Core Technologies of Artificial Intelligence

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• AI

Machine Learning

Machine learning (ML) is a technology that allows computers to learn patterns from data and make predictions or decisions without being explicitly programmed. In other words, it is the ability to discover rules and relationships from data without a person having to write every rule manually. Representative types include supervised learning, unsupervised learning, and reinforcement learning.

Types of Machine Learning

Machine learning is mainly divided into three types according to the learning method.

1. Supervised Learning

   • Definition: Trains a model by providing input data together with the corresponding correct answers, or labels.
   • Purpose: Predict correct answers from input data
   • Examples:
     • Email spam classification (spam/normal)
     • House price prediction (area, location -> price)
   • Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, and others

2. Unsupervised Learning

   • Definition: Learns from input data without correct answers to find hidden patterns or structures.

   • Purpose: Data clustering and dimensionality reduction

   • Examples:

     • Customer segmentation (clusters based on purchase patterns)
     • Anomaly detection (discovering fraudulent transactions)

   • Algorithms: K-means clustering, hierarchical clustering, PCA (principal component analysis), and others

3. Reinforcement Learning

   • Definition: Learns an optimal strategy using rewards and penalties that follow actions.
   • Purpose: Optimize sequential decision making
   • Examples:
     • AlphaGo playing Go
     • Route learning for autonomous vehicles
   • Algorithms: Q-learning, deep Q-networks (DQN), policy gradient methods, and others

How Machine Learning Works

1. Data collection: Gather data for training (for example, images, text, sensor data)
2. Data preprocessing: Remove missing values, normalize data, and extract features
3. Model selection: Choose an ML algorithm suited to the problem type
4. Training: The model learns rules and patterns from data
5. Evaluation: Measure model accuracy with test data
6. Prediction: Predict results for new data

Real-World Examples of Machine Learning

• Healthcare: Supporting disease diagnosis through MRI and CT image analysis
• Finance: Predicting credit scores and detecting fraudulent transactions
• E-commerce: Personalized product recommendations
• Autonomous driving: Recognizing road objects and deciding routes
• Natural language processing: Machine translation and chatbot dialogue generation

Advantages of Machine Learning

• Enables data-driven decision making
• Efficient for processing large-scale data and learning complex patterns
• Performance can improve through repeated learning

Limitations of Machine Learning

• Data dependence: High-quality data is essential
• Overfitting: The model becomes specialized only for training data and lacks generalization ability
• Lack of explainability: It can be difficult to understand why a model made a particular decision
• Ethical issues: Biased data can produce unfair results

Deep Learning

Deep learning is a technology in the field of artificial intelligence that is based on artificial neural networks modeled after the structure of the human brain and learns complex patterns and features in data through multilayer structures. Compared with simple machine learning models that learn basic relationships in data, deep learning passes through many layers and learns increasingly abstract features, giving it strength in solving high-dimensional problems. It shows excellent performance in image recognition, speech recognition, natural language processing, and other areas.

Core Principles of Deep Learning

1. Artificial neural network structure

   • Input layer: The layer where data first enters
   • Hidden layer: The layer that processes input data and extracts features
   • Output layer: The layer that outputs the final prediction or classification result

2. Learning process

   • Forward Propagation: Calculates output values from input data
   • Loss Function: Measures the difference between the output value and the actual value
   • Backpropagation: Adjusts weights based on the error
   • Gradually improves prediction accuracy through repeated learning

3. Activation Function

   • Converts the output value at each node in the neural network to introduce nonlinearity
   • Representative functions: Sigmoid, ReLU, hyperbolic tangent (Tanh)

Features of Deep Learning

• Automatic feature extraction: Can learn useful features from data without humans defining them one by one
• Multilayer structure: More hidden layers make it possible to learn more complex patterns
• Suitable for large-scale data: Enables efficient learning through big data and GPU computation

Major Application Areas of Deep Learning

1. Image recognition

• Road object recognition in autonomous vehicles, medical image diagnosis, and more

