Thread by @AndresJCremades

𝐀𝐈 𝐆𝐥𝐨𝐬𝐬𝐚𝐫𝐲 𝐀–𝐙: 𝐊𝐞𝐲 𝐓𝐞𝐫𝐦𝐬, 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧𝐬, 𝐚𝐧𝐝 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬

𝐀

𝐀𝐒𝐈 (𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐒𝐮𝐩𝐞𝐫 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞)
Definition: An AI system that surpasses human intelligence in every way.

Example: Like how humans are smarter than animals. This hypothetical AI would think faster, understand more deeply, and solve problems beyond human capability. It’s still science fiction, but represents what some researchers think could eventually develop from AGI.

𝐀𝐆𝐈 (𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞)
Definition: An AI system that can understand, learn, and perform any intellectual task that a human can do.

Example: Unlike today’s AI that’s great at specific things (like playing chess), AGI would be like having a digital human brain. It could switch between writing poetry, solving math problems, and having conversations just as easily as people do.

𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫
Definition: Special computer chips designed to make AI programs run much faster than regular processors.

Example: Think of them like turbo engines specifically built for AI tasks. They’re especially good at the math-heavy calculations that AI models need.

𝐀𝐠𝐞𝐧𝐭𝐬
Definition: AI programs that can work independently to complete tasks without constant human supervision.

Example: Like having a smart assistant that can browse the web, use calculators, and make decisions on its own. An AI agent might automatically research a topic and write a summary report.

𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭
Definition: Making sure AI systems want the same things humans want and behave according to human values.

Example: Like training a powerful assistant to understand the spirit behind orders, not just follow them blindly. If you ask an AI to “make me happy,” alignment ensures it won’t do something harmful thinking it’s helping.

𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧
Definition: A technique that helps AI models focus on the most important parts of information.

Example: Like how you focus on specific words when reading. When translating “The red car is fast,” the AI pays more attention to “red” and “car” when deciding how to translate those concepts. This selective focus helps AI understand context and relationships.

𝐁

𝐁𝐚𝐜𝐤 𝐏𝐫𝐨𝐩𝐚𝐠𝐚𝐭𝐢𝐨𝐧
Definition: The process of teaching an AI neural network by working backwards from its mistakes.

Example: Imagine a student getting a math test back with corrections – they trace back through their work to see where they went wrong. Similarly, the AI looks at its incorrect answers and adjusts its internal settings to do better next time.

𝐁𝐢𝐚𝐬
Definition: Built-in assumptions that AI models make about data.

Example: How humans have preconceptions. If an AI is trained mostly on photos of golden retrievers, it might assume all dogs are golden and fluffy. Some bias is helpful (assuming dogs have four legs), but too much can lead to unfair or inaccurate results.

𝐂

𝐂𝐡𝐚𝐢𝐧 𝐨𝐟 𝐓𝐡𝐨𝐮𝐠𝐡𝐭
Definition: The step-by-step reasoning process an AI uses to solve problems.

Example: Think of it like showing your work in math class. Instead of jumping straight to an answer, the AI breaks down complex problems into smaller steps. When asked “How many legs do 3 cats have?”, it might think: “Each cat has 4 legs, 3 cats × 4 legs = 12 legs total.”

𝐂𝐡𝐚𝐭𝐛𝐨𝐭
Definition: A computer program designed to have conversations with people through text or voice.

Example: Like texting with a robot friend. Modern chatbots use AI to understand what you’re saying and respond naturally. Examples include customer service bots on websites or virtual assistants like Siri.

𝐂𝐨𝐧𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐚𝐥 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 (𝐂𝐍𝐍)
Definition: A type of AI specifically designed to understand images.

Example: Like having digital eyes with pattern-recognition superpowers. CNNs look at small pieces of an image at a time, gradually building up understanding from simple shapes to complex objects. They’re what power features like photo tagging on social media and medical image analysis.

