Artificial intelligence (AI) is a broad field of technology that enables computer systems to perform tasks such as recognizing patterns, processing language, making predictions, generating content, and supporting decisions.
You may already interact with AI when using search engines, translation tools, recommendation systems, voice assistants, fraud-detection systems, image-recognition software, or generative AI applications.
Although AI can seem complicated, its basic ideas are easier to understand when you separate the technology into a few key concepts: data, algorithms, models, machine learning, and generated or predicted outputs.
What Is Artificial Intelligence?
Artificial intelligence refers to computer-based systems designed to perform tasks that can involve capabilities such as perception, learning, reasoning, prediction, language processing, planning, or decision-making.
There is no single way to build an AI system. Some systems use machine learning, while others can use rules, statistical methods, optimization techniques, or combinations of different approaches. NIST’s terminology recognizes AI broadly as machine-based systems that can perform functions such as making predictions, recommendations, or decisions for defined objectives.
AI also does not necessarily work like a human brain. A system may be highly capable at one task while being poor at another. For example, an AI model designed to identify objects in images is built for a different purpose from a model designed to generate text.
A simple example
Suppose an AI system is designed to identify whether an image contains a cat.
The system receives an image as input, processes information within that image, and produces an output such as “cat detected.”
A different AI system might receive a written question and generate a text response instead.
The underlying technology can vary considerably between these systems, even though both are described as AI.
How Does AI Work?
At a basic level, many modern AI systems involve data, algorithms, models, and computing resources.
| Component | Simple explanation | Example |
|---|---|---|
| Data | Information used to develop or operate a system | Text, images, numbers |
| Algorithm | A computational method for processing information | Classification method |
| Model | A trained computational system that produces outputs | Image-recognition model |
| Input | Information provided to the system | A photograph or question |
| Output | The result produced by the system | A label, prediction, or response |
In machine learning, a model is trained using data so that it can identify useful patterns or relationships. Once trained, the model can process new inputs and produce predictions, classifications, recommendations, or other outputs.
The exact process depends on the AI system. Some models require large amounts of data and substantial computing resources, while others can be designed for narrower tasks.
What Is Machine Learning?
Machine learning (ML) is a major approach within artificial intelligence.
Instead of manually writing a separate rule for every possible situation, developers can train a machine-learning model using examples or other forms of data. The model learns patterns that can then be used when it encounters new information.
For example, consider an email spam filter.
A machine-learning system can be trained using examples of spam and legitimate messages. It may learn patterns associated with unwanted messages and use those patterns when assessing new emails.
Machine learning is used in many areas, including:
- Image classification
- Speech recognition
- Recommendation systems
- Fraud detection
- Search systems
- Predictive modeling
- Text processing
However, machine learning does not mean that a system automatically becomes more accurate simply because it is being used. Its performance depends on factors such as the model, training process, data quality, evaluation methods, and the environment in which it is deployed. IBM similarly describes machine learning as a way of creating models that make predictions or decisions based on data.
AI, Machine Learning, Deep Learning, and Generative AI
These terms are related, but they do not mean exactly the same thing.
Artificial intelligence is the broadest category.
Machine learning is a major approach within AI in which models learn patterns from data.
Deep learning uses neural networks with multiple layers and is particularly important for many modern language, image, audio, and other complex AI applications.
Generative AI refers to AI models designed to generate content such as text, images, audio, video, or software code.
A useful way to think about the relationship is:
Artificial intelligence → Machine learning → Deep learning → Many modern generative-AI systems
This is a simplified relationship rather than a complete description of every AI technology.
What Is Generative AI?
Generative AI is designed to produce content in response to an input or instruction.
Depending on the system, the output might include:
- Text
- Images
- Audio
- Video
- Software code
For example, a user might provide a prompt asking a generative AI system to summarize a document or create an image based on a description.
Generative models learn patterns and relationships from their training data and use those learned representations to generate new outputs. NIST describes generative AI as a class of models that generates derived synthetic content, including text, images, video, and audio.
The output should not automatically be treated as factual simply because it sounds convincing. Generative AI systems can produce incorrect, incomplete, outdated, or misleading information. Important claims should therefore be checked against appropriate sources before they are relied upon.
Where Is AI Used?
AI is already incorporated into many types of software and services.
Common examples include:
- Search: Systems can help rank or interpret search results.
- Recommendations: Services can predict content, products, or other items that may interest users.
- Language tools: AI can support translation, speech recognition, transcription, and text processing.
- Fraud detection: Machine-learning systems can identify unusual patterns that may warrant further investigation.
- Computer vision: AI can analyze images and video for particular tasks.
- Customer support: Chatbots can answer questions or route requests.
- Healthcare: AI can support certain research, administrative, analytical, and clinical workflows, depending on the application and applicable safeguards.
- Education: AI tools can assist with explanations, practice, summarization, and other learning activities.
The capabilities and reliability of AI vary significantly between applications. An AI system designed for one narrow task should not automatically be assumed to perform well at another.
