Artificial Intelligence for Human Computer Interaction

Summary

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 James L. Crowley:

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