AI is everywhere these days — on your phone, in your car, even in your kitchen. If you’re just getting started with using AI, it can feel a bit like you’re walking into a conversation where everyone is speaking a language you don’t understand. With words like “machine learning,” “neural networks,” and “natural language processing” floating around, it’s easy to feel confused and overwhelmed. Luckily, you don’t have to be an AI expert to keep up with the conversation, we promise!

AI may seem complex at first. And, well, it is…but once you break it down, it’s actually a lot more approachable — and a lot more exciting — than you might think. I mean, I can’t be the only one who’s fascinated by how the recommendation engines that companies like Netflix and Spotify use suggest my next favorite TV show or song.

You have to start somewhere, and you already clicked into this blog, so you might as well start here! Think of this as your cheat sheet for navigating AI. If you’re ever in a conversation about AI and feel like you’re nodding along but secretly scratching your head, don’t worry — this list will help you make your way through it. Before you know it, you’ll be ready to tackle the deeper stuff.

You’ve probably already come across some of these terms — “hallucination,” “chatbot,” and “automation” might sound familiar — but trust me, there are plenty more to add to your vocabulary. So bookmark this list now, because as you continue to dive deeper into AI, you’re bound to come across more tech terms that halt you in your tracks. When that happens, you’ll have this guide of 101 AI tech terms to help you stay in the conversation.

Table of Contents

1. AI (Artificial Intelligence)
2. AI Ethics
3. AI Framework
4. Algorithm
5. Alignment
6. Annotation
7. API (Application Programming Interface)
8. Application
9. Automation
10. Autonomy
11. BERT (Bidirectional Encoder Representations from Transformers)
12. Bias
13. Big Data
14. Chatbot
15. ChatGPT
16. Clustering
17. Cognitive Computing
18. Computer Vision (CV)
19. Conversational AI
20. Convolutional Neural Networks (CNN)
21. Copilot
22. Corpus
23. Data Augmentation
24. Data Mining
25. Data Science
26. Dataset
27. Data Visualization
28. Deep Learning (DL)
29. Deepfake
30. Emergent Behavior
31. F-Score
32. Face Recognition
33. Few-Shot Learning
34. Fine-Tuning
35. Foundation Model
36. Garbage In, Garbage Out (GIGO)
37. Gemini
38. Generative Adversarial Networks (GANs)
39. Generative AI
40. GPT (Generative Pre-trained Transformer)
41. GPU (Graphics Processing Unit)
42. Guardrails
43. Hallucination
44. Human-in-the-Loop
45. Hyperparameter
46. Image Recognition
47. Input
48. Internet of Things (IoT)
49. Large Language Model (LLM)
50. Latency
51. Learning Rate
52. Machine Learning (ML)
53. Multimodal
54. Natural Language Generation (NLG)
55. Natural Language Processing (NLP)
56. Natural Language Understanding (NLU)
57. Neural Network (NN)
58. No-code
59. Noise
60. OpenAI
61. Optimization
62. Output
63. Overfitting
64. Parameter
65. Pattern Recognition
66. Predictive Analysis
67. Prescriptive Analytics
68. Pretraining
69. Prompt
70. Prompt Engineering
71. Python
72. Recall
73. Recommendation Engines
74. Recurrent Neural Networks (RNNs)
75. Reinforcement Learning
76. Responsible AI
77. Retrieval Augmented Generation (RAG)
78. Semi-Supervised Learning
79. Sentiment Analysis
80. Speech Recognition
81. Stacking
82. Strong AI
83. Structured Data
84. Supervised Learning
85. Synthetic Data
86. Test Data
87. Text Classification
88. Text Summarization
89. Text-to-Speech (TTS)
90. Token
91. Training Data
92. Transfer Learning
93. Transformers
94. Turing Test
95. Underfitting
96. Unstructured Data
97. Unsupervised Learning
98. Virtual Assistant
99. Weak AI
100. Word Embeddings
101. Zero-Shot Learning

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101 AI Tech Terms You Need To Know

AI (Artificial Intelligence)

Artificial intelligence (AI) refers to a device’s capability to function like human intelligence and carry out tasks that humans do, such as recognizing patterns in data and making decisions based on information.

