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 NLP / Natural Language Processing

Natural Language Processing (NLP) is a vital field in computer science and artificial intelligence that focuses on the interaction between computers and human languages. It aims to enable computers to understand, interpret, and generate human language effectively. The applications of NLP are extensive, including machine translation, sentiment analysis, text classification, and speech recognition.

The history of NLP dates back to the 1950s, and with advancements in technology—especially the rise of deep learning—NLP has made significant strides. Modern NLP relies on large corpora and powerful computational capabilities to enhance the accuracy and efficiency of language processing.

NLP typically operates through several steps: text preprocessing (like removing punctuation and tokenization), feature extraction (such as bag-of-words and TF-IDF), model training (using machine learning algorithms), and ultimately making predictions or generating results. As technology progresses, training NLP systems increasingly depends on extensive datasets and complex neural network architectures.

Typical scenarios for NLP include intelligent customer service, voice assistants, and social media analysis. In the future, as AI continues to evolve, NLP may play a more significant role in fields like education, healthcare, and law. However, NLP also faces challenges, such as the diversity and complexity of languages and the difficulty of context understanding.

The advantages of NLP include significant efficiency improvements in information processing and reduced human intervention, while also helping businesses gain insights into user emotions and needs. On the downside, training NLP systems requires vast amounts of data and may carry biases and misinterpretations.

When using NLP, there are several considerations: first, ensure the diversity and representativeness of the data to minimize model bias; second, continuously monitor and optimize model performance to adapt to language changes and evolving user needs.