Glossary

0-9

1-shot learning 2-stage detector 3D convolution 4D data 5G + AI 6DoF pose estimation 7D representation 8-bit quantization 9-layer network 0-shot learning

A

Algorithm Autoencoder Artificial Intelligence (AI)Attention AGI / Artificial General Intelligence

B

Batch Normalization Boosting Backpropagation Bias BERT

C

Classifier / Classification Chatbot Cross-Validation Clustering CNN / Convolutional Neural Network

D

Deterministic Model Data Augmentation Deep Learning Discriminative Model Deepfake

E

Explainable AI (XAI)Ensemble Learning Encoder Embedding Epoch

F

Foundation Model Fine-tuning Forward Propagation Feature Extraction Fusion / Multimodal Fusion

G

Gradient Descent Graph Neural Network (GNN)Generative AI GAN / Generative Adversarial Network Grounding

H

Hierarchical Model Hidden Layer Hyperparameter Hallucination Heuristic

I

Imbalanced Data Instance / Sample Intelligence Amplification / Augmentation Interpretability Instruction tuning

J

Juxtaposition Joint Embedding Jittering JAX JSONL / JSON-lines

K

Knowledge Distillation Kernel Trick K-means Clustering K-Shot Learning KL Divergence (Kullback–Leibler Divergence)

L

Large Language Model (LLM)Loss Function Latent Variable Learning Rate LSTM / Long Short-Term Memory

M

Machine Learning (ML)Multimodal / Multimodality Multi-head Attention Meta-learning Model

N

Novelty Detection / Anomaly Detection Neural Network Normalization NLP / Natural Language Processing NLU / Natural Language Understanding

O

Objective Function Overfitting Online Learning Optimizer One-hot Encoding

P

Parameter Policy / Reinforcement Learning Policy Prompt Pretraining Pooling

Q

Quantization Query Queue / Buffer Quality Estimation Q-learning

R

Reinforcement Learning (RL)Retrieval Augmented Generation (RAG)Regularization Representation Learning RNN / Recurrent Neural Network

S

Supervised Learning Sampling Sequence Modeling Self-Supervised Learning Softmax

T

Transfer Learning Tokenizer Tuning / Hyperparameter Tuning Transformer Training Data

U

Uncertainty Estimation Unsupervised Learning Underfitting Universal Approximation Theorem U-Net

V

Validation Set Vector Embedding Variational Autoencoder (VAE)Vanishing / Exploding Gradient Vision Transformer (ViT)

W

Whitening / Whitening Transformation Weak Supervision Word Embedding Workflow Weight Decay

X

X-axis / feature axis XAI / Explainable AI XLM XLNet XOR problem

Y

Yield (model yield / throughput)Yoga of AI Y-transform / YUV Y-axis / feature axis YAGNI (You Aren't Gonna Need It)

Z

Zero-gradient phenomenon Zero-centric / Zero-bias initialization Zero-shot Learning / Zero-shot inference Zygosity in augmentation Z-score Normalization

Grounding이란 무엇인가

Grounding은 개인과 시스템이 효과적으로 기능할 수 있도록 돕는 다양한 개념을 나타냅니다. 심리학에서 Grounding 기술은 개인이 현재 순간에 집중하여 불안과 스트레스를 관리하도록 돕습니다. 이 기술에는 심호흡과 신체적 접촉이 포함되어 개인을 현실에 고정시켜 감정 조절을 향상시킵니다.

전기 공학에서 Grounding은 전기 시스템을 지구에 연결하여 전기 쇼크를 방지하고 장비가 안전하게 작동하도록 보장하는 중요한 안전 조치입니다. 이 실천은 사람과 장치를 잠재적 위험으로부터 보호하는 데 필수적입니다.

철학적으로 Grounding은 존재의 기초를 가리킬 수 있으며, 사물이 존재하는 이유와 그 본질에 대해 질문합니다. 교육에서 Grounding은 학생들이 복잡한 개념을 이해하는 데 도움을 주는 기반을 제공합니다.