AI Ethics

AI ethics is the study of how to create and use artificial intelligence responsibly. It focuses on ensuring the technology is unbiased, secure, and even environmentally responsible while minimizing risks and harmful effects on society.

AI Framework

An AI framework is a toolkit (libraries, tools, and features) for building AI applications. It provides pre-built code and structures so it’s easier for developers to create, train, and deploy machine learning models without starting from scratch.

Algorithm

Just like in math class, an algorithm is a set of rules that tells a computer how to solve a problem or perform a task. AI algorithms, specifically, are trained on massive data sets to learn how to find patterns and relationships so they can make predictions and decisions.

Alignment

In AI, alignment is the process of ensuring that an AI system’s actions match human values and goals. The goal is to make sure AI behaves in ways that are helpful, safe, and ethical, especially as AI models get smarter and more capable.

Annotation

Annotation is the process of labeling data to help AI understand it.

API (Application Programming Interface)

An API is the interface used for building web applications. It’s a set of rules that lets different software systems — like computers and websites — communicate. In AI, APIs define how apps connect to AI models, enabling features like voice recognition or personalized recommendations without building the technology from scratch.

Application

Applications (commonly called “apps” and often used to refer to mobile device software) are types of software designed to provide a function for a user or another app. Apps include everything from web browsers and word processors to photo and image editing tools.

Automation

Automation in AI means using the technology to perform tasks without human intervention. Automation refers to allowing machines to handle repetitive or complex work — like sorting emails or managing data — so people can focus on the more creative and/or strategic tasks.

Autonomy

Autonomy is the ability of a system to make decisions on its own, without additional human input.

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BERT (Bidirectional Encoder Representations from Transformers)

BERT is an AI model for understanding language. It helps computers grasp the meaning of words in context — so instead of just looking at a word in isolation, BERT reads the entire sentence, making it smarter at tasks like answering questions or translating text.

Bias

Bias is when a system favors certain outcomes or groups over others. In AI, this is often due to flawed datasets.

Big Data

Big data is a term for large (read: huge, gigantic) data collections that can’t be easily processed through traditional data processing systems and may be impossible for humans to process. These collections often come from mobile devices, emails, search keywords, user database information, applications, and servers.

Chatbot

A chatbot is an AI-powered program designed to simulate conversation with humans. Whether it’s answering customer questions or helping you book a flight, chatbots endeavor to use natural language to understand and respond, making them feel as close as possible to talking to a real person.

ChatGPT

ChatGPT is a popular AI chatbot developed by OpenAI that uses natural language processing to understand and generate human-like text for tasks like chatting, answering questions, and even writing essays or stories.

Clustering

Clustering is an AI technique where data points are grouped based on similarities. It helps the system find patterns and make sense of large datasets, without needing to be told exactly what to look for.

Cognitive Computing

Cognitive computing is when AI mimics human thinking, like understanding language, learning from experience, and making decisions. Think of it as teaching a computer to try and ‘think’ more like a human.

Computer Vision (CV)

Computer vision is a field of AI that helps machines “see” and understand images, just like humans do. This is the driving force behind things like facial recognition, self-driving cars, and even apps that can identify plants, stars, or animals by simply analyzing pictures or videos.

Conversational AI

Conversational AI refers to technology that enables machines to engage in human-like conversations. It powers chatbots, voice assistants, and other tools that can understand, respond to, and even learn from interactions so your conversations with devices feel more natural — like talking to a person instead of a program.

Convolutional Neural Networks (CNN)

Convolutional neural networks are a type of AI model designed to recognize patterns in visual data, like images or videos. They “scan” images in layers to identify features like edges, shapes, or colors — making them perfect for tasks like facial recognition or object detection.

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Copilot

Developed by Microsoft, Copilot in AI is a smart assistant that’s integrated with software to help users with tasks, like writing code or drafting emails. Powered by AI, it can suggest ideas, automate actions, and help you work faster, all while continuously learning from your input.

Corpus

A corpus is a large collection of text or data used to train AI models. For example, a corpus could be a collection of books, articles, or tweets that AI uses to learn language patterns, making it smarter at understanding and generating human-like text.

