Artificial Intelligence (AI)

These terms represent the foundational concepts, methodologies, and application areas that are central to the field of AI, encompassing the theoretical underpinnings, the development of algorithms, and the implementation of systems capable of intelligent behavior.

Accuracy - A fundamental measure in AI for evaluating the correctness of a model's predictions.

Action Recognition - A task in AI focused on identifying actions in sequences, such as videos, which is a core part of understanding and interpreting dynamic data.

Activation Function - A key component in neural networks that determines the output of a node, given a set of inputs.

Agent - In AI, an agent is an entity that perceives its environment and takes actions to achieve certain goals, a foundational concept in many AI systems.

AI Governance - This concept involves frameworks and strategies to ensure the responsible and ethical use of advanced technologies, focusing on compliance, oversight, and accountability.

AlphaFold - An AI program developed by DeepMind that predicts protein folding structures.

Anomaly Detection - The use of AI to identify patterns in data that do not conform to expected behavior.

Artificial General Intelligence (AGI) - A theoretical form of AI that has the ability to understand, learn, and apply knowledge in ways that are indistinguishable from human intelligence.

Artificial Intelligence (AI) - The overarching field that encompasses all the methodologies, technologies, and applications aimed at creating machines capable of performing tasks that would require intelligence if done by humans.

Artificial Narrow Intelligence (ANI) - AI systems that are designed and trained for a specific task, representing the current state of most AI technologies.

Artificial Neural Network (ANN) - Inspired by the biological neural networks, these computational models are fundamental to deep learning and many AI applications.

Association - A concept in AI related to finding and establishing connections between data points or features, fundamental in rule-based systems and association rule learning.

Autoencoder - A type of artificial neural network used for unsupervised learning of efficient codings, fundamental for understanding deep learning architectures.

Automated Machine Learning (AutoML) - A core advancement in AI that automates the process of applying machine learning, making AI more accessible.

Big Data - Refers to extremely large datasets that traditional data processing software cannot manage, a foundational concept in understanding the scale of data AI technologies can work with.

ChatGPT - An example of a large language model that represents a significant advancement in natural language processing, a core area in AI.

Classification - A fundamental machine learning task where models are trained to categorize input data, a core technique in many AI applications.

Clustering - An unsupervised learning technique where models group sets of data points, fundamental for data analysis and pattern recognition in AI.

Convolutional Neural Network (CNN) - A specialized kind of neural network for processing data with a grid-like topology, crucial for computer vision tasks, a core application area in AI.

Deep Learning (DL) - A subset of machine learning involving neural networks with many layers, central to the significant advances in AI capabilities.

Dimensionality Reduction - The process of reducing the number of input variables in a dataset, crucial for simplifying AI models and reducing computational complexity.

Feature Engineering - The process of selecting, modifying, or creating new input variables to improve model performance, a core practice in the development of AI models.

Generative AI - AI systems capable of generating new content, offering a core methodology for creative AI applications.

Image Recognition - The ability of AI to identify objects, places, people, writing, and actions in images, a fundamental task in image processing.

Large Language Model (LLM) - These models, trained on vast amounts of text data, are at the core of modern natural language processing and generation tasks.

Machine Learning (ML) - A core subset of AI, focusing on the development of algorithms that allow computers to learn from and make predictions or decisions based on data.

Markov Decision Process (MDP) - A mathematical framework for modeling decision making, foundational in reinforcement learning, a key area in AI.

Model - In AI, a model is the representation of what has been learned by a machine learning algorithm, central to all AI applications.

Neuron - The basic computational unit of an artificial neural network, inspired by biological neurons, and foundational to understanding how neural networks function.

Reinforcement Learning (RL) - A type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve some notion of reward, a core methodology in AI.

Self-Supervised Learning - An innovative learning paradigm where models learn to predict part of the input from other parts, reducing the dependency on labeled data.

Supervised Learning - A fundamental machine learning paradigm where models learn from labeled training data, crucial for a wide range of AI applications.

Transformer Architecture - A model architecture that has revolutionized natural language processing, a core area in AI.

Unsupervised Learning - Learning patterns from unlabeled data, a core machine learning paradigm that underpins many exploratory data analysis methods in AI.