𝐃

𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
Definition: A powerful form of AI that uses neural networks with many layers.

Example: Like having multiple levels of thinking. Each layer learns increasingly complex patterns – the first might recognize simple lines, the next recognizes shapes, and deeper layers recognize entire objects. It’s called “deep” because of these many layers, not because it’s philosophical!

𝐃𝐚𝐭𝐚 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧
Definition: Creating variations of training data to help AI learn better.

Example: Like showing a student the same math problem with different numbers. If you’re training an AI to recognize cats, you might flip, rotate, or slightly change the color of cat photos to create more examples. This helps the AI become more robust and recognize cats even in unusual conditions.

𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧
Definition: A technique for generating new images by learning to remove noise.

Example: Like teaching an AI to clean up blurry photos in reverse. The AI starts with pure noise and gradually removes it to create clear, realistic images. It’s like watching a Polaroid photo slowly develop, but in reverse – starting with static and ending with a beautiful picture.

𝐄

𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠
Definition: A way of converting words, images, or other data into numbers that capture their meaning.

Example: Like creating a numerical “fingerprint” for each concept. Similar things get similar numbers – so “cat” and “kitten” would have very similar embeddings. This helps AI understand that “king” relates to “queen” the same way “man” relates to “woman.”

𝐄𝐦𝐞𝐫𝐠𝐞𝐧𝐜𝐞 / 𝐄𝐦𝐞𝐫𝐠𝐞𝐧𝐭 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫
Definition: When AI systems develop unexpected abilities that weren’t explicitly programmed.

Example: Like how a flock of birds creates complex patterns from simple individual rules. “Sharp left turns” and “intelligence explosions” refer to scenarios where AI development suddenly accelerates in unpredictable ways. It’s like teaching a child arithmetic and suddenly discovering they’ve figured out calculus on their own.

𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
Definition: Training AI to learn everything from raw input to final output without human-designed intermediate steps.

Example: Like teaching someone to cook by just showing them ingredients and final dishes. Instead of manually programming each step, the AI figures out its own way to get from start to finish. This often leads to surprising and more efficient solutions than human-designed approaches.

𝐄𝐱𝐩𝐞𝐫𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
Definition: AI programs designed to solve problems in specific fields using human expert knowledge.

Example: Like having a digital doctor or lawyer. They follow rules that human experts have defined to make decisions in their domain. A medical expert system might help diagnose diseases by following the same logical steps a doctor would use.

𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐀𝐈 (𝐗𝐀𝐈)
Definition: AI systems that can explain their decisions in human-understandable terms.

Example: Like showing your work on a math test. Instead of just saying “this is cancer,” an explainable medical AI might say “I detected cancer because of these three specific features in the scan.” This helps build trust and allows humans to verify AI decisions.

𝐅

𝐅𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠
Definition: Taking a pre-trained AI model and teaching it to excel at a specific task.

Example: Like taking a generally educated person and training them for a specific job. You might take a general language AI and fine-tune it to be a medical assistant by training it on medical texts. This is much faster than training from scratch and often produces better results.

𝐅𝐨𝐫𝐰𝐚𝐫𝐝 𝐏𝐫𝐨𝐩𝐚𝐠𝐚𝐭𝐢𝐨𝐧
Definition: The process where information flows through a neural network from input to output.

Example: Like water flowing through a series of pipes. Data enters at the input layer, gets processed through hidden layers, and emerges as a prediction at the output. Each layer transforms the data a bit more, gradually turning raw input into useful answers.

𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥
Definition: Large, versatile AI models trained on massive amounts of diverse data that can be adapted for many different tasks.

Example: Think of them as having a broad liberal arts education that can be specialized later – they know a little about everything. Examples include GPT models that can write, translate, code, and answer questions all from the same base training.

𝐆

𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈
Definition: AI that creates new content like text, images, music, or videos.

