The Rise of Multimodal Learning: Multimodal learning combines text, images, and audio to enable deep learning models to grasp complex data relationships.
Development of New Architectures: Constant innovation leads to improved deep learning model performance across various tasks, including multimodal learning.
Use of Federated Learning: Privacy-sensitive fields like healthcare and finance benefit from federated learning, enabling collaboration without data sharing.
Use of Reinforcement Learning: Reinforcement learning empowers agents to learn through trial and error, impacting robotics and game playing.
Development of New Hardware: Quantum computers and neuromorphic chips drive more efficient training and deployment of deep learning models.