AI and Deep Learning: Understanding Advanced Technologies

AI and Deep Learning: Understanding Advanced Technologies

Artificial intelligence (AI) has penetrated deeply into our lives and society, bringing revolutionary changes in various fields. Among them, deep learning has attracted attention as one of the most important technologies driving the evolution of AI. In this article, we will explain in detail the basic concepts of AI and deep learning, their mechanisms, application fields, and the future, to deepen your understanding of this advanced technology.

What are AI and deep learning?

Definition and types of AI (artificial intelligence)

AI is a general term for technology that mimics human intelligence and enables computers to perform intellectual activities such as learning, reasoning, and judgment. AI can be broadly divided into two types depending on its level of ability and autonomy.

1. Strong AI (General Purpose AI): AI with intelligence equal to or greater than that of humans, capable of performing a variety of tasks autonomously. This has not yet been realized.
2. Weak AI (Narrow-focused AI): AI designed to specialize in a specific task, such as image recognition, natural language processing, or game playing, and is capable of outperforming humans in a specific area.

Current AI research is primarily focused on developing weak AI.

History of AI development

The history of AI began in the 1950s. To date, there have been three AI booms.

1. First AI boom (1950s to 1960s): Basic AI techniques such as inference and search were developed, but due to the limitations of computers at the time, they were unable to solve complex problems.
2. Second AI boom (1980s to 1990s): Expert systems, AI that incorporates specialized knowledge in specific fields into computers, were developed. However, the boom came to an end due to the difficulty of expressing knowledge and limitations in responding to changes in the situation.
3. The Third AI Boom (2010s to present): With the advent of machine learning, especially deep learning, AI has made great strides. AI that exceeds human capabilities in fields such as image recognition, natural language processing, and voice recognition is being developed one after another.

Definition and characteristics of deep learning

Deep learning is a type of machine learning technology that uses neural networks that mimic the neural circuits of the human brain to learn complex patterns and features from data.

The characteristics of deep learning can be summarized into the following three points:

1. Multi-layer structure: By using a neural network consisting of multiple layers (input layer, hidden layer, output layer), it is possible to learn complex features.
2. Automatic feature extraction: Traditional machine learning requires humans to design features, but deep learning makes it possible to automatically extract features from data.
3. Learning from large amounts of data: High accuracy can be achieved by learning from large amounts of data.

Deep learning and the revolution it brings to AI

The emergence of deep learning has brought about a major revolution in AI research. Deep learning has made great strides in areas that were difficult to solve with conventional AI, such as image recognition, natural language processing, and voice recognition, and has produced AI that can surpass human capabilities.

Deep learning has greatly expanded the scope of applications of AI, bringing about innovation in a variety of fields, including medicine, finance, manufacturing, and autonomous driving.

Machine Learning vs. Deep Learning

Both machine learning and deep learning are technologies for realizing AI, but they differ in several ways.

Features	Machine Learning	Deep Learning
Feature design	Must be designed by humans	Automatically extract from your data
Model Complexity	Relatively simple	complicated
amount of data	Small amounts of learning are possible	Large amounts of data required
accuracy	Inferior to deep learning for some tasks	High accuracy possible

Machine learning is effective when the task is relatively simple and there is little data, whereas deep learning excels when the task is complex and there is a lot of data.

How deep learning works

At the core of deep learning is a “neural network” that mimics the neural circuits of the human brain. Here, we will explain in detail the structure and learning process of neural networks.

Neural network structure

A neural network is a network structure in which many nodes (neurons) are interconnected. Each node receives input from other nodes, performs calculations, and outputs the results.

• Input layer: This is the layer that receives data from outside. In the case of image recognition, the pixel values of the image are the input.
• Hidden layer: A layer between the input layer and the output layer. By stacking multiple layers, it is possible to learn more complex features.
• Output layer: This is the layer that outputs the final result. In the case of image recognition, the output is a label that indicates what is in the image.

The connections between each node have parameters called “weights,” and by adjusting these weights, the neural network learns.

perceptron

Perceptron is the most basic model of neural network. It consists of only input and output layers and does not have hidden layers. Perceptron can learn linearly separable problems (e.g., AND and OR circuits), but cannot learn nonlinearly separable problems (e.g., XOR circuits).

