A
Accelerator
A type of microprocessor specifically designed to speed up AI applications, enhancing their processing power.
Adaptive Learning
In AI, this refers to learning environments or systems that adjust their content or difficulty based on the learner's performance, providing a personalized experience.
Agents
AI systems designed to perform tasks autonomously, often utilizing various tools to achieve specific goals without constant human oversight.
AGI (Artificial General Intelligence)
A hypothetical form of AI that would possess intelligence equal to or surpassing human capabilities across a broad spectrum of intellectual tasks.
AI (Artificial Intelligence)
The broad field of computer science dedicated to creating systems that can perform tasks traditionally requiring human intelligence, such as learning, problem-solving, and decision-making.
AI Ethics
The considerations and frameworks necessary to ensure the responsible development and use of AI technology, addressing issues of fairness, safety, and accountability.
Alignment
The crucial task of ensuring that an AI system's goals and actions are consistent with human values and intentions, preventing unintended or harmful behaviors.
Algorithm
A step-by-step set of instructions or rules that an AI system follows to solve a specific problem or accomplish a task.
API (Application Programming Interface)
A set of rules and protocols that define how different software applications interact with each other, allowing them to communicate and share data.
Artificial Neural Network (ANN)
A computing system inspired by the structure of the human brain, using interconnected nodes ("neurons") to process data in layers and make decisions or predictions.
ASI (Artificial Super Intelligence)
A hypothetical AI system with intelligence far surpassing that of the most capable humans.
ASR (Automatic Speech Recognition)
The technology that enables computers to convert spoken language into text, essential for voice assistants and transcription services.
Attention (Self-Attention Mechanism)
Mechanisms within AI models, particularly Transformer models, that help the system focus on important parts of the input data to better understand context and generate relevant outputs.
Augmented Intelligence
A collaborative approach where humans and AI work together to enhance decision-making and performance, combining human intuition and judgment with AI's analytical capabilities.
Augmented Reality (AR)
Technology that overlays digital information or objects onto the real world, creating an enhanced or interactive experience.
Automation
The use of AI and other technologies to perform repetitive tasks with minimal human intervention, streamlining processes and increasing efficiency.
AutoML (Automated Machine Learning)
Tools and platforms that automate the process of building, training, and deploying machine learning models, making AI more accessible.
B
Backpropagation
An algorithm used in training neural networks to adjust the network's internal parameters (weights and biases) based on the error in the output, leading to improved performance.
BERT (Bidirectional Encoder Representations from Transformers)
A powerful natural language processing model that uses a Transformer architecture to understand the context of words in a sentence by considering the words that come before and after.
Bias (in AI)
Systematic prejudice or unfairness in an AI system, often stemming from biased training data or limitations in the algorithm's design, which can lead to discriminatory outcomes.
Big Data
Extremely large and complex datasets that AI systems analyze to uncover patterns, trends, and insights that might be hidden in smaller datasets.
Black Boxes
A term for AI models, especially deep learning models, where the internal workings and decision-making processes are not easily understandable or interpretable by humans.
Bots
Software programs designed to perform specific tasks within a software application, often interacting with users through conversational interfaces.
Bounding Box
In computer vision, a rectangular box drawn around an object in an image or video to identify and locate it for AI analysis.
C
Chain-of-thought Prompting
A technique that encourages AI models, particularly Large Language Models, to break down complex problems into a series of intermediate steps, leading to more accurate and reliable responses.
Chatbot
A software application designed to simulate human conversation, often used for customer service, information retrieval, or other interactive tasks.
ChatGPT
A specific example of a large language model (LLM) developed by OpenAI, known for its ability to generate human-like text and engage in conversational interactions.
CLIP (Contrastive Language–Image Pretraining)
A neural network trained to connect text descriptions with images, enabling tasks like image search based on text prompts.
Cognitive Computing
An AI approach focused on building systems that mimic human cognitive abilities, such as learning, reasoning, and understanding natural language.
Composite AI
An AI system that combines different AI techniques and models to address complex problems that cannot be solved by a single approach.
Computational Learning Theory
A field within AI that studies the theoretical foundations of machine learning algorithms, focusing on how machines learn and improve performance.
Computer Vision
A field of AI that enables machines to "see" and interpret visual information, such as images and videos, allowing them to identify objects, recognize patterns, and understand scenes.
Context Window
The limited amount of text or data that a language model can process and consider at any given time when generating a response.
Conversational AI
AI systems designed to engage in natural language conversations with humans, used in applications like chatbots and virtual assistants.
Convolutional Neural Network (CNN)
A type of neural network particularly effective at processing and analyzing visual data, commonly used in image recognition tasks.
Corpus
A large collection of text or spoken language data used to train and develop AI models, especially in natural language processing.
Critical AI
An approach to examining AI that emphasizes critical assessment and critique, focusing on understanding and challenging existing biases and societal impacts within AI systems.
D
Data
Units of information that AI systems use to learn, identify patterns, and make decisions or generate outputs.
Data Augmentation
Techniques used to artificially increase the size and diversity of training data by creating modified versions of existing examples, improving the robustness of AI models.
