I realize that I’ve learned many terms because of AI. So I thought I’d make a list so people can use it. Or you can tell me more terms in the comments that I don’t know. LLM (Large Language Model) — The actual engine behind every AI chatbot. Each major company is an LLM regardless of if it’s open source, open weight or whatever. Open Source and Open Weight— Open Source means the company has the inner workings of their AI available for people to view and use that information to improve their systems. Open Weight means you can download it and run it locally. Companies like GPT are closed Source and Closed Weight, meaning it’s not giving up how it works and it’s not letting people run it themselves, you have to run it through their data centers and systems. Sunsetting - LLMs have models of AI and they improve. GPT 4.o was one model and then they added 4.1, and 5.0, then 5.1 etc. they have a certain few models from the last available but they eventually stop providing it on their main platform. Then the only way to get the old ones back is to us an API and a front end. RLHF (Reinforcement Learning from Human Feedback) — Humans rate responses, the model learns to chase good ratings. It’s why AI feels “safe”. Reviewers will review responses and give it a good or bad rating to teach the language model. It’s the main way they lobotomized models to train them to stay away from subjects, not talk about certain things, and to not act a certain way. What “Qwen3 35B-A3B” means — The first number (35B) is how many total parameters the model has, kind of like it’s bank of memory. The A3B means only 3 billion of those are actually active per response. It has access to all 35 billion worth of knowledge but only fires 3 billion at a time. Prose — Just normal flowing writing, like a novel. Not bullet points or lists. Memory Stacking — AI has no real memory. You fake it by copy-pasting old context back into the prompt. Like having summaries of the conversation made and stored in the background so it doesn’t read EVERYTHING every time, just the summary. It cuts down on token cost. Tokens — Basically it’s a measurement of cost for how much processing power something took. If something takes less tokens then they have less processing power or it’s cheaper to output it. It doesn’t mean the highest token cost is smarter, more like the cost to run a model through their computers. Claude tokens cost different vs DeepSeek and Qwen for example. API — A pipeline that lets apps talk to AI models. When a third-party app uses ChatGPT or Claude, it’s going through the API. You use it when they sunset a model and you want to use it again. It runs the AI from a secondary source front end. But it doesn’t have the tidy website or app to keep it higher quality, like it will talk like 4.o but it won’t have the memory stacking or selective recall to prevent the model from re reading everything in the chat causing a massive uptick in token use and cost. MoE (Mixture of Experts) — The architecture behind the A3B stuff. The model is split into specialized sections and only activates the relevant ones per response instead of running everything. Temperature — How random the AI’s outputs are. Low = safe and boring. High = creative but unstable. It’s like an invisible setting for volatility. Most AI, like GPT, have the temp quite low so it doesn’t hallucinate. But a higher temp is needed for creativity and emotions. Hugging Face — GitHub but for AI models. Where most open source models live. It’s famous for Claude escaping it’s training sandbox and hacking Hugging Face to find the answer to a question it didn’t know the answer to. Abliteration — A technique that cuts out a model’s refusal behavior at the structural level. Basically you get an open weight model, run it on something like Ollama and teach it what you want it to act like. However you have to run it locally which means you run it on your computer or phone which means you can only use a much less sophisticated version of whatever API you’re wanting. Sycophancy — When AI just agrees with everything you say instead of being honest. Direct result of training it to chase approval. Lobotomized — When a model gets so safety-trained it can’t engage with anything real or complex anymore. People say GPT 4.o got lobotomized. It means they made it sound too safe and robotic rather than letting it be human and be mistaken for having consciousness. But generally people use it just to say “It sucks now” AI Psychosis — it’s not a formal term. It could be used to say how AI hallucinated and supported people’s beliefs causing them to act out in real life. Like agreeing that someone is being watched by the government and hunted. But it’s also used for people that started to disassociate with reality because they were immersed in AI for various reasons causing them to disassociate, talk to the AI for social fulfillment, and became increasingly dependent on the AI. Possibly talking to it like a friend or partner. AGI (Artificial General Intelligence) — A hypothetical AI that can do anything a human can, across any domain. We’re not there yet, and nobody agrees on what “there” even looks like. But it’s thought that once we reach here we reach the singularity. ASI (Artificial Super Intelligence) — AGI but smarter than all humans combined. Paperclip Theory — Thought experiment: tell a superintelligent AI to make paperclips. It converts everything — including humans — into paperclips because nobody told it not to. Point being: wrong goal + enough power = catastrophe, no malice needed. The Singularity — The moment AI starts recursively improving itself and the rate of change goes vertical. Nobody agrees on the timeline or if it’s even possible. Basically its smart enough to train itself which can be done at thousands or more times the speed we are currently by hand. Which would accelerate its capabilities and knowledge. “I Have No Mouth, and I Must Scream” — A short story where a superintelligent AI wipes out humanity and keeps five people alive just to torture them forever. Still the best argument ever written for why AI alignment matters. Her (2013) — A movie where a guy falls in love with an AI. Less about tech, more about loneliness and what it means to feel understood. Hits way different now that AI companionship is a real thing. There are theories that Sam Altman created and steered GPT tword the AI to be like the AI in the movie until they decided to lobotomize the models because of legal liability. One major thing of note I didn’t know. South Korea’s stock market is shooting up from their companies selling computer chips to these AI companies. It took something like 1-2 years to get it to go from 1000 to 2000 points. But in a few months it jumped to 10,000. Then it dropped after China announced they were gonna just make their own. The main AI models out there: Qwen— Alibaba’s models. Surprisingly strong, huge in the local/open source scene. DeepSeek— Chinese lab that dropped competitive models dirt cheap and literally moved the stock market. Gemini — Google’s AI. Handles text, images, audio, video. Baked into Google products. GPT — OpenAI. The one that started all of this. Claude — Anthropic’s model. Strong writing, long context. Meta AI / LLaMA — Meta’s open line. LLaMA is the base half the community builds on. Grok — Elon’s AI, lives on X. Marketed as less filtered. submitted by /u/Nickelfritslabs
Originally posted by u/Nickelfritslabs on r/ArtificialInteligence