25 AI Terms that You Need to Know in 2025

Artificial Intelligence (AI) has expanded rapidly in the past years and now it powers voice assistants, recommendation systems, chatbots, self-driving technology, and even creative tools that generate text, images, and music. Whether we realize it or not, AI influences how we work, communicate, shop, and consume content.

Yet, despite its growing presence, AI remains a mystery for many. Terms like machine learning, deep learning, LLMs, and multimodal AI often sound technical, cryptic, or overwhelming. With so many buzzwords floating around, it’s easy to get lost in the jargon.

This guide breaks down the most important AI terms in simple, plain English—helping you understand what they mean, how they work, and why they matter. Whether you’re a tech enthusiast, a business professional, or just curious about AI, this article will help you navigate the world of artificial intelligence with confidence.

1/ Artificial Intelligence (AI)

Artificial Intelligence (AI) is a field of computer science that focuses on building machines capable of performing tasks that typically require human intelligence. These tasks include speech recognition, language processing, image identification, decision-making, and content generation.

AI is used in everyday applications like voice assistants (Siri, Alexa), recommendation systems (Netflix, Spotify), and chatbots that handle customer service inquiries. It helps businesses automate tasks, improve efficiency, and provide personalized experiences.

2/ Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) refers to an advanced type of AI that can perform any intellectual task a human can. Unlike today’s AI, which is specialized for narrow tasks like playing chess or translating text, AGI would be able to think, learn, and adapt across a wide range of activities.

AGI remains theoretical but is often discussed in the context of AI surpassing human intelligence. Some researchers believe it could revolutionize society, while others warn about its potential risks, such as loss of control over AI decision-making.

3/ Machine Learning (ML)

Machine Learning (ML) is a subset of AI that enables computers to learn from data without being explicitly programmed. Instead of following strict rules, ML models analyze patterns in large datasets and improve their accuracy over time.

For example, spam filters learn to detect unwanted emails based on past messages. The more data they process, the better they become. ML powers various applications, including fraud detection, recommendation systems, and medical diagnoses.

4/ Deep Learning

Deep Learning is a more advanced form of machine learning that uses neural networks with multiple layers to analyze complex data. Inspired by the human brain, deep learning allows AI to process speech, recognize faces, and even drive cars.

This technology has led to major breakthroughs, including facial recognition, realistic voice synthesis, and self-driving vehicles. However, deep learning requires vast amounts of data and computing power, making it resource-intensive.

5/ Large Language Model (LLM)

Large Language Models (LLMs) are advanced AI systems trained on massive text datasets to understand, process, and generate human-like responses. Popular LLMs include ChatGPT, Claude, and Gemini.

LLMs work by identifying patterns in language and predicting the most likely sequence of words. This allows them to perform tasks like writing articles, summarizing text, answering questions, and generating code. They are used in customer service, education, content creation, and software development.

As LLMs evolve, they become more accurate, context-aware, and multimodal—meaning they can process text, images, and voice simultaneously. However, challenges like bias, misinformation, and high computational costs remain.

6/ Chatbot

A chatbot is an AI-powered program that simulates human conversation. You’ve likely interacted with chatbots when messaging a company’s support team or using virtual assistants like Alexa.

Advanced chatbots powered by LLMs can answer questions, assist with tasks, generate text, and even write code. They are widely used in customer service, e-commerce, healthcare, and productivity apps.

7/ Generative AI

Generative AI refers to AI models that create original content, such as text, images, music, and videos. Popular generative AI tools include ChatGPT (text), DALL·E (images), and Stable Diffusion (art).

These models analyze large datasets and generate new, unique content based on learned patterns. Businesses use generative AI for marketing, entertainment, design, and automation. However, ethical concerns like deepfakes and AI-generated misinformation are rising.

8/ AI Hallucination

AI hallucination occurs when an AI system generates false or misleading information while presenting it as fact. This happens because AI models don’t actually “know” things—they predict words based on patterns.

For example, a chatbot might cite a fake academic paper or provide incorrect medical advice. This is a major issue in healthcare, law, and journalism, where accuracy is critical. AI developers work to reduce hallucinations, but eliminating them completely is difficult.

9/ AI Ethics

AI ethics refers to the moral principles and guidelines that shape how AI should be designed and used. The goal is to ensure AI operates fairly, transparently, and without harm.

Key concerns include:

• Privacy – Protecting user data from misuse
• Bias – Ensuring AI doesn’t discriminate
• Accountability – Determining who is responsible when AI makes mistakes

Governments and organizations are working on AI regulations, but the rapid advancement of AI makes it hard to control all risks.

10/ Bias in AI

AI bias occurs when an AI system produces unfair, prejudiced, or discriminatory outcomes due to biased training data. Since AI learns from historical data, it can reinforce and amplify existing biases.

For example, an AI hiring tool may favor men over women if past hiring data reflects gender discrimination. Similarly, a financial AI may deny loans unfairly due to biased credit data. Developers try to reduce bias using diverse datasets and bias-detection algorithms, but eliminating bias is challenging.

11/ Vibe Coding

Vibe coding is an emerging approach where developers describe problems in plain language, and AI generates code automatically. Tools like GitHub Copilot, ChatGPT, and Claude help programmers write and debug code faster.

