Sub-symbolic AI
Sub-symbolic AI refers to a category of artificial intelligence that operates without explicit symbolic representation of knowledge, relying instead on connectionist or statistical methods.
AI
PaLM-E (embodied language model)
PaLM-E is an advanced embodied language model that integrates vision and language processing, enhancing the interaction between AI and the physical world.
Class-incremental learning
Class-incremental learning is a subfield of machine learning that focuses on the ability of models to learn new classes of data incrementally without forgetting previously learned information.
Gemini (language model)
Gemini is a language model developed to advance natural language processing tasks. It aims to improve understanding, generation, and interaction in AI systems through innovative architecture and training techniques.
GloVe (machine learning)
GloVe (Global Vectors for Word Representation) is a machine learning algorithm designed for natural language processing tasks, focusing on generating word embeddings.
DreamerV2
DreamerV2 is an advanced machine learning model designed for generating and understanding complex data patterns. It enhances predictive capabilities across various applications.
FeUdal networks (hierarchical RL)
FeUdal networks are a model in hierarchical reinforcement learning that enables efficient learning by structuring tasks into layers of subgoals.
Prompt engineering
Prompt engineering is the practice of designing and refining input prompts for artificial intelligence models, especially large language models, to achieve desired outputs. It involves crafting queries or instructions that guide AI systems to generate more accurate, relevant, or contextually appropriate responses.
State alignment for imitation
State alignment for imitation refers to the process in artificial intelligence and robotics where the internal state of an agent is synchronized or aligned with that of a demonstrator to facilitate learning by imitation. This concept is critical in enabling machines to replicate behaviors by understanding and matching the underlying states that generate observed actions.
Hyperparameter optimization
Hyperparameter optimization is a crucial process in machine learning that involves tuning the parameters of a model to improve its performance.