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

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

B

BERT Boosting Bias Backpropagation Batch Normalization

C

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

D

Deepfake Discriminative Model Data Augmentation Deep Learning Deterministic Model

E

Epoch Explainable AI (XAI)Embedding Encoder Ensemble Learning

F

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

G

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

H

Hierarchical Model Hallucination Heuristic Hyperparameter Hidden Layer

I

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

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

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

L

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

M

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

N

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

O

One-hot Encoding Optimizer Objective Function Online Learning Overfitting

P

Prompt Pooling Parameter Policy / Reinforcement Learning Policy Pretraining

Q

Q-learning Quality Estimation Query Quantization Queue / Buffer

R

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

S

Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

Tokenizer Transformer Training Data Tuning / Hyperparameter Tuning Transfer Learning

U

U-Net Uncertainty Estimation Underfitting Unsupervised Learning Universal Approximation Theorem

V

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

W

Weight Decay Workflow Weak Supervision Whitening / Whitening Transformation Word Embedding

X

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

Y

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

Z

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

什麼是Deepfake

Deepfake是一種利用人工智慧技術生成偽造視頻或音頻的技術。它通常採用深度學習技術，特別是生成對抗網絡（GAN），將一個人的面孔或聲音替換為另一個人的。

Deepfake的背景可以追溯到近年來人工智慧和深度學習技術的快速發展。隨著這些技術的進步，Deepfake的應用範圍正在從娛樂行業擴展到社交媒體，甚至政治、教育和心理治療等領域。

儘管Deepfake在某些情況下具有創造性和娛樂性，但其潛在的負面影響也引起了廣泛關注。假新聞、身份盜用和隱私侵犯等問題層出不窮，導致社會對這一技術的信任度下降。因此，許多國家和地區開始考慮立法以應對這些挑戰。

在未來，隨著技術的進一步發展，Deepfake的生成將變得更加簡單和普遍，因此對其倫理和法律框架的探討將愈發重要。儘管其優點包括創造性和藝術表達，但也不可忽視其帶來的風險和負面影響。