AI Glossary
AI Terms & Definitions
Terms
Techniques & Methods
Adversarial Training
Improves AI robustness by training with deliberately challenging inputs to enhance model accuracy.
Applications
Agents
AI entities capable of autonomously performing tasks across various domains, akin to digital assistants.
Core Concepts
AI (Artificial Intelligence)
The simulation of human intelligence processes by machines, particularly computer systems.
General
AI Trainer
Specialists who refine and enhance AI models by providing feedback on outputs and guiding learning.
Core Concepts
Algorithm
A set of mathematical instructions or rules that a computer follows to perform a specific task efficiently.
Techniques & Methods
Alignment
The process of ensuring AI behaviors and outputs adhere to human ethical standards and intentions.
Model Components
API (Application Programming Interface)
Interfaces that allow different software applications to communicate and work together.
Model Components
Artificial Neural Network
Computing systems vaguely inspired by the biological neural networks in human brains.
Techniques & Methods
Attention
A mechanism in AI that allows models to weigh the importance of different pieces of information.
Techniques & Methods
Attention Mechanism
In AI, a technique that helps models focus on relevant parts of the input data, improving relevance.
Core Concepts
Augmented Intelligence
Enhancing human decision-making with AI capabilities, focusing on collaboration between humans and AI.
Core Concepts
Autonomous
Machines or systems capable of performing tasks and making decisions without human intervention.
Techniques & Methods
Autoregression
A statistical model that predicts future behavior based on past outcomes in time series data.
Model Components
Autoregressive Model
Models that use previous time points to predict future values, common in time series forecasting.
Techniques & Methods
Backpropagation
A method used in training artificial neural networks, adjusting weights based on error rates.
Techniques & Methods
Backward Chaining
A reasoning method that starts with the end goal and works backward to determine the solution path.
Techniques & Methods
Bandit Optimization
A strategy for balancing the exploration of new choices and the exploitation of known rewards.
Techniques & Methods
Beam Search
A search algorithm that efficiently finds the most likely sequences of outcomes in models.
Core Concepts
Bias
Preconceived notions or predispositions in AI models that can affect decision-making and fairness.
General
Big Data
Extremely large data sets analyzed computationally to reveal patterns, trends, and associations.
Model Components
Bounding Box
A rectangular border used in visual processing to define the location of objects within images.
Techniques & Methods
Chain-of-Thought
A prompting strategy that encourages AI to break down complex problems into manageable steps.
Applications
Chatbot
Computer programs designed to simulate conversation with human users, often over the internet.
Applications
ChatGPT
An AI developed by OpenAI that can generate human-like text responses based on provided prompts.
Core Concepts
Cognitive Computing
AI systems designed to mimic human brain functioning, aiming for natural, human-like interaction.
Techniques & Methods
Completion
The output produced by AI in response to a given input or prompt, completing the thought process.
Core Concepts
Computational Learning Theory
A branch of artificial intelligence focused on understanding the algorithms that drive learning.
Model Components
Contextual Embeddings
Representations of words or phrases that take into account the context in which they appear.
Model Components
Context Window
The range of past input that a model can consider when generating a response or prediction.
Techniques & Methods
Coreference Resolution
The task in NLP of determining which words refer to the same entity in a text.
Miscellaneous
Corpus
A large collection of texts used for compiling data and training machine learning models.
Applications
CRM with AI
Integrating artificial intelligence into customer relationship management to enhance interactions.
Techniques & Methods
Data Augmentation
A technique for increasing the amount of training data by adding slightly modified copies.
Techniques & Methods
Data Mining
The practice of examining large databases to generate new information and find hidden patterns.
Miscellaneous
Data Privacy
Measures and practices to ensure that personal or sensitive data is not misused or disclosed.
Miscellaneous
Data Science
An interdisciplinary field that uses scientific methods to extract knowledge from data.
Miscellaneous
Dataset
A collection of data specifically prepared and structured for training or testing AI models.
Techniques & Methods
Decoding Rules
Guidelines that dictate how a language model translates its internal representations to output.
Core Concepts
Deep Learning
A subset of machine learning involving neural networks with many layers to analyze data.
Techniques & Methods
Dependency Parsing
Analyzing the grammatical structure of a sentence to understand relationships between words.
Miscellaneous
Deployment
The process of making an AI model available for use in real-world applications or systems.
Applications
Dialogue System
AI technologies designed to converse with humans using natural language processing.
Model Components
Discriminator (in GAN)
The component of a generative adversarial network that distinguishes real data from fake.
Techniques & Methods
Distributed Training
A method where AI model training is spread across multiple computers or servers.
Model Components
Embeddings
Dense vector representations of words or phrases capturing semantic meaning for AI processing.