Activation Function

An activation function is a function that determines how to convert input values into output values at each node of a neural network. There are various types of activation functions, such as ReLU (Rectified Linear Unit), sigmoid function, and tanh function.

Activation functions are responsible for introducing nonlinearity into neural networks, without which they would not be able to learn complex patterns.

Weights and Biases

Weights are parameters that represent the strength of the connections between each node in a neural network. The larger the weight, the stronger the signal that passes through that connection. Bias is a parameter that each node has that adjusts how easily the node is activated.

Neural network training is the process of adjusting the weights and biases to optimal values. Training data is input, the error between the output result and the correct data is calculated, and the weights and biases are updated to reduce that error.

Types of Neural Networks

In deep learning, various types of neural networks have been developed, and the appropriate model for each task is selected. Here, we will explain the main types of neural networks, their characteristics, and applications.

Convolutional Neural Network (CNN)

CNN is a neural network specialized for image recognition tasks. It achieves high-precision image recognition by stacking multiple convolutional layers and pooling layers to extract image features.

• Applications: Image classification, object detection, segmentation, face recognition, etc.
• Features:
  • Local Feature Extraction: Convolutional layers extract local features of an image (edges, corners, etc.).
  • Translation invariance: The pooling layer is able to recognize features as the same even if their location in the image is slightly shifted.
  • Parameter sharing: In a convolutional layer, the same filters (weights) are applied to the entire image, which reduces the number of parameters.

Recurrent Neural Network (RNN)

RNN is a neural network specialized for processing time series data (voice, text, etc.). Because it has a hidden state that remembers past information, it is possible to process time series data while taking into account its context.

• Applications: Natural language processing (machine translation, text generation, sentiment analysis, etc.), speech recognition, time series prediction, etc.
• Features:
  • Time series data processing: Past information can be stored and combined with current input for processing.
  • Contextual understanding: Time series data can be processed in context.
  • Supports variable length input: There is no limit to the length of the input data.

Transformer

Transformer is a relatively new neural network architecture that was announced in 2017. Unlike RNNs, it does not have a recursive structure and processes time-series data using the Attention mechanism.

• Uses: Natural language processing (machine translation, text generation, question answering, etc.)
• Features:
  • Parallel processing: Faster processing than RNNs is possible.
  • Learning long-range dependencies: Accurately capture the relationships between words even in long sentences.
  • Versatility: It is applied to a variety of tasks, including not only natural language processing but also image processing and speech processing.

Uses and features of each network

kinds	Applications	Features
CNN	Image Recognition	Local feature extraction, translation invariance, parameter sharing
RNN	Time Series Data Processing	Memory of past information, understanding of context, variable length input
Transformer	Natural language processing, image processing, voice processing	Parallel processing, learning long-distance dependencies, generality

Learning process

The deep learning process can be broadly divided into three steps:

1. Forward propagation: The input data passes through each layer of the neural network in order, and finally the result is output from the output layer. During this process, each node multiplies the input value by a weight, adds them together, and converts them into an output value through an activation function.
2. Loss calculation: Calculate the error (loss) between the output result and the correct data. The loss function is an index for evaluating the performance of the model, and the smaller the error, the better the model’s performance.
3. Backpropagation: Update the weights and biases of each layer based on the loss. Using an algorithm called backpropagation, we can efficiently update the weights and biases of each layer, working from the output layer to the input layer.

By repeating this cycle of forward propagation, loss calculation, and backpropagation, the neural network gradually learns and is able to output more accurate results.

Supervised learning, unsupervised learning, reinforcement learning

There are three main types of deep learning methods:

• Supervised Learning: A method of training a model by pairing input data with its correct answer label (teacher data). It is suitable for tasks where the correct answer is clear, such as image classification and object detection.
• Unsupervised Learning: A method in which a model discovers patterns and features by itself from data without correct labels. It is suitable for tasks that understand the structure of data, such as data clustering and dimensionality reduction.
• Reinforcement Learning: A method of learning in which a model is trained by giving rewards or penalties for the results of taking a certain action. This method is suitable for tasks that learn optimal actions through trial and error, such as game AI and robot control.

