An Easy-to-Understand Guide to Types of Artificial Intelligence (AI) and How They Work

What is Artificial Intelligence (AI)?

The term artificial intelligence (AI) is now commonly heard, but surprisingly few people have a correct understanding of its definition or how it works. AI is a technology that has the potential to have a major impact on our lives and society, and to greatly change the future.

In this article, we will explain in an easy-to-understand manner the definition and history of AI, the types of AI, how it works, and the latest research trends. Let’s learn the basics of AI and think together about the future that AI will bring.

Definition and History of AI

Definition of AI: Intelligent Machines

AI is a general term for technology that mimics human intelligence and enables computers to perform intellectual activities such as learning, inference, and judgment. AI can think and act like a human by analyzing given data and discovering patterns and regularities from it.

For example, image recognition AI can learn the characteristics of cats from large amounts of image data and determine whether a cat is in an unknown image, while natural language processing AI can understand human language, answer questions, and generate sentences.

The history of AI research: from the dawn of AI to the present

The history of AI research began in the 1950s. Although it has seen periods of boom and stagnation, it has made steady progress up to the present day.

• Dartmouth Conference (1956): This conference is considered the starting point of AI research. The term “artificial intelligence” was used for the first time here, and the goals and challenges of AI research were discussed.
• The First AI Boom (1950s to 1960s): Basic AI technologies such as inference and search were developed, but due to the limitations of computer performance at the time, they were unable to solve complex problems. This boom entered a period of stagnation in the 1970s, known as the “AI Winter.”
• The Second AI Boom (1980s to 1990s): AI called expert systems, which incorporate specialized knowledge in specific fields into computers, was developed. However, the difficulty of expressing knowledge and limitations in responding to changing situations led to another AI winter in the late 1990s.
• 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.

Classification of AI: Strong AI and Weak AI, Specialized AI and General AI

AI can be broadly divided into two types depending on its level of capabilities and autonomy.

• Strong AI (Artificial General Intelligence, AGI): AI that has intelligence equal to or greater than that of humans and can perform a variety of tasks autonomously. Although strong AI has not yet been realized, it is considered one of the ultimate goals of AI research.
• Weak AI (Specialized AI, Narrow AI): AI designed to specialize in a specific task. For example, image recognition AI, natural language processing AI, shogi AI, etc. Weak AI performs well in a specific task, but cannot handle other tasks.

Most current AI is weak AI. However, advances in AI technology are remarkable, and it is expected that strong AI may become a reality in the future.

How AI works

How does AI imitate human intelligence and perform intelligent activities on a computer? Here we explain the main technologies that support AI: machine learning, neural networks, and deep learning.

Machine Learning: Learning from Data

Machine learning is a technology that allows computers to learn from data and discover patterns and regularities, enabling them to automatically acquire knowledge from data without being explicitly programmed by humans.

There are three main types of machine learning:

• Supervised learning: This is a method of learning by pairing input data with its correct label (teacher data). For example, by pairing a large amount of image data with labels such as “dog” and “cat” and training it, it becomes possible to distinguish the type of animal in the image.
• Unsupervised learning: A method of learning the structure and characteristics of data from data that does not have a correct label. For example, you can analyze customer purchase history data and classify customers into several groups.
• Reinforcement learning: A method of learning through trial and error to maximize the reward obtained as a result of an action. For example, AI for playing Go or Shogi can become stronger by repeatedly playing against itself through reinforcement learning.

Neural networks: Mimicking how the brain works

A neural network is a mathematical model that mimics the network structure of nerve cells (neurons) in the human brain. Just as neurons in the brain work together to process information, neural networks also have many nodes (artificial neurons) that are interconnected and perform complex calculations to learn and make inferences.

