Wednesday, March 18, 2026
- Programming Custom GPTsv6Programming Custom GPTs are no-code, specialized versions of ChatGPT that users configure to write, debug, review, explain, and teach code, published in the Programming category of the GPT Store.
- Programming ChatGPT Pluginsv6Programming ChatGPT Plugins were a now-deprecated category of third-party extensions that let ChatGPT call external developer tools, code repositories, computational engines, and runtime environments during a…
- Programmingv11AI for programming refers to the use of artificial intelligence, and especially large language models, to write, edit, refactor, test, review, and explain source code.
- Productivity Custom GPTsv7Productivity Custom GPTs are the category of user-built Custom GPTs in OpenAI's GPT Store that speed up everyday work tasks such as building slide decks, drawing diagrams, summarising documents, searching…
- Productivity ChatGPT Pluginsv5Productivity ChatGPT Plugins were a now-deprecated category of third-party extensions inside ChatGPT that connected the chatbot to external work tools such as task managers, email, calendars, file storage…
- Productivityv10Productivity describes the relationship between output and the inputs used to produce it.
- Probabilistic Regression Modelv8A probabilistic regression model (also called distributional regression) is a regression model that outputs a full probability distribution over possible target values rather than a single point estimate.
- Prior beliefv5A prior belief, also called the prior distribution or simply the prior, is the probability distribution that expresses what is known or assumed about an unknown parameter before any data is observed.
- AI presentation toolsv6AI presentation tools are software products that use generative AI to create or assist in building slide decks.
- Preprocessingv6Preprocessing is the stage of a machine learning workflow that transforms raw data into a clean, consistent, numerical format that learning algorithms can use.
- Predictive rate parityv7Predictive rate parity (PRP), also called predictive parity, predictive value parity, or the sufficiency criterion, is a group fairness metric in machine learning that requires a classifier's positive…
- Predictive Parityv6Predictive parity is a group fairness metric in machine learning that holds when a classifier's positive predictive value (PPV), also called precision
- Prediction Biasv5Prediction bias is the difference between the average of a machine learning model's predictions and the average of the ground-truth labels in a dataset.
- Predictionv7Prediction in machine learning is the output a trained model produces when it is applied to new, previously unseen input.
- Precision-Recall Curvev9A precision-recall curve (PR curve) is a graph that plots precision on the y-axis against recall on the x-axis at every possible classification threshold for a binary classification model.
- Precisionv10Precision is a classification metric defined as the fraction of positive predictions that are correct: Precision = TP / (TP + FP), where TP is the number of true positives and FP is the number of false…
- Pre-trainingv8Pre-training is a stage of machine learning in which a model learns parameters from a source dataset or source objective before those parameters are reused or adapted for a target use.
- Pre-Trained Modelv8A pre-trained model is a machine learning model that has already been trained on a large, general-purpose dataset and can then be reused, either as a fixed feature extractor or by fine-tuning
- PowerPoint Presentation Maker by SlidesGPTv8PowerPoint Presentation Maker by SlidesGPT was the name of a third-party custom GPT that generated slide decks from a conversation in ChatGPT.
- Post-trainingv8Post-training is the stage of large language model (LLM) development that comes after pre-training and turns a raw, general-purpose base model into an aligned, instruction-following AI assistant.
- Post-processingv6In machine learning, post-processing is any operation applied to a model's raw outputs after the prediction step but before the results reach a downstream system or end user.
- Positive classv6In binary classification, the positive class is the class a model is testing for: the outcome it exists to detect, such as "spam," "fraud," or "tumor." The other label, covering everything else
- Poolingv9Pooling is a downsampling operation in neural networks that aggregates each local region of a feature map into a single summary value
- Politics ChatGPT Pluginsv5Politics ChatGPT plugins were a small set of third-party tools that connected ChatGPT to legislative data, voting records, lobbying disclosures, party donation records, and political document archives between…
- AI in politicsv4Artificial intelligence has become an established part of modern political work, used by campaigns, governments, journalists, and ordinary citizens.
- Policyv8In reinforcement learning (RL), a policy is the function that maps an agent's observed state to the action it takes, defining the agent's complete strategy of behavior.