Loss Functions and Optimization

A loss function is a function that quantifies the error between the model output and the correct data. The smaller the loss function value, the better the model’s performance.

Optimization is the process of adjusting the parameters (weights and biases) of a model to minimize the value of a loss function. There are many different optimization algorithms, including gradient descent, Adam, and RMSprop.

Backpropagation

Backpropagation is an algorithm for efficiently updating the weights and biases of each layer based on the loss in neural network training. The parameters of each layer are updated by backpropagating the error from the output layer to the input layer.

Gradient descent

Gradient descent is an optimization algorithm that updates parameters in the direction that minimizes the value of a loss function. There are many variations of gradient descent, including SGD (Stochastic Gradient Descent), Adam, and RMSprop.

• SGD: Stochastic Gradient Descent. Updates parameters using randomly selected portions of data. It is computationally inexpensive and can train efficiently on large datasets, but training can be unstable.
• Adam: An improved version of SGD that takes into account past gradient information to improve training stability and convergence speed.
• RMSprop: An improved version of SGD that automatically adjusts the learning rate to make learning more efficient.

Each of these optimization algorithms has its own strengths and weaknesses, so it is important to choose the right one for your task and the characteristics of your dataset.

Deep learning challenges and solutions

Deep learning has achieved remarkable results, but at the same time it also faces some challenges. Here we will explain the major challenges and how to deal with them.

Overfitting

Overfitting is a phenomenon in which a model becomes overly adapted to the training data and is unable to respond well to unknown data. For example, it is like a student who has perfectly memorized past exam questions but is unable to handle applied questions in the actual exam.

Causes of overfitting

Overfitting mainly occurs due to the following reasons:

• Lack of training data: If there is little training data, the model can only learn a limited number of patterns and will not be able to react when it encounters unknown data.
• Model complexity: If the model is too complex, it may learn into the noise in the training data, resulting in a model that fits the training data well but generalizes poorly to unseen data.
• Excessive training time: Training for too long can cause the model to overfit the training data, resulting in overfitting.

Countermeasures against overfitting

The following measures are effective in preventing overfitting:

• Increase training data: Training with more data allows the model to learn more diverse patterns and improve generalization performance.
• Simplify the model: Reducing the number of layers and parameters in the model can help reduce overfitting.
• Regularization: A technique for reducing the complexity of a model. There are various regularization techniques, such as L1 regularization, L2 regularization, and dropout.
• Early stopping: Stopping training prematurely prevents overfitting. Use validation data to monitor the model’s generalization performance and stop training if performance starts to degrade.

Countermeasures against overfitting

The following measures are effective in preventing overfitting:

• Increase training data: Training with more data allows the model to learn more diverse patterns and improve generalization performance.
• Simplify the model: Reducing the number of layers and parameters in the model can help reduce overfitting.
• Regularization: A technique for reducing the complexity of a model. There are various regularization techniques, such as L1 regularization, L2 regularization, and dropout.
• Early stopping: Stopping training prematurely prevents overfitting. Use validation data to monitor the model’s generalization performance and stop training if performance starts to degrade.

Vanishing Gradient Problem

The vanishing gradient problem is a phenomenon in which the gradient of the error becomes smaller as it goes back up the layers during learning using the backpropagation method, and the parameters in layers close to the input layer are hardly updated at all. This problem is particularly likely to occur in deep networks such as RNNs.

What causes the vanishing gradient problem?

The vanishing gradient problem mainly occurs when using sigmoid or tanh activation functions, which have the property that the gradient becomes very small when the input value is large or small.

Solutions to the vanishing gradient problem

The following measures are effective in solving the vanishing gradient problem:

• ReLU activation function: The ReLU function can alleviate the vanishing gradient problem because its gradient is 1 for input values greater than or equal to 0.
• LSTM: LSTM (Long Short-Term Memory) is a type of RNN with a structure designed to solve the vanishing gradient problem.
• Gradient Clipping: We can mitigate the vanishing gradient problem by limiting the error gradient to no more than a certain value.
• Batch normalization: Normalizing the inputs of each layer can stabilize training and mitigate the vanishing gradient problem.

By combining these measures, we can effectively solve the vanishing gradient problem and perform stable learning even in deep neural networks.