Example: It’s like having a creative assistant that can write stories, paint pictures, or compose songs based on what you ask for. Examples include AI that generates art from text descriptions or writes poetry in different styles.

𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 (𝐆𝐀𝐍)
Definition: Two AI networks competing against each other to create realistic fake data.

Example: Like a counterfeiter versus a detective. One network (the generator) tries to create convincing fake images, while the other (the discriminator) tries to spot the fakes. Through this competition, the generator gets so good that it can create incredibly realistic images, videos, or other content.

𝐆𝐏𝐔 (𝐆𝐫𝐚𝐩𝐡𝐢𝐜𝐬 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐔𝐧𝐢𝐭)
Definition: Powerful computer chips originally designed for rendering video game graphics, but now essential for AI training.

Example: They excel at doing many simple calculations simultaneously, which is exactly what AI models need. Think of them as having thousands of simple calculators working together instead of one super-fast calculator.

𝐆𝐫𝐚𝐝𝐢𝐞𝐧𝐭 𝐃𝐞𝐬𝐜𝐞𝐧𝐭
Definition: A method for improving AI models by gradually adjusting them in the direction that reduces errors.

Example: Like rolling a ball down a hill to find the bottom. If the AI makes a mistake, gradient descent figures out which way to “nudge” the model’s settings to make fewer mistakes next time. It’s like learning to throw a basketball by adjusting your aim after each shot.

𝐇

𝐇𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐞 / 𝐇𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧
Definition: When AI generates information that sounds convincing but is actually false or made-up.

Example: Like a confident person giving wrong directions. An AI might claim a fake book exists or invent scientific facts that sound plausible but aren’t true. This happens because AI predicts what sounds right rather than checking if information is actually correct.

𝐇𝐢𝐝𝐝𝐞𝐧 𝐋𝐚𝐲𝐞𝐫
Definition: The middle layers of a neural network where the actual “thinking” happens.

Example: Like the thought processes between hearing a question and giving an answer. These layers are “hidden” because we can’t directly see what they’re doing – they transform input data into increasingly complex representations. More hidden layers typically allow for more sophisticated understanding.

𝐇𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐓𝐮𝐧𝐢𝐧𝐠
Definition: Adjusting the “settings” of an AI model to get the best performance.

Example: Like tuning a radio to get the clearest signal. These settings aren’t learned from data but are chosen by humans – things like learning speed, network size, or training duration. Finding the right combination often requires lots of experimentation and can dramatically improve results.

𝐈

𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞
Definition: The process of using a trained AI model to make predictions or generate outputs.

Example: Like using your knowledge to answer a test question. After an AI has learned from training data, inference is when it applies that learning to new, unseen situations. This is the “working” phase of AI – when it actually does useful tasks for users.

𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 𝐓𝐮𝐧𝐢𝐧𝐠
Definition: Training AI models to follow specific commands or instructions better.

Example: Like teaching a student to read and follow directions carefully. Instead of just predicting the next word, the AI learns to understand what humans want when they give instructions. This makes AI more helpful and better at tasks like “write a summary” or “translate this text.”

𝐉

𝐉𝐨𝐢𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
Definition: Training multiple AI tasks together so they help each other learn better.

Example: Like studying math and physics together – skills from one improve the other. An AI learning to recognize faces and emotions together might become better at both.

𝐉𝐨𝐢𝐧𝐭 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠
Definition: A way of teaching AI to understand how different things relate to each other across formats – like text and images.

Example: Imagine showing the AI a picture of a dog and the word “dog,” and it learns they mean the same thing. Joint embedding helps AI match captions with photos or find similar items even when they look or sound different.

𝐉𝐮𝐩𝐲𝐭𝐞𝐫 𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤
Definition: An interactive coding environment used by many AI developers and data scientists.

Example: Think of it like a digital lab notebook where you can write code, run it, and see results – all in one place. It’s great for experimenting and explaining AI concepts with both code and visuals.