• Input layer, hidden layer, output layer:
  *A neural network consists of three layers: an input layer, a hidden layer, and an output layer.
• Input layer is a layer that receives data from outside (e.g. pixel values from an image or words from a sentence).
• A hidden layer is a layer between the input layer and the output layer, and by stacking multiple layers, it is possible to learn more complex features and patterns.
• The output layer is the layer that outputs the final result (e.g. what is in an image, the meaning of a sentence, etc.).
• Activation functions, weights and biases:
• Each node receives inputs from other nodes, multiplies them by parameters called weights, adds them together, then adds a value called a bias and converts them into an output value through a nonlinear function called an activation function.
• Activation functions introduce nonlinearity into neural networks, allowing them to learn complex patterns.
• The weights and biases are adjusted based on the training data to improve the performance of the neural network.

Deep Learning: Multi-layered Neural Networks

Deep learning is a type of machine learning that uses multi-layered neural networks to learn complex patterns and features from data. By increasing the number of hidden layers, it becomes possible to learn more abstract features, and it demonstrates high performance in tasks such as image recognition and natural language processing.

There are various types of neural networks in deep learning. Here, we will introduce three representative architectures.

• Convolutional Neural Network (CNN):
• A neural network specialized for image recognition. By stacking multiple convolutional layers and pooling layers to extract image features, highly accurate image recognition is achieved.
• Application examples: Face recognition, object detection, image classification, medical image diagnosis, etc.
• Recurrent Neural Network (RNN):
• 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.
• Application examples: Machine translation, text generation, sentiment analysis, speech recognition, etc.
• Transformer:
• A neural network that uses a mechanism called the Attention Mechanism to process input data while taking into account the relationships between each part of the data. It demonstrates high performance in natural language processing, and in recent years has also been applied to image processing.
• Application examples: Machine translation, text generation, question answering, image recognition, etc.

These deep learning models can perform tasks with accuracy that exceeds that of humans by training them with large amounts of data, but they also require large amounts of computing resources for training and have issues such as low interpretability of the models.

Types of AI: Various AI and their characteristics

AI is classified into various types depending on its capabilities and applications. Here, we will explain in detail the characteristics and use cases of specialized AI, which is currently widely used, and artificial general intelligence (AGI), which is expected to become a reality in the future.

Specialized AI (Narrow AI)

Specialized AI is AI designed to specialize in a specific task. It can outperform humans in a specific field, but cannot perform other tasks. Most of the AI currently in practical use falls into this specialized AI category.

Image Recognition AI

Image recognition AI is an AI that recognizes what is in an image or video. It specializes in various tasks such as facial recognition, object detection, and image classification.

• Facial Recognition:
• A technology that identifies individuals from facial images. It is used in a variety of applications, including security systems, facial recognition payments, and photo organization apps.
• Case Study:
• Face unlock for smartphones
• Facial recognition gates at airport passport control
• Facebook auto-tagging
• Object Detection:
• A technology that detects specific objects in images and videos. It is used in a variety of fields, including self-driving cars, drones, robot vision, and factory automation.
• Case Study:
• Pedestrian and obstacle detection in autonomous vehicles
• Object detection from aerial footage taken by drones
• Visual inspection of products at the factory
• Image Classification:
• A technology that classifies images into multiple categories. It is used in a variety of fields, including medical image diagnosis, product quality inspection, and image filtering on social media.
• Case Study:
• Assists in diagnosing diseases from X-rays and CT scans
• Factory defect detection
• Detecting inappropriate image posts on social media

Natural Language Processing AI

Natural language processing AI is AI that understands and processes human language. It specializes in various tasks such as machine translation, text generation, sentiment analysis, and chatbots.

• Machine Translation:
• A technology that automatically translates between different languages. Highly accurate machine translation services such as Google Translate and DeepL are now available.
• Case Study:
• Translate web pages and documents
• Communication when traveling abroad
• International Business Communication
• Sentence generation:
• A technology that generates new text from text data. It can generate a variety of texts, including news articles, novels, poems, and advertising copy.
• Case Study:
• Automatic generation of news articles
• Support for writing novels and poems
• Auto-generate ad copy
• Sentiment Analysis:
• A technology that analyzes emotions (positive, negative, etc.) contained in text data. It is used in the marketing field, such as customer satisfaction surveys and social media analysis.
• Case Study:
• Product review analysis
• Social media post analysis
• Call center conversation analysis
• Chatbot:
• An AI system that answers human questions in natural language. It is used in a variety of fields, including customer support and information provision.
• Case Study:
• Customer support on EC sites
• Online consultation services for banks and insurance companies
• Handling inquiries on company websites

Speech recognition AI

Speech recognition AI is an AI that recognizes human speech and converts it into text. It is used in a variety of applications, including voice input, voice search, and voice translation.