What Are the Main Benefits of AI?
AI can be useful when applied to an appropriate task.
| Potential benefit | Example | Important consideration |
|---|---|---|
| Automation | Handling repetitive tasks | The process still needs appropriate oversight |
| Fast processing | Analyzing large datasets | Speed does not guarantee accuracy |
| Pattern recognition | Identifying unusual data patterns | Results depend on the quality of the data and model |
| Personalization | Recommending content or products | Personal data and privacy should be considered |
| Content generation | Drafting text or creating images | Generated material should be reviewed |
One of AI’s strengths is its ability to process large quantities of information quickly. However, that does not mean an AI system understands every situation or produces correct results every time.
What Are the Limitations and Risks of AI?
AI systems have limitations that users should understand before relying on them.
Incorrect or misleading outputs
An AI system can produce an answer that sounds convincing but is inaccurate. This is particularly important when the information concerns health, finances, law, safety, or other high-impact decisions.
Data quality
AI systems depend heavily on the data used to train, evaluate, or operate them. Poor-quality, incomplete, biased, or unsuitable data can affect the resulting system.
Bias
AI systems can reproduce or amplify problematic patterns present in data or in the way a system has been designed and deployed. Evaluating the context and limitations of an AI application is therefore important.
Privacy
Users should think carefully before entering sensitive personal, financial, confidential business, or other private information into an AI service. The appropriate precautions depend on the particular product, its settings, and how the service handles submitted information.
Security
AI systems can face security threats, and generative AI introduces additional risks in some applications. NIST’s AI security work identifies issues including privacy and adversarial attacks, while its generative-AI risk framework emphasizes that risks vary according to the model, system, data, and use case.
How to Use AI Responsibly
You do not need to avoid AI simply because it has limitations. A better approach is to understand what a particular tool is designed to do and use appropriate judgment.
Useful practices include:
- Check important information. Verify significant factual claims using reliable sources.
- Protect sensitive information. Avoid entering confidential data unless you understand how the service handles it and have appropriate authorization.
- Review generated content. Look for factual errors, missing context, misleading statements, and inappropriate recommendations.
- Understand the tool’s purpose. A system designed for generating text may not be suitable for making an important professional or personal decision.
- Keep a human in the loop. For consequential decisions, appropriate human review remains important.
- Consider the source. If an AI-generated claim matters, check the underlying evidence rather than relying on the AI’s confidence or writing style.
Responsible AI use is ultimately about matching the tool to the task and understanding its limitations.
How to Get Started With AI
If you are new to artificial intelligence, start with practical applications rather than trying to learn every technical detail immediately.
For example, you can learn the basics by experimenting with:
- A text-generation or question-answering tool.
- An image-recognition application.
- A translation or transcription service.
- A recommendation system.
- A simple machine-learning example.
As you experiment, ask yourself three questions:
What information does the system receive?
What does the system produce?
How reliable is that output for this particular task?
These questions provide a useful foundation for understanding both the capabilities and limitations of AI.
Conclusion
Artificial intelligence is a broad area of technology involving computer systems that can perform tasks such as recognizing patterns, processing language, making predictions, supporting decisions, and generating content.
Machine learning is an important approach within AI that allows models to learn useful patterns from data. Deep learning is a machine-learning approach based on multilayer neural networks, while generative AI focuses on producing new content such as text, images, audio, video, or code.
AI can save time, automate tasks, identify patterns, and support many types of work. At the same time, its outputs can be wrong or unsuitable, and AI systems can raise concerns involving data quality, privacy, bias, security, and misuse.
The most useful approach for beginners is neither to treat AI as magic nor to dismiss it because it has limitations. Understand what the particular system is designed to do, check important results, protect sensitive information, and use human judgment when the consequences matter.
Frequently Asked Questions
What is artificial intelligence in simple terms?
Artificial intelligence is a broad field of technology involving computer systems that can perform tasks such as recognizing patterns, processing language, making predictions, or generating content.
What is machine learning?
Machine learning is an approach to AI in which computational models learn patterns or relationships from data and use what they have learned to make predictions, classifications, or other outputs.
What is generative AI?
Generative AI is a type of AI designed to generate content such as text, images, audio, video, or software code in response to an input or instruction.
Is AI always accurate?
No. AI systems can produce incorrect or misleading outputs. Reliability depends on the particular system, task, data, and circumstances, so important information should be independently checked.
Where is artificial intelligence used?
AI is used across many areas, including search, recommendation systems, language processing, image analysis, fraud detection, customer service, healthcare, education, transportation, and industrial applications.
Is artificial intelligence the same as machine learning?
No. Artificial intelligence is the broader field, while machine learning is one major approach used to build AI systems. Deep learning is a specialized form of machine learning, and many modern generative-AI systems use deep-learning techniques.
Should I enter private information into an AI tool?
Be cautious. Before submitting sensitive information, understand the particular service’s privacy practices, data controls, and organizational requirements. When you do not need to provide sensitive information, avoid sharing it unnecessarily.