# AI Major Techniques - Unsupervised learning - Clustering - Identify groups/sub-groups (clusters) in a dataset - The algorithm receives data, and has to find interesting "facts" about it - Major issue: it requires a ton of labeled data (much more than human childs need to learn) - Unsupervised learning has applications for various things, including NLP (e.g., improving Web search) - Transfer Learning (TL) - Learn from a task A (e.g., car detection), and help on another task B - Very valuable technique, used for instance by computer vision systems - Reinforcement Learning (RL) - Similar to how you train a pet dog to behave - Let the dog whatever it wants to do - Whenever he does something good, you praise him - Whenever he does something bad, you go "bad dog!" - Over time the dog improves his behavior - Use a "reward signal" when it's doing well or poorly. Large positive number when positive and large negative number when performing poorly. The goal being to maximize positive rewards - Use for games such as Go, Chess, videogames, etc - One downside is that it can require a huge amount of data - Generative Adversarial Networks (GANs) - Created by Ian Goodfellow, a former student of Andrew Ng - Example: synthetize new images from scratch - Knowledge Graph (KG) - Huge economic potential and impact - Capture data (different dimensions) about things/concepts/people - Connect the data together to create knowledge graphs