Abstract: Artificial Intelligence/Machine learning has seen large growth and a number of successes over the past few years. In this talk I will describe some of the core ideas and algorithms along with corresponding results. We will start by thinking about intelligence in general. This will lead us to the problem of perception, where I will describe the core technology behind much of machine learning's recent success: artificial neural networks. I will discuss how simple modifications to the network architecture can be used for object recognition and detection, image captioning, speech recognition and synthesis, and language translation. These methods (with more sophistication) are the current state of the art systems and are employed by major technology companies. Next, I will discuss how a system can learn to make decisions, describing policy gradients - a simple yet general and effective algorithm. Finally, I will describe a number of methods that go beyond the basics such as planning and AlphaGo Zero, unsupervised learning and generative models.