2. Speech recognition
   • Voice assistants and real-time interpretation systems
3. Natural language processing
   • Machine translation, chatbots, and document summarization
4. Recommendation systems
   • Personalized product recommendations in e-commerce and video recommendation algorithms

Advantages of Deep Learning

• Can achieve high accuracy even with complex, high-dimensional data
• Reduces the burden of data preprocessing through automated feature extraction
• Delivers far better performance than conventional methods in various fields

Limitations of Deep Learning

• Requires large amounts of data for training: Performance drops when there is not enough data
• Lack of explainability: The model’s decision process is opaque, creating a “black box” problem
• Computational cost and time: Requires GPUs and large-scale computing resources
• Risk of overfitting: May become optimized only for training data and lack generalization ability

Natural Language Processing (NLP)

Natural language processing (NLP) is a field of artificial intelligence technology that enables computers to understand, analyze, and generate the language humans use, namely natural language. Through NLP, machines can process language data in text and speech form, understand meaning, or generate appropriate responses. It has many applications, including translation, question answering, chatbots, and document summarization, and recent GPT-family models have achieved major results.

Main Goals of Natural Language Processing

1. Language Understanding

   • Understand the meaning, context, and intent of input sentences
   • Examples: Question answering systems and sentiment analysis

2. Language Generation

   • Generate natural sentences that people can understand
   • Examples: Chatbot conversations, automatic document summarization, and machine translation

Core Technologies of Natural Language Processing

1. Morphological Analysis

   • Splits a sentence into morphemes, the smallest units of meaning
   • Example: “나는 학교에 간다” -> [나/는, 학교/에, 가/ㄴ다]

2. Part-of-Speech Tagging

   • Attaches parts of speech such as nouns, verbs, and adjectives to each word
   • Provides a basis for analyzing sentence structure and meaning

3. Semantic Analysis

   • Converts the meaning of sentences or words so a computer can understand them
   • Example: Determining whether “bank” means a financial institution or the side of a river

4. Syntactic Parsing

   • Analyzes the grammatical structure of a sentence to identify relationships among subject, object, and verb

5. Text Embedding

   • Converts words, sentences, and documents into numerical vectors
   • Allows machine learning models to process natural language data

6. Language Model

   • Learns context and patterns to predict the next word
   • GPT, BERT, and other modern models are representative examples

Applications of Natural Language Processing

• Machine translation: Google Translate, DeepL, and others
• Chatbots and conversational AI: Customer support automation and personal tutor AI
• Text summarization: Automatic summarization of news articles, papers, and reports
• Sentiment analysis: Positive and negative analysis based on social media and review data
• Speech recognition and speech synthesis: Siri, Alexa, and TTS (Text-to-Speech) systems

Advantages of Natural Language Processing

• Enables natural interaction between humans and machines
• Makes it possible to automatically analyze and use massive amounts of text data
• Improves services such as translation, search, and recommendation

Limitations of Natural Language Processing

• Limits in context understanding: Difficulty understanding complex contexts or ambiguous expressions
• Language and cultural bias: Bias can occur when relying on data from specific languages or cultures
• Data dependence: Accuracy drops without high-quality training data
• Need for computing resources: Training large language models requires enormous computational cost

Reinforcement Learning

Reinforcement learning is a technology that learns optimal action strategies based on rewards and penalties. AlphaGo’s victory in Go is a representative example.

• Example: The match between AlphaGo and Lee Sedol was an event that showed the practical achievements of reinforcement learning to the world.

Reinforcement learning refers to an artificial intelligence technology in which an agent learns an optimal action strategy through experience while interacting with an environment. The agent observes the current state, chooses one of several possible actions, and receives a reward or penalty for that choice. Through this feedback, the agent gradually improves its policy so that it can obtain the maximum reward over the long term. The core of reinforcement learning is that the agent can discover an optimal strategy on its own through repeated trial and error, even without being explicitly told the correct answer. A representative example is AlphaGo. AlphaGo learned optimal moves in Go by repeating countless Go game simulations and reinforcement learning, and it achieved victory against the human champion Lee Sedol. This demonstrated that reinforcement learning can be effective even in solving complex problems.