Artificial Intelligence (AI)
What it is: Think of AI as a smart, data-driven brain inside your devices. It studies and learns from huge amounts of information to recognize patterns, make predictions, and automate decisions; whether that’s flagging spam emails, translating text, or detecting faces in photos. AI is the broad umbrella under which tools can simply analyze and classify data or, in the special case of Generative AI, go a step further to create entirely new content like text, images, or music.
Example: When you log into Netflix and see “Top Picks for You” or “Because you watched Stranger Things,” that’s AI analyzing your viewing history, spotting patterns in what shows and movies you like (and what people like you watch), then serving up personalized recommendations so you can dive straight into something you’ll enjoy.
Generative AI
What it is: Generative AI is a type of artificial intelligence that can create new content (text, images, code, music, and more), based on patterns it’s learned from huge datasets. Instead of just analyzing or classifying inputs, it generates novel outputs that didn’t exist before.
Example: Imagine you type into a tool, “Create a cozy illustration of a cat curled up by a fireplace,” and within seconds you get a brand-new, high-quality image matching that description. Behind the scenes, a generative AI model has learned from millions of pictures to blend colors, shapes, and textures in just the right way to bring your idea to life
Here are some of the most widely used generative-AI tools:
ChatGPT (OpenAI)
A versatile conversational agent built on OpenAI’s GPT-4 family. It’s used for drafting emails, writing code snippets, brainstorming ideas, and more. Over 400 million weekly active users make it the market leader in Gen AI chatbots.Google Gemini
Google’s flagship large-language model, integrated into Workspace products for drafting docs, summarizing emails, and powering conversational experiences. Gemini competes directly with ChatGPT across both consumer and enterprise applications.Anthropic Claude
A privacy-focused chat assistant that emphasizes safety and steerability. Often used by enterprises for internal knowledge-base queries and customer support automation.Microsoft 365 Copilot
Embedded in Word, Excel, and Teams, it leverages OpenAI models plus Microsoft Graph to summarize meetings, draft reports, and generate data-visualizations from your documents and spreadsheets.GitHub Copilot
An AI pair-programmer that suggests code completions, entire functions, and tests in real time, trained on billions of lines of public code. Widely adopted by developers inside VS Code and other IDEs.Midjourney
A community-driven image-generation service known for its painterly and artistic renderings. Popular among designers for concept art, social-media graphics, and rapid prototyping.DALL·E 3 (OpenAI)
An easy-to-use text-to-image model that creates photorealistic or stylized visuals from natural-language prompts, now baked into ChatGPT Plus and Enterprise plans.Adobe Firefly (and Photoshop AI)
Adobe’s suite of generative tools for in-app image editing, background removal, and style transfers. Integrated directly into Photoshop and Illustrator to streamline creative workflows.Synthesia
A no-code platform for creating AI-generated videos from text scripts. Often used for training, marketing explainers, and multilingual content without a film crew.Jasper
A marketing-focused writing assistant with templates for blog posts, ad copy, and social-media captions. Known for its user-friendly interface and brand-tone customization.Notion AI
Built into the Notion workspace, it helps you summarize notes, generate to-do lists, and draft documentation, all inside your team’s wiki and project boards.
These tools span text, code, image, and video generation - and together they form the backbone of most Gen AI workflows in U.S. businesses and creative teams today.
AI vs. Generative AI
At a high level, AI is the umbrella term for any system that mimics aspects of human “thinking” or decision-making, things like sorting your email into “important” vs. “spam,” recognizing that you’re talking to it, or predicting the next word you’ll type.
Generative AI, on the other hand, is a subset of AI that’s all about creating new content (text, images, code, music, you name it), rather than just analyzing or classifying existing data.
Here’s a quick breakdown:
Goal
AI: Understand, classify, predict, or optimize based on data
Generative AI: Produce brand-new data that didn’t exist before
Typical models
AI: Decision trees, recommendation engines, image-classification CNNs
Generative AI: Large language models (ChatGPT, Bard), diffusion models (DALL·E, Stable Diffusion)
Common tasks
AI:
Flagging fraudulent transactions
Translating a webpage
Detecting objects in a photo
Generative AI:
Writing you a poem or email draft
Creating an original illustration from a prompt
Composing a jazz solo in the style of Miles Davis
In short: all generative AI is AI, but not all AI is generative - only those systems designed to create new content.
Machine Learning (ML)
What it is: Machine Learning is a way for computers to learn from examples instead of being told exact step-by-step instructions. You feed them data (like photos labeled “cat” or “dog”), and they figure out the differences on their own.
Example: Show the computer thousands of cat and dog photos. Over time, it learns “cat things” vs. “dog things” and can then look at a new photo and say “That’s a cat!” with high accuracy.
Deep Learning (DL)
What it is: Deep Learning is a special kind of machine learning that uses layers of “virtual neurons” (like a mini-brain) to tackle very complex problems - such as recognizing a face in a crowd or translating entire sentences in real time.
Example: Ever use Google Photos and it magically shows all pictures of “Grandma” even if you didn’t tag them? That’s deep learning spotting the same face across hundreds of photos.
Large Language Model (LLM)
What it is: An LLM is a giant “word machine” trained on huge amounts of text (books, websites, articles) so it can write or answer questions in natural English (or other languages). Think of it as a library in your computer that also knows how to talk.
Example: ChatGPT is an LLM. You ask “Write me a thank-you note to my neighbor,” and it composes a polite, customized letter - almost like a human would.
Prompt & Prompt Engineering
What it is: A prompt is simply the question or instruction you give to an AI. Prompt engineering is learning how to phrase that question clearly so the AI gives you exactly what you want.
Example: Instead of asking “Tell me about trees,” you might say, “Write a three-sentence description of an oak tree, using friendly language for a child.” That extra detail is prompt engineering.
Transformer
What it is: A transformer is the computer “recipe” (architecture) most modern AIs use to understand entire sentences at once, rather than word by word. It helps the AI know which words matter most in context.
Example: In the sentence “I couldn’t not laugh,” a transformer-based AI knows that “not” flips the meaning, because it looks at the whole phrase together.
Retrieval-Augmented Generation (RAG)
What it is: RAG first fetches real documents (your company’s manual, your own notes) and then feeds them to an AI so its answers stay accurate and up-to-date.
Example: Suppose your employee handbook says “Vacation requests need two weeks’ notice.” A RAG system will look up that exact rule before the AI writes, “Please submit vacation plans 14 days in advance,” ensuring it matches your real policy.
Hallucination
What it is: This is when an AI makes up convincing but false information—like a confident “mistake.”
Example: You ask “What year did Einstein win his second Nobel Prize?” and the AI might fabricate “1929” even though he only won once in 1921. Always double-check important facts and ask the AI to provide sources!