AI Glossary G-P

Garbage In, Garbage Out

A principle stating that flawed input data will inevitably lead to misleading results and nonsensical output, often referred to as "garbage."

Gated Recurrent Units (GRU)

A gating mechanism in recurrent neural networks designed to address the vanishing gradient problem, thereby enhancing the learning process in sequential data.

A regulation in EU law focused on data protection and privacy for individuals within the European Union, aiming to give citizens control over their personal data.

Generalisation

The capability of a learning system to handle new data that it hasn't encountered before. For a neural network to generalize effectively, the training data must represent the problem accurately, and an appropriate network architecture is necessary.

Generative 3D Modeling

A technique for representing three-dimensional shapes through a series of processing steps using generative algorithms, streamlining design and fabrication processes.

Generative Adversarial Network (GAN)

A machine learning framework that generates realistic data by pitting a generative model against a discriminative model. Comprising two neural networks, a generator, and a discriminator, GANs produce outputs that become increasingly realistic through competition.

Generative Design

A design approach powered by AI, where algorithms create multiple design options based on specified constraints and objectives.

Generative Model

A machine learning model designed to generate new data instances, applicable across various domains.

Generative Pre-Trained Transformer (GPT)

A large language model developed by OpenAI that employs transformer architecture to process language, creating coherent and contextually relevant text. GPT excels in tasks like text generation, question answering, and summarization.

Generator

An AI-based tool that generates new content from a given input. It learns from training data to create output that mimics learned patterns. ChatGPT by OpenAI is a popular example.

Genetic Algorithm

A heuristic optimization technique inspired by natural selection and genetic processes, used to solve complex optimization problems by emulating the biological evolution process.

Global Vectors for Word Representation (GLoVe)

A word embedding technique that captures semantic relationships between words by analyzing global co-occurrence statistics.

GLUE Benchmark

The GLUE Benchmark is a set of nine natural language processing tasks designed to evaluate model performance across diverse language understanding challenges, providing a unified framework for assessing NLP models.

Gradient

The rate of change of a function with respect to its input variables, used in optimization algorithms like gradient descent to iteratively adjust model parameters and minimize errors.

Gradient Descent

A fundamental optimization algorithm that uses gradients to minimize errors in machine learning models, iteratively adjusting parameters to improve model performance.

Graph-Based Model

A machine learning model that leverages graph structures to represent and analyze relationships between data points.

Greedy algorithms

An algorithm type that makes the optimal choice to achieve an immediate objective at each decision point, without considering long-term solutions.

Grid Search

A hyperparameter tuning technique that systematically explores a predefined hyperparameter space to optimize model performance.

Ground Truth

The absolute or objectively known state of a dataset, serving as a reference against which the quality of AI system outputs is measured for accuracy and reliability.

Grounding LLMs

Grounding LLMs involves using relevant information not available in training data when interacting with a large language model.

Guardrails

Constraints and guidelines implemented to ensure ethical, accurate, and responsible content generation using large language models.

Hallucination

The tendency of AI models to generate unrealistic or nonsensical data, often due to errors or limitations in training or processing capabilities.

Hetro-associative Network

The mapping between input and output data in a neural network, where desired output differs from presented input, such as outputting a cow image from a "mooing" sound.

Hierarchical RL

A reinforcement learning paradigm that breaks down complex processes into simpler subtasks to enhance decision-making across multiple abstraction levels.

Hindsight Experience Replay

A technique in reinforcement learning that replays failed experiences with alternate goals to improve learning efficiency.

Human in the loop

A design paradigm incorporating human oversight and interaction in AI system operations, combining AI with human intelligence for optimal outcomes.

Human-AI Collaboration

Cooperation between humans and AI to collectively accomplish tasks by leveraging their respective strengths.

Human-Centered Machine Learning

An approach to machine learning that emphasizes human needs, ethics, and user experience in model development.

Human-centric AI

An AI design approach prioritizing human well-being, autonomy, and values, aiming to amplify human abilities rather than undermine them.

Hybrid Search

A technique that merges dense vector-based search with sparse vector-based search, enabling simultaneous semantic and literal matching during searches.

Hyperautomation

An advanced automation approach integrating AI, machine learning, and robotic process automation to automate complex business processes.

Hyperparameter

A configurable setting in machine learning models that influences training behavior and performance.

