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 NLU / 自然语言理解

自然语言理解（NLU）是人工智能领域中的一个重要分支，专注于使计算机能够理解和处理人类语言的含义。它是自然语言处理（NLP）的一部分，主要目的是让机器不仅能够识别文字，还能理解其语境和意图。

NLU 涉及多个技术领域，包括机器学习、语义理解和逻辑推理。随着大数据和人工智能技术的发展，NLU 的应用越来越广泛，例如在智能助手、客服机器人、情感分析和信息提取等领域。

NLU 的工作流程通常包括文本分析、意图识别、实体识别和上下文理解等步骤。例如，当用户向智能助手询问天气时，NLU 系统首先分析请求的语法和结构，然后识别出用户的意图，以及提取出关键实体（如时间和地点）。

NLU 的优点在于能够提高人机交互的自然性和效率，用户可以以自然语言进行交流，而不需要遵循特定的命令格式。然而，NLU 也面临挑战，例如多义词、方言和上下文变化等问题，这些都可能导致理解上的歧义。

未来，随着自然语言处理技术的不断进步，NLU 将变得更加智能，能够处理更复杂的语言结构和情感。同时，结合深度学习和大数据分析，NLU 可能会在更多领域，如教育、医疗和社交媒体等，发挥更大的作用。

在设计 NLU 系统时，开发者需要注意用户的隐私保护和数据安全，确保所处理的语言数据不会泄露个人信息。此外，持续的模型训练和优化也是提升 NLU 准确性的关键。