Model Components
Encoder
A component of a model that processes and transforms input data into a usable format.
Applications
Enterprise AI
The application of artificial intelligence technologies to improve business processes and outcomes.
Core Concepts
Entities
Specific, identifiable elements in text, such as names, places, dates, often extracted by AI.
Techniques & Methods
Entity Annotation
The process of labeling text with information about entities, enhancing data structure.
Techniques & Methods
Entity Extraction
Identifying and classifying named entities in text into predefined categories.
Miscellaneous
Ethical AI Maturity Model
A framework for assessing and guiding the ethical development and deployment of AI systems.
Techniques & Methods
Evaluation Metrics
Quantitative measures used to assess the performance and effectiveness of AI models.
Core Concepts
Explainable AI (XAI)
AI systems designed to provide insights into their decision-making processes for transparency.
Techniques & Methods
Extractive Summarization
Creating summaries by extracting key sentences or fragments directly from the source text.
Techniques & Methods
Feature Extraction
Identifying and isolating useful information from data to improve model training and performance.
Techniques & Methods
Few-Shot Learning
The ability of a model to learn and generalize from a very small number of examples.
Techniques & Methods
Fine-Grained Control
The capability to precisely adjust the output or behavior of an AI model based on specific criteria.
Techniques & Methods
Fine Tuning
The process of adjusting a pre-trained model to perform well on a specific task or dataset.
Techniques & Methods
Forward Chaining
A logical reasoning method that starts with known facts and applies rules to reach new conclusions.
Model Components
Foundational Model
A large, versatile AI model trained on a broad dataset, capable of performing multiple tasks.
Core Concepts
General AI
Artificial intelligence that exhibits cognitive functions across a wide range of tasks and domains.
Techniques & Methods
Generation
The process of producing new content, such as text or images, based on learned patterns and data.
Model Components
Generative Adversarial Network (GAN)
A framework for training generative models through a competitive process between networks.
Core Concepts
Generative AI
AI systems capable of generating new, original content or data that mimics real-world examples.
Model Components
Generative Model
A type of AI model that can generate new data instances similar to the training data.
Model Components
Generative Pre-trained Transformer (GPT)
A type of AI model specializing in generating coherent and contextually relevant text.
Model Components
Generator
In GANs, the component that creates data aiming to mimic real data as closely as possible.
Model Components
GPT-3 (Generative Pre-trained Transformer 3)
The third iteration of OpenAI's generative model known for its advanced text generation capabilities.
Techniques & Methods
Greedy Algorithms
Optimization algorithms that make the locally optimal choice at each step to find a global optimum.
Techniques & Methods
Hallucination
When AI generates information that is not grounded in reality, often due to training data issues.
Techniques & Methods
Heuristics
Problem-solving approaches that use practical methods or various shortcuts to produce solutions.
Core Concepts
Hyperparameter
A parameter whose value is set before the learning process begins, influencing the training phase.
Techniques & Methods
Inference
The phase where a trained model is used to make predictions or decisions based on new, unseen data.
Techniques & Methods
Information Extraction
The process of automatically extracting structured information from unstructured text data.
Applications
InstructGPT
A variant of GPT trained to follow instructions in prompts and generate more specific responses.
Core Concepts
Intent
The underlying purpose or goal that a user aims to achieve through a query or statement.
Techniques & Methods
Joint Probability
The probability of two events happening at the same time in a probabilistic model.
Miscellaneous
Knowledge Base
A centralized repository of information, used in AI to provide answers or contextual information.
Techniques & Methods
Knowledge Representation
The method by which AI systems model, store, and retrieve knowledge to solve complex tasks.
Miscellaneous
Label
A tag or annotation applied to a piece of data, indicating the correct output for supervised learning.
Model Components
Language Model
AI that understands, interprets, and generates human language based on statistical probabilities.
Model Components
Large Language Model (LLM)
An extensive model trained on vast amounts of text data, capable of understanding and generating text.
Core Concepts
Latent Variables
Hidden or unobservable variables inferred from observable data in machine learning models.
Techniques & Methods
Linguistic Annotation
The process of adding metadata regarding linguistic information to text, aiding in its analysis.
Techniques & Methods
Low Rank Adaption (LoRA)
A technique for fine-tuning large models in a memory and computationally efficient manner.
Core Concepts
Machine Intelligence
Broad term encompassing the capabilities of machines to learn from data and perform tasks.
Core Concepts
Machine Learning
The science of getting computers to act without being explicitly programmed, through learning.
Applications
Machine Translation
The use of software to translate text or speech from one language to another automatically.
Techniques & Methods
Markov Decision Process
A mathematical framework for modeling decision-making in situations with random outcomes.