Imitation Learning

A machine learning approach where models learn by mimicking human behavior, often used for training autonomous systems.

Impact assessment

An evaluation process designed to identify and mitigate potential ethical, legal, and societal implications of an AI system in a specific use case.

Inaccessible Data

Information that is not readily available or searchable due to a lack of structure or necessary tools.

Indexing

Metadata that provides information about other data, such as characteristics and properties of specific content.

Inference

The process where a trained AI model applies learned information to new data to generate predictions or decisions, transitioning from training to real-world application.

Information Retrieval

The study of searching for information within documents, metadata, and databases of various media like text, images, or sounds.

Input

Data provided to an AI system to explain a problem or request, essential throughout an AI model's lifecycle from training to deployment.

Instance

A single data point in a dataset, characterized by features and, in supervised learning, associated with a label.

Instance Segmentation

A computer vision task identifying and categorizing individual objects within an image, crucial for object detection.

Interpretability

The study of explaining machine learning model decisions and designing systems whose decisions can be easily understood.

Intrinsic Curiosity Module (ICM)

A reinforcement learning concept where agents are driven by curiosity signals to explore their environment.

Intrinsic Motivation

A reinforcement learning mechanism where models are driven to exhibit inherently rewarding behaviors like exploration.

Inverse Reinforcement Learning

A technique where agents learn a reward function by observing human behavior.

Inverted Index

Discover how inverted indexes enhance search engines and databases by enabling efficient and accurate data retrieval.

Knowledge Distillation

A technique in artificial intelligence where a larger, more complex model imparts its learned insights to a smaller, less complex model. This process enhances the efficiency of the smaller model by retaining the essential knowledge of the original, larger model, ensuring that it remains effective and accurate while being more resource-efficient.

Knowledge Engineering

Knowledge Engineering is the discipline focused on constructing systems that utilize explicit knowledge bases, including rules, facts, and structured data. These systems mimic human decision-making processes, allowing for intelligent problem-solving by leveraging organized knowledge frameworks like ontologies and databases, thereby facilitating advanced data-driven insights and solutions.

Knowledge Graph

A Knowledge Graph is a structured data model that organizes information through entities and their interconnections. This approach enhances semantic comprehension and allows for more sophisticated data retrieval, enabling systems to understand context and relationships within complex datasets, greatly aiding in tasks such as search and information extraction.

Labeled Data

Labeled data consists of datasets where each example is paired with a specific label or tag, providing meaningful context. This labeling is crucial for training supervised learning models, as it allows algorithms to learn from examples and make accurate predictions based on the labeled information provided during the learning phase.

Labeling

Labeling is the process of annotating data with class labels, which is essential for supervised machine learning. By assigning labels to data points, models can learn to distinguish between different categories, enabling them to make informed predictions and classifications based on the labeled training data they have been exposed to.

Language Model

A language model is a sophisticated machine learning system trained to comprehend and generate human language. It plays a critical role in natural language processing tasks, enabling applications such as translation, text generation, and conversational agents by learning linguistic patterns and contextual information from large datasets.

Large Language Model (LLM)

Large Language Models (LLMs) are a class of AI systems designed to process and generate human-like text. By learning intricate patterns and relationships within vast text corpora, LLMs can perform tasks such as language translation, text generation, and question answering, producing coherent and contextually relevant outputs from given inputs.

Latent Semantic Analysis (LSA)

Latent Semantic Analysis (LSA) is an NLP technique used to discover relationships between terms and documents within a text corpus. By reducing high-dimensional data into a lower-dimensional form through Singular Value Decomposition (SVD), LSA captures the latent semantic structure, facilitating improved document clustering, retrieval, and topic modeling.

Layer (Hidden Layer)

In an Artificial Neural Network, a layer refers to a collection of neurons that process input features or outputs from other neurons. Hidden layers, in particular, are intermediary layers that are not directly visible in the output but play a crucial role in complex transformations, allowing the network to learn intricate patterns in the data.

Learning Rate

The learning rate is a crucial hyperparameter in gradient-based optimization algorithms, dictating the step size during model training. It determines how quickly or slowly a model learns by influencing the magnitude of updates to the model's parameters, thereby impacting the speed and convergence of the training process.

Learning-to-Learn

Learning-to-Learn, also known as meta-learning, is an emerging area in machine learning that explores how algorithms can improve their ability to generalize by reflecting on and optimizing their learning processes. This approach aims to enhance adaptability and performance across various tasks by refining the learning strategies themselves.

