What Is All This AI Stuff?
Welcome! At Gloo AI, we believe everyone deserves to understand how modern artificial intelligence works—without needing a PhD or vibe coding your own app. If you’re just getting started, we will walk you through the core concepts you’ll hear about all the time in AI conversations. These are the foundational ideas behind everything from ChatGPT to image generators and smart search engines.Artificial Intelligence (AI)
What it means: AI is the broad field of teaching machines to do things that typically require human intelligence—like understanding language, recognizing images, making decisions, or learning from data. Analogy: Think of AI as the umbrella term. It’s like saying “sports”—under that, you have soccer, tennis, basketball. In AI, those subfields include things like machine learning and computer vision. How it shows up in Gloo: Gloo uses AI to help organizations deliver personalized, accurate, and aligned experiences using their own content. AI powers search, assists staff with Chat for Teams, and enhances content understanding in the Data Engine and Studio.Machine Learning (ML)
What it means: Machine Learning is a branch of AI where computers learn patterns from data instead of being explicitly programmed. Instead of writing rules, we feed the machine examples, and it figures out how to make predictions or decisions. Example: You don’t teach a machine every rule for recognizing cats in photos. You just show it 10,000 pictures labeled “cat” and “not cat.” It learns the patterns that typically define a cat.Deep Learning
What it means: Deep Learning is a type of machine learning that uses multi-layered “neural networks” to learn complex patterns—especially good for things like language, images, and audio. Analogy: If machine learning is like learning to recognize apples by color and shape, deep learning figures it out by examining thousands of layers of features—like stem angle, skin texture, or reflection—without being told what those features are.Neural Network
What it means: A neural network is a system of algorithms inspired by the way human brains process information. It’s made up of interconnected “neurons” that process and pass along signals. Visualize it: Picture rows of dots connected by lines. Each dot processes a tiny piece of information and passes it along. The final output is the decision or prediction the system makes.Transformer
What it means: Transformers are a special type of neural network architecture that revolutionized how machines handle sequences—like words in a sentence. They’re the core architecture behind most modern language models. Why it matters: Before transformers, AI struggled with long text or understanding meaning across multiple sentences. Transformers fixed that by focusing attention on important parts of input, not just the most recent word. Famous example: ChatGPT and nearly every powerful text model today is built on transformer architecture.Diffusion
What it means: Diffusion is a technique used in image and video generation. The model starts with pure noise and gradually “denoises” it into a coherent image based on a prompt. Analogy: Imagine taking a photo and gradually adding static until it’s just noise. Diffusion models learn how to reverse that process—starting with noise and ending with an image of, say, “a dog riding a skateboard in space.”Model
What it means: A model is the result of training an AI system. It’s a learned set of rules and parameters that can take an input (like a sentence or image) and return an output (like a summary or classification). How to think about it: If data is the fuel, and algorithms are the engine, then the model is the car you build from both. It’s what you deploy when you’re ready for your AI to start working in the real world. How it shows up in Gloo: Gloo does not train new models. Instead, Gloo integrates frontier and foundation models and adds alignment layers, safeguards, and rights management so organizations can safely apply AI to their own content.Large Language Model (LLM)
What it means: An LLM is a type of AI trained on huge amounts of text to understand, generate, and interact using natural language. How it works: It doesn’t “know” facts in the human sense. Instead, it’s learned patterns in language—how likely one word is to follow another—and uses that to respond in surprisingly smart ways. Example: GPT-4, Claude, Gemini, and LLaMA are all examples of LLMs. How it shows up in Gloo: LLMs power many of Gloo’s key capabilities, including search, content question answering, metadata enrichment, and aligned generative responses in Chat for Teams.Multimodal AI
What it means: Multimodal AI can understand and generate more than one kind of input—like text, images, audio, or video—all within the same system. Use case: You can upload a picture and ask, “What’s happening in this photo?” and the AI can understand and respond. Or you might give it a chart and ask it to write a summary. How it shows up in Gloo: Gloo supports multimodal ingestion by allowing organizations to upload documents such as PDFs, transcripts, and scanned pages. The platform converts these into usable, searchable content for RAG.Artificial General Intelligence (AGI)
What it means: AGI is a hypothetical form of AI that can perform any intellectual task a human can. It’s still far off and the subject of much debate. Why it matters: Right now, AI is narrow—it does specific things well (like answering questions or generating images). AGI would be flexible, able to learn new tasks on the fly like a person.Next Up: How AI Models Learn In the next section, we’ll walk through how models are trained, fine-tuned, and made useful—answering the question: “How do AI models learn, improve, and adapt after they’re built?”