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

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

B

BERT Boosting Bias Backpropagation Batch Normalization

C

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

D

Deepfake Discriminative Model Data Augmentation Deep Learning Deterministic Model

E

Epoch Explainable AI (XAI)Embedding Encoder Ensemble Learning

F

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

G

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

H

Hierarchical Model Hallucination Heuristic Hyperparameter Hidden Layer

I

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

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

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

L

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

M

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

N

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

O

One-hot Encoding Optimizer Objective Function Online Learning Overfitting

P

Prompt Pooling Parameter Policy / Reinforcement Learning Policy Pretraining

Q

Q-learning Quality Estimation Query Quantization Queue / Buffer

R

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

S

Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

Tokenizer Transformer Training Data Tuning / Hyperparameter Tuning Transfer Learning

U

U-Net Uncertainty Estimation Underfitting Unsupervised Learning Universal Approximation Theorem

V

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

W

Weight Decay Workflow Weak Supervision Whitening / Whitening Transformation Word Embedding

X

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

Y

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

Z

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

可解釋性是什麼

可解釋性是指人類理解模型或算法所做決策原因的程度。在人工智慧和機器學習領域，隨著模型複雜性的增加，可解釋性變得越來越重要。

隨著模型變得更加複雜，決策過程可能類似於「黑箱」，使得用戶難以理解結論是如何得出的。這種複雜性引發了對可解釋性研究的興趣，特別是在醫療和金融等高風險領域，模型決策的透明度直接影響倫理和法律責任。

實現可解釋性的技術包括特徵重要性分析、可視化工具以及局部可解釋模型（如 LIME 和 SHAP）。這些工具幫助用戶理解模型決策的基礎。

隨著法規和標準的日益重視，尤其是歐盟的AI法規，可解釋性將成為模型設計和開發的關鍵方面。

可解釋性的優點包括增強信任和透明度，但過分追求可解釋性可能會限制模型的複雜性和性能。開發者必須在模型的準確性與可解釋性之間尋求平衡，以確保最終用戶獲得有用信息。