AI in one sense means doing what human do when they learn. The field is thick with references to ‘learning’, the most common being machine learning, deep learning and reinforcement learning. Machine learning uses algorithms and statistical models and applies patterns and inferences to perform tasks, so that it learns from experience. Deep learning is a machine learning technique that uses layered neural networks with data, supervised, semi-supervised or unsupervised, to perform tasks. Reinforcement learning operates on maximising reward by exploring existing knowledge and exploring new knowledge. 

Digging deeper there is decision-tree learning, lazy and eager learning, supervised and unsupervised learning, incremental, feature, federated and ontology learning. The noun is used so frequently, in so many contexts, for so many techniques that it has become a general term for software that gets better as it proceeds. This is why it AI is a topic of immediate interest to those in the learning game. 

John McCarthy and Marvin Minsky convened a conference in 1956, whose aim was to 

“to proceed on the basis of the conjecture that every aspect of learning (my bold) or any other features of intelligence can in principle be so precisely described that a machine can be made to simulate it”. 

There were successes, like Arthur Samuel’s checkers software, but the promise was never realised and the first AI winter arrived in the 60s. The early 80s saw a resurgence of interest in expert systems and a second winter came. It was only when probability and statistics were literally introduced into the equations that deep learning gave us translation, speech recognition and image recognition. But AI, with exponential growth in processing power, data and devices can now deliver on some of those early promises.

We are now revisiting, and implementing that 1956 objective, with a focus on how AI can be used to accelerate learning. AI makes us rethink learning. It holds the possibility that we do not have to learn some old knowledge and skills, it may help us learn new knowledge and skills, even improve the process and speed of learning. What greater objective that AI helping us to learn; learn to deal with the consequences of this bountiful technology, learn to solve intractable problems, learn to control AI, if necessary. 

Stuart Russell, a major figure in AI, rightly claims in ‘Human Compatible’, that 

“With AI tutors, the potential of each child, no matter how poor can be realised. The cost per child would negligible and that child would live a far richer and more productive life.” I think he’s right. Even if he’s only part right, this is the right direction of travel.