What is AI Technology? Why is AI Becoming a Global Trend?

AI technology is transforming the way people work, learn, and interact with the world. From virtual assistants like Siri and Google Assistant to AI - powered automation systems in manufacturing, this technology is rapidly evolving and becoming an integral part of modern life.

But what exactly is AI technology? How does it work? Why are more and more businesses investing in AI and applying it in practice? If you are looking for a comprehensive article about artificial intelligence technology, this piece will help you understand it from A to Z. Let’s explore in detail with Tokyo Tech Lab in the article below.

I. What is AI Technology?

AI technology (Artificial Intelligence Technology) is a collection of systems and algorithms that enable machines to simulate human intelligence, including learning, reasoning, language processing, and decision-making.

Simply put, AI technology allows machines to "think" and perform tasks that require intelligence - tasks that were previously only possible for humans. AI systems can analyze vast amounts of data in a short time, learn from past experiences, and continuously improve their accuracy.

AI is not just a theoretical concept but has become a core technology in various fields such as healthcare, finance, e-commerce, manufacturing, and transportation. AI technology includes several branches such as Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, and Expert Systems.

Thanks to AI, machines can analyze massive datasets, automate workflows, personalize user experiences, and optimize business operations. With its rapid advancements, AI technology is reshaping the way people work and interact with the digital world.

II. Why is Artificial Intelligence Becoming a Global Trend?

Artificial intelligence (AI) is becoming a global trend because it brings outstanding benefits across multiple sectors, from business to daily life. One of the most significant reasons is its ability to automate and optimize workflows. AI helps businesses reduce costs, increase productivity, and minimize errors. With intelligent algorithms, AI can handle repetitive tasks more accurately and efficiently than humans, thereby improving the operational effectiveness of enterprises and organizations.

Additionally, AI has the capability to process data at incredible speeds with high accuracy. In the era of Big Data, humans find it challenging to analyze and extract valuable insights effectively. AI helps filter essential information, predict trends, support decision-making, and even protect data from cybersecurity threats. By leveraging AI, organizations can make more strategic and data-driven decisions, from marketing strategies to financial management.

AI is also widely applied across many critical industries. In healthcare, AI aids in diagnosing diseases through medical imaging, supporting drug research, and personalizing treatment plans. In finance, AI detects fraud, assesses risks, and optimizes investments. In e-commerce, AI analyzes user behavior to recommend relevant products, enhancing the shopping experience. Furthermore, AI plays a crucial role in transportation with self-driving vehicles, route optimization, and smart city management.

The rapid development of cloud computing and advanced hardware is also accelerating AI’s expansion. Cloud computing services from Google Cloud, Microsoft Azure, and Amazon Web Services allow businesses to access AI technology without investing in expensive infrastructure. Meanwhile, AI-specific processors such as NVIDIA GPUs and Google TPUs enhance computing speed, enabling AI to perform more powerful functions. The combination of AI and 5G technology is unlocking new potentials, especially in IoT and smart city applications.

Beyond technological factors, fierce competition among major corporations like Google, Microsoft, OpenAI, Tesla, and Amazon is driving AI’s rapid growth. These companies continuously invest in AI research, developing cutting-edge products and services to gain a competitive edge. For example, Google owns DeepMind, Microsoft collaborates with OpenAI to develop ChatGPT, Tesla integrates AI into self-driving cars, and Amazon leverages AI to optimize supply chains and e-commerce operations. This competition not only fosters technological innovation but also makes AI more accessible in everyday life.

AI not only benefits businesses but also contributes to economic and social development. AI improves education quality through intelligent learning platforms and personalized curriculums. In urban management, AI helps build smart cities, optimize energy use, and reduce environmental pollution. While AI may replace some traditional jobs, it also creates new career opportunities in fields such as data science, AI engineering, and cybersecurity.

III. Classification of Artificial Intelligence Technology

AI can be classified in various ways. One of the most common methods is categorizing AI based on its complexity and cognitive capabilities.

Type 1: Reactive Machine AI

Reactive AI is the most basic form of artificial intelligence, capable only of responding to input data without storing or using past experiences. These systems cannot learn or make decisions based on historical data.

A notable example of reactive AI is Deep Blue, the chess-playing system developed by IBM that defeated Grandmaster Garry Kasparov in 1997. Deep Blue could evaluate the best possible move using algorithms and computational power, but it could not improve its strategy based on previous games.

Reactive AI is commonly used in low-complexity systems that require high accuracy, such as:

• Spam filtering systems in emails.
• Image recognition software, such as Google's photo classification technology.
• Chess or strategic gaming programs.

Type 2: Limited Memory AI

Unlike reactive AI, limited memory AI can store temporary data and use past experiences to make decisions. This is the most widely used AI today, applied in areas such as self-driving cars, virtual assistants, and data analysis.

A prime example of limited memory AI is Tesla’s self-driving cars, which can remember recent traffic data to adjust driving behavior, such as avoiding obstacles or stopping at red lights. Additionally, chatbots and virtual assistants like Siri, Alexa, and Google Assistant use this type of AI to analyze user queries based on past interactions and provide appropriate responses.

Applications of limited memory AI include:

• Autonomous vehicles, analyzing sensor data in real time to make driving decisions.
• Recommendation systems on Netflix, Spotify, or YouTube, which suggest content based on previous user behavior.
• Virtual assistants like Siri, Alexa, and Google Assistant, learning from user interactions to enhance their responses.