Learning-to-Rank

Learning-to-Rank applies machine learning techniques to develop ranking models for information retrieval systems. By learning from data, these models can effectively order search results or recommendations, improving the relevance and quality of retrieved information based on user queries and preferences.

Lemmatization

Lemmatization is a text normalization technique in natural language processing that reduces words to their base or root form. By converting inflected words to their lemmas, this process enhances text analysis and processing efficiency, aiding in tasks like search, information retrieval, and language understanding.

Lifelong Learning

Lifelong Learning, also known as continual learning, refers to an approach where machine learning models continuously learn from new data and adapt to evolving tasks. This capability ensures that models remain relevant and effective over time, as they are able to update their knowledge and skills in response to changing environments.

Llama

Meta has developed Llama, a series of advanced AI models, aiming to make them accessible to developers for creating various applications. By providing these models freely, Meta envisions Llama as a foundational tool within the AI ecosystem, fostering innovation and collaboration across diverse AI-driven products and services.

LLM Bias

LLM bias refers to the presence of systemic preferences or prejudices within the outputs generated by a Large Language Model. These biases often reflect the underlying biases inherent in the training data, which can influence the fairness and accuracy of the model's predictions and decisions.

LLM Connector

An LLM connector is a component or module designed to enable seamless integration with a Large Language Model service. It facilitates communication and data exchange between applications and LLMs, enhancing the ability to leverage the model's capabilities in various tasks such as text generation and analysis.

LLM Hyperparameters

LLM Hyperparameters are predefined settings that dictate the behavior and performance of a Large Language Model during training. These include factors like learning rate, batch size, and optimizer choice, which significantly influence how the model learns and adapts to the data it's exposed to.

LLM Jailbreaking

LLM Jailbreaking involves techniques designed to bypass restrictions and constraints imposed on Large Language Models. These methods aim to extract hidden information or generate outputs beyond intended guidelines, often by reverse engineering prompts or exploiting vulnerabilities in the model's design.

LLM Quality Assurance

LLM Quality Assurance encompasses processes and tools aimed at ensuring the accuracy, reliability, and usefulness of content generated by Large Language Models. Through systematic testing and validation, QA processes uphold quality standards, monitoring the model's performance and alignment with desired outcomes.

LLMOps

LLMOps refers to the specialized DevOps and MLOps procedures tailored for managing Large Language Models. This includes tasks such as deployment, monitoring, and maintenance, ensuring that LLMs are efficiently operationalized and integrated within production environments for optimal performance and scalability.

Local Interpretable Model-Agnostic Explanations (LIME)

LIME is a technique in model interpretability that uses a local, interpretable model to provide human-understandable explanations for the predictions made by a black box model. By approximating the model's behavior in a localized manner, LIME enhances transparency and trust in AI systems.

Logit Function

The logit function is the inverse of the logistic (sigmoidal) function, commonly used in statistics and mathematics. It transforms probabilities into odds, serving as a crucial component in logistic regression models for binary classification tasks.

Long Short-Term Memory (LSTM)

LSTM is a type of recurrent neural network architecture designed to overcome the vanishing gradient problem, making it essential for processing sequential data. Its unique structure allows it to retain information over long time periods, making it effective in tasks like language modeling and time series prediction.

Loss Function

A loss function is a mathematical expression used to quantify the difference between predicted and actual values in machine learning models. It guides the optimization process by providing feedback on model performance, enabling adjustments to improve accuracy and effectiveness.

Machine Learning

Machine learning is a branch of AI focused on developing algorithms that enable computers to learn from data and make predictions. By training on large datasets, these algorithms recognize patterns and improve over time, applying learned knowledge to new situations without explicit programming.

Markov Decision Process (MDP)

A Markov Decision Process is a mathematical framework used to model decision-making scenarios with sequential actions and uncertain outcomes. It provides a structured approach for optimizing decisions by evaluating potential future states and rewards.

Markov Models

Markov models, or Markov chains, are probabilistic models that describe sequences of events where each event depends only on the state of the previous one. They are widely used in various fields to model stochastic processes and predict future states.

Massive Text Embedding Benchmark (MTEB)

The Massive Text Embedding Benchmark (MTEB) is a standardized evaluation framework for text embedding models. It assesses model performance across diverse tasks and languages, aiming to improve text embedding quality for various applications by providing consistent benchmarks and comparisons.