• Voice input:
• A technology for inputting text using voice. It is installed on a variety of devices, including smartphones, PCs, and smart speakers.
• Case Study:
• Voice input function of smartphone
• Control home appliances with a smart speaker
• Meeting minutes creation with voice recognition
• Voice Search:
• A technology for entering search keywords by voice. It is used in the voice search functions of smartphones and smart speakers.
• Case Study:
• Voice search function on smartphones
• Information search using smart speakers
• Audio Translation:
• A technology that translates speech into different languages. It is used in real-time translation apps and translation devices.
• Case Study:
• Communication when traveling abroad
• Simultaneous interpretation at international conferences

Recommendation engine

A recommendation engine is an AI system that recommends products and content based on a user’s behavioral history and preferences. It is used in a variety of services, including e-commerce sites, video distribution services, and music streaming services.

• Case Study:
• Amazon product recommendations
• Recommended movies and dramas on Netflix
• Recommended music playlists from Spotify

others

• Game AI: AI that controls the actions of characters in the game. There are various types, such as enemy character AI and ally character AI.
• Spam Filter: AI that automatically identifies and keeps spam emails out of your inbox.
• Fraud Detection System: An AI that detects fraudulent use of credit cards and unauthorized logins.

Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) is the ultimate goal in AI research, and its realization has the potential to fundamentally change our society and lives. Unlike AI specialized for specific tasks, AGI refers to AI that has a wide range of knowledge and abilities like humans and can adapt to a variety of situations.

Definition and characteristics of AGI

AGI is defined as AI that has intelligence equal to or greater than that of humans and the ability to learn and execute various tasks autonomously. AGI is not limited to a specific field, and is said to be able to carry out a wide range of human intellectual activities, including learning, reasoning, judgment, problem solving, and creativity.

The characteristics of AGI are as follows:

• Versatility: The ability to perform a variety of tasks without being limited to a specific one.
• Autonomy: The ability to set goals, make plans and take action independently.
• Learning Ability: The ability to acquire new knowledge and skills independently.
• Creativity: The ability to generate new ideas and concepts.
• Adaptability: The ability to adapt flexibly to changes in the environment.

Status of AGI research and development

Research and development into AGI is being actively conducted by research institutes and companies around the world, but it has not yet been realized. To achieve AGI, it is necessary to elucidate the complex information processing mechanisms of the human brain and reproduce them on a computer.

Current AI research focuses on machine learning techniques such as deep learning, but it is thought that it will be difficult to realize AGI using these techniques alone. To achieve AGI, a new approach that integrates knowledge from various fields such as symbol processing AI, neuroscience, and cognitive science will be necessary.

Challenges to achieving AGI

To achieve AGI, the following technical and ethical challenges must be overcome:

• Technical challenges:
• Limits of Computing Power: In order to achieve AGI, we need computers with computing power equal to or greater than that of the human brain. Even current supercomputers are said to be unable to reach the computing power of the human brain.
• Algorithm Complexity: AGI requires highly complex algorithms to perform a wide variety of tasks. With current AI technology, it is not easy to develop such complex algorithms.
• Lack of data quality and quantity: AI improves its performance by learning from large amounts of data. However, collecting and building the diverse datasets required for AGI is not easy.
• Difficulties in evaluation metrics: Appropriate evaluation metrics are necessary to evaluate the performance of AGI. However, because the tasks that AGI must perform are diverse, it is difficult to measure AGI performance with a single evaluation metric.
• Ethical Issues:
• AI Safety: There is concern that AGI may exceed human intelligence and become uncontrollable. To ensure the safety of AGI, it is necessary to develop ethical AI design and technology to monitor and control AI behavior.
• Ethical judgment of AI: Will AGI be able to make ethical judgments? When AI faces an ethical issue, there are problems such as what kind of judgment it should make, who will decide the standard, and how.
• Relationship between AI and humans: How will the relationship between humans and AI change as AGI penetrates society? There are concerns that AI will take over human jobs and that we will be dominated by AI.
• Impact on employment: AGI could result in a large number of jobs being replaced, resulting in a large number of unemployed people. It is necessary to predict in advance what kind of employment changes will occur as a result of the introduction of AGI and take measures.

