Artificial intelligence is the field of technology that aims to give computer systems cognitive abilities associated with human intelligence, such as learning, problem solving, decision making, and language understanding. AI systems learn from large datasets and algorithms, make predictions, and carry out specific tasks automatically. Today, artificial intelligence is used across an extremely wide range of settings, from everyday life to enterprise systems.
Table of Contents
- What Does Artificial Intelligence Mean?
- What Are the Types of Artificial Intelligence?
- What Is the Difference Between AI, Machine Learning, and Deep Learning?
- Types and Subfields of Artificial Intelligence
- What Are AI Models?
- The Best-Known AI Names
- How Do AI Systems Work?
- What Are AI Algorithms?
- What Can AI Do?
- Where Is AI Used?
- Simple Examples of AI
- What Is Generative AI?
- How Can You Learn AI?
- Frequently Asked Questions
What Does Artificial Intelligence Mean?
Artificial intelligence, usually shortened to AI, is the umbrella term for systems that perform tasks normally associated with human thinking. The core goal of AI is not for machines merely to execute the commands they are given, but to learn from data and make independent decisions.
AI aims to imitate human-like thought processes. These include learning, reasoning, pattern recognition, and language understanding.
What Are the Types of Artificial Intelligence?
AI is divided into three main types according to capability level.
Narrow AI (ANI)
Narrow AI refers to systems designed to perform one specific task. All AI applications in use today fall into this category.
- Voice assistants
- Recommendation systems
- Facial recognition software
General AI (AGI)
General AI is a type of AI that can learn a variety of tasks at a level close to human intelligence. AGI would be able to transfer what it has learned to new domains. General AI is currently theoretical, and there is no practically usable example of it.
Super AI (ASI)
Super AI is a type of AI that is expected to surpass human intelligence. ASI is expected to learn and make decisions faster than humans. For now, super AI is only a theoretical concept.
What Is the Difference Between AI, Machine Learning, and Deep Learning?
These three concepts are often confused because they are actually nested within one another — they are not three separate, independent fields:
- Artificial Intelligence is the broadest term — it covers every approach that enables machines to exhibit intelligent behavior (including rule-based systems).
- Machine Learning is a subset of AI; it lets systems make decisions by learning patterns from data, without rules being written by hand.
- Deep Learning is a subset of machine learning; it learns using multi-layer artificial neural networks, especially on complex data such as images, audio, and language.
In short: Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence. For example, a large language model such as ChatGPT is trained with deep learning techniques; deep learning is a branch of machine learning; and machine learning is a subfield of AI.
Types and Subfields of Artificial Intelligence
AI is developed across several different technical areas.
- Machine Learning: Algorithms that learn from data
- Deep Learning: Learning models built on artificial neural networks
- Natural Language Processing (NLP): Understanding and generating human language
- Computer Vision: Analyzing visual data
- Expert Systems: Systems that imitate expert decisions in specific domains
- Robotics: AI systems that can interact with the physical world
What Are AI Models?
AI models should be classified from two different angles: how the model is trained (learning method) and what kind of data or architecture it works with (model type).
By Learning Method
| Learning Method | Description |
|---|---|
| Supervised Learning | Learning from labeled data |
| Unsupervised Learning | Finding patterns in unlabeled data |
| Reinforcement Learning | Learning through a reward-and-penalty approach |
By Architecture / Use Case
| Model Type | Description |
|---|---|
| Large Language Models (LLMs) | Models focused on understanding and generating text (e.g., GPT, Gemini) |
| Diffusion Models | Models that generate images or video from noise (e.g., Midjourney, Stable Diffusion) |
| Speech/Audio Models | Models focused on speech recognition and synthesis |
The Best-Known AI Names
General-Purpose AI
ChatGPT, Claude, Gemini, and Copilot are general-purpose AI assistants, but more specialized solutions also exist for images, video, research, meetings, and coding. To compare tools by intended use, you can review a list of the best AI applications.
These systems can handle a large number of different tasks. Some are closed-source (commercial), while others are open-weight — meaning the model itself can be downloaded and customized.
