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

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

B

BERT Boosting Backpropagation Batch Normalization Bias

C

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

D

Discriminative Model Deterministic Model Deep Learning Data Augmentation Deepfake

E

Epoch Encoder Ensemble Learning Explainable AI (XAI)Embedding

F

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

G

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

H

Hierarchical Model Hyperparameter Hallucination Heuristic Hidden Layer

I

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

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

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

L

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

M

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

N

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

O

One-hot Encoding Overfitting Objective Function Optimizer Online Learning

P

Prompt Parameter Pretraining Policy / Reinforcement Learning Policy Pooling

Q

Q-learning Query Queue / Buffer Quantization Quality Estimation

R

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

S

Softmax Sampling Supervised Learning Sequence Modeling Self-Supervised Learning

T

Tokenizer Transformer Tuning / Hyperparameter Tuning Transfer Learning Training Data

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

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

W

Weight Decay Word Embedding Workflow Whitening / Whitening Transformation Weak Supervision

X

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

Y

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

Z

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

什么是AGI / 人工通用智能

AGI，即人工通用智能，指的是一种能够理解、学习和运用知识的人工智能，类似于人类的智能。与狭义人工智能相比，AGI具备更广泛的能力，能够处理多种任务、适应新环境并进行推理和决策。

AGI的概念可以追溯到20世纪中期的人工智能研究。许多科学家和研究人员认为，达到AGI的关键在于理解人类智能的本质并在计算机中复制这些特征。AGI的实现可能对社会产生深远的影响，包括经济、教育和医疗等领域的革命性变化。

AGI的运作机制尚未完全明朗，但理论上，它将依赖于深度学习、强化学习以及其他复杂算法的组合，以模拟人类的思维过程。典型的场景包括自动驾驶汽车、智能助手和复杂问题解决系统。

然而，AGI的发展也伴随着许多挑战和风险。安全性和伦理问题是主要关注点之一。AGI可能导致失业、隐私侵犯和社会不平等加剧。因此，在推进AGI研究的同时，建立相应的伦理框架和政策尤为重要。

未来，AGI研究预计将持续增长，特别是在计算能力和大数据分析的推动下。随着技术的进步，AGI可能逐步接近人类的智能水平，这将引发更深入的讨论和研究。

AGI的优点包括提高生产力、解决复杂问题和推动科学技术的进步。然而，它的缺点也包括失控的潜在风险以及对人类社会的深远影响。