Memory Networks

Memory Networks are AI models that integrate reasoning capabilities with long-term memory, enhancing predictive performance. By reading from and writing to memory, these networks leverage stored knowledge to generate relevant responses, particularly useful in question-answering systems.

Meta Reinforcement Learning

Meta Reinforcement Learning is a higher-level paradigm where agents learn to adapt and generalize across multiple tasks. By acquiring knowledge from previous experiences, these agents improve their ability to tackle new challenges, enhancing learning efficiency and versatility.

Meta-Learning

Meta-learning, or learning to learn, involves machine learning models acquiring knowledge that aids rapid adaptation to new tasks. By leveraging insights from previous learning experiences, these models enhance their ability to generalize and perform well in diverse environments.

MLOps

Machine Learning Operations (MLOps) is a set of practices that combine machine learning, DevOps, and data engineering to streamline the ML lifecycle. MLOps focuses on deploying, monitoring, and maintaining ML models in production, ensuring reliability and scalability.

MMLU Benchmark

The MMLU (Massive Multitask Language Understanding) Benchmark evaluates language models' multitask capabilities across various subjects. Covering 57 diverse tasks, it assesses models' ability to generalize knowledge and reason across domains, providing a comprehensive test of advanced language models.

Modality

In AI, modality refers to the type of data generated or processed by a model, such as text, images, or audio. Different modalities require specific processing techniques, and multimodal models can handle and integrate multiple data types simultaneously.

Model

A model in AI is a computational framework that processes data, learns patterns, and performs tasks like text generation or decision-making. Trained on large datasets, models recognize patterns and make predictions, with their architecture and parameters defining their capabilities.

Model Compression

Model compression involves optimizing deep learning models for deployment on resource-constrained devices without sacrificing performance. Techniques like pruning and quantization reduce model size and complexity, enabling efficient operation on edge devices.

Model Deployment

Model deployment is the process of operationalizing machine learning models, making them accessible for real-world applications. It involves integrating models into production environments, ensuring they can effectively interact with users and systems.

Model Quantization

Model quantization is a compression technique that reduces the memory and computational requirements of a model by representing its parameters with fewer bits. This enables more efficient deployment on devices with limited resources.

Model Robustness

Model robustness refers to a machine learning model's ability to perform consistently across various input data and scenarios. Robust models can handle noise, variability, and adversarial conditions, maintaining accuracy and reliability.

Model Validation

Model validation involves assessing a machine learning model's performance and generalization capabilities on unseen data. This process ensures that the model effectively applies learned knowledge to new situations, maintaining accuracy and reliability.

Model-Based RL

Model-Based Reinforcement Learning is an approach where agents learn a model of the environment to make informed decisions and plan actions. By simulating potential outcomes, agents optimize their strategies for better performance.

Model-Free RL (Reinforcement Learning)

Model-Free Reinforcement Learning is a method where agents learn optimal actions through trial-and-error, without relying on explicit models of the environment. This approach allows agents to explore and discover effective strategies based on direct experience.

Momentum

Momentum is a technique used in gradient-based optimization algorithms to accelerate convergence by incorporating past gradients. By smoothing updates, it helps escape local minima and improves the efficiency of the optimization process.

Monte Carlo

Monte Carlo methods are approximate techniques that use repeated random sampling to generate simulated data. They are widely used in various fields for modeling and solving complex problems involving uncertainty and probabilistic processes.

Monte Carlo Tree Search (MCTS)

Monte Carlo Tree Search is a decision-making algorithm used in game-playing AI. It efficiently navigates vast search spaces by combining random sampling with strategic exploration, excelling in complex games where traditional methods struggle.

Motion Prediction

Motion Prediction involves forecasting the future movements or actions of objects, often used in autonomous systems. By analyzing past trajectories and contextual information, models predict future paths, enabling safer and more efficient navigation.

Multi-Agent RL

Multi-Agent Reinforcement Learning is a subfield where multiple agents interact and learn in a shared environment. These agents collaborate or compete, adapting their strategies to optimize individual or collective objectives.

Multi-Armed Bandit

The Multi-Armed Bandit problem involves choosing between multiple actions, or "arms," to maximize cumulative rewards. It models decision-making scenarios with trade-offs between exploration and exploitation, widely used in optimization and recommendation systems.

