A/B Testing
A/B testing is an experimental method that compares two versions of a model, prompt, or interface to determine which performs better. In AI, A/B testing helps evaluate model outputs, UI changes, and prompt strategies by measuring user engagement or accuracy.
Data ScienceAbstractive Summarization
Abstractive summarization generates new text that captures the key points of a longer document, rather than simply extracting existing sentences. It requires deep language understanding and generation capabilities.
Natural Language ProcessingAccuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Machine LearningActivation Function
An activation function is a mathematical function applied to a neuron's output to introduce non-linearity into a neural network. Common activation functions include ReLU, sigmoid, and tanh, each with different properties for gradient flow.
Deep LearningActive Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Machine LearningAdam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm that combines the benefits of AdaGrad and RMSProp. It adapts learning rates for each parameter using estimates of first and second moments of gradients.
Deep LearningAdapter Layers
Adapter layers are small trainable modules inserted into a pre-trained model to enable parameter-efficient fine-tuning. They allow task adaptation while keeping the original model weights frozen.
Deep LearningAdversarial Attack
An adversarial attack is a technique that creates deliberately crafted inputs designed to fool a machine learning model into making incorrect predictions. These attacks reveal vulnerabilities in AI systems and are critical to AI safety research.
AI Ethics & SafetyAdversarial Training
Adversarial training is a defense strategy that improves model robustness by including adversarial examples in the training data. The model learns to correctly classify both normal and adversarially perturbed inputs.
AI Ethics & SafetyAgent
An AI agent is an autonomous system that perceives its environment, makes decisions, and takes actions to achieve specific goals. Modern AI agents can use tools, browse the web, write code, and chain multiple reasoning steps together.
AI ApplicationsAgentic AI
Agentic AI refers to AI systems that can autonomously plan, reason, and execute multi-step tasks with minimal human oversight. These systems use tool calling, memory, and iterative problem-solving to accomplish complex goals.
AI ApplicationsAGI
Artificial General Intelligence (AGI) refers to a hypothetical AI system with human-level cognitive abilities across all intellectual tasks. Unlike narrow AI, AGI would be able to learn, reason, and solve problems in any domain without task-specific training.
FundamentalsAI Alignment
AI alignment is the research field focused on ensuring that AI systems pursue goals and behaviors consistent with human values and intentions. Alignment is considered one of the most important challenges in AI safety.
AI Ethics & SafetyAI Chip
An AI chip is a specialized processor designed specifically for artificial intelligence workloads like neural network training and inference. Examples include NVIDIA's GPUs, Google's TPUs, and custom ASICs.
AI InfrastructureAI Ethics
AI ethics is the branch of ethics that examines the moral implications of developing and deploying artificial intelligence systems. It addresses fairness, transparency, privacy, accountability, and the societal impact of AI technology.
AI Ethics & SafetyAI Safety
AI safety is the interdisciplinary field focused on ensuring AI systems operate reliably, beneficially, and without causing unintended harm. It encompasses alignment, robustness, interpretability, and governance research.
AI Ethics & SafetyAI Visibility
AI visibility refers to how prominently a brand, product, or entity appears in AI-generated responses from systems like ChatGPT, Perplexity, and Gemini. As AI-powered search grows, visibility in AI recommendations becomes a critical marketing metric.
AI ApplicationsAI Winter
An AI winter is a period of reduced funding, interest, and research progress in artificial intelligence. Historical AI winters occurred in the 1970s and late 1980s, often following inflated expectations and undelivered promises.
FundamentalsAlgorithm
An algorithm is a step-by-step procedure or set of rules for solving a computational problem. In AI, algorithms define how models learn from data, make predictions, and optimize their performance.
FundamentalsAnnotation
Annotation is the process of adding labels or metadata to raw data to create training datasets for supervised learning. Data annotation can involve labeling images, tagging text, or marking audio segments.
Data ScienceAnomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
Machine LearningAPI
An API (Application Programming Interface) is a set of protocols and tools that allows different software systems to communicate. AI APIs enable developers to integrate machine learning capabilities like text generation, image recognition, and speech processing into applications.
AI InfrastructureArtificial General Intelligence
Artificial General Intelligence is a theoretical form of AI that would match or exceed human cognitive abilities across all domains. AGI remains an aspirational goal rather than a current reality in AI research.
FundamentalsArtificial Intelligence
Artificial Intelligence is the broad field of computer science focused on creating systems that can perform tasks requiring human-like intelligence. AI encompasses machine learning, natural language processing, computer vision, and robotics.
FundamentalsArtificial Narrow Intelligence
Artificial Narrow Intelligence (ANI) refers to AI systems designed to perform specific tasks, such as image recognition or language translation. All current AI systems, including large language models, are forms of narrow intelligence.
FundamentalsArtificial Superintelligence
Artificial Superintelligence (ASI) is a hypothetical AI that would surpass human intelligence in every cognitive dimension. The prospect of ASI raises profound questions about control, alignment, and the future of humanity.
FundamentalsAttention Mechanism
An attention mechanism allows neural networks to focus on the most relevant parts of the input when producing each element of the output. Attention is the foundational innovation behind the Transformer architecture and modern large language models.
Deep LearningAutoencoder
An autoencoder is a neural network trained to compress input data into a compact representation and then reconstruct it. Autoencoders are used for dimensionality reduction, denoising, and learning latent representations.
Deep LearningAutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Machine LearningAutonomous Systems
Autonomous systems are AI-powered machines that can operate and make decisions independently without continuous human supervision. Examples include self-driving cars, delivery drones, and robotic warehouse systems.
Robotics & AutomationBackpropagation
Backpropagation is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight. It propagates error signals backward through the network to update weights and minimize prediction errors.
Deep LearningBagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Machine LearningBatch Normalization
Batch normalization is a technique that normalizes layer inputs across mini-batches during training to stabilize and accelerate neural network training. It reduces internal covariate shift and allows higher learning rates.
Deep LearningBatch Size
Batch size is the number of training examples used in one iteration of gradient descent. Larger batches provide more stable gradient estimates but require more memory, while smaller batches add beneficial noise.
Deep LearningBayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Machine LearningBeam Search
Beam search is a decoding algorithm that explores multiple candidate sequences simultaneously, keeping only the top-k most promising at each step. It balances between greedy decoding and exhaustive search in text generation.
Natural Language ProcessingBenchmark
A benchmark is a standardized test or dataset used to evaluate and compare the performance of different AI models. Common benchmarks include MMLU, HumanEval, and ImageNet.
Data ScienceBERT
BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that reads text in both directions simultaneously. BERT revolutionized NLP by enabling deep bidirectional pre-training for language understanding tasks.
Natural Language ProcessingBias in AI
Bias in AI refers to systematic errors or unfair outcomes in machine learning models that arise from biased training data, flawed assumptions, or problematic design choices. Addressing AI bias is essential for building fair and equitable systems.
AI Ethics & SafetyBias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Machine LearningBigram
A bigram is a contiguous sequence of two items (typically words or characters) from a given text. Bigram models estimate the probability of a word based on the immediately preceding word.
Natural Language ProcessingBinary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.
Machine LearningBoltzmann Machine
A Boltzmann machine is a stochastic recurrent neural network that can learn a probability distribution over its inputs. Restricted Boltzmann Machines (RBMs) were influential in the deep learning revolution as building blocks for deep belief networks.
Deep LearningBoosting
Boosting is an ensemble method that trains models sequentially, with each new model focusing on correcting the errors of previous ones. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.
