Glossary

0-9

0-shot learning 1-shot learning 2-stage detector 3D convolution 4D data 5G + AI 6DoF pose estimation 7D representation 8-bit quantization 9-layer network

A

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

B

Backpropagation Batch Normalization BERT Bias Boosting

C

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

D

Data Augmentation Deep Learning Deepfake Deterministic Model Discriminative Model

E

Embedding Encoder Ensemble Learning Epoch Explainable AI (XAI)

F

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

G

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

H

Hallucination Heuristic Hidden Layer Hierarchical Model Hyperparameter

I

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

J

JAX Jittering Joint Embedding JSONL / JSON-lines Juxtaposition

K

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

L

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

M

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

N

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

O

Objective Function One-hot Encoding Online Learning Optimizer Overfitting

P

Parameter Policy / Reinforcement Learning Policy Pooling Pretraining Prompt

Q

Q-learning Quality Estimation Quantization Query Queue / Buffer

R

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

S

Sampling Self-Supervised Learning Sequence Modeling Softmax Supervised Learning

T

Tokenizer Training Data Transfer Learning Transformer Tuning / Hyperparameter Tuning

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

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

W

Weak Supervision Weight Decay Whitening / Whitening Transformation Word Embedding Workflow

X

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

Y

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

Z

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

What is NLU / Natural Language Understanding

Natural Language Understanding (NLU) is a crucial subfield of artificial intelligence that focuses on enabling computers to comprehend and process human language meaningfully. It is part of Natural Language Processing (NLP) and aims to allow machines to not only recognize text but also understand its context and intent.

NLU involves various technological domains, including machine learning, semantic understanding, and logical reasoning. With the advancement of big data and AI technologies, NLU's applications are becoming increasingly widespread, such as in intelligent assistants, customer service bots, sentiment analysis, and information extraction.

The workflow of NLU typically includes text analysis, intent recognition, entity recognition, and context understanding. For instance, when a user asks an intelligent assistant about the weather, the NLU system first analyzes the request's syntax and structure, then identifies the user's intent and extracts key entities like time and location.

The advantages of NLU lie in enhancing the naturalness and efficiency of human-computer interaction, allowing users to communicate in natural language without adhering to specific command formats. However, NLU also faces challenges such as polysemy, dialects, and contextual variations that may lead to interpretive ambiguities.

In the future, as natural language processing technologies continue to advance, NLU will become smarter, capable of handling more complex language structures and emotions. Coupled with deep learning and big data analytics, NLU is likely to play a more significant role in various fields, such as education, healthcare, and social media.

When designing NLU systems, developers should be mindful of user privacy and data security, ensuring that the language data processed does not leak personal information. Continuous model training and optimization are also key to improving NLU accuracy.