“The Building Blocks of AI: An Insight into Key Algorithms and Their Real-World Impact”

Here are some commonly used algorithms under each of the branches of AI, along with a brief description of their real-world applications:

1. Machine Learning (ML):

- Linear Regression: Used for predicting a continuous outcome variable (like temperature) based on one or more predictor variables.

- Logistic Regression: Used for predicting a categorical outcome (like spam or not-spam) based on one or more predictor variables.

- Decision Trees: Used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal.

- Support Vector Machines: Used in classification and regression analysis.

- K-Nearest Neighbors: Used in pattern recognition for either regression or classification.

2. Deep Learning (DL):

- Convolutional Neural Networks (CNNs): Used for image and video processing.

- Long Short Term Memory Networks (LSTMs): Used for sequence prediction problems and natural language processing.

- Recurrent Neural Networks (RNNs): Used for working with sequential data and language modeling.

- Generative Adversarial Networks (GANs): Used for generating new data instances that resemble your training data.

- Radial Basis Function Networks (RBFNs): Used for function approximation and regression.

3. Natural Language Processing (NLP):

- Tokenization: Used for breaking down text into words, phrases, symbols, or other meaningful elements.

- Named Entity Recognition: Used for extracting information like names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. from text.

- Text Summarization: Used for creating a short, accurate, and fluent summary of a longer text document.

- Topic Modeling: Used for discovering the abstract "topics" that occur in a collection of documents.

- Text Classification: Used for classifying text into organized groups.

4. Computer Vision:

- Convolutional Neural Networks (CNNs): Used for image and video processing.

- Edge Detection: Used for identifying points in a digital image where the image brightness changes sharply.

- Feature Extraction: Used for reducing the amount of resources required to describe a large set of data accurately.

- Image Segmentation: Used for dividing a digital image into multiple segments to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.

- Object Recognition: Used for finding and identifying objects in an image or video sequence.

5. Robotics:

- Anytime A* Algorithm: Used for pathfinding and graph traversal.

- D* Algorithm: Used for planning paths in a graph with dynamically changing arc costs.

- PRM Algorithm: Used for robot motion planning.

- Zero Moment Point (ZMP) Algorithm: Used for balance control in humanoid robots.

- Proportional Integral Differential (PID) Control Algorithm: Used for controlling the robots.

6. Neural Networks:

- Convolutional Neural Networks (CNNs): Used for image and video processing.

- Long Short Term Memory Networks (LSTMs): Used for sequence prediction problems and natural language processing.

- Recurrent Neural Networks (RNNs): Used for working with sequential data and language modeling.

- Generative Adversarial Networks (GANs): Used for generating new data instances that resemble your training data.

- Radial Basis Function Networks (RBFNs): Used for function approximation and regression.

7. Transfer Learning:

- Pre-trained Models: Used for leveraging a pre-trained model on a new problem.

- Fine-tuning: Used for slightly adjusting the pre-trained model for a specific task.

- Feature Extraction: Used for using the representations learned by a previous network to extract meaningful features from new samples.

- Domain Adaptation: Used for adapting a model from a source domain to a different target domain.

- Multi-task Learning: Used for improving generalization by learning a problem together with other related problems at the same time.

8. Generative Adversarial Networks (GANs):

- Vanilla GAN: Used for generating new data instances that resemble your training data.

- Conditional GAN (CGAN): Used for generating new data instances conditioned on certain types of labels.

- Deep Convolutional GAN (DCGAN): Used for generating high-quality images.

- Wasserstein GAN (WGAN): Used for improving the stability of learning.

- Cycle GAN: Used for image-to-image translation.

I hope this gives you a good overview of some of the commonly used algorithms in these branches of AI and their applications.