Multi-Dimensional Scaling (MDS)

Multi-Dimensional Scaling is a technique for visualizing high-dimensional data by projecting it onto a lower-dimensional space. It preserves the similarity structure of the data, aiding in the exploration and interpretation of complex datasets.

Multi-Head Attention

Multi-Head Attention is an attention mechanism that allows a model to focus on different aspects of input data simultaneously. By using multiple attention heads, models capture diverse relationships and dependencies, enhancing performance in tasks like language translation.

Multi-Instance Learning

Multi-Instance Learning is a machine learning paradigm where each example comprises multiple instances. Models learn from groups of data, enabling them to make predictions based on aggregated information, useful in scenarios like drug discovery and image classification.

Multi-Label Classification

Multi-Label Classification involves assigning multiple class labels to each data instance. This task requires models to learn complex dependencies between labels, enabling them to handle scenarios where data points belong to multiple categories simultaneously.

Multi-Modal Learning is a subfield of machine learning that integrates information from multiple data types or modalities, such as text, images, and audio. By leveraging complementary signals, models improve their performance and understanding of complex tasks.

Multi-Objective RL

Multi-Objective Reinforcement Learning involves optimizing multiple objectives simultaneously, often leading to trade-offs. Agents balance competing goals, such as speed and accuracy, to achieve optimal solutions in complex decision-making scenarios.

Multi-Task Learning

Multi-Task Learning is an approach where a single model is trained to perform multiple related tasks simultaneously. By sharing knowledge across tasks, models improve their generalization and efficiency, often outperforming task-specific models.

Multimodal AI

Multimodal AI refers to machine learning models capable of processing and integrating information from multiple modalities, such as text, images, and audio. These models leverage diverse data types to enhance understanding and performance in complex tasks.

Multimodal Learning

Multimodal Learning involves training models to leverage data from multiple modalities, like text, image, and audio, to improve performance. By integrating diverse information sources, these models gain a richer understanding and can tackle complex tasks more effectively.

Multimodal models

Multimodal models in machine learning can process multiple types of input or output data, or "modalities," simultaneously. For example, they can analyze both images and text captions, making them versatile for tasks like image captioning and speech recognition.

Multimodal RAG

Multimodal Retrieval-Augmented Generation (RAG) is a specific type of RAG pipeline capable of retrieving and generating content across multiple modalities. It enhances information retrieval and synthesis by integrating diverse data types, improving the quality of generated outputs.

Multitask Learning

Multitask Learning is an approach where a single machine learning model is trained to perform multiple related tasks at once. By leveraging shared information across tasks, these models improve their efficiency and generalization, often achieving better results than task-specific models.

Multivector RAG

Multivector RAG is a variant of Retrieval-Augmented Generation where a document is represented by multiple vectors. This approach enhances the search process by embedding additional information, such as summaries or hypothetical questions, to improve retrieval accuracy and relevance.

N-Gram

A contiguous sequence of items, typically words, from a block of text used in language modeling and text analysis. These sequences help in understanding patterns and predicting the next word in a sequence, enhancing the capabilities of language processing models.

Naive Bayes

A set of probabilistic classifiers that apply Bayes' theorem with the assumption of feature independence. Despite its simplicity, it is effective for large datasets and is widely used in text classification and spam filtering.

Named Entity Recognition (NER)

A task in natural language processing that identifies and categorizes named entities in text. This process involves recognizing entities like individuals, organizations, and locations, enhancing text understanding and information extraction.

Narrow AI

Refers to AI models designed to perform specific tasks without generalizing their experience to other tasks. Current AI systems excel in narrow AI, such as algorithms detecting medical conditions from X-rays with high accuracy.

NATS

Refers to the GLUE Benchmark, a collection of nine NLP tasks evaluating model performance across various language challenges. It standardizes evaluation metrics, serving as a benchmark for general-purpose language model advancement.

Natural Language Generation (NLG)

The process of creating human-like text from structured data or other information sources. NLG systems are employed in applications like chatbots and content creation, facilitating human-computer interaction.

Natural Language Processing

A subfield of AI enabling computers to understand and process human language. It involves transforming text or speech into data, allowing machines to translate languages, interpret meaning, and determine sentiment.

Neural Network

An AI model inspired by the human brain, consisting of interconnected nodes that process data. These networks excel in deep learning, facilitating complex data analysis and pattern recognition.