The future brought about by AGI

If AGI becomes a reality, it could bring about major changes to our society and lives, potentially bringing about revolutionary advances in a variety of fields, including scientific research, medicine, education, and business.

• Acceleration of scientific research: AGI can analyze vast amounts of papers and data at speeds far surpassing those of human researchers, leading to new discoveries and hypotheses. This is expected to accelerate research in a variety of fields, including new drug development, new material development, and space exploration.
• Medical Evolution: AGI has the potential to bring about revolutionary changes in the medical field, including improving the accuracy of medical image diagnosis, realizing personalized medicine, and accelerating new drug development. AGI can make diagnoses more accurately and quickly than human doctors and suggest optimal treatments tailored to each patient’s constitution and condition.
• Individualized optimization of education: AGI can provide optimal learning materials and study plans tailored to each individual learner’s level of understanding and progress. This will enable students to study efficiently at their own pace, and is expected to dramatically improve the quality of education.
• Business Efficiency: AGI can be used in various business situations, such as management decisions, process automation, and customer support. AGI can analyze large amounts of data and propose optimal strategies and decisions. In addition, by linking it with RPA (Robotic Process Automation), it can automate routine tasks and reduce the burden on employees.
• Solving social issues: AGI can contribute to solving various social issues facing humanity, such as environmental issues, poverty, and disaster prevention. AGI can analyze complex systems and propose optimal solutions. For example, AGI can perform climate change simulations and provide information that is useful for measures against global warming.

The future that AGI will bring gives us hope and anxiety at the same time, but by giving careful thought to the ethical aspects of AGI and taking appropriate measures, we may be able to coexist with AGI and build a better future.

Deep dive into how AI works: Key technologies and algorithms

AI intelligence is realized by complex algorithms and the technologies that support them. Here, we will explain the machine learning algorithms that form the basis of AI, dividing them into three major categories: supervised learning, unsupervised learning, and reinforcement learning, and introduce representative algorithms for each, their characteristics, and examples of their use.

Machine Learning Algorithms

Machine learning is the core technology of AI, where computers learn from data and discover patterns and regularities to perform tasks such as prediction and classification. Machine learning algorithms are broadly divided into three types according to the learning method: supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning

Supervised learning is a method of pairing input data with its correct label (teacher data) and training the model. For example, by pairing a large amount of image data with labels such as “dog” and “cat” and training the model, it will be possible to distinguish the type of animal in the image.

Supervised learning can be divided into two main tasks:

• Classification: The task of sorting data into categories. Applications include, for example, spam detection, customer segmentation, image classification, etc.
• Regression: The task of modeling relationships between data and predicting values. For example, it is used in sales forecasting, stock price forecasting, demand forecasting, etc.

Representative algorithms for supervised learning

• Linear Regression: An algorithm used when there is a linear relationship between input and output variables, for example analyzing the relationship between advertising costs and sales, or predicting the relationship between temperature and ice cream sales.
• Logistic Regression: An algorithm used when the output variable is binary (e.g. success/failure, positive/negative), for example to predict whether a customer will purchase a product or to help diagnose a disease.
• Decision Tree: An algorithm that classifies data in a tree structure. It has the advantage of being highly interpretable and easy to explain the reasons for decision-making. For example, it can be used in a recommendation system that decides which products to recommend based on customer attribute information.
• Random Forest: An algorithm that improves prediction accuracy by combining multiple decision trees. It is one of the methods known as ensemble learning, and demonstrates high performance in a variety of fields.
• Support Vector Machine (SVM): An algorithm that classifies data into two groups. It is good at classifying high-dimensional data and has a built-in mechanism to prevent overfitting. For example, it is used in handwritten character recognition and face recognition.

