Artificial intelligence refers, among other things, to machines’ capacity to demonstrate some degree of what humans consider “intelligence”. This process is being driven by the rapid advancement of machine learning: getting machines to think for themselves rather than pre-programming them with an absolute concept.
Take image recognition. Humans excel at this task, but it’s proved difficult to simulate artificially. Training a machine to recognise a cat doesn’t mean inputting a set definition of what a cat looks like. Instead, many different images of cats are inputted; the aim is that the computer learns to distil the underlying “cat-like” pattern of pixels.
This dependence on data is a powerful training tool. But it comes with potential pitfalls. If machines are trained to find and exploit patterns in data then, in certain instances, they only perpetuate the race, gender or class prejudices specific to current human intelligence.
But the data-processing facility inherent to machine learning also has the potential to generate applications that can improve human lives. “Intelligent” machines could help scientists to more efficiently detect cancer or better understand mental health.
Most of the progress in machine learning so far has been classical: the techniques that machines use to learn follow the laws of classical physics. The data they learn from has a classical form. The machines on which the algorithms run are also classical.
We work in the emerging field of quantum machine learning, which is exploring whether the branch of physics called quantum mechanics might improve machine learning. Quantum mechanics is different to classical physics on a fundamental level: it deals in probabilities and makes a principle out of uncertainty. Quantum mechanics also expands physics to include interesting phenomena which cannot be explained using classical intuition.
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