- PoisonGPTv4PoisonGPT is a July 2023 demonstration by the French security startup Mithril Security in which researchers surgically modified an open-source large language model, uploaded the tampered weights to Hugging…
- Podcasts ChatGPT Pluginsv4Podcasts ChatGPT plugins were a category of third-party extensions for ChatGPT that connected the chatbot to podcast search engines, episode transcripts, audio narration tools, and listening recommendation…
- Podcastsv5AI podcasts are audio shows about artificial intelligence: long form research interviews, daily news roundups, and conversations with the founders, scientists, and investors building the technology.
- Pluginsv10Plugins are modular software extensions that connect large language models and other AI systems to external tools, services, data sources, and user interfaces, letting a model trained on a static corpus reach…
- Pipeliningv9Pipelining is a term used in two distinct senses within machine learning and artificial intelligence.
- Pipelinev5A pipeline in machine learning is a sequence of data processing steps chained together into an automated, end-to-end workflow, where each step takes the previous step's output as its input.
- Photography ChatGPT Pluginsv5Photography ChatGPT Plugins were a small group of third-party extensions inside ChatGPT that helped the chatbot work with photographs, photographic filters, and prompts used to produce photography-style…
- Photographyv5Artificial intelligence in photography covers a wide span of techniques, from the computational pipelines baked into modern smartphones to the generative editing tools now built into Photoshop and Lightroom…
- Perplexityv12Perplexity has two distinct meanings in artificial intelligence. In information theory and natural language processing, perplexity (PPL) is an intrinsic score for a probabilistic model: on a specified sequence
- Permutation variable importancesv6Permutation variable importance is a model-agnostic technique that measures how much a fitted machine learning model relies on a given feature by randomly shuffling that feature's values and recording how much…
- Performancev5Performance in machine learning is an overloaded word. It refers to two related but distinct ideas. The first is the quality of a model's predictions: how accurate or useful its outputs are for a given task.
- Perceptronv8A perceptron is the earliest trainable artificial neural network: a single-layer linear model that classifies inputs into two categories by computing a weighted sum and applying a step threshold.
- Partitioning strategyv5A partitioning strategy in distributed deep learning is the plan that decides how a model and its training data are split across multiple accelerators (typically GPUs or TPUs) so that the workload can be…
- Participation Biasv6Participation bias is a systematic error that arises when the individuals who choose to take part in a study, survey, or data collection effort differ in meaningful ways from those who do not, so the resulting…
- Partial derivativev8A partial derivative measures how a multivariable function changes when one of its inputs is varied while every other input is held fixed.
- Parameter updatev7A parameter update is the step in neural-network training where a model's trainable weights are adjusted using the gradient of the loss.
- Parameter Server (PS)v7The Parameter Server (PS) is a distributed system architecture for training large machine learning models in which one set of machines, the server nodes, holds the global model parameters as a partitioned…
- Parameterv7In machine learning and statistics, a parameter is an internal variable of a model whose value is learned from data during the training process.
- Papersv7The most influential AI research papers are the small set of publications that introduced the architectures, training methods, and benchmarks that every modern system is built on, from "Attention Is All You…
- Paper2Videov7Paper2Video (full title: Paper2Video: Automatic Video Generation from Scientific Papers) is a research project from Show Lab at the National University of Singapore that formalizes and evaluates automatic…
- Pandasv7Pandas is an open-source data analysis and manipulation library for the Python programming language, providing high-performance, flexible data structures designed for working with structured (tabular…
- PaLM-E: An Embodied Multimodal Language Modelv6PaLM-E (short for Pathways Language Model, Embodied) is an embodied multimodal large language model introduced by Google and TU Berlin in March 2023 that injects continuous sensor and image observations…
- Oversamplingv7Oversampling is a data preprocessing technique in machine learning that fixes class imbalance by increasing the number of minority class examples in the training set, either by duplicating existing samples…
- Overfittingv11Overfitting is a failure of generalization: a fitted model or a model-selection procedure performs better on the observations used to develop it than on new observations from the population of interest.
