AI Concepts

The Action Value Function evaluates the expected reward for taking a specific action in a given state in reinforcement learning.

An Actor Network is a concept in sociology that describes the interconnected relationships between human and non-human entities.

Agentic Architecture refers to systems designed to empower users to act and make decisions autonomously.

Agentic scaffolding refers to support structures that enhance an agent's ability to make decisions and take actions autonomously.

Aligned AI refers to artificial intelligence systems designed to align with human values and goals.

Anthropic Uncertainty refers to the uncertainty about human preferences and values in AI system design.

Anticipatory Thinking involves predicting future scenarios to inform decision-making and planning.

Argmax identifies the input value that yields the maximum output in a function or dataset.

An associative array is a data structure that pairs keys with values for efficient data retrieval.

An attention map visualizes the focus areas of a neural network during processing, highlighting important input features.

Attention Score measures the importance of input data in AI models, particularly in neural networks.

An attention sink is a phenomenon where attention is drawn to a specific area, often in visual tasks or AI interactions.

Attention weight determines the importance of different inputs in neural networks, especially in transformer models.

Autonomy Gradient refers to the measurement of an AI system's ability to make independent decisions.

Autoregressive Drift refers to a phenomenon in time series forecasting where predictions deviate over time.

Axiom Extraction is the process of identifying and deriving fundamental truths from data or models in AI systems.

The base rate fallacy occurs when the base rate (prior probability) is ignored in favor of specific information.

Bayes' Theorem is a mathematical formula used to calculate conditional probabilities, fundamental in statistics and machine learning.

A Bayesian Belief Network (BBN) is a graphical model that represents probabilistic relationships among variables.

The Bayesian Information Criterion (BIC) is a statistical tool used for model selection.

The Bayesian Posterior is the updated probability of a hypothesis after observing evidence, central to Bayesian inference.

A belief network is a graphical model that represents probabilistic relationships among variables.

BERTScore is an evaluation metric for natural language processing that uses BERT embeddings to assess text similarity.

A Beta Distribution Prior is a statistical model used in Bayesian statistics to represent beliefs about probabilities.

The bias-variance tradeoff is a fundamental concept in machine learning that balances model complexity and accuracy.

A binary tree is a hierarchical data structure with at most two children per node.

A bipartite graph is a type of graph that has two distinct sets of vertices with edges only between the sets.

A Black Box Model is an AI system whose internal workings are not accessible or interpretable by users.