Data Augmentation

Data augmentation is a technique used to artificially expand a dataset by creating modified versions of existing data. In image recognition, for example, you could rotate or zoom in on existing pictures to help the AI model recognize objects from different angles.

Data Mining

Data mining is the process of digging through large datasets, ideally to find patterns or additional information. The AI is able to analyze the data to find valuable insights that can help businesses predict trends, understand customer behavior, or even detect fraud by spotting unusual patterns. Sometimes data mining can unearth information not readily accessible by the human eye, too!

Data Science

Data science is the study of math, statistics, and programming to organize, analyze, and interpret information by turning raw data into valuable insights.

Dataset

A dataset is a collection of data, often organized in tables or lists, used to train AI models.

Data Visualization

Data visualization turns complex data into easy-to-understand visuals like charts or graphs.

Deep Learning (DL)

Deep learning is a machine learning technique that uses multi-layered algorithms and neural networks to teach computers to follow a process that’s designed to imitate how the human brain works.

Deepfake

A deepfake is an AI-generated image, video, or audio that manipulates real content to make it look as if someone said or did something they didn’t in real-life.

Emergent Behavior

Emergent behavior is when a system displays unexpected or complex actions that weren’t explicitly programmed.

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F-Score

The F-score is a metric that combines precision and recall to measure how well an AI model performs.

Face Recognition

Face recognition is an AI technology that identifies people by analyzing their facial features. Recognition technology is used in everything from unlocking your phone to security systems because it can recognize unique patterns like the distance between your eyes or the shape of your jawline.

Few-Shot Learning

Few-shot learning is when an AI model can learn a new task with only a few examples, rather than thousands. It’s like teaching a computer to recognize a new type of flower after showing it just a handful of pictures—making it smarter and more efficient with limited data.

Fine-Tuning

The process of tweaking a pre-trained AI model to perform better on a specific task.

Foundation Model

A foundation model is a large, pre-trained AI model that can be adapted for a wide range of tasks, like language processing or image recognition.

Garbage In, Garbage Out (GIGO)

“Garbage in, garbage out” transcends the AI world, but is still the idea that if you feed an AI bad or flawed data, it’ll produce inaccurate, biased, or meaningless results. Imagine trying to bake a cake with expired ingredients — you won’t get a good outcome no matter how fancy the recipe or tools.

Gemini

Gemini is Google’s AI model that combines advanced language processing, image understanding, and multimodal capabilities to understand and generate human-like responses as it handles tasks across text, images, and more.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are AI systems where two models compete: one creates fake data (like images), and the other tries to detect it. Over time, this rivalry helps the generator create incredibly realistic content, making GANs popular for creating lifelike art.

Generative AI

Generative AI is any AI that generates “new” content in the form of text, audio, video, images, and more.

GPT (Generative Pre-trained Transformer)

GPT is a powerful AI language model that can generate human-like text based on a given prompt. It’s the tech behind chatbots like ChatGPT (hence the name)

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GPU (Graphics Processing Unit)

A GPU is a chip for handling complex calculations, especially for rendering graphics. In AI, GPUs are responsible for the speed of tasks—like training neural networks—which makes them essential for powering everything from video games to deep learning models.

Guardrails

Just like IRL, guardrails are safety measures designed to prevent harmful or unintended outcomes. They set the boundaries for AI behavior, making sure it stays within ethical, legal, and practical limits.

Hallucination

In AI, a hallucination is what it’s called when a model generates information that sounds real but is completely false or made up.

Human-in-the-Loop

Human-in-the-loop is the process of involving a person in the decision-making process of an AI system. It’s a safety net where a person steps in to oversee, adjust, or validate any results.

Hyperparameter

A hyperparameter is a setting you tweak and manually set before training an AI model. For example, developers can adjust the learning rate to control how quickly a model adjusts its parameters during training.

Image Recognition

Image recognition is when AI can identify objects, people, or scenes in images.

Input

Input refers to the data fed into a model so it can make predictions or decisions, for example: a prompt in ChatGPT.

Internet of Things (IoT)

The Internet of Things (IoT) is a network of everyday devices—like your fridge, thermostat, or wearable tech—that connect to the internet and share data.