𝐉𝐢𝐭𝐭𝐞𝐫
Definition: Adding small, random changes to data to make AI training more robust.

Example: Imagine shaking up your training images just a bit – changing brightness, shifting pixels, or tilting slightly – so the AI doesn’t get too used to one perfect version and learns to handle real-world messiness.

𝐊

𝐤-𝐍𝐞𝐚𝐫𝐞𝐬𝐭 𝐍𝐞𝐢𝐠𝐡𝐛𝐨𝐫𝐬 (𝐤-𝐍𝐍)
Definition: A simple AI technique that finds the most similar examples to make a prediction.

Example: If you’re trying to guess someone’s favorite food, k-NN looks at people with similar tastes and suggests what they liked. It’s like asking your friend group for restaurant recommendations based on shared preferences.

𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧
Definition: Pulling useful facts or insights from massive amounts of text or data.

Example: It’s like an AI playing detective – reading tons of content and picking out things like names, dates, or relationships. Handy for turning unstructured info into something organized.

𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐆𝐫𝐚𝐩𝐡
Definition: A smart map of how different ideas, objects, or facts connect.

Example: Imagine a web linking “Paris” to “France,” “Eiffel Tower,” and “capital city.” AI uses these graphs to understand context and meaning – so when you ask “Where’s the Eiffel Tower?”, it doesn’t get confused.

𝐊-𝐦𝐞𝐚𝐧𝐬 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠
Definition: A way for AI to group similar things together without being told how.

Example: It’s like giving a bunch of photos to an AI and asking it to organize them. It figures out what looks alike – say, beach pics vs. mountain pics – and creates clusters, or “buckets.”

𝐊𝐞𝐲𝐟𝐫𝐚𝐦𝐞
Definition: A specific frame in a video that represents an important moment or change.

Example: AI uses keyframes to save effort – like summarizing a scene or detecting when action starts. Think of it as AI’s way of bookmarking highlights.

𝐋

𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 (𝐋𝐋𝐌)
Definition: Massive AI systems trained on enormous amounts of text to understand and generate human language.

Example: Like having read most of the internet. They can write, translate, answer questions, and even code because they’ve learned patterns from billions of examples. The “large” refers to both the amount of training data and the size of the neural network.

𝐋𝐚𝐭𝐞𝐧𝐭 𝐒𝐩𝐚𝐜𝐞
Definition: A hidden mathematical space where AI represents the essence of data.

Example: Like a secret code that captures the most important features. In this space, similar things cluster together – all dogs would be near each other, separate from cats. It’s where AI does its “thinking” by manipulating these compressed representations of the real world.

𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 (𝐂𝐨𝐬𝐭 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧)
Definition: A mathematical way to measure how wrong an AI model’s predictions are.

Example: Like keeping score of mistakes during practice. The goal is to minimize this “loss” – the fewer mistakes, the lower the score. It’s like a coach who points out errors so the player can improve their game.

𝐌

𝐌𝐢𝐱𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐱𝐩𝐞𝐫𝐭𝐬
Definition: An AI architecture that uses multiple specialized sub-models, each expert at different tasks.

Example: Like having a team of specialists. When given a problem, the system decides which expert (or combination of experts) should handle it. It’s like having separate doctors for different medical conditions rather than one doctor trying to handle everything.

𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
Definition: A way of teaching computers to learn patterns and make decisions from data, without explicitly programming every possible scenario. Instead of writing specific instructions for every situation, you show the computer lots of examples and let it figure out the rules.

Example: Like teaching a child to recognize animals by showing them thousands of photos rather than describing every possible animal.

𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥
Definition: AI systems that can understand and work with different types of data like text, images, audio, and video all at once.

Example: Like a person who can read, see, and hear simultaneously to understand the world. For example, a multimodal AI could watch a cooking video and then write out the recipe in text.

𝐍

𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤
Definition: A computer system inspired by how the human brain works, made up of interconnected processing units called neurons.