Unsupervised learning

Unsupervised learning is a method in which a model discovers patterns and features by itself from data that does not have correct labels. It is used for tasks that understand the structure of data, such as data clustering, dimensionality reduction, and anomaly detection.

Representative algorithms for unsupervised learning

• k-means method: An algorithm that divides data into k clusters. It is used in customer segmentation, image compression, etc.
• Principal Component Analysis (PCA): An algorithm that converts high-dimensional data into lower-dimensional data. It is used for data visualization and noise removal.
• Self-Organizing Map (SOM): An algorithm that maps data into a low-dimensional space while preserving the topology of the data. It is used for data visualization and anomaly detection.

Reinforcement Learning

Reinforcement learning is a method in which an agent learns from interactions with the environment through trial and error, and acquires actions that maximize rewards. It is applied in various fields such as game AI, robot control, autonomous driving, and financial trading.

Representative algorithms for reinforcement learning

• Q-learning: An algorithm that learns an action-value function Q. Based on the Q value, the agent can choose the optimal action.
• SARSA (State-Action-Reward-State-Action): Similar to Q-learning, this is an algorithm that learns an action-value function Q, but chooses the next action based on the current policy, thus mimicking more realistic situations.
• Actor-Critic: This is an algorithm that uses two networks, an actor and a critic. The actor selects the action, and the critic evaluates the value of the action. This allows for more efficient learning.

Each of these algorithms has different characteristics and is applicable to different tasks. AI developers can maximize the performance of their AI models by understanding these algorithms and selecting the appropriate one.

Deep Learning Architecture

Deep learning uses multi-layered neural networks to learn complex patterns and features from data. Here we explain the most common architectures often used in deep learning, their features, and examples of their use.

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.

• Convolutional Layer: It performs calculations on small regions (filters) of the image to generate a new representation called a feature map. By sliding the filters across the image, it is possible to extract different features.
• Pooling Layers: It reduces the size of the feature maps and reduces the amount of computation. Pooling layers also help obtain feature representations that are robust to image translation.

CNNs perform well in a variety of image recognition tasks, including image classification, object detection, and segmentation. For example, self-driving cars use CNNs to recognize surrounding objects and help them drive safely. In the medical field, research is also underway to use CNNs to detect lesions in X-ray and CT scan images.

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.

• Hidden state: RNNs store information from past inputs as hidden states and process them in conjunction with the current input, allowing them to make predictions and generate results that take into account past context.

RNNs are widely used in natural language processing (machine translation, text generation, sentiment analysis, etc.) and time series data analysis (stock price prediction, demand forecasting, etc.). For example, conversational AI such as ChatGPT is developed based on Transformer, a type of RNN.

Transformer

Transformer is a neural network architecture announced by a Google research team in 2017. Unlike RNNs, it does not have a recursive structure and uses the Attention mechanism to process time-series data.

• Attention mechanism: For each element of the input data, we calculate the relevance to other elements and pay attention to the more important elements. This allows us to learn long-distance dependencies efficiently.

Transformer has demonstrated performance superior to RNNs in natural language processing (machine translation, text generation, question answering, etc.) In recent years, it has also been applied to other fields such as image processing and speech processing.

Other architectures

• Autoencoder: A neural network used for data dimensionality reduction and feature extraction. It compresses input data into a low-dimensional latent space and learns to recover the original data from that latent space.
• Generative Adversarial Network (GAN): A model in which two neural networks (a generative network and a discriminative network) compete with each other to learn. It can generate realistic images, videos, and audio.

Natural language processing technology

Natural Language Processing (NLP) is a technique for enabling computers to understand and process human language. This article explains word embeddings, Transformer-based models, and other NLP techniques that are commonly used in NLP.

Word Embedding

Word embedding is a technique for representing words as vectors (arrays of numbers). Representing the meaning and context of words in a vector space makes it easier for computers to understand the relationships between words.

