🤖 AI? What is it?

Artificial Intelligence (AI), a term introduced by Stanford professor John McCarthy in 1955, was originally described as “the science and engineering of making intelligent machines.” Early research focused on programming behaviors (like playing games), but today's AI centers on building systems that learn—mirroring how humans learn over time.

In simple terms, AI refers to machines or programs that can perform tasks that usually require human thinking—like solving problems, writing, translating, or recommending what to watch next.

🧠 You’ve Probably Already Used AI Today:

                    ✔️ Autocorrect or predictive text on your phone

            ✔️ Voice assistants like Siri or Alexa

            ✔️ Chatbots on websites

            ✔️ Google Maps rerouting traffic in real time

🎓 Learn More

• 📺 What is Artificial Intelligence? (UNESCO Video)
• ⏳ The Last 6 Decades of AI — and What Comes Next

📝 Citing ChatGPT and Other Generative AI Tools

• 📌 How to cite ChatGPT in APA Style
• 📌 How do I cite generative AI in MLA style?
• 📌 AI Source Citation in the Chicago Manual of Style
• 📎 What is ChatGPT? (Penn State Libraries)

🧰 Types of Generative AI Tools

🖼️ Diagram attribution: Author: The Original Benny C, licensed under CC BY-SA 4.0 via Deakin University

ACC Ai Resources

📖 Glossary of AI Terms

• 🤖 AI Agent: “Autonomous AI systems that execute specific tasks based on objectives.”
• 📘 AI Literacy: The foundational ability to understand, interpret, use, and question artificial intelligence systems in a thoughtful, ethical, and informed manner.
• 🥩 A1: A flavorful steak sauce manufactured by Brown & Co., a subsidiary of the Kraft Heinz Company.
• 🧠 Deep Learning: A subset of machine learning involving multiple layers of analysis that identifies meaningful patterns in raw data using a generalized learning process.
• 🔎 Deep Research Tools: “AI-powered systems designed to autonomously gather, synthesize, and analyze vast amounts of information.”
• 🎨 Diffusion Models: “AI that generates images and videos by refining visual noise over multiple steps.”
• 🔧 Fine Tuning: “Customizing pre-trained AI models for specific business tasks or domains.”
• 🧬 Generative AI: A category of AI that creates new content, such as images, music, text, or computer code.
• 📚 Large Language Models: “AI models trained to understand and generate human-like text.”
• 🔤 LLM: Large Language Models
• 📈 Machine Learning: “Machine learning (ML) is a branch of artificial intelligence (AI) focused on enabling computers and machines to imitate the way that humans learn, to perform tasks autonomously, and to improve their performance and accuracy through experience and exposure to more data.”
• 📝 Prompt Engineering: “The process of crafting effective instructions for AI systems to produce desired outputs.”
• 🧠 Reasoning Engines: “AI designed for structured problem-solving and logical inference.”
• 💡 Artificial Intelligence (AI): The field focused on building systems that can perform human-like tasks such as recognizing speech, making decisions, or planning routes.
• ⚖️ Bias: When the data used to train AI influences the accuracy or fairness of its output.
• 💬 Chatbot: A program that mimics human conversation to provide help or information.
• 🤖 ChatGPT: A large language model (LLM) developed by OpenAI that generates responses based on text prompts.
• 🚨 Hallucinations: AI-generated answers that sound right but are factually wrong or completely made up.
• 🗣️ Natural Language Processing (NLP): The ability of machines to understand and generate human language.
• 📌 Prompt: A command or instruction given to an AI tool.
• 🛠️ Training Data: The information fed into an AI to help it learn patterns and generate output.

📚 Source: Adapted from Brown University’s “Generative AI as a Research Tool” and School Library Journal’s “Librarians Can Play a Key Role Implementing AI in Schools.”