Machine LearningBounding Box
A bounding box is a rectangular border drawn around an object in an image to indicate its location and extent. Bounding boxes are the primary output format for object detection models.
Computer VisionByte Pair Encoding
Byte Pair Encoding (BPE) is a subword tokenization algorithm that iteratively merges the most frequent pairs of characters or character sequences. BPE is widely used in modern language models to handle rare words and multilingual text.
Natural Language ProcessingCatastrophic Forgetting
Catastrophic forgetting is the tendency of neural networks to abruptly lose previously learned knowledge when trained on new tasks. Continual learning research aims to overcome this limitation.
Deep LearningCausal Inference
Causal inference is the process of determining cause-and-effect relationships from data, going beyond mere correlation. AI systems increasingly use causal reasoning to make more robust and interpretable decisions.
Data ScienceChain of Thought
Chain of thought is a prompting technique that encourages large language models to break down complex reasoning into intermediate steps. This approach significantly improves performance on math, logic, and multi-step reasoning tasks.
Generative AIChatbot
A chatbot is a software application that simulates human conversation through text or voice interactions. Modern AI chatbots use large language models to generate contextually relevant, natural-sounding responses.
AI ApplicationsChatGPT
ChatGPT is an AI chatbot developed by OpenAI that uses large language models to generate human-like conversational responses. It became one of the fastest-growing consumer applications in history after its launch in November 2022.
Generative AIClassification
Classification is a supervised learning task where the model predicts which category or class an input belongs to. Examples include email spam detection, image recognition, and sentiment analysis.
Machine LearningClaude
Claude is an AI assistant developed by Anthropic, designed to be helpful, harmless, and honest. It is built using Constitutional AI techniques and competes with models like GPT-4 and Gemini.
Generative AIClustering
Clustering is an unsupervised learning technique that groups similar data points together without predefined labels. Common clustering algorithms include K-Means, DBSCAN, and hierarchical clustering.
Machine LearningCNN
A CNN (Convolutional Neural Network) is a deep learning architecture designed to process grid-structured data like images. CNNs use convolutional filters to automatically learn spatial hierarchies of features.
Deep LearningComputer Vision
Computer vision is a field of AI that enables machines to interpret and understand visual information from images and videos. Applications include facial recognition, autonomous driving, medical imaging, and augmented reality.
Computer VisionConfusion Matrix
A confusion matrix is a table that summarizes a classification model's performance by showing true positives, true negatives, false positives, and false negatives. It provides a detailed breakdown beyond simple accuracy.
Machine LearningConstitutional AI
Constitutional AI is an approach developed by Anthropic that trains AI systems to be helpful, harmless, and honest using a set of written principles. The model critiques and revises its own outputs based on these constitutional rules.
AI Ethics & SafetyContinual Learning
Continual learning is the ability of an AI system to learn new tasks or knowledge over time without forgetting previously learned information. It aims to create more human-like learning that accumulates knowledge incrementally.
Machine LearningContrastive Learning
Contrastive learning is a self-supervised technique that trains models to distinguish between similar and dissimilar data pairs. It learns useful representations by pulling similar examples closer and pushing dissimilar ones apart in embedding space.
Deep LearningConvolutional Neural Network
A convolutional neural network is a specialized deep learning architecture that applies learned filters across input data to detect patterns. CNNs excel at image recognition, object detection, and visual understanding tasks.
Deep LearningCorpus
A corpus is a large, structured collection of text documents used for training and evaluating natural language processing models. The quality and diversity of a training corpus significantly impacts model performance.
Natural Language ProcessingCross-Entropy
Cross-entropy is a loss function that measures the difference between two probability distributions — typically the model's predictions and the true labels. It is the standard loss function for classification tasks in deep learning.
Machine LearningCross-Validation
Cross-validation is a model evaluation technique that splits data into multiple folds, training and testing on different subsets in rotation. K-fold cross-validation provides more reliable performance estimates than a single train-test split.
Data ScienceCUDA
CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform that allows developers to use GPUs for general-purpose processing. CUDA is the foundation of GPU-accelerated deep learning training.
AI InfrastructureCurriculum Learning
Curriculum learning is a training strategy that presents examples to a model in a meaningful order, starting with easier examples and progressively introducing harder ones. This mimics human learning and can improve convergence.
Machine LearningData Augmentation
Data augmentation is a technique that artificially increases training dataset size by creating modified versions of existing data. In computer vision, this includes rotations, flips, and color changes; in NLP, it includes paraphrasing and synonym replacement.
Data ScienceData Drift
Data drift occurs when the statistical properties of production data change over time compared to the training data. Drift can degrade model performance and requires monitoring and retraining strategies to address.
Data ScienceData Labeling
Data labeling is the process of assigning meaningful tags or annotations to raw data to create supervised learning datasets. High-quality labeled data is essential for training accurate machine learning models.
Data ScienceData Lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format. AI systems often draw training data from data lakes that store structured, semi-structured, and unstructured information.
AI InfrastructureData Pipeline
A data pipeline is an automated series of data processing steps that moves and transforms data from source systems to a destination. ML data pipelines handle ingestion, cleaning, feature engineering, and model training workflows.
AI InfrastructureData Warehouse
A data warehouse is a centralized repository for structured, processed data optimized for analysis and reporting. AI and ML systems often source their training data from enterprise data warehouses.
AI InfrastructureDecision Boundary
A decision boundary is the surface or line that separates different classes in a classification model's feature space. The shape and complexity of decision boundaries depend on the model architecture and training data.
Machine LearningDecision Tree
A decision tree is a supervised learning algorithm that makes predictions by learning a series of if-then rules from training data. Decision trees are interpretable and form the basis of powerful ensemble methods like Random Forest.
Machine LearningDeep Learning
Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to learn hierarchical representations of data. Deep learning has achieved breakthrough results in vision, language, and speech.
Deep LearningDeep Reinforcement Learning
Deep reinforcement learning combines deep neural networks with reinforcement learning algorithms to handle complex, high-dimensional environments. It has achieved superhuman performance in games like Go, chess, and Atari.
Reinforcement LearningDeepfake
A deepfake is AI-generated synthetic media that convincingly replaces a person's likeness, voice, or actions in images, audio, or video. Deepfakes raise significant concerns about misinformation and identity fraud.
AI Ethics & SafetyDepthwise Separable Convolution
Depthwise separable convolution is an efficient convolution variant that factorizes a standard convolution into depthwise and pointwise operations. It dramatically reduces computation while maintaining accuracy, enabling mobile AI.
Deep LearningDiffusion Model
A diffusion model is a generative AI model that creates data by learning to reverse a gradual noise-adding process. Diffusion models power state-of-the-art image generation systems like Stable Diffusion and DALL-E.
Generative AIDimensionality Reduction
Dimensionality reduction is the process of reducing the number of features in a dataset while preserving its essential structure. Techniques like PCA and t-SNE help with visualization, noise reduction, and computational efficiency.
Data ScienceDiscriminator
A discriminator is the component of a GAN that learns to distinguish between real and generated data. It provides feedback to the generator, creating an adversarial training dynamic that improves output quality.
Generative AIDistillation
Knowledge distillation is a model compression technique where a smaller student model learns to replicate the behavior of a larger teacher model. Distillation makes it possible to deploy powerful AI in resource-constrained environments.
Deep LearningDistributed Training
Distributed training is the practice of splitting model training across multiple GPUs or machines to handle large models and datasets. It uses data parallelism or model parallelism to accelerate training.
