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 Chatbot

A chatbot is a software program designed to simulate human conversation using artificial intelligence (AI) technologies. Its main purpose is to interact with users, providing information, answering questions, or completing tasks. As natural language processing (NLP) and machine learning technologies advance rapidly, the applications of chatbots are expanding.

Chatbots play a crucial role in various industries, particularly in customer service, sales, education, and entertainment. They provide 24/7 service, significantly improve operational efficiency, reduce labor costs, and enhance user experience.

Typically, a chatbot operates by being trained on specific algorithms and datasets to understand user intent and generate appropriate responses. They can be rule-based systems using predefined answers or AI-based systems that learn and improve their responses over time.

Many businesses have deployed chatbots on their websites and social media platforms. For example, numerous e-commerce sites use chatbots to help customers check order statuses and product information. Social media platforms like Facebook Messenger also allow users to interact with chatbots.

Looking ahead, the intelligence level of chatbots will continue to rise. With more advanced neural networks and deep learning technologies, chatbots will be able to understand more complex conversations and provide more personalized services. Furthermore, the application of chatbots in voice assistants will become increasingly common.

While chatbots offer advantages such as increased efficiency, cost reduction, and 24/7 service, they also have limitations in understanding complex issues, which can lead to poor user experiences. Businesses need to balance these factors when deploying chatbots and ensure that human customer support is available when necessary.

When designing and implementing chatbots, businesses should pay attention to user privacy and data security, ensuring compliance with relevant laws and regulations. Additionally, continuously monitoring and optimizing the chatbot's performance is crucial for improving user experience.