What is the relationship between machine learning, deep learning, and artificial intelligence?

1 What is artificial intelligence

Artificial Intelligence: Artificial intelligence is a broad concept that refers to making computer systems have human-like intelligence and capabilities. Artificial intelligence covers a variety of methods and technologies, including machine learning and deep learning, designed to enable computers to perceive, understand, reason, learn and solve problems. The goal of artificial intelligence is to simulate and realize all aspects of human intelligence to improve life, increase efficiency and solve complex problems.

1.1 The development history of artificial intelligence

1.2 Strong AI and Weak AI

Artificial intelligence is divided into strong artificial intelligence (Artificial General Intelligence, AGI) and weak artificial intelligence (Artificial Narrow Intelligence, ANI).

Narrow AI refers to AI systems that specialize in specific tasks or domains. These systems are designed and trained to perform specific tasks such as speech recognition, image classification, natural language processing, etc. They excel in narrow domains, but lack intelligence and generality across domains. NAI systems can be very powerful at specific tasks, but they don't have the broad adaptability of human intelligence.
Strong artificial intelligence refers to an artificial intelligence system that is as intelligent and adaptable as humans. Strong artificial intelligence can understand and deal with various tasks and problems, has a level of intelligence similar to that of humans, and can demonstrate high levels of performance in multiple fields. A strong artificial intelligence system has the ability of autonomous learning, reasoning, problem solving and creation, can handle challenges in unknown fields, and has intelligence beyond human beings.

At present, the artificial intelligence technology we have is mainly weak artificial intelligence, and these technologies have high application value in specific fields. Strong artificial intelligence is still a field of research and exploration. Scientists are working hard to develop more intelligent and comprehensive artificial intelligence systems, but true strong artificial intelligence has not yet been achieved.

2 What is machine learning

Machine Learning: Machine learning is a method and technique of artificial intelligence designed to enable computer systems to learn and improve from data without being explicitly programmed. Machine learning algorithms discover patterns and laws in data by training models, and use these patterns and laws to perform tasks such as prediction, classification, and decision-making. Machine learning algorithms can be classified into different types such as supervised learning, unsupervised learning, and reinforcement learning.

Usually what we call machine learning refers to traditional machine learning algorithms, also known as classical machine learning. These algorithms are mainly based on statistical and mathematical methods to make predictions or decisions by learning and modeling input data. Traditional machine learning algorithms learn patterns and rules from data in the training phase, and then predict or classify new unseen data in the testing phase. The machine learning algorithms for some scenarios are listed below:

Linear Regression: A task used to predict a continuous value, such as house price prediction, sales prediction, etc.
Logistic Regression (Logistic Regression): Mainly used for binary classification problems, such as spam classification, user churn prediction, etc.
Decision Trees: For classification and regression problems. Decision trees can generate easy-to-understand rules for tasks such as customer segmentation, medical diagnosis, and more.
Random Forests: Improve predictive accuracy and robustness by combining multiple decision trees. Commonly used in image classification, risk assessment, etc.
Support Vector Machines: Used for classification and regression problems, especially for problems with complex decision boundaries.
Naive Bayes: Based on Bayes theorem, it is used for text classification, spam filtering and other tasks.
K-Nearest Neighbors (KNN): Classification or regression prediction based on the distance between samples.
K-Means Clustering (K-Means Clustering): For clustering problems in unsupervised learning, data points are divided into different clusters.
Principal Component Analysis (PCA): Used for data dimensionality reduction, reducing feature dimensions and retaining key information of datasets.

At present, many machine learning algorithms have been replaced by deep learning, and deep learning algorithms are powerful as soon as possible, but traditional machine learning algorithms still perform well in many tasks and have their unique advantages (such as small amount of calculation, requiring less data set training) wait). When choosing a machine learning algorithm, it is necessary to comprehensively consider the specific problem, the characteristics of the data set, and the available resources to determine the most suitable method.

3 What is deep learning

Deep Learning: Deep learning is a branch of artificial intelligence technology, which simulates the connection and signal transmission between neurons in the human brain based on artificial neural networks. Deep learning performs feature extraction and learning through a multi-layer neural network, and adjusts network parameters through a backpropagation algorithm to achieve modeling and analysis of complex data. Deep learning performs well in processing large-scale data and complex tasks, such as image recognition, speech recognition, natural language processing, etc.