Deep learning applications

Deep learning is being applied in a variety of fields due to its high learning ability and versatility. Here, we will introduce some representative application fields and specific examples.

Image Recognition

Image recognition is one of the areas deep learning excels in. By learning from large amounts of image data, it has become possible to recognize images with accuracy that exceeds that of humans.

• Facial recognition: It is used in a variety of situations, including security systems, facial recognition payments, photo organization apps, etc. For example, facial recognition technology is used in everyday situations such as unlocking smartphones and passport control at airports.
• Object detection: Image recognition technology is essential for self-driving cars, drones, robot vision, etc. to recognize the surrounding environment and decide the appropriate action. For example, in self-driving cars, it is used to detect pedestrians and other vehicles and avoid collisions.
• Medical imaging diagnosis: Tumors and lesions can be detected from medical images such as X-rays, CT scans, and MRIs. Deep learning can assist doctors in making diagnoses and contribute to early detection and treatment. For example, one study reported that deep learning could be used to improve the accuracy of breast cancer detection.

Natural Language Processing

Natural language processing is a technology that allows computers to understand human language. Deep learning has demonstrated high performance in various natural language processing tasks, such as machine translation, text generation, and sentiment analysis.

• Machine translation: Highly accurate machine translation services such as Google Translate and DeepL are now available, facilitating communication between different languages and promoting international exchange and business.
• Text generation: AI such as GPT-3 and ChatGPT are emerging that can generate natural text that sounds like it was written by a human. They are used to create a variety of texts, including novels, poems, news articles, and advertising copy.
• Sentiment analysis: Sentiments (positive, negative, etc.) can be analyzed from text data such as social media posts and customer reviews. It is used for marketing, customer satisfaction surveys, etc.
• Chatbots: Chatbots that automatically respond to customer inquiries on websites and apps are becoming more common, making customer support more efficient and enabling 24/7 support.

voice recognition

Speech recognition is a technology that enables a computer to recognize human speech. Deep learning has significantly improved the accuracy of speech recognition, contributing to the spread of voice assistants and voice input systems.

• Voice assistants: AI assistants that can be operated by voice, such as Siri, Alexa, and Google Assistant, are widely used. They can perform various tasks by voice, such as playing music, getting the weather forecast, and managing schedules.
• Voice input: The voice input function on smartphones and computers is used by many people because it is easier and more efficient than typing.
• Speech-to-text transcription: Transcription services that convert audio from meetings, interviews, etc. into text help improve work efficiency.

others

Deep learning is also used in a variety of other fields besides those mentioned above.

• Autonomous driving: Based on information from onboard cameras and sensors, the vehicle recognizes the surrounding situation and automatically controls the steering, accelerator, brakes, etc.
• Robot control: Controlling the movements of robots to enable them to carry out complex tasks. They are used in factory assembly work and rescue operations at disaster sites.
• Finance: It is used in a variety of financial applications, including stock price prediction, fraud detection, and loan screening.
• Marketing: Recommendation systems, targeted advertising, and more provide personalized information based on customer behavior and attributes.

It is expected that the range of applications of deep learning will continue to expand in the future.

Deep Learning Frameworks and Libraries

Tools called frameworks and libraries are essential for building and training deep learning models. These tools provide functions for efficiently performing complex calculations and easily defining the structure of models. This article introduces the major deep learning frameworks and libraries, explains their respective advantages and disadvantages, and how to choose the right one for you.

Key Frameworks and Libraries

1. TensorFlow (Google): An open source deep learning framework developed by Google. It is suitable for training large datasets and complex models, and also supports distributed processing. TensorFlow provides a low-level API, allowing flexible model construction. It also comes with a visualization tool called TensorBoard, which is useful for monitoring and debugging the training process.
2. PyTorch (Meta): An open source deep learning framework developed by Meta (formerly Facebook). It has high compatibility with Python and supports dynamic computational graphs, making it suitable for research and development and prototyping. PyTorch provides an intuitive API, making it relatively easy for even beginners to build models.
3. Keras: A library that provides a high-level API on top of TensorFlow. It is easy to use even for beginners because models can be defined with simple code. Keras supports not only TensorFlow but also other backends (Theano, CNTK).