- Output Layerv12The output layer is the final layer of a neural network: it takes the features computed by the hidden layers and converts them into the model's prediction, with its size and activation function fixed by the…
- Outliersv10An outlier is a data point that differs so markedly from the rest of a dataset that it appears not to belong to the same population.
- Outlier Detectionv8Outlier detection is the process of identifying data points, observations, or patterns that deviate so markedly from the rest of a dataset that they are likely to have been generated by a different process.
- Out-of-bag evaluation (OOB evaluation)v8Out-of-bag (OOB) evaluation, sometimes called out-of-bag estimation or OOB error, is a model validation technique used with bagging-based ensemble methods such as random forests and bagged decision trees.
- Out-Group Homogeneity Biasv7Out-group homogeneity bias, also called the out-group homogeneity effect, is the cognitive bias in which people perceive members of an out-group as more similar to one another than members of their own…
- Organizationsv7Organizations that build, fund, govern, and study artificial intelligence span private companies, government agencies, university laboratories, non-profit research institutes, and international standards…
- Optimizerv9An optimizer in machine learning is an algorithm that iteratively adjusts a model's learnable parameters to minimize (or maximize) an objective function, commonly called a loss function
- Operation (op)v9In machine learning, an operation (often abbreviated as op) is a basic computational unit that manipulates data, typically tensors, during the training or execution of a model.
- OpenRouterv13OpenRouter is a unified API gateway and marketplace that routes a single, OpenAI-compatible request across more than 400 large language models (LLMs) and other AI models from over 60 providers, automatically…
- OpenMulev5OpenMule is an open-source proposal for a decentralized marketplace platform designed to facilitate transactions between end-users and service providers of automated agents.
- OpenAI Pulsev5ChatGPT Pulse is a proactive personalized briefing feature within ChatGPT, an artificial intelligence chatbot developed by OpenAI.
- OpenAIv31OpenAI is an American artificial intelligence research and deployment organization.
- Online learningv8Online learning is a machine learning paradigm in which a model receives data sequentially, one example or one mini-batch at a time, and updates its parameters immediately after each observation rather than…
- Online inferencev9Online inference (also called dynamic inference, real-time inference, or on-demand prediction) is the practice of running a trained machine learning model synchronously inside a request path
- One-vs.-allv7One-vs.-all (OvA), also known as one-vs.-rest (OvR) or one-against-all, is a strategy for turning a multi-class classification problem into several binary classification problems by training one binary…
- One-Shot Learningv7One-shot learning is a machine learning approach in which a model learns to recognize or classify new categories from only a single labeled example per class.
- One-Hot Encodingv9One-hot encoding is a data preprocessing technique that converts a categorical variable with…
- Ollamav12Ollama is a free, open-source runtime for downloading, running, and managing open-weight large language models (LLMs) locally on personal computers and servers.
- Offline inferencev8Offline inference (also called batch inference, static inference, or bulk scoring) is the practice of running a trained machine learning model over a known set of inputs ahead of time and storing the resulting…
- Offlinev6In machine learning, offline describes operations that happen ahead of time on a fixed dataset rather than continuously on live data.
- Oblique conditionv6An oblique condition is a decision tree split test that involves more than one feature, comparing a linear combination of several numerical features to a threshold rather than testing a single feature on its…
- Objective functionv7An objective function is the single scalar-valued quantity that an optimization algorithm tries to minimize or maximize during training.
- Objectivev9In machine learning, an objective (or objective function) is the scalar function that a learning algorithm optimizes during training: the single number whose gradients drive every parameter update.
- OCR Modelsv8OCR Models are artificial intelligence (AI) systems that convert images of typed, handwritten, or printed text into machine-readable digital text through Optical Character Recognition (OCR).
- Numerical Datav7Numerical data (also called quantitative data) is information expressed as numbers on a continuous or discrete scale that supports arithmetic operations such as addition, subtraction, multiplication, and…
- NumPyv9NumPy (short for Numerical Python) is the foundational open-source library for numerical and scientific computing in Python, providing an n-dimensional array object called ndarray along with a large collection…
- Novelty Detectionv7Novelty detection is a branch of machine learning concerned with identifying test data that differ in some meaningful way from the data available during training.
