The Turing test defines intelligence as human-level performance at interaction. After more than 50 years of research, Machine Learning has provided an enabling technology for constructing intelligent systems with abilities for interaction at or beyond human level. This technology creates a fundamental rupture in the way we build systems, and in the kind of systems that we can be built.

In this talk I will provide a survey of recent progress in Machine Learning, and examine how these technologies will impact the domain of Human-Computer Interaction.  Starting with a summary of the multi-layer perceptron and back propagation, I will describe how massive computing power combined with planetary scale data and advances in optimization theory have created the rupture technology known as deep learning.  I will survey common architectures and popular programming tools for building convolutional and recurrent neural networks, and review recent advances such as Generative Adversarial Networks and Deep Reinforcement Learning. I will examine how these technologies can be used to build realistic systems for vision, robotics, natural language understanding and conversation. I will discuss open problems concerning explainable, verifiable, and trustworthy artificial systems.