Example: Like brain cells that pass signals to each other, artificial neurons receive information, process it, and pass results to other neurons. These networks can learn to recognize patterns, make decisions, and solve complex problems.

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

Example: Like English or Spanish. It’s what allows computers to read books, understand speech, translate languages, and have conversations. NLP bridges the gap between human communication and computer understanding.

𝐎

𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠
Definition: When an AI model becomes too specialized on its training data and can’t handle new situations.

Example: Like a student who memorizes practice tests but fails on actual exams. The model learns the training examples so perfectly that it can’t generalize to slightly different cases. It’s the difference between understanding concepts versus just memorizing answers.

𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧
Definition: The goal or target that an AI model tries to achieve during training.

Example: Like having a clear mission statement. This could be “minimize prediction errors” or “maximize accuracy.” The objective function guides the learning process, telling the AI what “good performance” looks like so it knows which direction to improve.

𝐏

𝐏𝐫𝐞-𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠
Definition: The initial education phase where an AI model learns general knowledge from vast amounts of data before specializing in specific tasks.

Example: Like how children learn basic language and world knowledge before focusing on specific subjects in school. This foundation provides the AI with broad understanding that can later be applied to many different tasks.

𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬
Definition: The internal settings or “knobs” that an AI model adjusts to learn from data.

Example: Like the synaptic strengths in a brain. These numbers determine how the model processes information and makes decisions. A typical AI model might have millions or billions of parameters that get fine-tuned during training to produce accurate results.

𝐏𝐫𝐨𝐦𝐩𝐭
Definition: The instructions or questions you give to an AI model to get it to perform a task.

Example: Like asking a helpful assistant what you want them to do. The way you phrase your prompt greatly affects the AI’s response – being clear and specific usually gets better results. Think of it as setting the context and giving directions for what you want the AI to accomplish.

𝐐

𝐐𝐮𝐞𝐫𝐲
Definition: A question or request given to an AI system to get information.
Example: Like when you type “best AI movies” into a chatbot or search bar – that’s your query. The AI reads it, processes it, and tries to give the most helpful response.

𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠
Definition: A new kind of computer that could solve problems regular computers struggle with.

Example: Like supercharging AI. Instead of using regular bits (0s and 1s), quantum computers use qubits, which can be both at once. While still experimental, they could one day make AI much faster and smarter.

𝐐𝐮𝐞𝐫𝐲 𝐄𝐱𝐩𝐚𝐧𝐬𝐢𝐨𝐧
Definition: Making your AI search smarter by adding related words to your question.

Example: Ask “AI art tools,” and the AI might also look for “image generation” or “creative apps.” It’s like giving your question a few helpful synonyms.

𝐐𝐮𝐢𝐜𝐤𝐃𝐫𝐚𝐰 𝐃𝐚𝐭𝐚𝐬𝐞𝐭
Definition: A giant collection of doodles from people around the world, used to train AI to recognize sketches.

Example: It’s like a global game of Pictionary turned into a training gym for AI models. Great for teaching machines to recognize hand-drawn images.

𝐐𝐮𝐚𝐧𝐭𝐢𝐬𝐚𝐭𝐢𝐨𝐧
Definition: Compressing AI models by rounding numbers to make them smaller and faster.

Example: Like packing a suitcase tightly – you lose a bit of precision, but the model runs more efficiently, especially on phones or edge devices.

𝐑

𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
Definition: A way of training AI by giving rewards for good actions and penalties for bad ones.

Example: Like training a pet with treats. The AI learns through trial and error, gradually figuring out which actions lead to the best outcomes. This is how AI learns to play games, control robots, or optimize complex systems.

𝐑𝐋𝐇𝐅 (𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐇𝐮𝐦𝐚𝐧 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤)
Definition: A training method where humans rate AI outputs as good or bad, helping the AI learn human preferences and values.

