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

什麼是XAI / 可解釋人工智慧

可解釋人工智慧（XAI）是個關鍵領域，旨在使人工智慧模型的決策過程透明且易於理解。隨著人工智慧在醫療、金融、自動駕駛等高風險領域的廣泛應用，確保模型的可解釋性和透明度變得至關重要。

XAI的運作方式通常涵蓋多種技術，如模型可視化、特徵重要性分析和決策規則生成等。這些技術幫助使用者理解模型如何得出特定結果。例如，LIME（局部可解釋模型-不變性）和SHAP（SHapley加性解釋）是常用的方法，能夠為複雜的黑箱模型提供解釋。

XAI的應用場景非常廣泛。在醫療領域，醫生可以透過可解釋的AI模型更好地理解診斷建議，從而作出更明智的決策。在金融行業，監管機構要求金融服務提供商能夠解釋其信貸決策，以確保公平性和透明度。

儘管XAI的優點顯而易見，但也存在一些挑戰。例如，某些複雜模型（如深度學習）本質上是高度非線性的，難以提供簡單的解釋。此外，過度簡化可能導致重要信息的喪失，因此在實現可解釋性時需要找到合適的平衡。

未來，XAI的發展趨勢將與技術的進步密切相關。隨著人們對AI透明度和公平性要求的提升，XAI領域的研究和應用將會越來越受到重視。企業和組織也需要遵循適當的倫理框架，以確保其AI系統不僅高效，還能被使用者和社會所接受。