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 Hallucination

Hallucination refers to a perception that occurs without an external stimulus, where an individual experiences sensations that are not present in reality. These can manifest in various forms, including visual, auditory, olfactory, or tactile hallucinations. The causes of hallucinations can vary widely, ranging from mental health disorders such as schizophrenia to substance abuse, extreme fatigue, or sleep deprivation. Understanding the mechanisms behind hallucinations is crucial for medical professionals in developing effective treatment plans for patients.

The impact and significance of hallucinations are profound. In the medical field, recognizing the reasons behind hallucinations helps in tailoring appropriate interventions. Furthermore, hallucinations play a significant role in psychological and neuroscientific research, providing insights into human perception and consciousness. For example, studies have shown that hallucinations can result from both psychological distress and neurological conditions, linking them to broader issues within mental health.

As for the future trends, advancements in neuroscience and psychology are driving research into the mechanisms and treatment methods related to hallucinations. Researchers are exploring how a better understanding of hallucinations can improve mental health treatment and even therapeutic applications, such as using psychedelics under controlled conditions. However, the experience of hallucinations can also lead to negative outcomes, including social isolation, anxiety, and other mental health issues.

In conclusion, while understanding hallucinations can aid in identifying and treating related disorders, the experiences themselves can pose significant challenges for individuals. It is essential to approach those experiencing hallucinations with empathy and support, ensuring they receive appropriate care rather than stigma.

What is Hallucination - Glossary