A
Ablation Study: An experiment where components of a model or pipeline are removed to assess their impact on performance.
Active Learning: A training strategy where a model identifies which data points would be most useful for humans to label next.
Adversarial Example: An input deliberately modified to cause a model to make an incorrect prediction.
Agentic AI: Systems that can plan, make decisions, and take actions across multiple steps, often using tools or other models.
Alignment: The extent to which a model’s behavior matches intended goals, values, or policies.
API (Application Programming Interface): A structured way for software to interact with an AI model or service programmatically.
Artificial Intelligence (AI): A broad field focused on creating systems that perform tasks normally requiring human intelligence, such as reasoning, learning, and decision-making.
Attention: A mechanism that allows models to focus on the most relevant parts of the input when processing information.
Autoencoder: A neural network designed to learn compact representations of data by reconstructing inputs from compressed versions.
B
Batch Job: A compute task submitted to a scheduler and run without interactive control.
Batch Processing: Running inference or training on many inputs simultaneously rather than one at a time.
Benchmark: A standardized dataset or task used to evaluate and compare model performance.
Bias: Systematic errors or skewed outputs arising from data, model design, or usage context.
C
Chain-of-Thought: A prompting approach that encourages models to generate intermediate reasoning steps.
Checkpointing: Saving model state during training so work can be resumed later.
Cloud Computing: On-demand access to remote computing resources over the internet.
Cluster: A group of connected computers that work together as a single computing resource.
Compute: Hardware resources, such as CPUs, GPUs, or TPUs, used to train or run AI models.
Computer Vision (CV): AI methods that allow machines to interpret visual data, including classification, detection, and segmentation.
Confidence Score: A numerical estimate of how certain a model is about a prediction.
Context Window: The maximum amount of information, measured in tokens, that a model can process at once.
CPU (Central Processing Unit): A general-purpose processor optimized for sequential tasks and system-level operations.
CUDA: A parallel computing platform and programming model used to run code on NVIDIA GPUs.
D
Data Leakage: When information outside the training set improperly influences model training or evaluation.
Data Provenance: Information describing where data originated, how it was collected, and how it has been processed.
Deep Learning (DL): A subset of machine learning using multi-layer neural networks to learn complex patterns.
Determinism: The degree to which a model produces the same output for the same input.
Diffusion Models: Generative models that create data by iteratively refining noise into structured outputs.
Distributed Training: Training models across multiple machines or GPUs to scale computation.
Domain Adaptation: Adjusting a model trained in one domain to perform well in another related domain.
Downstream Task: A specific application a pre-trained model is adapted or evaluated for.
E
Edge AI: Running AI models locally on devices rather than on cloud infrastructure.
Embeddings: Numeric vector representations of data that capture semantic or structural relationships.
Emergent Behavior: Capabilities that arise in large models without being explicitly programmed.
Evaluation Metrics: Quantitative measures used to assess model performance, such as accuracy or recall.
Explainability: The degree to which a model’s outputs and behavior can be understood by humans.
F
Federated Learning: A training approach where models learn from decentralized data without moving it to a central location.
Few-Shot Learning: A model’s ability to perform tasks with only a small number of examples.
Fine-Tuning: Adapting a pre-trained model to a specific task or dataset.
Foundation Models: Large, general-purpose models designed to be adapted across many tasks.
G
Generative AI (GenAI): Models that create new content, such as text, images, code, or audio.
Generalization: A model’s ability to perform well on unseen data.
GPU (Graphics Processing Unit): A processor optimized for parallel computation, widely used for training and running deep learning models.
Gradient: A signal used during training to update model parameters in order to reduce error.
Ground Truth: Trusted reference data used for training or evaluation.
Guardrails: Constraints or checks designed to limit unsafe or unintended model behavior.
H
Hallucination: When a model produces incorrect or fabricated information presented as factual.
Human-in-the-Loop: A workflow where human judgment is integrated into model training or decision-making.
Hyperparameters: Configuration settings chosen before training that influence how a model learns.
I
Inference: Using a trained model to generate outputs on new data.
Inference Cost: The computational or financial cost of running a model.
Interpretability: Methods for understanding how model inputs influence outputs.
J
K
Knowledge Distillation: Training a smaller model to mimic a larger model’s behavior.
L
Large Language Models (LLMs): Models trained on large text datasets to understand and generate natural language.
Latency: The delay between providing input to a model and receiving output.
M
Machine Learning (ML): A subset of AI where systems learn patterns from data rather than explicit rules.
Model Cards: Documentation describing a model’s data, intended use, limitations, and ethical considerations.
Model Collapse: Degradation in model quality caused by repeated training on synthetic or low-diversity data.
Model Drift: Performance degradation over time due to changing data or conditions.
Multimodal AI: Models that work across multiple data types, such as text and images.
Multitask Learning: Training a single model to perform multiple tasks simultaneously.
N
Natural Language Processing (NLP): AI methods for analyzing, understanding, and generating human language.
Neural Networks: Computational models composed of connected layers of processing units inspired by the brain.
Node: An individual machine within a computing cluster.
O
On-Device Learning: Updating models directly on local hardware rather than centralized servers.
Open-Source Models: Models whose code and weights are publicly available.
Out-of-Distribution Data: Inputs that differ significantly from the data a model was trained on.
Overfitting: When a model performs well on training data but poorly on new data.
P
Parameters: Internal values learned by a model during training.
Post-Training Evaluation: Assessing model performance after deployment.
Pretraining: Initial training of a model on large, general datasets.
Privacy-Preserving AI: Techniques that protect sensitive data during model training or inference.
Probabilistic Output: Predictions expressed as likelihoods rather than fixed answers.
Prompt Injection: A security risk where inputs manipulate a model’s instructions or behavior.
Prompting: Writing inputs that guide how a model responds.
Q
R
RAM: System memory used by the CPU to store active data and programs.
Reinforcement Learning (RL): A learning approach where agents learn by receiving rewards or penalties.
Regularization: Techniques used to reduce overfitting during training.
Reproducibility: The ability to replicate results using the same data and methods.
Responsible AI: Practices emphasizing fairness, transparency, accountability, and ethics.
Retrieval-Augmented Generation (RAG): A method where models retrieve relevant documents before generating responses.
S
Sampling: Methods that control how generative models select outputs.
Safety Filtering: Processes that detect and block harmful outputs.
Scaling Laws: Empirical relationships linking model size, data, compute, and performance.
Segmentation: A computer vision task that assigns labels to individual pixels or regions.
Supervised Learning: Training models using labeled examples.
Synthetic Data: Artificially generated data used for training or testing models.
System Prompt: Instructions that set a model’s overall behavior before user input.
T
Temperature: A parameter controlling randomness in generative outputs.
Token: A basic unit of text processed by language models.
Tool Use: A model’s ability to call external functions or software.
TPU (Tensor Processing Unit): A specialized accelerator designed for machine learning workloads.
Training Data: The examples used to teach a model.
Transfer Learning: Applying knowledge from one task or dataset to another.
Transformers: A neural network architecture based on attention mechanisms.
U
Unsupervised Learning: Learning patterns from data without labeled outputs.
V
Validation Data: Data used to tune and evaluate models during training.
Virtual Environment: An isolated software environment used to manage dependencies.
Vision Models: Models designed to interpret visual data.
VRAM: Dedicated memory on GPUs used to store model parameters and intermediate computations.
W
X
Y
Z
Zero-Shot Learning: Performing tasks without task-specific training examples.