Example: Like having a human teacher grade the AI’s homework and explain what makes a good answer. This helps create AI systems that are more helpful, harmless, and aligned with what humans actually want.

𝐒

𝐒𝐲𝐦𝐛𝐨𝐥𝐢𝐜 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞
Definition: An approach to AI that uses logical rules and symbols to represent knowledge and solve problems.

Example: Like programming with if-then statements. Instead of learning from data, symbolic AI follows explicit rules that humans create. Think of it as building an AI that follows a detailed manual rather than learning from examples.

𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
Definition: Training AI using examples that already have correct answers.

Example: Like learning with an answer key. You show the AI thousands of photos labeled “cat” or “dog,” and it learns to recognize the differences. It’s the most common type of machine learning, similar to how students learn with textbooks that provide both questions and solutions.

𝐒𝐢𝐧𝐠𝐮𝐥𝐚𝐫𝐢𝐭𝐲
Definition: A hypothetical future moment when AI becomes so advanced that it triggers runaway technological growth, fundamentally changing human civilization forever.

Example: Like reaching a point where AI becomes better at improving AI than humans are, leading to an explosion of capabilities. It’s a speculative concept that represents the ultimate “what if” scenario for AI development.

𝐓

𝐓𝐏𝐔 (𝐓𝐞𝐧𝐬𝐨𝐫 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐔𝐧𝐢𝐭)
Definition: Google’s custom-designed computer chips specifically optimized for machine learning tasks.

Example: Like having a race car engine built just for racing. TPUs are even more specialized than GPUs for AI work, designed to handle the specific mathematical operations that neural networks need. They can train AI models faster and more efficiently than general-purpose processors.

𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫
Definition: A powerful neural network architecture that revolutionized AI by using attention mechanisms to understand relationships in data.

Example: Like having excellent reading comprehension that can connect ideas across long passages of text. Transformers power most modern language AI, including ChatGPT, and are excellent at understanding context and long-range dependencies in information.

𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
Definition: Using knowledge from one AI task to help with a different but related task.

Example: Like how learning to ride a bicycle helps with learning to ride a motorcycle. Instead of training from scratch, you take an AI that’s already learned something useful and adapt it for a new purpose. This saves time and often produces better results than starting over.

𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐃𝐚𝐭𝐚
Definition: The collection of examples used to teach an AI model how to perform tasks.

Example: Like textbooks and practice problems for students. This data shows the AI what inputs and correct outputs look like, allowing it to learn patterns and relationships. The quality and quantity of training data largely determines how well the AI will perform.

𝐔

𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
Definition: Training AI to find hidden patterns in data without being given the “right answers.”

Example: Like asking someone to organize a messy room without telling them how. The AI discovers natural groupings, relationships, or structures on its own. It’s like learning by exploration rather than following instructions.

𝐔𝐧𝐝𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠
Definition: When an AI model is too simple to capture the important patterns in data.

Example: Like trying to solve calculus with only basic arithmetic. The model performs poorly because it hasn’t learned enough about the relationships in the data. It’s the opposite of overfitting – instead of memorizing too much, it’s learned too little to be useful.

𝐕

𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐃𝐚𝐭𝐚
Definition: A separate set of data used to test how well an AI model is learning during training.

Example: Like practice exams that don’t count toward the final grade. This helps developers tune the model’s settings and catch problems like overfitting before deploying the AI. It’s different from both training data (used for learning) and test data (used for final evaluation).

𝐖

𝐖𝐢𝐧𝐝𝐨𝐰𝐢𝐧𝐠
Definition: Breaking big chunks of data into smaller slices so AI can process them better.

Example: Like listening to music 10 seconds at a time to analyze rhythm or chopping a long sentence into parts to better understand meaning. Especially useful in audio and time-series data.

𝐖𝐞𝐢𝐠𝐡𝐭𝐬
Definition: The numbers inside an AI model that decide how important different pieces of information are.