Neural Radiance Fields (NeRF)

A technique for producing detailed 3D reconstructions from 2D images, enhancing visualization and modeling in computer graphics and virtual reality applications.

Neural Rendering

A computer graphics method that employs neural networks to create realistic images. By simulating light transport, it enhances the realism of generated scenes based on existing data.

Neural Style Transfer

A technique that blends the content of one image with the artistic style of another, producing visually unique artworks. This method is used in image editing and artistic applications.

Neural Turing Machine (NTM)

A neural network model with a differentiable memory interface, allowing it to learn complex tasks through gradient descent. It combines neural networks with memory mechanisms for advanced problem-solving.

Neuromorphic Computing

Computer architecture mimicking the human brain's structure, using electronic circuits to perform tasks like pattern recognition and cognitive processes. It aims to enhance computational efficiency and adaptability.

Neuron

A fundamental unit in artificial neural networks, processing multiple inputs to generate a single output. Neurons are the building blocks of neural networks, facilitating data analysis and learning.

Neuroscience

The scientific study of the nervous system, including the brain, spinal cord, and nerves. Neuroscientists explore development, function, and behavior, aiming to understand the biological and physiological aspects of the nervous system.

Neurosymbolic AI

A model combining statistical AI and symbolic reasoning, aiming for general AI capabilities. By integrating data-driven and logic-based approaches, it enhances problem-solving and decision-making.

NLP or Natural Language Programming

A field intersecting computer science, AI, and linguistics, focused on enabling computers to understand and generate human language. It involves developing systems that analyze and respond to text and voice data meaningfully.

Object Detection

A computer vision technique identifying objects within images or videos, crucial for applications like autonomous driving and surveillance. It uses instance segmentation to recognize and locate objects.

Observability

Understanding system behavior through data it produces, enabling insights into state and performance. Observability helps detect issues, ensuring systems operate as expected through logs, metrics, and traces.

Off-Policy Learning

A reinforcement learning strategy that updates actions based on past experiences, allowing models to learn from previously collected data. It enhances learning efficiency by leveraging existing knowledge.

On-Policy Learning

A reinforcement learning approach that refines actions by evaluating the same policy during environment interactions. It focuses on improving decision-making through continuous policy assessment.

One-Hot Encoding

A method of transforming categorical data into binary vectors, commonly used in machine learning. This technique facilitates the representation of categorical variables for model training.

One-Shot Learning

Training models to recognize new objects or concepts with minimal examples, valuable when data is limited. It enables models to learn efficiently from a few instances.

Ontology

A structured representation of knowledge defining concepts and their relationships, used to organize information. Ontologies transform unstructured data into structured formats, aiding data analysis and understanding.

Optical Flow

A computer vision technique estimating object movement and velocity in images or videos, aiding in tracking and motion analysis. It is used in applications like video surveillance and autonomous navigation.

Optimization

The process of fine-tuning AI models to maximize efficiency. Optimization techniques seek the best solutions within constraints, improving model performance and resource utilization.

Option-Critic Architecture

A reinforcement learning framework incorporating options to enhance decision-making flexibility in agents. It enables more adaptable and efficient learning strategies.

Out-of-Distribution Detection

Identifying data instances that deviate from a model's training distribution, defending against adversarial attacks. It enhances model robustness and reliability in unpredictable scenarios.

Output

The response generated by an AI model, whether text, image, or other forms. Outputs are the results of automation workflows, representing the completion of tasks or decision-making processes.

Overfitting

Occurs when a machine learning model performs well on training data but poorly on new data due to excessive fitting. Overfitting leads to poor generalization, affecting model accuracy on unseen datasets.

Oversight

The process of monitoring and supervising AI systems to mitigate risks and ensure compliance. Effective oversight involves certification, assessments, and regulatory enforcement for responsible AI governance.

Panoptic Segmentation

An advanced computer vision task combining instance and semantic segmentation, allowing models to understand entire scenes. It enhances scene comprehension and object recognition.

Parameters

Internal variables learned by an AI model during training, determining its behavior and processing capabilities. Parameters include weights and biases, crucial for model performance and accuracy.

Parsing

The process of analyzing text data to extract meaningful information and understand its structure. Parsing transforms unstructured data into a structured format for further analysis.