AI InfrastructureDropout
Dropout is a regularization technique that randomly deactivates a fraction of neurons during training to prevent overfitting. It forces the network to learn redundant representations and improves generalization.
Deep LearningDynamic Programming
Dynamic programming is an algorithmic technique that solves complex problems by breaking them into simpler overlapping subproblems. It is used in reinforcement learning, sequence alignment, and optimal control.
FundamentalsEdge AI
Edge AI refers to running artificial intelligence algorithms locally on hardware devices rather than in the cloud. Edge AI enables real-time inference with lower latency, better privacy, and reduced bandwidth requirements.
AI InfrastructureEmbedding
An embedding is a dense vector representation that captures the semantic meaning of data like words, sentences, or images in a continuous mathematical space. Similar items are mapped to nearby points, enabling semantic search and comparison.
Deep LearningEmergent Behavior
Emergent behavior refers to capabilities that appear in large AI models that were not explicitly trained for or predicted. Examples include in-context learning and chain-of-thought reasoning in large language models.
FundamentalsEncoder-Decoder
An encoder-decoder is a neural network architecture where the encoder compresses input into a latent representation and the decoder generates output from it. This architecture is foundational for translation, summarization, and image captioning.
Deep LearningEnsemble Learning
Ensemble learning combines multiple models to produce better predictions than any individual model alone. Techniques include bagging, boosting, and stacking, which reduce variance, bias, or both.
Machine LearningEpoch
An epoch is one complete pass through the entire training dataset during model training. Training typically requires multiple epochs for the model to converge to good performance.
Machine LearningEvaluation Metric
An evaluation metric is a quantitative measure used to assess model performance on a given task. Common metrics include accuracy, precision, recall, F1 score, AUC-ROC, and perplexity.
Machine LearningExplainable AI
Explainable AI (XAI) encompasses techniques that make AI system decisions understandable to humans. XAI is crucial for building trust, meeting regulatory requirements, and debugging model behavior.
AI Ethics & SafetyExploration vs Exploitation
Exploration vs exploitation is a fundamental dilemma in reinforcement learning between trying new actions to discover better rewards versus leveraging known good actions. Balancing both is key to optimal long-term performance.
Reinforcement LearningExtractive Summarization
Extractive summarization selects and combines the most important sentences directly from a source document to create a summary. It preserves the original wording but may lack the coherence of abstractive approaches.
Natural Language ProcessingF1 Score
The F1 score is the harmonic mean of precision and recall, providing a single metric that balances both. An F1 score of 1 indicates perfect precision and recall, while 0 indicates total failure.
Machine LearningFace Recognition
Face recognition is a computer vision technology that identifies or verifies individuals by analyzing facial features in images or video. It is used in security systems, phone unlocking, and photo organization.
Computer VisionFeature Engineering
Feature engineering is the process of creating, selecting, and transforming input variables to improve machine learning model performance. Good feature engineering often matters more than model choice for traditional ML tasks.
Data ScienceFeature Extraction
Feature extraction is the process of automatically identifying and selecting the most informative representations from raw data. Deep learning models learn to extract features hierarchically, from simple edges to complex patterns.
Machine LearningFeature Map
A feature map is the output of applying a convolutional filter to an input, representing the presence and location of detected features. Deeper layers produce feature maps capturing increasingly abstract patterns.
Deep LearningFeature Store
A feature store is a centralized repository for storing, managing, and serving machine learning features. It enables feature reuse, consistency between training and serving, and collaboration across ML teams.
AI InfrastructureFederated Learning
Federated learning is a machine learning approach where models are trained across decentralized devices without sharing raw data. It enables privacy-preserving AI by keeping data on local devices while aggregating model updates.
Machine LearningFew-Shot Learning
Few-shot learning is the ability of a model to learn and generalize from only a small number of labeled examples. Large language models demonstrate impressive few-shot capabilities through in-context learning.
Machine LearningFew-Shot Prompting
Few-shot prompting provides a language model with a small number of input-output examples in the prompt to demonstrate the desired task format. This technique helps models understand task requirements without any fine-tuning.
Generative AIFine-Tuning
Fine-tuning is the process of taking a pre-trained model and continuing training on a smaller, task-specific dataset. It adapts general knowledge to specialized domains while requiring far less data and compute than training from scratch.
Deep LearningFoundation Model
A foundation model is a large AI model trained on broad data that can be adapted to a wide range of downstream tasks. GPT-4, Claude, Gemini, and DALL-E are examples of foundation models that serve as bases for specialized applications.
Generative AIFrozen Layers
Frozen layers are neural network layers whose weights are not updated during fine-tuning. Freezing preserves learned representations from pre-training while allowing later layers to adapt to new tasks.
Deep LearningGAN
A GAN (Generative Adversarial Network) is a generative model consisting of two competing neural networks — a generator and a discriminator. GANs produce realistic synthetic data by training these networks in an adversarial game.
Generative AIGaussian Process
A Gaussian process is a probabilistic model that defines a distribution over functions, providing both predictions and uncertainty estimates. Gaussian processes are used in Bayesian optimization and surrogate modeling.
Machine LearningGemini
Gemini is Google's family of multimodal AI models capable of processing text, images, audio, and video. It represents Google's most advanced AI system and competes with models like GPT-4 and Claude.
Generative AIGenerative Adversarial Network
A Generative Adversarial Network is a deep learning framework where two neural networks compete: a generator creates synthetic data while a discriminator evaluates authenticity. This adversarial process produces remarkably realistic outputs.
Generative AIGenerative AI
Generative AI refers to artificial intelligence systems that can create new content including text, images, music, code, and video. Technologies like GPT, DALL-E, and Stable Diffusion have made generative AI accessible to millions.
Generative AIGenerative Model
A generative model learns the underlying data distribution and can create new data samples that resemble the training data. Examples include GANs, VAEs, diffusion models, and autoregressive language models.
Generative AIGenerative Pre-trained Transformer
A Generative Pre-trained Transformer (GPT) is a type of large language model that generates text by predicting the next token in a sequence. Pre-trained on vast text corpora, GPT models exhibit broad language understanding and generation capabilities.
Generative AIGenetic Algorithm
A genetic algorithm is an optimization technique inspired by natural selection that evolves solutions through selection, crossover, and mutation. It is used for complex optimization problems where gradient-based methods are impractical.
FundamentalsGPT
GPT (Generative Pre-trained Transformer) is a series of large language models developed by OpenAI that generate human-quality text. GPT models are trained to predict the next token and can perform a wide range of language tasks.
Generative AIGPU
A GPU (Graphics Processing Unit) is a specialized processor designed for parallel computation that has become essential for training deep learning models. GPUs from NVIDIA dominate AI computing with architectures optimized for matrix operations.
AI InfrastructureGradient
A gradient is a vector of partial derivatives that indicates the direction and rate of steepest increase of a function. In neural networks, gradients are used to update weights in the direction that minimizes the loss function.
Deep LearningGradient Clipping
Gradient clipping is a technique that limits the magnitude of gradients during training to prevent exploding gradients. It is essential for stable training of deep networks and recurrent architectures.
Deep LearningGradient Descent
Gradient descent is an iterative optimization algorithm that adjusts model parameters in the direction that reduces the loss function. Variants include stochastic gradient descent (SGD), mini-batch SGD, and Adam.
Deep LearningGraph Neural Network
A graph neural network (GNN) is a deep learning architecture designed to operate on graph-structured data like social networks, molecules, and knowledge graphs. GNNs learn by passing messages between connected nodes.