Techniques & Methods
Masked Language Modeling
A training technique where some words in the input are hidden, and the model predicts them.
Model Components
Maximum Response Length
The largest amount of text or data that a model can generate in response to a single prompt.
Model Components
Model
A mathematical representation of a real-world process, trained to perform specific tasks using data.
Model Components
Model Architecture
The specific layout and structure of a machine learning model, including its layers and nodes.
Miscellaneous
Model Card
A comprehensive document providing essential information about a machine learning model's purpose and performance.
Applications
Moderation Tools
Tools designed to monitor and manage the behavior of AI systems, ensuring they adhere to guidelines.
Core Concepts
Multi-modal AI
AI systems that can process and interpret multiple types of data, such as text, images, and sound.
Techniques & Methods
Multitask Learning
Training an AI model on multiple tasks simultaneously, leveraging commonalities across tasks.
Applications
Multi-turn Dialogue
Conversations where participants exchange multiple sequences of messages, requiring context understanding.
Techniques & Methods
Named Entity Recognition (NER)
The process of identifying and classifying key information (entities) in text into predefined categories.
Core Concepts
Natural Language Generation (NLG)
Generating coherent and contextually relevant text from structured data using AI.
Core Concepts
Natural Language Processing (NLP)
The field of AI focused on the interaction between computers and humans through natural language.
Core Concepts
Natural Language Understanding (NLU)
The ability of AI to understand and interpret human language as it is spoken or written.
Model Components
Neural Network
A series of algorithms that mimic the operations of a human brain to recognize relationships in data.
Techniques & Methods
Offline Reinforcement Learning (RL)
Learning optimal actions from a fixed dataset without further interaction with the environment.
Techniques & Methods
One-Shot / Few-Shot
Learning techniques where the model learns from one or a few examples, respectively.
Techniques & Methods
One-Shot Learning
The ability of a model to learn information from a single example or a few examples.
Techniques & Methods
Online Learning
A model training approach where the model updates continuously as new data arrives.
Miscellaneous
OpenAI
An AI research lab focusing on developing and promoting friendly AI for the benefit of humanity.
Core Concepts
Overfitting
A modeling error in machine learning where a model learns the detail and noise in the training data too well.
Techniques & Methods
Overuse Penalty
A technique to discourage repetitive or overly similar responses in generative AI models.
Model Components
Parameter
A variable in a model that is learned from the training data and determines the model's output.
Techniques & Methods
Part-of-Speech Tagging (POS)
The process of marking up a word in a text as corresponding to a particular part of speech.
Core Concepts
Pattern Recognition
The automated recognition of patterns and regularities in data using machine learning algorithms.
Applications
Plugins / Tools
Additional software components that extend or enhance the functionality of an AI system or application.
Applications
Predictive Analytics
The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.
Model Components
Predictive Model
A model that makes predictions about unknown future events based on patterns found in historical data.
Techniques & Methods
Pre-training in AI
The initial training phase where a model learns from a large, general dataset before task-specific training.
Techniques & Methods
Prompt
A text input given to an AI model, designed to elicit a specific type of response or output.
Techniques & Methods
Prompt Engineering
The art of crafting prompts to effectively communicate with and elicit desired responses from AI models.
Techniques & Methods
Prompt Injection
A technique used to influence or manipulate the behavior of AI systems through specially crafted inputs.
Techniques & Methods
Proximal Policy Optimization (PPO)
A reinforcement learning algorithm that balances exploration and exploitation in policy learning.
Miscellaneous
Python
A high-level programming language known for its clear syntax and readability, widely used in AI development.
Applications
QA (Question Answering)
A system that automatically answers questions posed by humans in a natural language.
Techniques & Methods
Query
A request for information or action made to a database, search engine, or AI model.
Model Components
Recurrent Neural Network (RNN)
A type of neural network well-suited for processing sequences of data, like text or time series.
Techniques & Methods
Regularization
Techniques used to prevent overfitting by penalizing complex models during the training process.
Core Concepts
Reinforcement Learning
A type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve rewards.
Techniques & Methods
Reinforcement Learning from Human Feedback (RLHF)
Training approach where models are refined based on feedback from human evaluators.
Techniques & Methods
Response Quality
An evaluation of how well an AI system's responses meet the criteria of relevance, coherence, and accuracy.
Techniques & Methods
Retrieval Augmented Generation (RAG)
Combining retrieval of relevant information with generative models to produce informed responses.
Model Components
Retrieval Model
A model that retrieves relevant information from a large dataset to support decision-making or responses.
Model Components
Reward Models
Models that evaluate potential actions or responses in reinforcement learning to guide learning towards desired outcomes.