The pros and cons of each

Frameworks/Libraries	merit	Demerit
TensorFlow	Supports large data sets and complex models, enables distributed processing, and provides flexible model construction	Somewhat steep learning curve, low-level API makes code more complex
PyTorch	Highly Python-friendly, supports dynamic computational graphs, and has an intuitive API	Debugging can be difficult; training large models requires ingenuity
Keras	You can define models with simple code, it is easy for beginners to use, and you can easily use TensorFlow’s functions.	Can be inflexible and requires knowledge of TensorFlow

How to choose and use a framework

The framework you choose will depend on your goals and skill level.

• Beginners: Keras is recommended for beginners as it allows you to define models with simple code.
• Research and Development: PyTorch’s support for dynamic computational graphs makes it well suited for research and development and prototyping.
• Training large models: TensorFlow is well suited for training large datasets and complex models.

For information on how to use a framework, refer to the official documentation and tutorials for each framework. There is also a wide range of information and sample code available on the Internet.

Deep learning frameworks and libraries are evolving every day. Every time a new tool appears or an existing tool is updated, its functionality and usability improve. Always keep an eye on the latest information, find the tool that suits you, and use it.

Deep Learning Resources

Deep learning is a field that has developed rapidly in recent years, and there is a lot to learn. However, with the right resources, even beginners can learn deep learning efficiently. Here we introduce recommended learning resources, including online courses, books, tutorials and documentation.

Online Courses

Online courses are a great way to learn deep learning in a structured way, with video lectures, exercises, and assignments that give you practical skills.

• Coursera: A wide selection of high-quality courses, including courses taught by Stanford University and deep learning authority Andrew Ng.
• Udemy: Offers a wide range of courses on deep learning, from the basics to advanced applications.
• edX: Take courses offered by some of the world’s top universities, including MIT and Harvard.
• fast.ai: Features courses focused on building practical skills in deep learning.

These platforms offer both free and paid courses, and while the free courses are sufficient, the paid courses offer more advanced content.

Books

Books are a great way to learn about deep learning at your own pace. There are books covering a wide range of topics, from basic theory to the latest research trends.

• Beginners:
  • Deep Learning from Scratch
  • Deep Learning Textbook Deep Learning G-Certification (Generalist) Official Text
  • Deep Learning with Python and Keras
• Intermediate:
  • Deep Learning with Python
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
  • Deep Learning for Coders with fastai and PyTorch
• For advanced users:
  • Deep Learning (Ian Goodfellow, Yoshua Bengio, Aaron Courville)
  • Pattern Recognition and Machine Learning (Christopher M. Bishop)

These books vary in difficulty and target audience, so it is important to choose a book that suits your level.

Tutorials and Documentation

The official documentation for deep learning frameworks and libraries provides detailed information on everything from basic usage to advanced techniques. Many frameworks and libraries also provide beginner-friendly tutorials. Masu.

• TensorFlow: https://www.tensorflow.org/tutorials
• PyTorch: https://pytorch.org/tutorials/
• Keras: https://keras.io/getting_started/

These tutorials and documentation will help you gain practical skills.

The most important part of learning deep learning is to actually get your hands dirty and write code. By working through various problems and gaining practical experience, you can deepen your understanding of deep learning by following online courses, books, tutorials, etc.

Latest research trends in deep learning

Deep learning research is advancing rapidly, with new technologies and models being announced one after another. Here we will introduce some of the latest research trends that are particularly noteworthy.

The evolution of large-scale language models

In recent years, large-scale language models (LLMs), such as GPT-3 and ChatGPT, have attracted a great deal of attention. These models have learned huge amounts of text data and can generate natural, human-like sentences. LLMs have demonstrated high performance in various natural language processing tasks, such as sentence generation, translation, summarization, and question answering, and their evolution is expected to continue accelerating.

Future research will likely involve learning from larger datasets, developing more efficient learning algorithms, and making models lighter. Ethical issues and measures against bias are also important issues to be addressed.

Advances in self-supervised learning

Self-supervised learning is a method in which a model learns by itself from unlabeled data. Conventional supervised learning requires a large amount of labeled data, but self-supervised learning can learn from unlabeled data, which can significantly reduce the cost of data collection.