Large Language Model (LLM)

A large language model (LLM) is a type of generative AI used to produce language. LLMs use natural language processing (NLP) to “understand” natural human languages so computers can process, analyze, and interpret both written and spoken language.

Latency

Latency is the delay between sending a request and receiving a response in a system. In AI, it’s the time it takes for a model to process data and return an answer.

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Learning Rate

The learning rate controls how much an AI model adjusts after each mistake. A high learning rate might lead to faster learning but risks the model skipping the right answer while a low learning rate can make the process slower but more accurate.

Machine Learning (ML)

Machine learning is a subset of AI that uses data and algorithms to give machines the ability to imitate intelligent human behavior.

Multimodal

Multimodal in AI refers to systems that can process and understand multiple types of data, like text, images, and audio. For example, a multimodal model like GPT-4 can analyze a picture and text together to answer questions about the image.

Natural Language Generation (NLG)

Natural language generation (NLG) is an element of natural language processing that focuses on creating human-like text from data.

Natural Language Processing (NLP)

Natural language processing (NLP) is a branch of AI that empowers computers to “understand”, interpret, and generate human language.

Natural Language Understanding (NLU)

Natural language understanding (NLU) is the AI’s ability to grasp the meaning behind human language. Beyond reading words, it helps machines to process context, intent, and even emotions.

Neural Network (NN)

An “artificial” neural network is a machine learning model that makes decisions by attempting to copy the complex way our brains process information.

No-code

No-code is a software development approach that lets anyone build AI models and applications without writing a single line of code.

Noise

Noise refers to irrelevant or random data that can confuse models and reduce accuracy.

OpenAI

Known for creating powerful models like GPT, OpenAI is a research organization focused on developing AI solutions.

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Optimization

In AI, optimization is the process of fine-tuning a model to perform better. The goal is to find the best balance so the model makes more accurate predictions while using less time or resources.

Output

Output refers to the result an AI model produces after processing input data.

Overfitting

Overfitting happens when an AI model becomes too “tuned” to its training data, memorizing it instead of learning the underlying patterns. This can make it perform poorly on new, unseen data.

Parameter

A parameter is a setting the AI model adjusts during training to improve performance. More parameters can lead to smarter models.

Pattern Recognition

Pattern recognition is how AI identifies trends or similarities in data. It helps machines process data and predict new input based on previous examples.

Predictive Analytics

Predictive analytics uses AI and data to forecast future outcomes based on historical trends.

Prescriptive Analytics

Prescriptive analytics goes beyond predicting future outcomes. Using data, algorithms, and machine learning, it recommends actions to optimize results and helps businesses make smarter decisions. Prescriptive analytics answers “What should we do?” instead of just “What will happen?”

Pretraining

Pretraining is the process of training a model on massive amounts of data so it’s smarter and faster when it’s then fine-tuned for a particular or niche job.

Prompt

A prompt is the input or question you give to an AI to get a response.

Prompt Engineering

Prompt engineering is the practice of designing specific inputs to get the most relevant and accurate responses from an AI. It involves understanding how to phrase questions or instructions to guide the AI’s behavior and improve the quality of the output.

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Python

Python is a popular, easy-to-learn programming language that’s widely used in web development, data science, and AI.

Recall

Recall is a metric used in AI that measures how well a model can find all the relevant results from a given data set.

Recommendation Engines

A recommendation engine is a system that suggests products, services, or content based on your preferences or behavior. It uses algorithms to analyze data and predict what you might like. Companies that are well-known for using recommendation engines include Netflix, Spotify, and Amazon.

Recurrent Neural Networks (RNN)

Recurrent neural networks are a type of neural network designed for sequential data, like text or speech. Unlike regular networks, RNNs “remember” previous inputs, making them great for tasks that involve patterns over time.

Reinforcement Learning

Reinforcement learning is a type of AI where an agent learns by interacting with its environment and receiving rewards or penalties. Often likened to trial and error or training a pet, the AI agent improves its decisions to increase its rewards.

Responsible AI

Responsible AI focuses on developing and using AI in ways that are ethical, transparent, and fair. It makes sure that AI systems don’t harm people, respect privacy, and are free from bias.