Deep LearningGround Truth
Ground truth refers to the correct, verified labels or annotations in a dataset used to train and evaluate machine learning models. The quality of ground truth directly impacts model reliability.
Data ScienceGrounding
Grounding in AI refers to connecting a model's language understanding to real-world knowledge, data, or sensory experience. Grounded AI systems produce more factual and contextually relevant outputs.
Natural Language ProcessingHallucination
Hallucination in AI refers to when a model generates plausible-sounding but factually incorrect or fabricated information. Reducing hallucinations is a major challenge for large language models used in high-stakes applications.
Generative AIHate Speech Detection
Hate speech detection is the AI task of automatically identifying harmful, abusive, or discriminatory language in text. It is a key component of content moderation systems on social media platforms.
AI ApplicationsHeuristic
A heuristic is a practical problem-solving approach that uses rules of thumb to find good-enough solutions efficiently. In AI search algorithms, heuristics guide exploration toward promising solutions.
FundamentalsHidden Layer
A hidden layer is any neural network layer between the input and output layers. Hidden layers progressively transform data into increasingly abstract representations that enable complex pattern recognition.
Deep LearningHierarchical Clustering
Hierarchical clustering is an unsupervised method that builds a tree-like hierarchy of nested clusters. It can be agglomerative (bottom-up merging) or divisive (top-down splitting) and produces a dendrogram visualization.
Machine LearningHugging Face
Hugging Face is a platform and community that provides open-source tools, pre-trained models, and datasets for natural language processing and machine learning. It has become the central hub for sharing and deploying AI models.
AI InfrastructureHuman-in-the-Loop
Human-in-the-loop (HITL) is an approach where humans actively participate in the AI decision-making or training process. HITL systems combine human judgment with AI speed to improve accuracy and safety.
AI ApplicationsHyperparameter
A hyperparameter is a configuration value set before training that controls the learning process, such as learning rate, batch size, or number of layers. Unlike model parameters, hyperparameters are not learned from data.
Machine LearningHyperparameter Tuning
Hyperparameter tuning is the process of finding optimal hyperparameter values to maximize model performance. Methods include grid search, random search, and Bayesian optimization.
Machine LearningHypothesis Testing
Hypothesis testing is a statistical method used to determine whether observed results are statistically significant or due to random chance. In AI, it helps validate whether model improvements are meaningful.
Data ScienceImage Captioning
Image captioning is the AI task of generating natural language descriptions of images. It requires both visual understanding (computer vision) and text generation (NLP) capabilities.
Computer VisionImage Classification
Image classification is the computer vision task of assigning a label to an entire image based on its visual content. Deep learning models like ResNet and Vision Transformers achieve near-human accuracy on this task.
Computer VisionImage Generation
Image generation is the AI task of creating new images from text prompts, sketches, or other inputs. Diffusion models and GANs are the leading approaches for photorealistic image synthesis.
Generative AIImage Segmentation
Image segmentation is the process of partitioning an image into meaningful regions or classifying each pixel into a category. It is used in medical imaging, autonomous driving, and satellite analysis.
Computer VisionImitation Learning
Imitation learning is a technique where an AI agent learns to perform tasks by observing and mimicking expert demonstrations. It bridges the gap between supervised learning and reinforcement learning.
Reinforcement LearningImputation
Imputation is the process of replacing missing data values with substituted values based on statistical methods or machine learning. Proper imputation prevents biased model training from incomplete datasets.
Data ScienceIn-Context Learning
In-context learning is the ability of large language models to learn new tasks from examples provided within the input prompt, without any parameter updates. This emergent capability enables flexible task adaptation at inference time.
Generative AIInference
Inference is the process of using a trained model to make predictions on new, unseen data. Optimizing inference speed and cost is critical for deploying AI in production applications.
Machine LearningInformation Gain
Information gain measures the reduction in entropy achieved by splitting data on a particular feature. It is the primary criterion for building decision trees and feature selection.
Machine LearningInformation Retrieval
Information retrieval is the science of searching and extracting relevant documents or data from large collections. Modern AI-powered search uses embeddings and language models to understand semantic meaning.
AI ApplicationsInstance Segmentation
Instance segmentation is a computer vision task that identifies each object in an image and delineates its exact pixel boundary. Unlike semantic segmentation, it distinguishes between individual instances of the same class.
Computer VisionInstruction Tuning
Instruction tuning is a fine-tuning process that trains language models to follow natural language instructions across diverse tasks. It greatly improves a model's ability to understand and execute user requests.
Generative AIIntelligent Agent
An intelligent agent is an autonomous entity that observes its environment through sensors and acts upon it through actuators to achieve goals. Modern AI agents combine perception, reasoning, and action in complex workflows.
AI ApplicationsInverse Reinforcement Learning
Inverse reinforcement learning infers the reward function that an expert is optimizing by observing their behavior. It enables AI systems to learn goals and preferences from demonstrations.
Reinforcement LearningIoT and AI
IoT and AI refers to the integration of artificial intelligence with Internet of Things devices to enable smart, autonomous decision-making at the edge. This combination powers smart homes, industrial IoT, and wearable health devices.
AI ApplicationsJaccard Index
The Jaccard index is a similarity metric that measures the overlap between two sets by dividing the size of their intersection by the size of their union. It is commonly used in object detection evaluation and text similarity.
Data ScienceJAX
JAX is a Google-developed numerical computing library that combines NumPy-like syntax with automatic differentiation and GPU/TPU acceleration. JAX is increasingly popular for high-performance machine learning research.
AI InfrastructureJoint Probability
Joint probability is the probability of two or more events occurring simultaneously. Understanding joint probability distributions is fundamental to probabilistic machine learning and Bayesian inference.
Data ScienceK-Means Clustering
K-Means is an unsupervised clustering algorithm that partitions data into K groups by minimizing the distance between points and their assigned cluster centroids. It is one of the most widely used clustering methods.
Machine LearningK-Nearest Neighbors
K-Nearest Neighbors (KNN) is a simple machine learning algorithm that classifies data points based on the majority class of their K closest neighbors. KNN requires no training phase but can be computationally expensive at inference.
Machine LearningKernel
A kernel is a function that computes similarity between data points, often used to map data into higher-dimensional spaces. Kernels enable Support Vector Machines and other algorithms to find non-linear decision boundaries.
Machine LearningKnowledge Distillation
Knowledge distillation is a technique where a smaller model (student) is trained to mimic the outputs of a larger model (teacher). This transfers the teacher's knowledge into a more efficient model suitable for deployment.
Deep LearningKnowledge Graph
A knowledge graph is a structured representation of real-world entities and the relationships between them. AI systems use knowledge graphs to enhance reasoning, question answering, and recommendation systems.
AI ApplicationsKnowledge Representation
Knowledge representation is the field of AI concerned with encoding information about the world in a form that AI systems can use for reasoning. It includes ontologies, semantic networks, and logic-based formalisms.
FundamentalsLabel
A label is the target output or ground truth annotation associated with a training example in supervised learning. Models learn to predict correct labels from input features during the training process.
Machine LearningLanguage Model
A language model is an AI system that learns the probability distribution of sequences of words in a language. Modern language models like GPT and Claude can generate text, answer questions, and perform complex reasoning.
Natural Language ProcessingLarge Language Model
A Large Language Model (LLM) is a neural network with billions of parameters trained on massive text datasets to understand and generate human language. LLMs like GPT-4, Claude, and Gemini demonstrate broad capabilities across language tasks.
