CycleGAN & Approaches to AI

Only supervised learning is a “solved” problem and what people generally mean by AI. In this talk, we will survey different approaches to AI: supervised, unsupervised, reinforcement learning and active learning. We will explore CycleGAN as a working example of automating the environment and progressively building towards sophisticated AI. Lastly, we’ll a tour of the state-of-the-art approaches to GANs that research groups have harnessed to achieve exciting and realistic results.

After spending nearly a decade at UC Berkeley, Kelvin decided to repay his debt to the public education system by helping build UC Merced. He spent 7 years teaching 4,500 students across 55 classes, while redesigning the undergraduate Computer Science curriculum as the Undergraduate Chair. Kelvin then architected NVIDIA’s Deep Learning Institute plan to reach over 100K developers worldwide directly and in collaboration with Udacity and Coursera/Deeplearning.ai. It became obvious that AI will be the most disruptive and defining technology of the 21st century. So, he joined Iluvatar as AI Architect to directly create the full AI stack of AI apps, SW and HW.