Terminologies

Artificial Intelligence (AI): The broad concept of machines being able to carry out tasks in a way that we would consider “smart.” It’s like teaching a computer to mimic human logic.

Machine Learning (ML): A subset of AI. Instead of giving the computer specific rules for everything, we give it lots of data and let it "learn" the patterns on its own.

Deep Learning: A more advanced version of Machine Learning that uses "Neural Networks" (layers of calculations) to solve very complex problems, like recognizing a face in a photo.

Gem: A Gem, in the context of Google's AI, is a customized version of the Gemini chatbot. It is designed to act as a personal expert for specific tasks.  

Impersonate: Making/ asking AI (Gemini or GPT) to someone- Like Elon Mask(Eg. You are elon mask...)

Glitch: Is a temporary, unexpected malfunction or minor error in a system, device, or program, often seen in electronics, software (like video games), or even complex plans, causing it to work improperly or briefly fail before potentially self-correcting.  

Algorithm: A set of step-by-step instructions. In AI, the algorithm is the "recipe" the computer follows to turn data into an answer.

Training: The process of "teaching" the AI. You feed it thousands of examples (like pictures of cats) so it learns what a cat looks like.

Model: This is the end result of the training. Once an AI is trained, the "Model" is the program you actually interact with.

Large Language Model (LLM): A type of AI trained on massive amounts of text. This is what powers tools like the one you are using right now! It predicts the next most likely word in a sentence.

Prompt: This is simply the instruction or question you give to the AI. Writing a "good prompt" is the key to getting a good answer.

Generative AI: AI that can create something new, like a poem, an image, a video, or computer code, rather than just analyzing existing data.

Hallucination: This is when an AI gives an answer that sounds confident and logical but is actually factually incorrect. It’s a "confident mistake."

Dataset: The collection of information (books, photos, website text) used to train the AI.

NLP (Natural Language Processing): The field of AI focused on helping computers understand, interpret, and generate human language.

Parameters: Think of these as the "knobs and dials" inside the AI. The more parameters a model has (often in the billions), the more complex information it can understand.

Knobs and Dials: how computers learns and make decision. (Input-Process-output) 

NotebookLM:  (formerly known as Project Tailwind) is an AI-powered research assistant and "thinking partner" developed by Google. 

Grounding: This means the AI is forced to answer questions based only on the files you uploaded. It prevents the AI from "making things up" using outside information.

Source Citations: When NotebookLM gives you an answer, it shows you exactly which part of your document it got the information from. It's like a digital "fact-check" button.

Context Window: This refers to how much information the AI can "remember" or look at at one time. NotebookLM has a massive context window, allowing it to read hundreds of pages of your notes or PDFs simultaneously.