Natural Language ProcessingLatent Space
Latent space is a compressed, lower-dimensional representation of data learned by a model. In generative AI, navigating latent space allows smooth interpolation between data points and controlled generation.
Deep LearningLayer
A layer is a fundamental building block of a neural network that performs a specific transformation on its input. Common layer types include dense, convolutional, recurrent, and attention layers.
Deep LearningLazy Learning
Lazy learning is an approach where the model delays computation until a query is made rather than building a model during training. K-Nearest Neighbors is the most well-known lazy learning algorithm.
Machine LearningLearning Rate
The learning rate is a hyperparameter that controls the step size during gradient descent optimization. Too high a learning rate causes instability, while too low a rate leads to slow convergence.
Deep LearningLinear Regression
Linear regression is a statistical method that models the relationship between a dependent variable and one or more independent variables using a linear equation. It is one of the simplest and most interpretable ML algorithms.
Machine LearningLogistic Regression
Logistic regression is a classification algorithm that uses a sigmoid function to model the probability of a binary outcome. Despite its name, it is a classification method rather than a regression technique.
Machine LearningLoRA
LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that adds small trainable matrices to frozen pre-trained model weights. LoRA dramatically reduces the memory and compute required for fine-tuning large models.
Generative AILoss Function
A loss function is a mathematical function that quantifies how far a model's predictions are from the actual values. The model training process minimizes the loss function through optimization.
Machine LearningLSTM
Long Short-Term Memory (LSTM) is a type of recurrent neural network architecture designed to learn long-range dependencies in sequential data. LSTMs use gate mechanisms to control information flow and avoid the vanishing gradient problem.
Deep LearningMachine Learning
Machine learning is a branch of artificial intelligence where systems learn patterns from data to make predictions or decisions without being explicitly programmed. It encompasses supervised, unsupervised, and reinforcement learning approaches.
Machine LearningMarkov Chain
A Markov chain is a mathematical model describing a sequence of events where the probability of each event depends only on the current state. Markov chains are used in language modeling, page ranking, and MCMC sampling.
FundamentalsMarkov Decision Process
A Markov Decision Process (MDP) is a mathematical framework for modeling sequential decision-making problems with probabilistic outcomes. MDPs are the formal foundation for reinforcement learning algorithms.
Reinforcement LearningMasked Autoencoder
A masked autoencoder is a self-supervised learning method that masks random patches of an image and trains the model to reconstruct them. It has proven highly effective for pre-training vision models.
Computer VisionMasked Language Model
A masked language model is a training approach where random tokens in a sentence are hidden and the model learns to predict them from context. BERT popularized masked language modeling as a pre-training objective.
Natural Language ProcessingMeta-Learning
Meta-learning, or learning to learn, is an approach where AI systems learn how to quickly adapt to new tasks from limited data. Meta-learning algorithms optimize the learning process itself rather than just task performance.
Machine LearningMinimax
Minimax is a decision-making algorithm used in adversarial settings where one player tries to maximize their score while the other minimizes it. It is the classical approach for game-playing AI systems.
Reinforcement LearningMixture of Experts
Mixture of Experts (MoE) is an architecture that uses multiple specialized sub-networks (experts) and a gating mechanism to route inputs to the most relevant experts. MoE enables scaling model capacity without proportionally increasing compute.
Deep LearningMLOps
MLOps (Machine Learning Operations) is the practice of applying DevOps principles to the machine learning lifecycle, including development, deployment, monitoring, and maintenance. MLOps ensures reliable, reproducible, and scalable ML systems.
AI InfrastructureModel Card
A model card is a documentation framework that provides essential information about a machine learning model, including its intended use, performance metrics, limitations, and ethical considerations.
AI Ethics & SafetyModel Collapse
Model collapse is a phenomenon where AI models trained on AI-generated data progressively lose diversity and quality over generations. It highlights the importance of maintaining high-quality human-generated training data.
Generative AIModel Serving
Model serving is the process of deploying trained machine learning models to production environments where they can respond to prediction requests. Efficient serving requires optimization for latency, throughput, and cost.
AI InfrastructureMonte Carlo Method
Monte Carlo methods are computational algorithms that use repeated random sampling to estimate mathematical results. In AI, they are used in reinforcement learning, probabilistic inference, and tree search algorithms.
FundamentalsMulti-Agent System
A multi-agent system consists of multiple AI agents that interact, cooperate, or compete to solve complex problems. These systems model real-world scenarios like traffic management, markets, and collaborative robotics.
AI ApplicationsMulti-Head Attention
Multi-head attention is a mechanism that runs multiple attention operations in parallel, allowing the model to attend to different aspects of the input simultaneously. It is a core component of the Transformer architecture.
Deep LearningMulti-Task Learning
Multi-task learning is a training approach where a model learns to perform multiple related tasks simultaneously. Sharing representations across tasks often improves performance and data efficiency.
Machine LearningMultimodal AI
Multimodal AI refers to systems that can process and understand multiple types of data, such as text, images, audio, and video. Models like GPT-4 and Gemini are multimodal, enabling richer human-AI interaction.
Generative AIN-gram
An N-gram is a contiguous sequence of N items from a text, used in language modeling and text analysis. Unigrams, bigrams, and trigrams capture local word patterns and co-occurrence statistics.
Natural Language ProcessingNamed Entity Recognition
Named Entity Recognition (NER) is an NLP task that identifies and classifies named entities like people, organizations, locations, and dates in text. NER is a fundamental building block for information extraction.
Natural Language ProcessingNatural Language Generation
Natural Language Generation (NLG) is the AI task of producing coherent, human-readable text from structured data or prompts. Large language models have made NLG remarkably fluent and contextually appropriate.
Natural Language ProcessingNatural Language Inference
Natural Language Inference (NLI) is the task of determining whether a hypothesis is entailed by, contradicts, or is neutral to a given premise. NLI benchmarks test a model's understanding of logical relationships in text.
Natural Language ProcessingNatural Language Processing
Natural Language Processing (NLP) is the field of AI focused on enabling machines to understand, interpret, and generate human language. NLP powers applications from chatbots and translation to sentiment analysis and search.
Natural Language ProcessingNatural Language Understanding
Natural Language Understanding (NLU) is the subfield of NLP focused on machine reading comprehension — extracting meaning, intent, and context from text. NLU is essential for virtual assistants and conversational AI.
Natural Language ProcessingNeural Architecture Search
Neural Architecture Search (NAS) is an automated process for discovering optimal neural network architectures for a given task. NAS uses search algorithms to explore vast design spaces that would be impractical to navigate manually.
Deep LearningNeural Network
A neural network is a computing system inspired by biological neurons that processes information through interconnected layers of nodes. Neural networks are the foundation of deep learning and power most modern AI applications.
Deep LearningNeural Radiance Field
A Neural Radiance Field (NeRF) is a deep learning method that represents 3D scenes as continuous functions, enabling photorealistic novel view synthesis from 2D images. NeRFs have transformed 3D reconstruction and rendering.
Computer VisionNoise
Noise in data science refers to random, irrelevant, or erroneous information in a dataset that can hinder model learning. Effective ML systems must distinguish meaningful signal from noise.
Data ScienceNoise Injection
Noise injection is a regularization technique that adds random noise to inputs, weights, or gradients during training. It improves model robustness and generalization by preventing over-reliance on specific patterns.