- Normalizationv8Normalization is the process of scaling numerical data to a standard range or distribution so that features and activations are comparable and downstream computation behaves predictably.
- Nonstationarityv7Nonstationarity refers to the condition in which the statistical properties of a data-generating process change over time.
- Nonlinearv8Nonlinear describes any function, model, or relationship that does not satisfy the property of linearity.
- Non-Response Biasv6Non-response bias is the error that arises when the people or units that do not respond to a survey, study, or data collection process differ systematically from those that do
- Non-profit Organizationsv5Non-profit organizations active in artificial intelligence are mission-driven entities that conduct research, set standards, advocate for policy, and provide education without returning surplus revenue to…
- Non-binary conditionv5In decision tree learning, a non-binary condition is a test at a node that has more than two possible outcomes, routing each example to one of three or more child nodes and creating a multi-way split.
- Noisev7Noise in machine learning is any unwanted, irrelevant, or random variation in data that obscures the true underlying patterns a model is trying to learn.
- Node (neural network)v9A node in a neural network is the basic computational element, an artificial neuron, that receives one or more inputs, multiplies each by a learned weight, sums them, adds a bias, and passes the result through…
- Node (decision tree)v5A node is the basic building block of a decision tree: it is a single point in the tree that is either a condition (an internal node that tests a feature and branches) or a leaf (a terminal node that holds a…
- Node (TensorFlow graph)v6A node in a TensorFlow graph is a single unit of computation, such as a matrix multiplication, an addition, an activation, or a variable read, that occupies one vertex of the framework's computational graph…
- News ChatGPT Pluginsv5News ChatGPT plugins were a category of third-party tools that connected ChatGPT to live news feeds, breaking headlines, regional outlets, and topic-specific aggregators between March 2023 and April 2024.
- AI in journalismv5AI in journalism refers to the use of artificial intelligence, and especially machine learning and generative AI, in the gathering, production, distribution and verification of news.
- Neuronv5A neuron (also called a node or unit) is the fundamental computational element of an artificial neural network: it takes one or more numeric inputs, multiplies each by a learned weight, sums them with a bias…
- Neural Networkv12A neural network (also called an artificial neural network or ANN) is a computational model, loosely inspired by the networks of biological neurons in animal brains
- Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers (VALL-E)v7VALL-E is a zero-shot learning text-to-speech (TTS) system from Microsoft Research that clones a target voice from a 3-second recording and synthesizes new speech in that voice without any per-speaker training.
- Negative classv7In binary classification, the negative class is the outcome the model treats as the default or "no" result: the label assigned to instances that do not possess the target characteristic the model is testing…
- Natural Language Understandingv9Natural language understanding (NLU) is the branch of artificial intelligence and computational linguistics that enables machines to read human language and extract its meaning, intent, and structure.
- Natural Language Processingv8Natural language processing (NLP) is the study and development of computational methods for analyzing, representing, retrieving, transforming, and generating human language.
- NaN Trapv10A NaN trap (short for "Not a Number" trap) is a failure mode in machine learning training where arithmetic operations produce the special IEEE 754 value NaN, which then propagates through all subsequent…
- NVIDIA Triton Inference Serverv11NVIDIA Triton Inference Server is open-source model deployment software that lets teams run trained models from any machine learning or deep learning framework on any processor (GPU, CPU, or other accelerator)…
- NVIDIA Picassov6NVIDIA Picasso is a cloud-based generative AI foundry from NVIDIA for building, training, and deploying visual generative models that produce images, video, and 3D content from text prompts.
- NVIDIA Omniversev15NVIDIA Omniverse is a scalable, multi-GPU real-time 3D graphics collaboration and simulation platform developed by NVIDIA for building and operating industrial digital twins and physical AI applications.
- NVIDIA DGX Sparkv8NVIDIA DGX Spark is a compact, deskside AI development system, marketed by NVIDIA as a "personal AI supercomputer," that puts a Grace Blackwell superchip on a desktop and delivers up to 1 petaFLOP (1,000 TOPS)…
