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 AGI / Artificial General Intelligence

AGI, or Artificial General Intelligence, refers to a type of AI that can understand, learn, and apply knowledge in a manner similar to human intelligence. Unlike narrow AI, AGI possesses a broad range of capabilities that allow it to handle various tasks, adapt to new environments, and engage in reasoning and decision-making.

The concept of AGI traces back to AI research in the mid-20th century. Many scientists and researchers believe that achieving AGI hinges on understanding the essence of human intelligence and replicating these features in computers. The realization of AGI could have profound impacts on society, including revolutionary changes in sectors such as economy, education, and healthcare.

The operational mechanisms of AGI are not yet fully understood; however, theoretically, it will rely on a combination of deep learning, reinforcement learning, and other complex algorithms to simulate human thought processes. Typical scenarios include autonomous vehicles, intelligent assistants, and complex problem-solving systems.

Nonetheless, the development of AGI is fraught with challenges and risks. Issues of safety and ethics are primary concerns. AGI could lead to unemployment, privacy infringements, and increased social inequality. Therefore, while advancing AGI research, it is crucial to establish corresponding ethical frameworks and policies.

Looking ahead, the research into AGI is expected to continue growing, particularly propelled by advancements in computational power and big data analysis. As technology progresses, AGI may gradually approach human intelligence levels, sparking deeper discussions and investigations.

The advantages of AGI include its potential to enhance productivity, solve complex problems, and drive advancements in science and technology. However, the downsides encompass potential risks of losing control and the profound impacts on human society.

What is AGI / Artificial General Intelligence - Glossary