Deep LearningNormalization
Normalization is the process of scaling input features to a standard range or distribution to improve model training. Common techniques include min-max scaling, z-score standardization, and layer normalization.
Data ScienceObject Detection
Object detection is a computer vision task that identifies and locates multiple objects within an image by predicting bounding boxes and class labels. YOLO, Faster R-CNN, and DETR are popular object detection models.
Computer VisionObject Tracking
Object tracking is the computer vision task of following the movement of specific objects across consecutive frames in a video. It is essential for surveillance, autonomous driving, and sports analytics.
Computer VisionOne-Hot Encoding
One-hot encoding is a technique that converts categorical variables into binary vectors where only one element is 1 and the rest are 0. It is a standard preprocessing step for feeding categorical data to machine learning models.
Data ScienceOne-Shot Learning
One-shot learning is the ability of a model to learn a new concept from just a single example. It is particularly important in applications like face verification where collecting many examples per person is impractical.
Machine LearningOnline Learning
Online learning is a training paradigm where the model updates its parameters incrementally as new data arrives, rather than retraining on the entire dataset. It is essential for streaming data and dynamic environments.
Machine LearningOpen Source AI
Open source AI refers to AI models, tools, and frameworks whose source code and weights are publicly available for use, modification, and distribution. Projects like LLaMA, Mistral, and PyTorch drive AI democratization.
AI InfrastructureOpenAI
OpenAI is an AI research company that created ChatGPT, GPT-4, and DALL-E. Founded in 2015, it has been instrumental in advancing large language models and bringing generative AI to mainstream adoption.
Generative AIOptical Character Recognition
Optical Character Recognition (OCR) is the technology that converts images of text into machine-readable text data. Modern OCR uses deep learning to handle diverse fonts, handwriting, and document layouts.
Computer VisionOptimization
Optimization in machine learning is the process of adjusting model parameters to minimize (or maximize) an objective function. Gradient-based optimization methods are the backbone of neural network training.
Machine LearningOverfitting
Overfitting occurs when a model learns the training data too well, including its noise and outliers, and fails to generalize to new data. Regularization, dropout, and early stopping are common strategies to combat overfitting.
Machine LearningPanoptic Segmentation
Panoptic segmentation unifies semantic and instance segmentation by assigning every pixel a semantic class and an instance identity. It provides a complete understanding of scene composition.
Computer VisionParameter
A parameter is a learnable variable within a model that is adjusted during training, such as weights and biases in a neural network. Large language models contain billions of parameters.
Machine LearningParameter-Efficient Fine-Tuning
Parameter-Efficient Fine-Tuning (PEFT) refers to techniques that adapt large models by updating only a small subset of parameters. Methods like LoRA, adapters, and prefix tuning enable fine-tuning with minimal compute.
Generative AIPerceptron
A perceptron is the simplest type of artificial neural network consisting of a single neuron that computes a weighted sum of inputs and applies a threshold function. It is the fundamental building block of more complex networks.
Deep LearningPerplexity
Perplexity is a metric that measures how well a language model predicts a text sequence — lower perplexity indicates better prediction. It is also the name of an AI-powered search engine that provides cited, conversational answers.
Natural Language ProcessingPipeline
A pipeline in ML is a sequence of data processing and modeling steps chained together to automate a workflow. ML pipelines include data preprocessing, feature engineering, model training, and evaluation stages.
AI InfrastructurePolicy
A policy in reinforcement learning is a function that maps states to actions, defining the agent's behavior strategy. The goal of RL is to learn an optimal policy that maximizes cumulative reward.
Reinforcement LearningPose Estimation
Pose estimation is the computer vision task of detecting the position and orientation of a person's body joints in images or video. It enables applications in fitness tracking, motion capture, and human-computer interaction.
Computer VisionPositional Encoding
Positional encoding adds information about token position to input embeddings in Transformer models, which otherwise have no inherent sense of sequence order. This enables the model to understand word order and sentence structure.
Deep LearningPre-training
Pre-training is the initial phase of training a model on a large, general dataset before fine-tuning on specific tasks. Pre-training enables models to learn broad language or visual understanding that transfers to many applications.
Deep LearningPrecision
Precision is a classification metric measuring the proportion of true positive predictions among all positive predictions. High precision means few false positives, which is important when the cost of false alarms is high.
Machine LearningPrediction
Prediction is the output of a trained model when given new input data. Machine learning predictions can be categorical (classification), numerical (regression), or generative (text, images).
Machine LearningPrincipal Component Analysis
Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms data into a new coordinate system where the greatest variance lies along the first coordinates. PCA is widely used for data visualization and noise reduction.
Data SciencePrompt
A prompt is the input text or instruction given to a language model to elicit a desired response. The quality and specificity of prompts significantly influence the relevance and accuracy of AI-generated outputs.
Generative AIPrompt Chaining
Prompt chaining is a technique where the output of one language model call becomes the input for the next, creating a pipeline of AI reasoning steps. It enables complex workflows that exceed what a single prompt can accomplish.
Generative AIPrompt Engineering
Prompt engineering is the practice of designing and optimizing input prompts to get the best possible responses from AI language models. Techniques include few-shot examples, chain of thought, and structured formatting.
Generative AIPrompt Injection
Prompt injection is a security vulnerability where malicious instructions embedded in user input override or manipulate an AI system's intended behavior. Defending against prompt injection is an active area of AI security research.
AI Ethics & SafetyPruning
Pruning is a model compression technique that removes unnecessary weights or neurons from a neural network to reduce its size and computational cost. Pruned models can be significantly smaller while maintaining most of their accuracy.
Deep LearningPyTorch
PyTorch is an open-source deep learning framework developed by Meta that provides flexible tensor computation with GPU acceleration. It is the most popular framework for AI research due to its intuitive design and dynamic computation graphs.
AI InfrastructureQ-Learning
Q-learning is a model-free reinforcement learning algorithm that learns the value of actions in states to find an optimal policy. It uses a Q-table or neural network to estimate expected cumulative rewards for each state-action pair.
Reinforcement LearningQuantization
Quantization is a technique that reduces model size and speeds up inference by converting high-precision weights to lower precision (e.g., 32-bit to 4-bit). It enables large models to run on consumer hardware with minimal accuracy loss.
AI InfrastructureQuery in Attention
In the attention mechanism, a query is a vector representing what information the current position is looking for. Queries interact with keys and values to compute attention weights that determine which parts of the input to focus on.
Deep LearningQuestion Answering
Question answering is the NLP task of automatically generating answers to questions posed in natural language. Modern QA systems range from extractive (finding answers in text) to generative (producing new answer text).
Natural Language ProcessingRandom Forest
Random Forest is an ensemble learning method that trains multiple decision trees on random data subsets and combines their predictions through voting. It is robust, requires minimal tuning, and handles both classification and regression.
Machine LearningRecall
Recall is a classification metric measuring the proportion of actual positives that were correctly identified by the model. High recall is critical in medical diagnosis and other applications where missing true positives is costly.
Machine LearningRecommendation System
A recommendation system is an AI application that predicts and suggests items a user might be interested in. Netflix, Spotify, and Amazon use recommendation systems powered by collaborative filtering and deep learning.
AI ApplicationsRecurrent Neural Network
A Recurrent Neural Network (RNN) is a neural architecture designed for sequential data that maintains a hidden state across time steps. While largely superseded by Transformers, RNNs introduced the concept of memory in neural networks.