- ChatGPT (OpenAI) — closed-source
- Gemini (Google) — closed-source
- Claude (Anthropic) — closed-source
- Copilot (Microsoft) — closed-source
- Grok (xAI) — closed-source
- Perplexity — search-focused AI assistant
- DeepSeek — open-weight model family known for its low-cost training
- Llama (Meta) — open-weight model family
- Mistral — open-weight, Europe-based model family
Image and Video AI
Used for image generation and video processing.
- Midjourney
- DALL·E
- Stable Diffusion
- Runway
Audio and Music AI
Used for audio generation and music composition.
- ElevenLabs
- Soundraw
- AIVA
Data and Automation AI
Used to analyze and automate enterprise processes.
- DataBot
- AutoML
- UiPath AI
How Do AI Systems Work?
AI systems are trained on large datasets. Thanks to algorithms, these systems can:
- Learn
- Make predictions
- Make decisions
- Automate processes
This process consists of data collection, model training, testing, and continuous improvement.
What Are AI Algorithms?
The core algorithms used in AI are as follows:
- Decision trees
- Artificial neural networks
- Support vector machines
- K-means clustering
- Transformer architecture
What Can AI Do?
AI can be used across a very broad range of tasks.
- Text generation and analysis
- Image creation
- Speech recognition and generation
- Forecasting and analysis
- Automation
- Decision support systems
Where Is AI Used?
| Field | Purpose of Use |
|---|---|
| Healthcare | Diagnosis, image analysis |
| Finance | Risk analysis, fraud detection |
| Education | Personalized learning |
| Marketing | Targeting, content creation |
| Manufacturing | Automation and quality control |
| Human Resources | Candidate screening |
| Security | Threat detection |
Simple Examples of AI
Simple examples of AI commonly used in daily life:
- Spam filters
- Netflix’s recommendation system
- Smart keyboards
- Navigation apps
What Is Generative AI?
Generative AI refers to AI systems that do more than analyze: they can also create new content. Some sources call this approach “AI 3.0” — but it should be noted that this is not an officially recognized classification in the industry, as Web 1.0/2.0/3.0 is; it is more of a popular label.
The types of output that fall under generative AI:
- Text
- Images
- Code
- Audio and music
How Can You Learn AI?
At BlueMark Academy, we offer training programs to support you on your AI learning journey. Here are our AI courses:
- Test Automation with AI Training
- AI-Powered User Experience and Interface Design Training
- UI/UX Design with Artificial Intelligence Training
- AI-Driven Business Analysis and PO Training
Frequently Asked Questions
What is the abbreviation for artificial intelligence?
The abbreviation for artificial intelligence is “AI.” In Turkish, it is commonly written as “YZ” (for yapay zekâ) or simply as “AI.”
Does general AI (AGI) exist?
No, not yet. General AI (AGI) is currently in the development and research stage; all existing AI systems (including ChatGPT and Gemini) fall into the “narrow AI” category, focused on specific tasks.
Does AI learn?
Yes. Thanks to machine learning algorithms, AI systems keep improving by extracting patterns from data; however, unlike human learning, this learning is limited to the training data and the algorithms that have been set.
Are AI and machine learning the same thing?
No. Machine learning is a subset of AI. AI is the broader umbrella term; machine learning is a specific approach under that umbrella, based on learning from data.
What is the difference between ChatGPT, Gemini, and Claude?
All three are general-purpose AI assistants based on large language models (LLMs); they are developed by different companies (OpenAI, Google, Anthropic) and differ in model architecture, training data, and areas of focus (for example, long context and multimodal support).
What are open-source AI models?
Models such as Llama, Mistral, and DeepSeek have weights (the model files) that are shared publicly. This allows developers to run and customize the model on their own servers; that is not possible with closed-source models such as ChatGPT.
Where should I start to learn AI?
Starting with the basic concepts (the difference between machine learning and deep learning), and then taking a hands-on course in a practical area (for example, prompt engineering, data analysis, or AI-assisted product design), offers a fast and effective path to learning.