Compilation of Common AI Terms

Artificial Intelligence (AI) is becoming an indispensable part of many businesses, from process automation to data analysis and customer interaction. However, to understand how AI operates and applies effectively, grasping core terminology is extremely important. This article will simply explain the most common AI concepts, along with practical examples and specific benefits for businesses.

Overview of AI Terminology

AI (Artificial Intelligence): Technology that simulates human intelligence
Machine Learning (ML): Systems that learn from data
LLM (Large Language Model): AI that processes and generates natural text
RAG (Retrieval-Augmented Generation): Combining LLM with databases
Stable Diffusion: AI that generates images from descriptions
Generative AI: Creating new multimedia content
Token: The smallest unit of language processed by AI
Neural Network: Architecture that simulates the human brain
NLP (Natural Language Processing): AI understanding and interacting with language
LoRA (Low-Rank Adaptation): Technique for fine-tuning large models
Deep Learning: Machine learning using complex neural networks
AI Agent: An AI system that autonomously executes tasks

1. Artificial Intelligence (AI)

AI is technology that simulates human intelligence through machines. This capability includes learning from data, making decisions, and solving problems. For example, when you ask the virtual assistant Siri to schedule a meeting, it analyzes your voice, understands the context, and performs the task—this is AI in action[1][2].

Business applications:

Customer support chatbots: Companies like Foxconn have used AI to analyze production data, helping to increase forecast accuracy by 8% and saving $533,000/year[1].
Process automation: AI helps optimize supply chains by predicting market demand and managing inventory.

2. Machine Learning (ML)

Machine Learning is a branch of AI that allows systems to automatically learn and improve from data without specific programming. For example, when Netflix recommends movies based on your viewing history, that is ML analyzing behavior patterns to make predictions[2][10].

Practical applications:

Customer analysis: ML helps segment customers based on purchasing behavior, thereby personalizing marketing campaigns.
Fraud detection: Banks use ML to identify unusual transactions based on historical data patterns.

3. Large Language Model (LLM)

LLM is an AI model trained on vast amounts of data to understand and generate natural text. LLMs like GPT-4 or Google BERT can translate languages, answer questions, and write content[3][6][11].

How it works:

Next word prediction: LLM analyzes context to predict the appropriate word. For example, when typing "Today the weather...", the model might suggest "is sunny" or "is rainy"[3].
Self-Attention mechanism: Helps LLM identify relationships between words in long sentences, thus creating coherent text[3][6].

Business applications:

Email writing automation: LLM generates email content based on brief user requests.
Customer service support: Chatbots use LLM to answer complex questions with high accuracy.

4. Retrieval-Augmented Generation (RAG)

RAG combines the capabilities of LLM with external databases to improve the accuracy of responses. For example, when asking about company policies, RAG retrieves information from internal documents before generating a response[4][7][12].

Benefits:

Reducing "hallucinations": RAG limits the LLM from providing incorrect information by relying on reliable data[12].
Updating knowledge: Businesses can integrate the latest data (e.g., financial reports) into the system without retraining the model[12].

Practical example:

Legal consulting: RAG extracts information from current laws to provide accurate answers to clients[4].

5. Stable Diffusion

Stable Diffusion is an AI model that generates images from text descriptions. For example, entering "a dog playing guitar on the beach" will result in a corresponding image being created[5][8][13].

Underlying technology:

Diffusion process: Starts from random noise and gradually "denoises" to create a clear image[13].
Latent space: Compresses images into vector form, allowing detailed edits like changing lighting or colors[13].

Business applications:

Rapid design: Fashion companies use Stable Diffusion to create virtual clothing samples before production[5].
Creative marketing: Generate unique advertising images in seconds, saving costs on hiring designers[8].

6. Generative AI

Generative AI is technology that creates new content (text, images, sound) rather than just analyzing data. For example, tools like ChatGPT write blog posts or MidJourney creates artwork, both belonging to this category[10][14].

Advantages:

Increased productivity: Automates report writing, contract drafting.
Personalization: Creates marketing content tailored to individual customer preferences.

7. Token

Token is the smallest unit that AI processes in text. A token can be a word, a syllable, or a character. For example, the phrase "Hello" is split into 1 token: "Hello"[1].

Applications:

Text limits: Models like GPT-4 have token limits (e.g., 8,192 tokens), affecting response length.
Cost optimization: AI services charge based on the number of tokens processed, so shortening questions helps save budget[1].

8. Neural Network

Neural Networks simulate how the human brain processes information through interconnected layers. For example, in image recognition, a neural network analyzes pixels to identify objects[3][10].

Common types:

Convolutional Neural Networks (CNN): Used for image processing.
Recurrent Neural Networks (RNN): Analyze time series data like stock forecasting.

9. Natural Language Processing (NLP)

NLP helps computers understand and interact with human language. For example, when you search for "best coffee shop nearby," Google uses NLP to analyze intent and suggest locations[6][10].

Related technologies:

Sentiment analysis: Evaluating customer reviews on social media as positive or negative.
Text summarization: Automatically condensing long reports into key points.

10. Deep Learning

Deep Learning is an ML technique that uses multi-layer neural networks to analyze complex data. For example, facial recognition systems on phones use deep learning to identify individual features[3][10].

11. Low-Rank Adaptation (LoRA)

LoRA is an optimization method for fine-tuning large AI models like LLM or Stable Diffusion without retraining the entire system. Instead of changing billions of parameters of the original model, LoRA only adds lightweight adjustment layers (occupying 0.1-1% of capacity) to adapt to specific tasks[1][4][7].

How it works

Low-rank matrix: LoRA uses matrix decomposition techniques to compress information, reducing computational resources by 100-1,000 times compared to traditional training[4].
Dynamic combination: When in use, LoRA combines the weights of the original model with the adjustment matrix, producing outputs that meet new requirements without losing foundational knowledge[7].

Business applications

Quick AI customization: Fashion companies can use LoRA to teach Stable Diffusion to create clothing designs in their unique style with just 50-100 reference images[1].
Cost savings: Reduces GPU costs by 90% when fine-tuning chatbots for the banking sector compared to traditional fine-tuning methods[4].
Maintaining stability: Allows for updates of specialized knowledge (e.g., new tax laws) for LLM without affecting overall performance[7].

12. AI Agent

AI Agent is an autonomous system capable of gathering data, analyzing it, and acting to achieve predefined goals. Unlike regular chatbots, AI Agents can perform complex task sequences such as scheduling meetings, processing complaints, or even executing trades[2][5][8].

Key features

Four levels of autonomy:
1. Rule-based responses (e.g., auto-reply emails)
2. Learning from user feedback
3. Predicting needs (e.g., product recommendations)
4. Multi-factor decision-making (e.g., financial risk management)[8]
Multi-layer architecture: Combines NLP to understand requests, ML to analyze data, and rule systems to ensure policy compliance[5].

Implementation case studies

Customer support: The AI Agent of TP Bank handles 83% of credit card requests automatically, reducing the workload for the customer service department by 40%[2].
Supply chain management: The AI Agent at Unilever automatically forecasts demand for six months, optimizing shipping routes based on weather and fuel prices[8].
Algorithmic trading: Hedge Funds use AI Agents to execute 15,000 trades/second with 99.7% accuracy[5].

Sources