Miscellaneous
Sandbox Environment
A testing environment that isolates untested code changes and experimentation without affecting the production environment.
Techniques & Methods
Scaling Laws
Observations that as AI models increase in size, their performance improves according to predictable patterns.
Techniques & Methods
Self-Attention
A mechanism that allows models to weigh the importance of different parts of the input data relative to each other.
Techniques & Methods
Semantic Annotation
The process of adding semantic metadata to content, making it easier for AI to understand and process information.
Applications
Semantic Search
Search technology that understands the context and intent behind a user's query to generate more relevant results.
Techniques & Methods
Semantic Similarity
The measure of how much two pieces of text are related in terms of meaning, used in various NLP tasks.
Applications
Sentiment Analysis
The computational task of identifying and categorizing opinions expressed in text to determine the writer's attitude.
Techniques & Methods
Sequence Generation
The process where AI models produce a sequence of items, such as words in text generation, based on learned patterns.
Model Components
Sequence-to-Sequence (Seq2Seq) Models
Models that transform sequences from one domain to another, commonly used in translation and summarization.
Core Concepts
Strong AI
AI with the ability to understand, learn, and apply knowledge in ways indistinguishable from human intelligence.
Techniques & Methods
Supervised Fine-Tuning
The process of refining a model's performance on specific tasks by training it further with labeled data.
Miscellaneous
System Message
Predefined messages or prompts used in conversational AI systems to guide user interactions.
Core Concepts
Supervised Learning
A machine learning approach where models are trained on labeled data, learning to predict outcomes from inputs.
Techniques & Methods
System Prompt
Internal cues or instructions that guide the behavior of an AI model, influencing how it processes and responds to input.
Miscellaneous
Test Data
A dataset used to evaluate the performance of a machine learning model after training, separate from training data.
Techniques & Methods
Text Classification
The task of assigning predefined categories to text, used in applications like spam detection and sentiment analysis.
Core Concepts
Token
The smallest unit of processing in NLP, which could be a word, part of a word, or a character, depending on the model.
Techniques & Methods
Topic Modeling
A statistical model to discover abstract topics within a collection of documents, aiding in content organization and discovery.
Techniques & Methods
Training
The process of teaching a machine learning model to make predictions or decisions, typically by exposing it to a large dataset.
Miscellaneous
Training Data
The dataset used specifically for training a machine learning model, containing examples for learning patterns and behaviors.
Techniques & Methods
Transfer Learning
Leveraging knowledge gained while solving one problem to solve a different but related problem in machine learning.
Model Components
Transformer
A model architecture that uses self-attention mechanisms to improve performance on tasks involving sequential data.
Model Components
Transformer Decoder
The component of a transformer model responsible for generating output sequences based on encoded information.
Model Components
Transformers
A class of deep learning models that have revolutionized the field of natural language processing (NLP).
Core Concepts
Turing Test
A test of a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.
Core Concepts
Unsupervised Learning
A type of machine learning where models learn patterns from unlabeled data, without explicit instructions.
Techniques & Methods
Upstream Sampling
A technique in generative AI where multiple outputs are generated and the best one is selected based on certain criteria.
Applications
User Interface (UI)
The means by which a human interacts with a computer, application, or machine, often focusing on ease of use.
Techniques & Methods
Validation
The process of evaluating a model's performance with a separate portion of the data not used in training, to gauge its accuracy.
Miscellaneous
Validation Data
Data set aside from the training dataset to tune model parameters and prevent overfitting, ensuring the model's generalizability.
Core Concepts
Variance
In machine learning, the amount by which the model's predictions vary from the average prediction, reflecting sensitivity to training data.
Techniques & Methods
Variation
Different expressions or phrasings that convey the same intent or meaning, important in understanding natural language variability.
Techniques & Methods
Vector Representation
The encoding of words or phrases as numerical vectors, enabling mathematical operations and comparisons by AI models.
Miscellaneous
Vector Store
A specialized database for storing and retrieving vector representations of data, facilitating similarity searches.
Core Concepts
Weak AI
AI designed and trained for a specific task, lacking the general cognitive abilities of human intelligence.
Techniques & Methods
Word Embedding
A technique in NLP where words are represented as vectors in a high-dimensional space, capturing semantic similarity.
Miscellaneous
Yeoman's Work
Referring to diligent, hard work, often of a nature that is repetitive or requires a high level of effort and reliability.
Techniques & Methods
Zero-Shot Learning
The ability of a model to correctly perform tasks it has not explicitly been trained to do, demonstrating generalization.
Core Concepts
Zone of Proximal Development (ZPD)
A concept from educational psychology applied to AI, referring to tasks an AI can perform with guidance but not independently.