Deep LearningRegression
Regression is a supervised learning task where the model predicts a continuous numerical value rather than a category. Examples include predicting house prices, stock returns, and temperature forecasts.
Machine LearningRegularization
Regularization is a set of techniques that prevent overfitting by adding constraints or penalties to the model during training. Common methods include L1/L2 regularization, dropout, and early stopping.
Machine LearningReinforcement Learning
Reinforcement learning is a machine learning paradigm where an agent learns to make decisions by receiving rewards or penalties for its actions in an environment. It has achieved breakthroughs in game playing, robotics, and AI alignment.
Reinforcement LearningReinforcement Learning from Human Feedback
RLHF is a training technique that uses human preferences to fine-tune AI models, aligning their outputs with human values and expectations. RLHF is key to making language models helpful, harmless, and honest.
Generative AIRepresentation Learning
Representation learning is the automatic discovery of useful data representations needed for machine learning tasks. Deep learning is fundamentally a form of representation learning that builds hierarchical feature abstractions.
Deep LearningResidual Network
A Residual Network (ResNet) is a deep neural network architecture that uses skip connections to enable training of very deep networks. ResNets solved the vanishing gradient problem and enabled networks with hundreds of layers.
Deep LearningResponsible AI
Responsible AI encompasses practices and principles for developing AI systems that are fair, transparent, accountable, and beneficial to society. It addresses bias, privacy, safety, and the broader social impact of AI technology.
AI Ethics & SafetyRetrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is a technique that enhances language model responses by first retrieving relevant documents from a knowledge base. RAG reduces hallucinations and keeps AI responses grounded in up-to-date, factual information.
Generative AIReward Model
A reward model is a trained model that predicts human preferences between different AI outputs, providing a scalar reward signal. Reward models are central to RLHF and are used to align language models with human values.
Reinforcement LearningReward Shaping
Reward shaping is the practice of designing intermediate reward signals to guide reinforcement learning agents toward desired behaviors more efficiently. Good reward shaping accelerates training while avoiding unintended shortcuts.
Reinforcement LearningRNN
An RNN (Recurrent Neural Network) is a class of neural networks where connections between nodes form cycles, allowing the network to maintain temporal state. While effective for sequences, RNNs struggle with long-range dependencies compared to Transformers.
Deep LearningRobotic Process Automation
Robotic Process Automation (RPA) uses software robots to automate repetitive, rule-based business tasks like data entry and form processing. AI-enhanced RPA can handle unstructured data and make intelligent decisions.
Robotics & AutomationRobotics
Robotics is the field of engineering and AI focused on designing, building, and programming robots that can interact with the physical world. AI-powered robotics combines computer vision, planning, and motor control.
Robotics & AutomationRobustness
Robustness in AI refers to a model's ability to maintain performance when faced with unexpected inputs, adversarial attacks, or distribution shifts. Building robust AI systems is essential for reliable real-world deployment.
AI Ethics & SafetyROC Curve
A ROC (Receiver Operating Characteristic) curve plots the true positive rate against the false positive rate at various classification thresholds. The area under the ROC curve (AUC) is a widely used metric for classifier performance.
Machine LearningSampling
Sampling in generative AI is the process of selecting tokens from a probability distribution during text generation. Different sampling strategies like top-k and top-p control the randomness and creativity of outputs.
Generative AIScaling Laws
Scaling laws are empirical relationships showing how model performance improves predictably with increases in model size, data, and compute. They guide decisions about resource allocation in training large AI models.
Deep LearningSelf-Attention
Self-attention is an attention mechanism where each element in a sequence computes attention scores with every other element in the same sequence. It enables Transformers to capture long-range dependencies regardless of distance.
Deep LearningSelf-Supervised Learning
Self-supervised learning is a training approach where models generate their own supervisory signals from unlabeled data. Pre-training large language models with next-token prediction is a form of self-supervised learning.
Machine LearningSemantic Search
Semantic search uses AI to understand the meaning and intent behind queries rather than just matching keywords. It leverages embeddings and language models to return results that are conceptually relevant.
AI ApplicationsSemantic Similarity
Semantic similarity is a measure of how closely two pieces of text convey the same meaning. AI computes semantic similarity using vector embeddings, enabling applications like duplicate detection and recommendation.
Natural Language ProcessingSemi-Supervised Learning
Semi-supervised learning uses a combination of a small amount of labeled data and a large amount of unlabeled data for training. It bridges the gap between supervised and unsupervised learning.
Machine LearningSentiment Analysis
Sentiment analysis is the NLP task of determining the emotional tone or opinion expressed in text — positive, negative, or neutral. It is widely used in brand monitoring, customer feedback analysis, and social media analytics.
Natural Language ProcessingSequence-to-Sequence
Sequence-to-sequence (Seq2Seq) is a model architecture that transforms one sequence into another, used in translation, summarization, and dialogue. It consists of an encoder that reads the input and a decoder that generates the output.
Deep LearningSHAP
SHAP (SHapley Additive exPlanations) is an explainability method based on game theory that assigns each feature an importance value for a particular prediction. SHAP provides consistent, locally accurate explanations for any ML model.
AI Ethics & SafetySigmoid Function
The sigmoid function is an activation function that maps any input to a value between 0 and 1, making it useful for binary classification outputs. It has been largely replaced by ReLU in hidden layers but remains standard for output layers.
Deep LearningSim-to-Real Transfer
Sim-to-real transfer is the process of training AI models in simulation and deploying them in the real world. It is crucial in robotics where real-world training is expensive, slow, or dangerous.
Robotics & AutomationSoftmax
Softmax is a function that converts a vector of raw scores into a probability distribution where all values sum to 1. It is the standard output activation for multi-class classification and attention mechanisms.
Deep LearningSparse Model
A sparse model activates only a subset of its parameters for each input, reducing computational cost while maintaining capacity. Mixture of Experts and pruned networks are common sparse model architectures.
Deep LearningSpeech Recognition
Speech recognition is the AI capability of converting spoken language into text. Modern systems like Whisper use deep learning to achieve near-human accuracy across multiple languages.
AI ApplicationsStable Diffusion
Stable Diffusion is an open-source AI image generation model that creates images from text descriptions using a latent diffusion process. Its open nature has spurred a large community of developers and artists.
Generative AIStochastic Gradient Descent
Stochastic Gradient Descent (SGD) is an optimization algorithm that updates model weights using the gradient computed from a random subset (mini-batch) of training data. SGD is computationally efficient and adds beneficial noise that helps escape local minima.
Deep LearningStyle Transfer
Style transfer is a computer vision technique that applies the artistic style of one image to the content of another. Neural style transfer uses deep learning to separate and recombine content and style representations.
Computer VisionSupervised Learning
Supervised learning is a machine learning approach where models learn from labeled training data — input-output pairs. It is the most common ML paradigm, powering classification and regression tasks.
Machine LearningSupport Vector Machine
A Support Vector Machine (SVM) is a classification algorithm that finds the optimal hyperplane separating different classes with maximum margin. SVMs are effective for high-dimensional data and small datasets.
Machine LearningSwarm Intelligence
Swarm intelligence is a collective behavior that emerges from groups of simple agents following local rules, inspired by natural systems like ant colonies and bird flocks. It is used in optimization and multi-robot coordination.
Robotics & AutomationSynthetic Data
Synthetic data is artificially generated data that mimics the statistical properties of real-world data. It is used to augment training datasets, protect privacy, and test models when real data is scarce or sensitive.