Example: Think of them like knobs on a sound mixer. If the AI is trying to recognize a cat, it might “turn up” the importance of pointy ears and “turn down” the color of the background. These weights get fine-tuned during training.

𝐖𝐞𝐚𝐤 𝐀𝐈
Definition: AI that’s good at specific tasks but can’t do everything a human can.

Example: Like a chess app that beats grandmasters – but doesn’t know how to write emails or drive a car. Most AI today is weak AI, even if it’s super powerful in its own area.

𝐖𝐚𝐯𝐞𝐆𝐀𝐍
Definition: A special AI model that creates new audio from scratch.

Example: Think of it like a robot DJ that’s never heard a song before – but can generate music that sounds real by learning patterns in waveforms.

𝐗

𝐗𝐀𝐈 (𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐀𝐈)Definition: AI systems designed to show how they make decisions – so humans can understand and trust them.

Example: If an AI says “You’re not approved for this loan,” XAI helps explain why, like “Your credit score is low.” It’s about opening the AI black box so humans stay in the loop.

𝐗𝐌𝐋 (𝐄𝐱𝐭𝐞𝐧𝐬𝐢𝐛𝐥𝐞 𝐌𝐚𝐫𝐤𝐮𝐩 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞)
Definition: A way of organizing data so both humans and machines can read it.

Example: Think of it like a labeled box for information – easy for AI to unpack and understand. It’s often used when transferring data between systems in AI applications.

𝐗𝐎𝐑 𝐏𝐫𝐨𝐛𝐥𝐞𝐦
Definition: A classic example used to show that simple AI models can’t solve everything.

Example: It’s like a logic puzzle where basic AI gets confused. The XOR problem helped push the development of more advanced neural networks (like those with hidden layers).

𝐗𝐚𝐯𝐢𝐞𝐫 𝐈𝐧𝐢𝐭𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧
Definition: A smart way to set up an AI model’s starting weights to help it learn faster.

Example: Think of it like setting up dominos so they fall evenly – not too slow, not too fast. Xavier Initialization keeps everything balanced at the start of training.

𝐘

𝐘𝐎𝐋𝐎 (𝐘𝐨𝐮 𝐎𝐧𝐥𝐲 𝐋𝐨𝐨𝐤 𝐎𝐧𝐜𝐞)
Definition: A fast AI model used to detect and recognize objects in images or videos – all in a single glance.

Example: Imagine watching a street camera, and the AI instantly spots “car,” “person,” and “dog” in real time. That’s YOLO at work, powering things like self-driving cars and security systems.

𝐘𝐀𝐌𝐋 (𝐘𝐀𝐌𝐋 𝐀𝐢𝐧’𝐭 𝐌𝐚𝐫𝐤𝐮𝐩 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞)
Definition: A human-friendly way to write data that AI systems can understand.

Example: It looks clean and simple, like writing settings in plain English. AI developers often use YAML to configure models – telling them what to do and how to behave.

𝐘𝐢𝐞𝐥𝐝
Definition: A keyword in programming (especially Python) that helps AI models work with large data more efficiently.

Example: It’s like handing over one page of a book at a time instead of printing the whole book at once – saving memory and speeding up training.

𝐘𝐮𝐥𝐞-𝐒𝐢𝐦𝐨𝐧 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧
Definition: A statistical pattern that shows how some things get super popular while others stay rare. AI uses it to understand things.

Example: Like why a few words (like “the”) dominate language or how some YouTube videos go viral while most don’t.

𝐙

𝐙𝐞𝐫𝐨-𝐬𝐡𝐨𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
Definition: When an AI can perform tasks it was never specifically trained on.

Example: Like a student taking a test on a subject they never studied but doing well based on general knowledge. The AI uses its broad understanding to handle completely new situations without needing examples. It’s like being able to recognize a zebra even if you’ve only seen horses and learned about stripes separately.

Thanks to my colleagues from @Nagarro.

@Nagarro @threadreaderapp unroll