Data Discovery
The process of exploring large datasets to uncover valuable insights, patterns, and relationships that can inform decision-making or model development.
Data Drift
A change in the distribution of input data over time, which can impact the performance of AI models trained on earlier data.
Data Extraction
The process of retrieving data from various sources for use in AI applications.
Data Ingestion
The process of gathering and consolidating data from multiple sources into a centralized location for AI processing and analysis.
Data Labeling
The process of tagging or annotating data with relevant information (e.g., classifying images or transcribing audio) to create labeled datasets for supervised machine learning.
Data Mining
The process of analyzing large datasets to discover new patterns, trends, and valuable insights, often used to improve AI model performance or inform business decisions.
Data Scarcity
A situation where there is insufficient data available to effectively train an AI model.
Data Science
An interdisciplinary field that combines statistics, computer science, and other disciplines to extract knowledge and insights from data, often used to develop and deploy AI solutions.
Dataset
A collection of related data points used to train or evaluate AI models.
Deep Learning
A subset of machine learning that uses artificial neural networks with multiple layers ("deep" neural networks) to learn complex patterns and representations from large datasets.
Deepfake
AI-generated media, typically videos or audio recordings, that have been altered to convincingly depict individuals doing or saying things they never did or said.
Diffusion Models
A class of generative AI models that create new data by gradually adding noise to existing data and then learning to reverse the process to generate realistic outputs, often used for image generation.
Double Descent
A phenomenon observed in some AI models where performance initially degrades as model complexity increases, but then improves significantly when complexity surpasses a certain threshold.
E
Embodied AI
AI systems that are integrated into physical bodies (e.g., robots) and interact with the real world through sensors and actuators, allowing them to learn from physical experiences.
Embeddings
Numerical representations of data (e.g., words, images) that capture their meaning and relationships in a multi-dimensional space, enabling AI models to process and understand them.
Emergent Behavior
Unpredictable or unintended capabilities that emerge in complex AI systems when individual components interact as a whole.
End-to-End Learning
An AI approach where a single model learns to perform a task directly from raw input data to the final output, without requiring intermediate steps or feature engineering.
Entity Annotation
The process of identifying and labeling specific entities (e.g., people, organizations) within text or other data to structure it for AI processing.
Entity Extraction
The overall process of structuring unstructured data by identifying and extracting relevant entities.
ESG (Environmental, Social, and Governance)
A framework used to evaluate a company's performance in these three areas, often incorporating AI for data analysis and reporting.
Expert Systems
Early AI systems that used explicitly programmed rules and knowledge bases to solve problems within a specific domain, mimicking the decision-making of a human expert.
Explainable AI (XAI)
Approaches and techniques that aim to make AI models more understandable and transparent, allowing humans to comprehend how the models arrive at their decisions and predictions.
F
Feature Engineering
The process of selecting, transforming, and creating relevant features from raw data to improve the performance of machine learning models.
Few-shot Learning
Training an AI model to recognize new concepts or objects using a very small number of examples.
Fine-tuned Model
An AI model that has been pre-trained on a large dataset and then further trained on a smaller, task-specific dataset to adapt it to a particular application.
Fine-tuning
The process of adjusting the parameters of a pre-trained AI model to specialize it for a specific task or dataset.
Foundation Models
Large-scale AI models pre-trained on massive datasets that serve as a base for developing more specialized models for various tasks.
Forward Chaining
A reasoning method used in AI where the system starts with known facts and applies rules to deduce new information or reach a conclusion.
Forward Propagation
The process of feeding data through a neural network to generate an output or prediction.
G
GAN (Generative Adversarial Network)
A type of generative AI model consisting of two neural networks, a generator and a discriminator, that are trained in competition with each other to generate realistic data.
Generative AI (GenAI)
A type of AI that focuses on creating new content, such as text, images, music, or videos, based on patterns and examples learned from its training data.
GPT (Generative Pre-trained Transformer)
A specific type of generative AI model that uses a Transformer architecture and is pre-trained on a large corpus of text data.
GPU (Graphics Processing Unit)
A specialized processor designed to handle the complex calculations required for training and running AI models, particularly deep learning models.
Gradient Descent
An optimization algorithm used in machine learning to minimize the error or "loss" of a model by iteratively adjusting its parameters in the direction of the steepest decrease in the loss function.
Grounding
The ability of AI systems to connect their outputs or responses to verifiable sources of information or real-world entities, ensuring factual accuracy.
Guardrails
Mechanisms and frameworks designed to ensure that AI systems operate within ethical, legal, and technical boundaries, preventing harmful or biased outcomes.
H
Hallucination
Instances where an AI model generates incorrect or fabricated information, presenting it as factual.
Hidden Layer
An intermediate layer of nodes within a neural network that processes data between the input and output layers.
Human in the Loop
A concept emphasizing the involvement of humans in the AI development and decision-making process to ensure accuracy, fairness, and safety.
Human-centered Perspective
An approach to AI development that focuses on designing systems to work collaboratively with humans, augmenting their abilities rather than replacing them.
Hybrid AI
An AI system that combines different AI techniques or models to leverage their respective strengths and address complex problems.
