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AGI / 人工通用智慧

AGI，即人工通用智慧，指的是一種能夠理解、學習和運用知識的人工智慧，類似於人類的智慧。與狹義人工智慧相比，AGI具備更廣泛的能力，能夠處理多種任務、適應新環境並進行推理和決策。

AGI的概念可以追溯到20世紀中期的人工智慧研究。許多科學家和研究人員認為，達到AGI的關鍵在於理解人類智慧的本質並在計算機中複製這些特徵。AGI的實現可能對社會產生深遠的影響，包括經濟、教育和醫療等領域的革命性變化。

AGI的運作機制尚未完全明朗，但理論上，它將依賴於深度學習、強化學習以及其他複雜算法的組合，以模擬人類的思維過程。典型的場景包括自動駕駛汽車、智能助手和複雜問題解決系統。

然而，AGI的發展也伴隨著許多挑戰和風險。安全性和倫理問題是主要關注點之一。AGI可能導致失業、隱私侵犯和社會不平等加劇。因此，在推進AGI研究的同時，建立相應的倫理框架和政策尤為重要。

未來，AGI研究預計將持續增長，特別是在計算能力和大數據分析的推動下。隨著技術的進步，AGI可能逐步接近人類的智能水平，這將引發更深入的討論和研究。

AGI的優點包括提高生產力、解決複雜問題和推動科學技術的進步。然而，它的缺點也包括失控的潛在風險以及對人類社會的深遠影響。