  It is ten 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 labeled data. However, deep
neural network models are not easily interpretable, and thus the debate between
blackbox and whitebox modeling has come to the fore. The rise of attention
networks, self-supervised learning, generative modeling, 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. Failure to meet high expectations in high profile, and much heralded
flagship projects like self-driving vehicles could trigger another AI winter.