Hyperparameter
Settings that control the learning process and behavior of a machine learning model, such as the learning rate or the number of hidden layers, typically set before training.
Hyperparameter Tuning
The process of optimizing hyperparameters to achieve the best possible performance for an AI model.
I
Image Recognition
The ability of AI systems to identify and classify objects, people, or scenes within images.
Inference
The process of using a trained AI model to make predictions or generate outputs based on new, unseen data.
Instruction Tuning
A technique used to train AI models, particularly LLMs, to follow specific instructions or prompts to perform various tasks.
Intelligent Tutoring Systems (ITS)
AI-powered educational systems that provide personalized feedback and support to students, adapting to their individual learning needs.
Intent
In natural language processing, the underlying purpose or goal of a user's input, used to guide AI responses.
Interpretable Machine Learning (IML)
An approach to designing AI models that are inherently transparent and provide explanations for their decisions, contrasting with "black box" models.
IOT (Internet of Things)
A network of interconnected devices that collect and exchange data, often incorporating AI for data analysis and decision-making.
R
Regularization
Techniques used in machine learning to prevent overfitting by adding constraints or penalties to the model's parameters during training.
Reinforcement Learning
A type of machine learning where an AI agent learns by interacting with an environment and receiving feedback (rewards or penalties) based on its actions. The agent learns to make decisions that maximize its cumulative reward.
Retrieval-Augmented Generation (RAG)
A technique that enhances the capabilities of LLMs by allowing them to retrieve information from external knowledge bases before generating a response. This helps ensure the generated content is accurate and grounded in verifiable sources.
Robotic Process Automation (RPA)
The use of software robots or AI to automate repetitive, rule-based digital tasks, streamlining business processes and improving efficiency.
Robotics
The field of engineering focused on the design, construction, operation, and application of robots. AI is often integrated into robots to enhance their capabilities, enabling them to perceive their environment, make autonomous decisions, and adapt to new situations.
Rule-based System
An AI system that uses a set of explicitly programmed rules and a knowledge base to solve problems within a specific domain.
S
Search Algorithm
A set of steps or rules that AI systems use to efficiently find information or solutions within a large dataset or search space.
Self-awareness (AI)
A hypothetical level of AI where a system would possess consciousness and an understanding of its own existence. This type of AI does not currently exist.
Self-attention Mechanism
Components within AI models, especially Transformers, that allow the system to weigh the importance of different parts of the input data when processing it, enhancing its ability to understand context and relationships.
Sentiment Analysis (Opinion Mining)
The use of natural language processing to determine the emotional tone or sentiment expressed in text data, such as positive, negative, or neutral.
Structured Data
Data organized in a fixed format or schema, making it easy to process and analyze using traditional methods.
Supervised Learning
A type of machine learning that uses labeled datasets, where each data point is paired with a corresponding output or target value. The model learns to map inputs to outputs by identifying patterns in the labeled data.
T
Temperature (AI Temperature)
A hyperparameter used in generative AI models that controls the randomness or creativity of the output. A higher temperature results in more diverse and potentially creative output, while a lower temperature produces more predictable and focused output.
Theory of Mind (AI)
A hypothetical level of AI capable of understanding and predicting the mental states and intentions of others, including humans. This is a crucial step towards creating AI systems that can interact with humans in a more natural and empathetic way.
Token
A basic unit of text or data used by AI models, particularly LLMs, when processing and generating content. Tokens can represent words, parts of words, or characters.
Training Data
The dataset used to train an AI model, enabling it to learn patterns and relationships. The quality and quantity of training data significantly impact the model's performance.
Transfer Learning
A machine learning technique where a model trained on one task is reused as a starting point for another, related task. This is particularly useful when limited data is available for the second task.
Transformer Model
A type of neural network architecture, widely used in natural language processing and generative AI, known for its ability to handle sequential data and capture long-range dependencies.
U
Unstructured Data
Data that does not have a predefined format or structure, such as text documents, images, audio files, and videos. AI plays a crucial role in extracting valuable insights from unstructured data.
Unsupervised Learning
A type of machine learning that focuses on identifying patterns and relationships in unlabeled data without human guidance. This is used for tasks like clustering data, dimensionality reduction, and anomaly detection.
V
Vector Embeddings
Numerical representations of data that capture their meaning and relationships in a multi-dimensional space, allowing AI models to process and understand them.
Validation Set
A subset of data used to evaluate the performance of an AI model during the training process, helping to identify and prevent overfitting.
Virtual Reality (VR)
Technology that creates immersive, interactive virtual environments. AI is often used in VR to enhance realism, create intelligent agents, and provide personalized experiences.
Voice Recognition (Speech Recognition)
The technology that allows computers to convert spoken language into text or commands, enabling voice-controlled interfaces and applications.
W
Weak AI (Artificial Narrow Intelligence - ANI)
AI systems designed and trained to perform specific, narrow tasks, such as playing chess or recognizing faces. They lack the broad, general intelligence of AGI.
Weight (in AI)
A numerical value associated with the connections between nodes in a neural network. These weights are adjusted during training to optimize the model's performance.