Vicuna, here is a list of AI, machine learning, and algorithm topics that I have some questions about, my first question is, of this list which topics are least relevant to artificial general intelligence:  1. Convolutional Neural Networks - Image processing and object detection, satellite images, process medical images, detect anomalies.

2. Long Short Term Memory Networks - time-series prediction

3. Recurrent Neural Networks - image captioning, time-series analysis, natural language processing, handwriting recognition, and machine translation.

4. Generative Adversarial Networks - image and graphic generation and improvment

5. Radial Basis Function Networks - classification, regression, and time-series prediction

6. Multilayer Perceptrons - speech recognition, image recognition, and machine translation software

7. Self Organizing Maps - Data Visualization

8. Deep Belief Networks - image recognition, video recognition, and motion capture data

9. Restricted Boltzmann Machines - dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling

10. Autoencoders - pharmaceutical discovery, popularity prediction, and image processing

1. Supervised Learning - Used for a targeted outcome.
Regression
Decision Tree
Random Forest
KNN
Logistic Regression
Classification
Transfer Learning
Ensemble Methods
2. Unsupervised Learning - Used for recommendation systems or clustering populations. Segmenting customers into different groups.
Apriori algorithm
K-means clustering
Clustering
Neural Networks & Deep Learning
Word Embeddings
Dimensionality Reduction (supervised and unsupervised)
Natural Language Processing
Reinforcement Learning (a subset of unspervised learning)
3. Reinforcement Learning - AI is exposed to an environment were it trains itself to make decisions through trial and error
Markov Decision Process

Machine Learning Algorithms - These can be applied to almost any data problem.

1. Linear Regression
2. Logistic Regression
3. Decision Tree
3. SVM
5. Naive Bayes
6. kNN
8. NAME_1
9. Dimensionality Reduction Algorithms
10. Gradient Boosting Algorithms
	GBM
	XGBoost
	LightGBM
	CatBoost

Topics Not Yet Covered

Vision models - Machine Vision, Facial Recognition, Object Recognition
Language models - Natural Language Processesing 
Gradient descent
Support Vector Machines
Principal component analysis
Transformers
Q-learning
Linear Discriminant Analysis
Classification and Regression
Learning Vector Quantization
AdaBoost






Here is an even more comprehensive li Use words with all capital letters to highlight key points, but make sure that words with all capital letters appear less than 14 times
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