a single node that sends a message to a surrounding node (a neighbouring neuron), and this creates a network of information that performs computations. In order for a human to survive, its brain needs to change in response to its environment, adapting to ever-changing circumstances. Neurons provide us the flexibility to both survive and perform the complex behaviors of modern society. In principle, a deep learning system possessing this exact same system of nodes could perform calculations millions of times quicker, and therefore would quickly achieve superiority in any domain that humans believe they’re good at.
This line of reasoning is often used to argue for the “Deep Learning” takeover of creative tasks. There are many AI proponents that believe a neural network will be able to paint a true masterpiece, write award-winning novels, or replace cinema by creating stories through its superhuman capacity to utilize visual effects. Any of the creative tasks that we value as humans could, in theory, be replaced by sufficiently advanced neural networks.
While this is probably going to be the case eventually, the question is how quickly will it happen, and whether the AI systems generating these works of art would actually understand what they’re doing.
