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

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

B

BERT Boosting Backpropagation Batch Normalization Bias

C

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

D

Discriminative Model Deterministic Model Deep Learning Data Augmentation Deepfake

E

Epoch Encoder Ensemble Learning Explainable AI (XAI)Embedding

F

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

G

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

H

Hierarchical Model Hyperparameter Hallucination Heuristic Hidden Layer

I

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

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

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

L

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

M

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

N

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

O

One-hot Encoding Overfitting Objective Function Optimizer Online Learning

P

Prompt Parameter Pretraining Policy / Reinforcement Learning Policy Pooling

Q

Q-learning Query Queue / Buffer Quantization Quality Estimation

R

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

S

Softmax Sampling Supervised Learning Sequence Modeling Self-Supervised Learning

T

Tokenizer Transformer Tuning / Hyperparameter Tuning Transfer Learning Training Data

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

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

W

Weight Decay Word Embedding Workflow Whitening / Whitening Transformation Weak Supervision

X

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

Y

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

Z

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

什么是XAI / 可解释人工智能

可解释人工智能（XAI）是一个关键领域，旨在使人工智能模型的决策过程透明和可理解。随着人工智能在医疗、金融、自动驾驶等高风险领域的广泛应用，确保模型的可解释性和透明度变得至关重要。

XAI的运作方式通常涵盖多种技术，如模型可视化、特征重要性分析和决策规则生成等。这些技术帮助用户理解模型如何得出特定结果。例如，LIME（局部可解释模型-不变性）和SHAP（SHapley加性解释）是常用的方法，能够为复杂的黑箱模型提供解释。

XAI的应用场景非常广泛。在医疗领域，医生可以通过可解释的AI模型更好地理解诊断建议，从而作出更为明智的决策。在金融行业，监管机构要求金融服务提供商能够解释其信贷决策，以确保公平性和透明度。

尽管XAI的优点显而易见，但也存在一些挑战。例如，某些复杂模型（如深度学习）本质上是高度非线性的，难以提供简单的解释。此外，过度简化可能导致重要信息丢失，因此在实现可解释性时需要找到合适的平衡。

未来，XAI的发展趋势将与技术的进步密切相关。随着人们对AI透明度和公平性要求的不断提升，XAI领域的研究和应用将会越来越受到重视。企业和组织也需要遵循适当的伦理框架，以确保其AI系统不仅高效，而且能够被用户和社会所接受。