• Word2Vec: A model that learns word embeddings based on word co-occurrences.
• GloVe (Global Vectors for Word Representation): A model that extracts word co-occurrence information from large corpora (text data) and learns word embeddings.
• fastText: A model that extends Word2Vec, it splits words into subwords (units of strings that make up words) and learns embeddings, allowing it to handle unknown words and morphological changes.

Transformer-based models

Transformer is a neural network architecture that demonstrates high performance in natural language processing. Various models based on Transformer have been developed and are used in various tasks in natural language processing.

• BERT (Bidirectional Encoder Representations from Transformers): A model developed by Google that learns contextual information from large amounts of text data through pre-training. It can handle a variety of tasks, including text classification, question answering, and named entity extraction, with high accuracy.
• GPT (Generative Pre-trained Transformer): A model developed by OpenAI that specializes in text generation. It has learned from a large amount of text data and can generate natural-sounding sentences that sound like they were written by a human.
• T5 (Text-to-Text Transfer Transformer): A model developed by Google that can handle various natural language processing tasks in a unified manner. A single model can train and execute various tasks such as translation, summarization, and question answering.

Other NLP techniques

• Hidden Markov Model (HMM): A method for modeling probabilistic relationships in time series data. It is used in a variety of tasks, including speech recognition and part-of-speech tagging.
• Conditional Random Field (CRF): A probabilistic model used for sequence labeling tasks (e.g. part-of-speech tagging, named entity extraction), which can model more complex dependencies than HMMs.

Latest trends in AI development and future prospects

AI technology is evolving day by day, and this evolution is having a major impact on our society and business. Here we take a closer look at the latest trends in AI development, the impact of AI on society, and the future of AI.

AI development trends

AI development is currently seeing several important trends that are helping to improve AI development efficiency and performance, and enabling the development of more advanced AI applications.

AutoML (Automated Machine Learning)

AutoML (Automated Machine Learning) is a technology that automates the construction, learning, and evaluation of machine learning models. Traditionally, the development of machine learning models required data scientists with specialized knowledge and skills, but the advent of AutoML has made it possible for even non-experts to easily develop AI models.

AutoML automates the process of:

• Data preprocessing: Missing value imputation, outlier removal, feature engineering, etc.
• Model Selection: Select the most appropriate machine learning algorithm based on the characteristics of your data.
• Hyperparameter tuning: Optimizing parameters that affect model performance, such as learning rate and regularization parameters.

AutoML has made a significant contribution to the democratization of AI development and is being used in a variety of industries. For example, in the medical field, efforts are underway to use AutoML to develop AI models for diagnosing diseases and predicting treatment outcomes.

Machine Learning Operations (MLOps)

MLOps (Machine Learning Operations) is a method to streamline the process from development to operation of machine learning models. By automating model version management, deployment, monitoring, re-learning, etc., it can improve the productivity of AI development.

MLOps applies the concept of DevOps (Development and Operations) to machine learning, and has the following benefits:

• Shortened development cycles: By automating the process from model development to operationalization, you can shorten the development cycle and more quickly introduce AI models into your business.
• Improve quality: You can improve the quality of your AI models by using model versioning and test automation.
• Reduced operational costs: Automating model monitoring and retraining can reduce operational costs.

MLOps is an essential element for improving the efficiency and quality of AI development, and is expected to become increasingly important in the future.

Edge AI

Edge AI is a technology that performs AI processing on the device side (edge) rather than in the cloud. Edge AI has the following advantages:

• Low latency: Real-time processing is possible since no communication with the cloud is required.
• Privacy Protection: No data leaves your device, so your privacy is protected.
• Cost Savings: Reduce your cloud bills.

Edge AI is expected to be installed in a variety of devices, including IoT devices, smartphones, and drones, and to make our lives more convenient. For example, AI installed in a smartphone camera can perform image recognition in real time, identify the subject, and blur the background.

Quantum computing and AI

Quantum computers are the next generation of computers that operate on different principles than conventional computers, and are expected to perform far superior to conventional computers in certain calculations.