3.1 Advantages of Deep Learning Algorithms

Compared with traditional machine learning algorithms, deep learning algorithms have the following advantages:

Handling large-scale data: Deep learning algorithms are good at processing large-scale data sets. Since deep learning models usually contain a large number of parameters and multi-level network structures, they can learn complex features and patterns from large amounts of data.
Automatic Feature Extraction: Traditional machine learning algorithms often require manual feature extraction and selection, which is a tedious and expertise-intensive process. Deep learning algorithms can automatically learn higher-level feature representations from raw data, reducing the burden of feature engineering.
Handling nonlinear relationships: Deep learning models can model and capture nonlinear relationships in input data through multi-level nonlinear transformations. This makes deep learning more expressive in dealing with complex tasks and nonlinear problems.
High performance and accuracy: Deep learning algorithms have achieved significant performance gains in many tasks, such as image recognition, speech recognition, and natural language processing. Deep learning models have powerful representation and generalization capabilities, and can better adapt to different types of data and complex patterns.
End-to-end learning: Deep learning algorithms support end-to-end learning, which can start learning directly from raw data and map input to output without manually designing a multi-stage processing flow. This simplifies the model development process and makes building and deploying deep learning models more efficient.

Deep learning models usually require a large amount of computing resources and large-scale data sets to train, the model has low interpretability, requires high data quality and labeling accuracy, and is sensitive to the selection and adjustment of hyperparameters. In practical applications, it is necessary to comprehensively consider the characteristics of the problem, available resources and performance requirements, and select the appropriate algorithm and method.

3.2 Training of deep learning algorithm

The training of deep learning algorithms usually includes the following steps:

Data preparation: First, the dataset for training needs to be prepared. A dataset should contain input data and corresponding targets or labels. The dataset should be pre-processed, such as normalization, standardization, denoising, etc., to improve the training effect and stability.
Model construction: According to specific tasks and problems, select the appropriate deep learning model architecture, such as convolutional neural network (CNN) for image tasks, recurrent neural network (RNN) for sequence tasks, etc. Building a model includes defining the network structure, selecting an activation function, setting parameters, etc.
Loss function definition: Choose an appropriate loss function to measure the difference between the model's predicted output and the true label. Common loss functions include mean square error (MSE), cross-entropy (Cross-Entropy), etc.
Parameter initialization: Initialize the weight and bias parameters of the model. Random initialization, parameters of pre-trained models, etc. can be used.
Forward propagation: By passing the input data through the network, forward propagation from the input layer to the output layer, to obtain the predicted output of the model.
Calculate Loss: Use a loss function to calculate the loss value between the model's predicted output and the true label.
Backpropagation: Through the backpropagation algorithm, the gradient of the loss function to the model parameters is calculated. These gradients indicate the direction of parameter updates.
Parameter update: Use an optimization algorithm (such as gradient descent) to update the parameters of the model according to the gradient to reduce the value of the loss function.
Repeated training: The steps of forward propagation, loss calculation, backpropagation, and parameter update are repeated until a specified stopping condition is reached, such as reaching the maximum number of iterations, loss function convergence, etc.
Model evaluation: Use the validation set or test set independent of the training data to evaluate the trained model, and calculate the performance indicators of the model on new data, such as accuracy rate, precision rate, recall rate, etc.
Hyperparameter tuning: adjust the hyperparameters of the model, such as learning rate, regularization parameters, etc., to further improve the performance of the model.

The training process of deep learning models usually requires a lot of computing resources and time. In practical applications, resources need to be planned and managed reasonably, and appropriate adjustments and optimizations should be made according to the characteristics and needs of the problem.

4 Summary of the relationship among the three

Machine learning, deep learning, and artificial intelligence are three related but distinct concepts that are interrelated and mutually supportive in the field of artificial intelligence. Machine learning and deep learning are one of the key technologies and methods for realizing artificial intelligence, which provide the ability to learn from data and automate decision-making. Artificial intelligence is a broader concept that covers a variety of technologies including machine learning and deep learning, aimed at enabling the development and application of intelligent systems. Deep learning is a branch of machine learning that uses multi-layer neural networks for advanced feature learning and complex pattern recognition.