Data ScienceSystem Prompt
A system prompt is a hidden instruction given to a language model that defines its behavior, persona, and constraints for a conversation. System prompts shape how AI assistants respond without being visible to end users.
Generative AITemperature
Temperature is a parameter in language model text generation that controls the randomness of output. Lower temperatures produce more deterministic, focused responses, while higher temperatures increase creativity and diversity.
Generative AITensor
A tensor is a multi-dimensional array of numbers that serves as the fundamental data structure in deep learning. Scalars, vectors, and matrices are all specific cases of tensors.
Deep LearningTensorFlow
TensorFlow is an open-source machine learning framework developed by Google that provides tools for building and deploying ML models. It supports distributed training, mobile deployment, and production serving.
AI InfrastructureText Classification
Text classification is the NLP task of assigning predefined categories to text documents. Applications include spam filtering, topic labeling, and content moderation.
Natural Language ProcessingText Generation
Text generation is the AI task of producing coherent, contextually relevant text, typically through autoregressive language models. Modern text generation powers chatbots, creative writing tools, and code assistants.
Generative AIText-to-Image
Text-to-image generation creates visual images from natural language descriptions using AI models like DALL-E, Midjourney, and Stable Diffusion. It has transformed creative workflows and content production.
Generative AIText-to-Speech
Text-to-speech (TTS) is the AI technology that converts written text into natural-sounding spoken audio. Modern TTS systems produce remarkably human-like voices with appropriate prosody and emotion.
AI ApplicationsToken
A token is the basic unit of text that a language model processes, which can be a word, subword, or character depending on the tokenizer. GPT-4 processes text in tokens, with roughly 4 characters per token in English.
Natural Language ProcessingTokenization
Tokenization is the process of splitting text into tokens that a language model can process. Modern tokenizers like BPE and SentencePiece balance vocabulary size with the ability to represent any text sequence.
Natural Language ProcessingTool Use
Tool use in AI refers to a language model's ability to interact with external tools like calculators, web browsers, code interpreters, and APIs. Tool use extends AI capabilities beyond pure text generation.
Generative AITop-k Sampling
Top-k sampling is a text generation strategy that restricts token selection to the k most probable next tokens. It prevents the model from selecting highly unlikely tokens while maintaining output diversity.
Generative AITop-p Sampling
Top-p sampling (nucleus sampling) selects from the smallest set of tokens whose cumulative probability exceeds a threshold p. It dynamically adjusts the candidate pool size based on the model's confidence.
Generative AITPU
A TPU (Tensor Processing Unit) is a custom-designed AI accelerator chip developed by Google specifically for neural network computations. TPUs power Google's AI services and are available through Google Cloud.
AI InfrastructureTraining Data
Training data is the dataset used to teach a machine learning model to recognize patterns and make predictions. The quality, quantity, and representativeness of training data fundamentally determine model capabilities.
Data ScienceTransfer Learning
Transfer learning is the technique of applying knowledge gained from training on one task to improve performance on a different but related task. It enables powerful AI models from limited domain-specific data by leveraging pre-trained knowledge.
Machine LearningTransformer
The Transformer is a neural network architecture based on self-attention mechanisms that processes all input positions in parallel. Introduced in 2017, it became the foundation for virtually all modern large language models and many vision models.
Deep LearningTrustworthy AI
Trustworthy AI is an approach to building AI systems that are reliable, fair, transparent, privacy-preserving, and safe. It encompasses technical, ethical, and governance dimensions of AI development.
AI Ethics & SafetyTuring Test
The Turing Test is a measure of machine intelligence proposed by Alan Turing where a human judge evaluates whether they are conversing with a human or a machine. If the judge cannot reliably distinguish, the machine is said to exhibit intelligent behavior.
FundamentalsType I and Type II Error
Type I error (false positive) occurs when a model incorrectly predicts a positive outcome, while Type II error (false negative) incorrectly predicts a negative. Understanding these errors is crucial for evaluating model performance in context.
Data ScienceUnderfitting
Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data. Increasing model complexity or training longer can address underfitting.
Machine LearningUnsupervised Learning
Unsupervised learning is a machine learning approach where models discover patterns and structure in data without labeled examples. Clustering, dimensionality reduction, and anomaly detection are common unsupervised tasks.
Machine LearningUnsupervised Pre-training
Unsupervised pre-training is the process of training a model on unlabeled data to learn general representations before fine-tuning on labeled data. It is the foundation of modern foundation models and transfer learning.
Deep LearningUpsampling
Upsampling is a technique that increases the spatial resolution of data, commonly used in image generation and segmentation to produce higher-resolution outputs. Transposed convolutions and interpolation are common upsampling methods.
Deep LearningValidation Set
A validation set is a portion of data held out from training to evaluate model performance during development and tune hyperparameters. It helps detect overfitting and guides model selection before final testing.
Data ScienceVanishing Gradient
The vanishing gradient problem occurs when gradients become extremely small during backpropagation through many layers, making it difficult to train deep networks. Skip connections and normalization techniques were developed to address this issue.
Deep LearningVariational Autoencoder
A Variational Autoencoder (VAE) is a generative model that learns a probabilistic latent representation of data. VAEs can generate new data by sampling from the learned latent space distribution.
Generative AIVector
A vector is an ordered array of numbers that represents a point or direction in multi-dimensional space. In AI, vectors (embeddings) encode the semantic meaning of words, images, and other data types.
FundamentalsVector Database
A vector database is a specialized storage system optimized for storing, indexing, and querying high-dimensional vector embeddings. It powers semantic search, recommendation systems, and RAG applications.
AI InfrastructureVision Transformer
A Vision Transformer (ViT) applies the Transformer architecture to image recognition by treating image patches as tokens. ViTs have matched or exceeded CNNs on many computer vision benchmarks.
Computer VisionWatermarking
AI watermarking is the technique of embedding hidden, detectable signals in AI-generated content to identify its origin. It helps distinguish AI-generated text and images from human-created content.
AI Ethics & SafetyWeight
A weight is a numerical parameter in a neural network that determines the strength of the connection between neurons. Weights are learned during training through backpropagation and gradient descent.
Deep LearningWeight Initialization
Weight initialization is the strategy for setting initial values of neural network weights before training begins. Proper initialization (like Xavier or He initialization) prevents vanishing or exploding gradients.
Deep LearningWord Embedding
A word embedding is a dense vector representation of a word that captures its semantic meaning and relationships to other words. Words with similar meanings are mapped to nearby points in embedding space.
Natural Language ProcessingWord2Vec
Word2Vec is a pioneering neural network model that learns word embeddings from large text corpora. Developed by Google in 2013, it demonstrated that vector arithmetic on word embeddings captures semantic relationships.
Natural Language ProcessingXAI
XAI (Explainable Artificial Intelligence) refers to methods and techniques that make AI decision-making processes transparent and interpretable to humans. XAI builds trust and enables accountability in AI systems.
AI Ethics & SafetyXGBoost
XGBoost (Extreme Gradient Boosting) is a highly optimized gradient boosting library known for its speed and performance in structured data competitions. It remains one of the most popular algorithms for tabular data.
Machine LearningZero-Shot Learning
Zero-shot learning is the ability of a model to correctly handle tasks or recognize classes it has never been explicitly trained on. Large language models demonstrate strong zero-shot capabilities across diverse tasks.
Machine LearningZero-Shot Prompting
Zero-shot prompting is providing a language model with task instructions and no examples, relying on its pre-trained knowledge to perform the task. It tests the model's ability to generalize from training to novel instructions.
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