Part of Speech Tagging

Labeling words in text with their grammatical roles, such as nouns or verbs. This task is vital for language understanding and processing in natural language tasks.

Partially Observable Markov Decision Process (POMDP)

A generalization of the Markov Decision Process, modeling decision-making in uncertain environments where agents lack complete information.

Pattern Recognition

A machine learning area focusing on recognizing patterns in data, whether supervised or unsupervised. It is foundational for tasks like image and speech recognition.

Perplexity

A measure of how well a language model predicts text, indicating its contextual understanding. Lower perplexity signifies better prediction accuracy and model reliability.

Personally Identifiable Information

Any information that can be used, alone or with other data, to identify an individual. Protecting PII is crucial for privacy and data security.

Physical Symbol System Hypothesis

A philosophical approach suggesting that manipulating symbols and structures can lead to general AI. It involves using symbols to represent physical objects and processes.

Policy Gradient

A set of reinforcement learning techniques optimizing policies through gradient descent. These methods improve policy performance by refining action selection.

Pooling (Max Pooling)

A process reducing the size of matrices generated by convolutional layers in neural networks, aiding in feature extraction and dimensionality reduction.

Pose Estimation

A computer vision task estimating the positions and orientations of objects or human body parts in images or videos, used in applications like motion capture and augmented reality.

Post processing

Steps taken after running a machine learning model to adjust its output, ensuring fairness and meeting business requirements. It involves refining model predictions for improved outcomes.

Pre-training LLM

The initial training phase of a Large Language Model using extensive datasets to learn language patterns. This process uses next-token prediction to build foundational language understanding.

Precision

A metric in classification models, defined as the ratio of true positive results to all positive results returned. It evaluates the accuracy of model predictions.

"Precision, Recall, and F1 Score"

Metrics assessing classification model performance based on true positives, false positives, and false negatives. These metrics provide insights into model accuracy and reliability.

Prediction

The act of an AI model forecasting the likelihood of certain outcomes, typically as probabilities. It is used in applications like social media ranking and recommendation systems.

Predictive Analytics

A branch of analytics using historical data, algorithms, and machine learning to forecast future outcomes. It informs decision-making by identifying patterns and trends.

Preprocessing

Steps to prepare data for machine learning model training, including cleaning, normalizing, and encoding. Preprocessing enhances data quality and model performance.

Principal Component Analysis (PCA)

A technique for dimensionality reduction and data visualization, removing redundant information. PCA simplifies complex datasets, aiding in analysis and interpretation.

Prior

The probability distribution representing preexisting beliefs about a quantity before considering new evidence. Priors are used in probabilistic modeling to incorporate prior knowledge.

Prioritized Experience Replay

A reinforcement learning technique that replays experiences with higher learning importance, improving training efficiency and model performance.

Probabilistic Modeling

An approach using probability distributions to model uncertainty in data and predictions. It aids in reasoning and decision-making under uncertainty.

Procedural Generation

An algorithmic content creation method, often used in video games to generate dynamic gameplay elements during play. It creates characters, environments, and scenarios.

ProGAN

A generative adversarial network using progressive growth to create high-resolution images, enhancing image quality and realism.

Progressive Growth of GANs

A training technique gradually increasing GAN image resolution, improving image diversity and quality through incremental learning.

Prometheus Metrics

An open-source tool tracking software system performance and health through metrics. It detects problems early, ensuring reliable and efficient services.

Prompt

An interaction where a human provides information to guide an AI model's output. Prompts come in various forms, such as text or images, directing AI responses.

Prompt as Code (Prompt Engineering)

Designing input prompts to optimize AI model performance in NLP. This practice involves crafting prompts to leverage AI understanding for desired outputs.

Prompt Chaining

A technique in NLP using structured prompts to break down complex tasks into smaller subtasks, enhancing coherence and accuracy in language model outputs.

Prompt Engineering

Crafting input prompts to guide AI models in generating specific outputs. It involves structuring prompts to optimize relevance and accuracy of AI-generated content.

Prompt Templates

Pre-defined recipes for generating prompts for language models. AIPRM manages these templates, augmenting them with user-defined variables and custom content.

Protocol

A set of rules governing data transmission between electronic devices, ensuring accurate and efficient communication.

Proximal Policy Optimization (PPO)

A reinforcement learning method optimizing policies through gradual updates, ensuring stable and efficient learning outcomes.

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