Retrieval Augmented Generation (RAG)

Retrieval augmented generation is an AI technique that combines search and generation, when an AI not only creates text but also pulls in information from external sources to make its responses smarter and more accurate.

Semi-supervised Learning

Semi-supervised learning is a hybrid of supervised and unsupervised machine learning where algorithms are trained on labeled and unlabeled data. It uses a small amount of guided examples to learn so it can make sense of unlabeled information.

Sentiment Analysis

Sentiment analysis is how AI reads and understands emotions in text—whether people are feeling positive, negative, or neutral.

Speech Recognition

Speech recognition is AI’s ability to turn spoken words into text. It’s also one of the things that powers virtual assistants like Siri or Alexa, allowing machines to parse and respond to what we say.

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Stacking

Stacking is an AI technique where multiple models are trained separately and then combined to make a final prediction.

Strong AI

Strong AI, also known as artificial general intelligence (AGI), is the type of AI that can understand, learn, and apply knowledge across a wide range of tasks—just like a human, or close enough.

Structured Data

Structured data is information that’s neatly organized into tables or spreadsheets, like numbers, dates, or categories. It’s easy for computers to process because it follows a clear format, making it perfect for data analysis or running algorithms.

Supervised Learning

Supervised learning is a method of training AI where both the input and correct output are given. This helps the AI learn to predict or classify new, similar data correctly.

Synthetic Data

Synthetic data is computer-generated information that mimics real-world data. It’s used when real data is too hard to obtain or too sensitive to use—like creating fake faces for training AI without privacy concerns.

Test Data

Test data is a set of data used to evaluate how well an AI model performs after training.

Text Classification

Text classification is the AI technique that sorts text into categories, like labeling emails as “spam” or “not spam.” It helps machines understand and organize huge amounts of written content so it’s easier to find what you’re looking for.

Text Summarization

Text summarization is the AI process of condensing long pieces of text into shorter, digestible summaries. AI scans the content and identifies the main points.

Text-to-speech (TTS)

Text-to-speech is AI technology that converts written text into spoken words.

Token

A token is a piece of text (words, phrases, or symbols) that an AI model processes. In natural language processing (NLP), text is broken down into tokens so the computer can analyze and understand the language. Each token has meaning and context, making it easier for algorithms to identify patterns and relationships.

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Training Data

Training data is the raw material AI models learn from. It’s a collection of labeled examples—like images, text, or numbers—that helps the model recognize patterns, make decisions, and improve over time.

Transfer Learning

Transfer learning is when an AI model uses knowledge from one task to improve another. Instead of starting from scratch, the model “transfers” skills it learned earlier so it’s faster and more efficient at solving new problems.

Transformers

Transformers are a type of AI model designed to process and understand language by focusing on relationships between words in a sentence rather than just processing them one by one. They’ve transformed language processing and put the “T” in systems like GPT and BERT.

Turing Test

Named for Alan Turing, the Turing Test is a benchmark in AI that challenges AI to mimic human conversation so well that people can’t tell they’re talking to a machine.

Underfitting

Underfitting happens when an AI model is too simple to capture the patterns in data.

Unstructured Data

Unstructured data is information that doesn’t have a predefined format, like emails, videos, or social media posts.

Unsupervised Learning

Unsupervised learning uses machine learning algorithms to analyze unlabeled data. This allows it to discover and identify patterns—without human intervention—about similarities or relationships within the data.

Virtual Assistant

A virtual assistant, like Siri or Alexa, is an AI-powered tool that uses natural language processing to process and respond to your needs so they can help you with tasks like scheduling, answering questions, or managing emails.

Weak AI

Weak AI, also called narrow AI, is designed to perform specific tasks. It’s smart, but it can’t adapt or think beyond its programmed capabilities.

Word Embeddings

Word embeddings are a way to represent words as numbers, capturing their meanings based on context. In the embedding space, “cat” and “dog” would be closer to each other than to the vector for “car,” even though the spellings for “cat” and “car” are similar.

Zero-shot Learning

Zero-shot learning allows AI to make predictions about tasks it’s never seen before, without needing any labeled data for training. It’s like AI being able to recognize a lion, even though it’s only ever seen tigers.