It is 10 years since neural networks made their spectacular
comeback. Prompted by this anniversary, we take a holistic
perspective on artificial intelligence (AI). Supervised learning for
cognitive tasks is effectively solved—provided we have enough
high-quality labelled data. However, deep neural network
models are not easily interpretable, and thus the debate between
blackbox and whitebox modelling has come to the fore. The rise
of attention networks, self-supervised learning, generative
modelling and graph neural networks has widened the
application space of AI. Deep learning has also propelled the
return of reinforcement learning as a core building block of
autonomous decision-making systems. The possible harms made
possible by new AI technologies have raised socio-technical
issues such as transparency, fairness and accountability. The
dominance of AI by Big Tech who control talent, computing
resources, and most importantly, data may lead to an extreme
AI divide. Despite the recent dramatic and unexpected success
in AI-driven conversational agents, progress in much-heralded
flagship projects like self-driving vehicles remains elusive. Care
must be taken to moderate the rhetoric surrounding the field
and align engineering progress with scientific principles