This is the continuation of the "Latest Trends in Artificial Intelligence" video Part 2.
Federated learning: Federated learning is a machine learning approach that allows multiple devices to collaborate on a single model without sharing their data with a central server. This is useful for applications where data privacy is important, such as healthcare and finance.
Generative adversarial networks (GANs): GANs are a type of neural network that can be used to generate new, realistic data based on existing data. This is useful for applications such as image generation, text generation, and music generation.
Explainable AI (XAI): XAI is an approach to AI that aims to increase the transparency and comprehension of machine learning models. This is important for ensuring that AI models are fair and accountable. Reinforcement learning: Reinforcement learning is a type of machine learning that allows an agent to learn how to behave in an environment by trial and error. This is useful for applications such as robotics and game playing.
Transfer learning: Transfer learning is a technique that allows a machine learning model to be trained on one task and then applied to a different task. This is useful for saving time and resources when training machine learning models.
