There's been way too much fear-mongering news articles around the latest version of DeepMind's AlphaGo. Let's set the record straight, AlphaGo is an incredible technology and it's not terrifying at all. I'll go over the technical details of how AlphaGo really works; a mixture of deep learning and reinforcement learning. That's what keeps me going.
Why was AlphaGo's Move 37 against Lee Sedol so significant? Why was it so important that I named my 10 week course on deep reinforcement learning on it? In this final video of my course, I'll explain what move 37 symbolized for humanity and detail 3 examples of how it will affect healthcare, design, and decision-making. We'll go through a code example of a Generative Adversarial Network and even discuss China ambitious 2030 AI initiative. Theres a lot that I cover in this video, I hope that it helps connect the dots.
We've seen advances in generative models, the AlphaGo victory, several data breach scandals, and so much more. I'm going to briefly review AI in 2018 before giving 10 predictions on where the space is going in 2019. Prepare yourself, my predictions range from more Kubernetes infused ML pipelines to the first business use case of generative modeling of 3D worlds. Happy New Year and enjoy! Thats what keeps me going.
Quantum computers are mind bogglingly powerful machines that take a novel approach to processing data. Built on the principles of quantum mechanics, they utilize complex and fascinating laws of nature that are always there, but usually remain hidden from view like superposition and entanglement. Specifically, we'll discuss the examples of quantum annealing, sampling, and quantum gates as layers in a neural network. We'll first try to cover quantum mechanics though, get hype! That's what keeps me going.