【Ａ】
A３C
AAAI
Accuracy
Activation function
Actor-Critic
Adaboost
AdaDelta
AdaGrad
Adam
AI100
Alan Turing
AIC
Allen Newell
AlexNet
AlphaFold
AlphaGo
AlphaGo Zero
Alpha Star
Andrew Ng
Annotation
Anomaly Detection
AP(Average Precision)
Arthur Samuel
Artificial Intelligence（AI）
arXiv
Attention
Attribute
AugMix
AugMixup
Autoencoder
Auto ML


【Ｂ】
Backpropagation
Bandit algorithm
Batch Normalization
Batch training
BERT
Big Data
Bootstrap method
Boltzmann machine


【Ｃ】
CAM（Class Activation Map）
Chainer
CIFAR-10
CIFAR-100
Classification
Confusion Matrix
Controller RNN
CNTK
Convolutional Neural Network（CNN）
Coursera
Count Encoding
CPU（Central Processing Unit）
CrRv（Category relevance Rarity value）
CTC（Connectionist Temporal Classification）
CUDA
Curse of dimensionality
CutMix
Cutout
Cyc
CycleGAN



【Ｄ】
Daniel Dennett
Daphne Koller
Dartmouth Conference
Data mining
Data Science
DBSCAN
DCGAN （Deep Convolutional GAN）
Decision tree
Deep belief network
Deep Blue
DEEP CNN
DeepDream
DeepFake
Deep Learning
DeepMind
Deep Neural Network（DNN）
Dendrogram
Depthwise Separable Convolution
Dilation Convolution
Discount Rate
Double Deep Q Network
DQN
Dropout
Dueling Network




【Ｅ】
early stopping
Efficitent NAS（ENAS）
EfficientNet
Ethically Aligned Design（EAD）
ELIZA
elman network
elmo
Embodiment
End to End Learning
Ensemble Learning
Experience Replay
Expert system
Explanatory variable
Exploitation



【Ｆ】
False Positive（FP）
Fashion MNIST
Faster R-CNN
Feature
Feature Engineering
Feature map
F-measure
Forward Propagation
Frame problem
Frank Rosenblatt


【Ｇ】
Genera tive Adversarial Networks；GAN
GELU (Gaussian Error Linear Units)
Geoffrey Hinton
GDPR
Gini impurity
Global Average Pooling
GLUE
GoogLeNet
Google Scholar
Gordon Moore
GPGPU
GPT-3
GPT-ｎ model（Generative Pre-Training）
GPU（Graphics Processing Unit）
Grad-CAM
Grad-CAM++
Gradient Boosting
group average method
Group normalization
GRU
Guided Backpropagation
Guided Grad-CAM



【Ｈ】
Hadoop
Herbert Simon
HMM（Hidden Markov Model）
Hyperbolic tangent function


【Ｉ】
IAAA
Ian Goodfellow
IEEE
ILSVRC（ImageNet Large Scale Visual Recognition Challenge）
Image Generation
ImageNet
Image Recognition
Inference
Instance Normalization
iteration



【Ｊ】
John McCarthy
John Searle
JUMAN



【Ｋ】
Kaggle（カグル）
Kaiming He
Keras
ｋ-means
K-Nearest Neighbor
Knowledge Base
Kuromoji




【Ｌ】
Latent Dirichlet Allocation：LDA
Latent Semantic Analysis：LSA
Layer Normalization
LAWS（Lethal Autonomous Weapons Systems）
LeNet
LightGBM
LIME（Local Interpretable Model-agnostic Explainations）
Loebner Contest
Logic Theorist
Logistic regression
LP pooling
LSI（Latent Semantic Indexing）
LSTM



【Ｍ】
Machine Learning
machine translation(MT)
mAP
Marvin Minsky
MeCab
Mini-Batch Stochastic Gradient Descent
Mini-batch training
minimax
Mixup
MnasNet
MNIST
MobileNet
Monte Carlo tree search
Morphological Analysis
multi-agent reinforcement learning：MARL
Multi-Agent Simulation（MAS）
Multilayer perceptron
Multimodal Artificial Intelligence



【Ｎ】
Naive Bayes
NAS（Neural Architecture Search）
NASNet
Natural Language Processing（NLP）
Neocognitron
Neural network
Neural Turing Machine（NTM）
NMT：Neural Machine Translation
noisy network
Normalization
Numpy


【Ｏ】
Occam's razor
OCR
Okapi BM25
one-hot-encoding
Online Learning
Ontology
OpenAI
OpenAI Five
OpenAI Gym
Open innovation
OpenPose
Oren Etzioni
Over-Training／Over-Fitting



【Ｐ】
Panoptic Segmentation
PARRY
Partnership on AI
Patrick Hayes
方策勾配法
Policy Gradient Method
Ponanza
Probabilistic Latent Semantic Analysis：PLSA
Pruning
PyTorch


【Ｒ】
RAINBOW
Random Erasing
Random forest
RAS 2020
Ray Kurzweil
Recall
Rectified Linear Unit
Reigional CNN（R-CNN）
RegNet
Region Proposal
Regression
Regularization
Reinforcement Learning（RL）
ResNet（Residual Network）
Response variable
RFC439
RNN
RMSprop
RMT：Rules Based Machine Translation
RNN(Recurrent Neural Network)
ROI



【Ｓ】
SAC（Soft Actor-Critic）
SAEJ3016自動運転レベルの定義
SARSA
scikit-learn
SciPy
score-CAM
Seaborn
Search
SegNet
Selective Search
Self-Attention
self-play
Semantic Network
Semantic Web
Semi-Supervised Learning
Seq2Seq（sequence-to-sequence）
SHAP（SHapley Additive exPlanations）
SHRDLU
Sigmoid function
SIGNATE
Sim2Real（Simulation To-Real）
singularity
Simple perceptron
SMT：Statistical Base Machine Translation
Source-Target attention
Speech processing
state representation learning
Stationary point
Step function Stephen Hawking
Stevan Harnad
STRIPS
strong AI and weak AI
Supervised learning
SWITCHBOARD
Symbol grounding problem


【Ｔ】
Tacotron2
Target encording
Target Mean Encoding
Tay
TD
teaching data
Tensorflow
Tomáš Mikolov
Toy problem
TPU
training data
True Negative（TN）
True Positive
Truncated BPTT
tーSNE
Turing machine
Turing Test



【Ｕ】
Ugly Duckling theorem
UI
Uncle Bernie's Rule
underfitting
U-Net
Universal Embedding
Unsupervised Learning


【Ｖ】
VAE
Vernor Vinge
VGG
Vision Transformer


【Ｗ】
WACV
WaveRNN
WaveGlow
WaveNet
Weight decay
WER
WSDM（Web Search and Data Mining）


【Ｘ】
XGBoost


【Ｙ】
Yann LeCun
YOLO