The collaboration between AI and quantum computers is attracting attention as a new frontier in AI research. Quantum computers have the potential to speed up the learning and inference processing of AI models, making it possible to develop larger, more complex AI models.

For example, quantum computers are expected to be particularly effective in fields such as drug discovery and materials development, where optimal solutions must be found from among a vast number of combinations.

The impact of AI on society

Advances in AI technology are having a variety of effects on our society. Here, we will explain the impact of AI on society from four perspectives: employment, economy, education, and ethics.

employment

The introduction of AI has the potential to take away jobs by automating some jobs. In particular, simple and routine tasks are considered to be at high risk of being replaced by AI. Examples include factory assembly work, data entry, and call center work.

However, AI has the potential to not only take away jobs from humans, but also create new jobs. New AI-related occupations, such as AI engineers, data scientists, and AI ethics consultants, are attracting attention. In addition, by using AI to handle repetitive tasks and routine work, humans may be able to focus on more creative work and work that involves building relationships.

The impact of AI on employment is expected to accelerate in the future. Governments and companies need to support education and training to acquire the skills needed in the AI era and adapt to changes in the labor market.

economy

The introduction of AI can also have a major impact on the economy. While automation using AI can lead to increased productivity and cost reductions, promoting economic growth, there are also concerns about job losses and widening inequality.

• Improved Productivity and Economic Growth: AI can improve productivity and contribute to economic growth by complementing and enhancing human capabilities. For example, in the manufacturing industry, AI robots can automate repetitive tasks, improving production efficiency and reducing costs. In addition, data analysis by AI can accurately grasp market trends and customer needs, enabling more effective marketing strategies and product development.
• Job loss and change: The introduction of AI may cause job loss by automating some jobs. Routine and simple tasks are considered to be at high risk of being replaced by AI. However, on the other hand, it is possible that new jobs will be created in developing and operating AI, and new business models will be created that utilize AI.
• Wider inequality: While companies with AI technology and people who can use AI effectively will gain more wealth, companies and people who do not have AI technology will lose their competitiveness and may find themselves at an economic disadvantage. For this reason, there are concerns that the spread of AI technology will widen the economic gap.

The impact of AI on the economy is expected to become even more pronounced in the future. Governments and companies need to predict the economic impact of the introduction of AI and take appropriate policies and measures. For example, it will be important to create new jobs to compensate for jobs lost due to AI and to develop human resources with AI skills.

Education

AI also has great potential in the field of education. AI has many possibilities for improving the quality of education, including individually optimized learning, efficient creation of teaching materials, and reducing the burden on teachers.

• Individually Optimized Learning: AI can provide optimal learning materials and tasks according to each student’s learning situation and level of understanding. This allows students to progress at their own pace and maximize their learning effect.
• Example: Knewton provides an AI-powered adaptive learning platform that adjusts the difficulty and content of learning materials based on a student’s learning history and level of understanding.
• Efficient teaching material creation: AI can also be used as a support tool for teachers when creating teaching materials. For example, text generation AI can help with writing and summarizing teaching materials, and image generation AI can create illustrations and diagrams for teaching materials.
• Example: OpenAI’s DALL-E 2 is an AI that can generate high-quality images from text, and is also used to create educational content.
• Reducing the burden on teachers: AI can reduce the burden on teachers by automating tasks such as grading and grade management, allowing teachers to spend more time communicating with students and providing individualized instruction.
• Example: Gradescope provides an automatic grading tool that uses AI to automatically grade handwritten answer sheets and programming code.

However, there are also several challenges to consider when introducing AI into education.

• Ethical use of AI: It is important that AI properly protects students’ personal information and ensures fairness.
• Changes in the role of teachers: The introduction of AI into the educational field will also change the role of teachers. Teachers will need to acquire the skills to use AI and work with AI to teach.
• Educational gap: While AI education becomes more widespread, there is a possibility that some students will not be able to receive AI education due to financial reasons, etc. Measures to eliminate educational gaps are also necessary.