Type 3: Theory of Mind AI

Theory of Mind AI represents an advanced level of artificial intelligence that can understand and react based on human emotions, thoughts, and intentions. This is a significant step forward in AI, as it not only relies on data but also comprehends human psychology to interact more naturally.

Although current AI has not yet reached this level, research in social robots and advanced AI assistants aims to develop systems capable of recognizing and responding to human emotions. For example, a robot might analyze a person’s facial expressions to determine whether they are happy or sad, then adjust its communication accordingly.

Potential applications of Theory of Mind AI include:

• Healthcare, with emotional support robots that assist patients with depression or elderly individuals.
• Customer service, where AI can understand customer emotions and provide more empathetic responses.
• Education, where AI personalizes learning programs based on student emotions and comprehension levels.

Type 4: Self-Aware AI

Self-aware AI represents the highest level of artificial intelligence, where the system is not only capable of understanding humans but also has self-awareness, emotions, and its own intentions. This AI is closest to human intelligence, where machines can think independently, experience emotions, and make decisions based on their own consciousness.

Currently, self-aware AI remains purely theoretical and is mostly depicted in science fiction. However, with rapid advancements in technology, many experts believe that self-aware AI could become a reality in the distant future.

If AI reaches this level, it could completely transform the way we work and live. However, it also raises significant ethical and control issues, such as ensuring AI does not develop beyond human control.

IV. Applications of AI Technology in Programming

In the field of programming, artificial intelligence (AI) is not just a helpful tool but is gradually transforming how developers create software. AI can assist developers in various aspects, from automating code creation, bug detection, performance optimization, to building intelligent systems. Below are some key applications of AI in programming.

1. Code Writing Assistance and Autocompletion

One of the most common applications of AI in programming is assisting with code writing, which helps developers save time and reduce syntax errors. AI tools such as GitHub Copilot (developed based on OpenAI Codex) can suggest and autocompletes code snippets based on the programming context.

For example, if a developer types a Python command to read a file, AI can automatically suggest the complete code, including error handling and optimization. This allows the coder to focus on the logic rather than remembering every detailed command.

Popular tools for code assistance:

• GitHub Copilot – AI-based code writing assistant.
• Tabnine – Code suggestion based on deep learning models.
• Kite – Autocompletion for various programming languages.

2. Bug Detection and Code Optimization

AI can automatically scan source code to detect bugs, security vulnerabilities, or inefficient code, helping developers quickly fix issues and improve software quality. Tools such as DeepCode, SonarQube, and Snyk use AI to analyze code and suggest optimization solutions.

For example, AI can identify security flaws like SQL Injection, XSS (Cross-Site Scripting), or memory overflow, and recommend fixes. As a result, the software runs not only faster but also more securely.

AI-based code review tools include:

• DeepCode – Real-time code analysis for security flaws.
• SonarQube – Code quality checks and improvement suggestions.
• Snyk – Detecting and fixing security issues in dependencies (external libraries).

3. Automatic Software Creation and Testing

AI does not just help developers write code but also automates the software testing process. Instead of manually testing each function, AI can automatically generate test cases, run experiments, and report bugs.

For instance, Testim and Applitools use AI for UI testing, helping detect display errors and user experience issues across different platforms. AI can also predict potential bugs based on historical data, optimizing the testing process.

AI tools for software testing include:

• Testim – Automates UI testing with AI.
• Applitools – Tests display on multiple devices.
• Functionize – Smart functional application testing.

4. AI in Mobile and Web Application Development

AI is transforming the way mobile and web applications are developed, improving user experience (UX/UI) and optimizing performance.

AI applications in web development: Platforms like Wix ADI (Artificial Design Intelligence) can automatically design professional websites based on user preferences. AI also optimizes HTML, CSS, and JavaScript code to enhance page load speed.

AI applications in mobile app development: AI helps analyze user behavior to personalize experiences. For example, Google’s Firebase Predictions uses AI to predict user behavior, enabling developers to adjust the interface or features accordingly.

AI technologies supporting web and mobile programming include:

• Wix ADI – Automatically designs websites using AI.
• Google Firebase Predictions – Predicts user behavior in applications.
• TensorFlow.js – Embeds AI models in web applications.

5. AI in Security and Cybersecurity

The cybersecurity field is leveraging AI to detect attacks and prevent network threats more effectively. AI systems can analyze network traffic, detect abnormal behaviors, and warn of potential attacks before they happen.

For example, tools like Darktrace and IBM Watson for Cybersecurity use AI to identify malware, phishing attempts, and DDoS attacks. AI can also learn from past attacks to improve future protection capabilities.

AI tools for cybersecurity include:

• Darktrace – AI detects and responds to security threats.
• IBM Watson for Cybersecurity – Analyzes and predicts security risks.
• CrowdStrike Falcon – Monitors and protects systems in real time.

V. Conclusion

Artificial intelligence is no longer a futuristic technology; it is already present everywhere. It not only enhances productivity but also creates smarter experiences for humans. However, with its rapid development, AI also presents challenges in terms of security, ethics, and the potential for replacing human labor.

Nonetheless, it is undeniable that AI is a crucial part of the future, and understanding this technology will help us make the most of its potential. Thank you for taking the time to read this article! Don't forget to follow our blog for the latest updates on AI technology and future innovations.