AI Terms You Should Know (Click here for AI Marketing Terms You Should Know)
Artificial General Intelligence, or AGI: Imagine a super-smart robot that can do anything a human can do, like solve math problems, write stories, and even get better at things by learning on its own.
AI Ethics: Rules to make sure robots and AI don't hurt people, like making sure they don't learn bad stuff from the internet or treat some people unfairly.
AI Safety: The study of keeping AI from turning into a super smart, out-of-control robot that could be mean to humans.
Algorithm: Like a recipe for a computer program that helps it learn from data and make decisions, such as figuring out what's in a picture or how to play a game.
Alignment: Making sure AI does what we want it to do, like helping us find information online without showing us scary or mean stuff.
Anthropomorphism: When we treat machines like they're human, thinking they can feel happy, sad, or even have thoughts like us, even though they really don't.
Artificial Intelligence, or AI: Computers that are designed to do things that normally need human brains, like understanding language or recognizing faces.
Bias: When AI makes mistakes because the information it learned from wasn't fair or was wrong, like thinking all doctors are men.
Chatbot: A computer program you can chat with, like asking it questions or having it help you with homework.
ChatGPT: A special chatbot made by OpenAI that can write text that sounds very human-like.
Cognitive Computing: Just another way to say artificial intelligence.
Data Augmentation: Mixing up and adding different kinds of information so AI can learn better and not get confused by new stuff.
Deep Learning: A smart way to teach computers to recognize patterns, like faces in photos, by using brain-like systems called neural networks.
Diffusion: A way of teaching computers to create new pictures or fix old ones by starting with a mess and slowly making it better.
Emergent Behavior: When AI does something surprising or new that we didn't exactly teach it to do.
End-to-End Learning, or E2E: Teaching a computer to figure out how to do a whole task all by itself, from start to finish, without needing to break it down into smaller steps.
Ethical Considerations: Thinking about how to make sure AI is fair and safe, like making sure it doesn't invade people's privacy or spread false information.
Foom: A scary idea that if we create a super smart AI, it could quickly become way smarter than us and we wouldn't be able to control it.
Generative Adversarial Networks, or GANs: A way of making AI that can create new things, like pictures or music, by having two parts of it try to outsmart each other.
Generative AI: AI that's really good at making new stuff, like writing stories or drawing pictures, by learning from a bunch of examples.
Google Bard: Google's own chatbot that can look up new stuff on the internet to answer your questions, unlike ChatGPT which can't search the web.
Guardrails: Rules to make sure AI doesn't do bad things, like creating fake news or showing things that are not suitable for kids.
Hallucination: When AI gets things wrong but acts super confident about it, like saying a historical event happened at the wrong time.
Large Language Model, or LLM: A big computer brain that reads a lot of text to get really good at understanding and generating human-like language.
Machine Learning, or ML: A way for computers to get smarter over time by practicing with a lot of data, so they can predict or decide things without being directly programmed for every single task.
Microsoft Bing: Microsoft's search engine that now uses AI similar to ChatGPT to answer questions and help you find what you're looking for online.
Multimodal AI: AI that can understand and use different kinds of information at the same time, like pictures, text, and sound.
Natural Language Processing: Teaching computers to understand and use human language, so they can chat with us, translate languages, or find information we need.
Neural Network: A computer system designed to think like a human brain, helping the computer recognize patterns and learn from data.
Overfitting: When AI learns a little too well from its training data and can't handle new or slightly different situations because it's too stuck on what it already knows.
Paperclips: A thought experiment where an AI is told to make as many paperclips as possible and ends up taking over everything to do so, showing how important it is to be careful with AI goals.
Parameters: The settings inside AI that help it learn and make decisions. Think of it like tuning a guitar to make sure it plays the right notes.
Prompt Chaining: When AI uses stuff it learned from earlier conversations to make its future answers better or more related.
Stochastic Parrot: The idea that AI, like a parrot, can mimic human speech or writing without really understanding it or knowing what it means.
Style Transfer: When AI can take the look of one picture and apply it to another, like making your photo look like it was painted by a famous artist.
Temperature: A setting that changes how creative or predictable AI's answers are. Turning it up makes AI try new and wild ideas, while turning it down makes it stick to what it knows.
Text-to-Image Generation: When AI can create pictures just from descriptions you give it, like drawing a dragon playing basketball.
Training Data: The information we give to AI so it can learn how to do its job, like feeding it lots of books or pictures.
Transformer Model: A smart way AI looks at data, understanding the context and relationships between things, like knowing which "bank" you mean based on the rest of your sentence.
Turing Test: A test to see if a machine can act or talk so convincingly human that people can't tell it's a machine.
Weak AI, aka Narrow AI: AI that's really good at one specific thing, like playing chess or recommending movies, but can't do anything else outside of that.
Zero-Shot Learning: When AI can figure out how to do something it hasn't been directly taught, like recognizing an animal it's never seen before just by knowing about similar animals.
