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

什麼是可解釋的人工智慧 (XAI)

可解釋的人工智慧 (XAI) 是指提供人類可理解的決策與行動解釋的人工智慧方法。隨著 AI 系統在醫療和金融等關鍵領域的普及，透明度的需求變得至關重要。用戶必須理解 AI 決策背後的邏輯，以信任這一技術。

XAI 通過多種技術運作，包括特徵重要性分析、模型可視化和生成可解釋的決策規則。這些方法使用戶能夠深入了解 AI 模型的輸出，從而增強他們對系統的信任。例如，像 LIME（局部可解釋模型無關解釋）和 SHAP（SHapley 加法解釋）等工具可以解釋特定輸入特徵對預測的影響。

展望未來，隨著 AI 應用的擴展，XAI 有望成為行業標準，尤其是在監管要求日益嚴格的背景下。它的優點包括提高用戶信任、改善模型接受度以及幫助開發者識別模型中的偏見和倫理問題。然而，XAI 也存在一些缺點，例如額外的計算開銷、可能的過度簡化解釋以及在所有 AI 類型中的適用性挑戰。

在實施 XAI 時，開發者必須平衡可解釋性與模型性能之間的權衡，確保提供的解釋對最終用戶真正有用。總之，XAI 是推動人工智慧透明度和責任的重要一步，有助於更安全和公平的 AI 發展。