Self-supervised learning has been successful in many fields, including image recognition and natural language processing, and is expected to continue to develop in the future. It is particularly expected to be applied to fields with a lot of data that is difficult to label (medical images, satellite images, etc.).

Applications of Graph Neural Networks

A graph neural network (GNN) is a neural network for handling graph-structured data (social networks, molecular structures, traffic networks, etc.). GNNs can learn by taking into account the relationships between nodes, so they can handle tasks that were difficult for conventional neural networks to handle.

GNNs are being applied in a variety of fields, including drug discovery, recommendation systems, and anomaly detection, and their potential is expected to continue to expand.

Explainable AI (XAI) research

Explainable AI (XAI) is a technology that explains the reasons for the results output by AI so that humans can understand them. Conventional deep learning models are often black boxes, making it difficult to explain why they output such results. However, as research into XAI progresses, it will become possible to understand the reasons for AI decisions, which is expected to improve the reliability of AI and enable its application to more advanced tasks.

XAI is particularly important in areas where AI decisions affect human life and property, such as medical diagnosis assistance, financial risk assessment, and autonomous driving.

The Future of Deep Learning

Deep learning is still evolving rapidly, and this evolution has the potential to greatly change our future. Here, we will consider the technical challenges and solutions of deep learning, as well as its impact on society.

Technical challenges and solutions

For deep learning to advance further, several technical challenges must be overcome.

• Explainability: Deep learning models have complex structures, so it can be difficult for humans to understand why they output a certain result. This “black box problem” is an important issue because it is directly related to the reliability and ethical issues of AI.
  • Solution: Research into explainable AI (XAI) is progressing. XAI is a technology that explains the reasons for AI decisions in a way that humans can understand, and by increasing the transparency of the model, it contributes to improving the trustworthiness of AI.
• Bias and Fairness: Deep learning models can pick up on biases in the training data, which can lead to discriminatory outcomes for certain groups.
  • Solutions: Solutions include training on diverse datasets, developing algorithms to detect and correct bias, and introducing fairness metrics.
• Computational cost and energy efficiency: Training deep learning models requires huge computational resources and energy, and training large-scale models in particular requires supercomputers or cloud computing environments.
  • Solutions: Progress is being made in developing more efficient learning algorithms, making models lighter, and developing dedicated hardware. In addition, efforts to reduce the burden on the environment, such as by using renewable energy, are also important.
• Data volume and data privacy: Deep learning models perform well when trained on large amounts of data. However, the collection and use of data that includes personal or confidential information requires caution from the perspective of privacy protection.
  • Solutions: Technologies for anonymizing personal information, the development of privacy protection technologies such as differential privacy, and the establishment of ethical guidelines for data collection and use are needed.

Impact on society

Deep learning has the potential to have a variety of impacts on our society.

• Employment: Deep learning automation technology may replace some jobs. In particular, simple and routine tasks are likely to be replaced by AI. On the other hand, new jobs that require the use of AI and jobs that require creativity and communication skills that cannot be replaced by AI may also be created.
• Economy: Deep learning has the potential to contribute to economic growth through improved productivity and the creation of new services. However, there are concerns that AI could widen inequality and lead to market monopolies by companies with AI technology.
• Ethics: The use of deep learning comes with ethical issues. When AI decisions affect human life or property, questions arise about who is responsible, and whether the AI’s decisions are transparent and fair. It is important to deepen discussions on AI ethics and reach a consensus across society.
• Education: There is an urgent need to develop human resources who can understand and utilize deep learning. Efforts to improve AI literacy are required not only in school education but also through adult education and recurrent education.

Summary: Deep learning is the technology that will shape the future

Deep learning is a technology that has the potential to dramatically change our lives and society. It has achieved remarkable results in various fields, including image recognition, natural language processing, and voice recognition, and its range of applications is expected to continue to expand.

However, as deep learning becomes more widespread in society, ethical and technical issues have also come to light. In order to resolve these issues and maximize the benefits of deep learning, not only technological development but also discussion and cooperation across society is essential.

Deep learning is a technology that will shape the future. It is important for each of us to understand the possibilities and challenges of deep learning and to actively participate in its development.