Ethics

The advancement of AI also raises ethical issues. Because AI has the potential to surpass human intelligence, its development and use entails a variety of ethical issues, including safety, ethical judgment, relationships with humans, and the impact on employment.

• AI Safety: If AI malfunctions or is used by malicious third parties, serious damage may occur. Ensuring the safety of AI requires ethical design of AI and the development of technology to monitor and control AI behavior.
• Ethical judgment of AI: Will AI be able to make ethical judgments? When AI faces an ethical issue, there are problems such as what kind of judgment it should make, who will decide the standard, and how.
• Relationship between AI and humans: How will the relationship between humans and AI change as AI penetrates society? There are concerns that AI will take over human jobs and that humans will be dominated by AI.
• Impact on employment: AI may replace many jobs, resulting in a large number of unemployed people. It is necessary to predict in advance what kind of employment changes will occur as a result of the introduction of AI and take measures.
• Privacy: AI may violate privacy when collecting and using personal information. When developing and using AI, it is necessary to comply with laws and regulations such as the Personal Information Protection Act and to design with privacy in mind.
• Discrimination and fairness: AI may reflect biases contained in the training data. This may lead to discriminatory judgments against certain groups. AI developers must continue to make efforts to eliminate bias and ensure fairness.
• Accountability: Developers and users need to be accountable for decisions made by AI. It is important to make the AI’s decision-making process transparent and to be able to explain why the AI made that decision.

Discussions on AI ethics have only just begun, and many issues remain to be addressed. However, as the impact of AI technology on society grows, the importance of AI ethics is increasing. We need to think seriously about AI ethics and deepen the discussion in order to build a better society that coexists with AI.

The Future of AI

The future of AI gives us hope and anxiety at the same time. However, the evolution of AI technology has the potential to enrich our lives and society. Here, we will discuss the future of AI from two perspectives: the realization of artificial general intelligence (AGI) and the coexistence of AI and humans.

Realizing Artificial General Intelligence (AGI)

AGI (Artificial General Intelligence) is a versatile AI that can perform a variety of tasks like humans. Unlike AI specialized for a specific task, AGI is said to have the ability to adapt to new situations and challenges, the ability to solve problems in unknown areas, and the ability to perform multiple tasks simultaneously.

The realization of AGI is one of the ultimate goals of AI research, and many researchers are working towards its realization. However, there are still many technical and ethical challenges to overcome before AGI can be realized.

If AGI becomes a reality, it could bring about major changes to our lives and society. For example, AGI could bring about revolutionary advances in various fields, including scientific research, medicine, education, and business.

Coexistence of AI and humans

The evolution of AI technology has the potential to have a major impact on our lives and society. Some people may be concerned that AI will take over human jobs or that we will come to be dominated by AI.

However, AI is merely a tool and cannot replace humans. By performing tasks that humans are not good at, AI can allow humans to focus on more creative work and work that builds relationships. In addition, the evolution of AI may lead to the creation of new jobs that have never existed before.

In order for AI and humans to coexist, it is necessary to pay attention to the following points.

• AI Transparency and Accountability: It is important to make the AI’s decision-making process transparent and to be able to explain why the AI made that decision. This will increase trust in AI and prevent its misuse or abuse.
• Adherence to AI ethics: The development and use of AI must adhere to ethical guidelines and respect human dignity and rights.
• Promotion of AI education: By acquiring knowledge and skills about AI, people will be able to correctly understand AI and use it appropriately. AI education is important not only in school education, but also in adult education and lifelong learning.

Summary: Deepening our understanding of AI and creating the future

AI is a technology that has the potential to greatly change our lives and society. By understanding the definition, history, types, mechanisms, and latest research trends of AI, you can acquire correct knowledge about AI.

AI has the potential to make our lives more convenient and enriching. However, as AI evolves, we must also consider ethical issues and social impacts. In order to build a future in which we coexist with AI, not only technological development but also discussion and cooperation throughout society is essential.

If each of us correctly understands the potential and challenges of AI and uses it appropriately, we can create a better future.