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 XAI / Explainable AI

Explainable AI (XAI) is a crucial field aimed at making the decision-making processes of artificial intelligence models transparent and understandable. As AI is increasingly applied across various sectors, particularly in high-stakes areas like healthcare, finance, and autonomous driving, ensuring the interpretability and transparency of models becomes essential.

The operation of XAI typically encompasses various techniques, such as model visualization, feature importance analysis, and decision rule generation. These techniques help users understand how models arrive at specific outcomes. For instance, LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) are commonly used methods that provide explanations for complex black-box models.

XAI has a wide range of applications. In healthcare, doctors can better understand diagnostic recommendations through explainable AI models, leading to more informed decisions. In the finance industry, regulators require financial service providers to be able to explain their credit decisions to ensure fairness and transparency.

While the advantages of XAI are evident, challenges also exist. For example, some complex models (like deep learning) are inherently highly nonlinear, making it difficult to provide simple explanations. Additionally, oversimplification may lead to the loss of critical information; thus, it is necessary to find an appropriate balance when achieving interpretability.

In the future, the development of XAI will be closely linked to advancements in technology. As demands for AI transparency and fairness continue to rise, research and application in the XAI field will gain increasing attention. Organizations also need to adhere to suitable ethical frameworks to ensure that their AI systems are not only efficient but also accepted by users and society.

What is XAI / Explainable AI - Glossary