This speeds up development by allowing programmers to focus on problem-solving rather than syntax details. However, AI-generated code still requires human oversight to ensure security and efficiency.

12/ Digital Watermarking

Digital watermarking is a method used to identify AI-generated content by embedding hidden markers that indicate it was produced by artificial intelligence. This is crucial for combating deepfake misinformation, AI-generated fake news, and copyright concerns.

Watermarks can be applied to:

• AI-generated images – Tools like DALL·E or Stable Diffusion can add hidden patterns that reveal the image’s AI origins.
• Text-based content – AI-generated text may include patterns or metadata invisible to humans but detectable by AI.
• Videos and audio – Watermarking ensures that AI-generated voices or synthetic videos can be traced and verified.

Tech companies and policymakers are exploring watermarking as a way to distinguish real vs. AI-generated content, preventing deception and maintaining trust in digital media.

13/ Multimodal AI

Multimodal AI can process multiple types of data simultaneously, including text, images, audio, and video. Unlike traditional AI that specializes in one format, multimodal AI understands complex relationships between different inputs.

Examples include Google Gemini, GPT-4o, and Meta’s AI models, which are changing how we interact with technology.

14/ Prompt Engineering

Prompt engineering is the practice of crafting well-structured instructions (prompts) to guide AI models in generating better responses. Because AI models don’t “think” the way humans do, the way you ask a question significantly impacts the quality of the answer.

For example, instead of asking “Write an article about AI,” a well-crafted prompt would be:
“Write a 500-word article explaining AI in simple language, covering its impact on daily life and future trends.”

Effective prompt engineering is key to getting AI models to produce accurate, creative, and useful outputs in fields like content creation, coding, data analysis, and research.

15/ Turing Test

The Turing Test, proposed by Alan Turing in 1950, is a way to measure whether a machine can exhibit human-like intelligence. The idea is simple: if a person interacts with an AI through conversation and cannot reliably tell if they are speaking to a human or a machine, the AI is said to have passed the test.

Early AI systems struggled with this, but modern chatbots like ChatGPT, Gemini, and Claude can generate highly convincing responses, making it harder to distinguish between AI and humans. However, as AI improves, some argue that the Turing Test is no longer a definitive measure of intelligence, since AI can mimic human speech patterns without actually understanding what it says.

16/ Autonomous Systems

Autonomous systems are AI-powered machines and software that operate independently without direct human control. These systems use AI, machine learning, and sensor data to make decisions and perform tasks on their own.

Examples of autonomous systems include:

• Self-driving cars – AI-powered vehicles that navigate roads, detect obstacles, and follow traffic laws.
• Delivery drones – Automated drones that transport packages without human pilots.
• Industrial robots – AI-driven machines that assemble products in factories without supervision.

While these technologies increase efficiency and reduce labor costs, they also raise concerns about job displacement, safety, and ethical risks related to AI decision-making.

17/ Natural Language Processing (NLP)

Natural Language Processing (NLP) is the AI branch that enables computers to understand, interpret, and generate human language. It powers tools like speech-to-text, language translation, chatbots, and sentiment analysis.

Common NLP applications include:

• Speech assistants (Siri, Google Assistant) that convert voice into text
• Language translation (Google Translate, DeepL)
• Chatbots that provide automated customer support
• Sentiment analysis for detecting emotions in reviews or social media

Despite advancements, NLP still struggles with language ambiguity, slang, and cultural nuances.

18/ GPU (Graphics Processing Unit)

A GPU is a specialized computer chip designed for parallel processing. Originally built for gaming and video rendering, GPUs are now essential for AI training and deep learning because they can process vast amounts of data simultaneously.

Companies like NVIDIA and AMD produce powerful GPUs that power AI applications, scientific simulations, and cryptocurrency mining.

19/ TPU (Tensor Processing Unit)

A TPU is a custom-built processor developed by Google specifically for AI workloads. It is optimized for machine learning tasks, making it more efficient than GPUs for certain AI applications.

TPUs are used in Google Search AI, YouTube recommendations, and Gemini AI. They consume less power while delivering high performance, making them valuable for large-scale AI projects.

20/ API (Application Programming Interface)

An API (Application Programming Interface) is a set of rules and tools that allows different software applications to communicate with each other. In AI, APIs make it easy for developers to integrate AI models into apps, websites, and other platforms without having to build everything from scratch.

Common AI-related APIs include:

• OpenAI’s API – Used to integrate ChatGPT into websites and apps.
• Google’s Vision API – Adds image recognition capabilities to applications.
• Speech-to-text APIs – Convert spoken words into written text for transcription services.

APIs have made AI more accessible by allowing businesses and developers to connect AI capabilities to their productswith minimal effort, accelerating AI adoption across industries.

Závěr

AI is no longer just a futuristic concept—it’s a fundamental part of our daily lives. From smart assistants and recommendation algorithms to chatbots and self-driving cars, artificial intelligence is shaping how we interact with technology.

Yet, with its rapid expansion comes a flood of technical terms and concepts that can feel overwhelming. Understanding AI doesn’t have to be complicated. Whether it’s machine learning, deep learning, LLMs, or generative AI, the key is recognizing how these technologies work and how they impact the world around us.

As AI continues to evolve, staying informed will help you navigate its opportunities and challenges. The more we understand AI, the better we can use it, question it, and shape its future responsibly.