Your AI Glossary - The Top 100

A

Algorithm
A set of clearly defined, step-by-step instructions for solving a problem or performing a task by a computer. Algorithms form the foundation of all computer programs, including AI systems, as they define the logic and rules for processing data and making decisions. Think of it like a recipe that tells a computer exactly what to do, step by step, to achieve a desired outcome.

Alignment
The goal of ensuring AI systems' outputs and behaviors align with human values and intentions. Alignment is crucial to make sure AI models not only work correctly but also behave ethically and safely, especially in autonomous or complex systems. In everyday terms, it’s like teaching a helper to understand not just what you say, but what you really mean and care about.

Artificial Intelligence (AI)
The field of computer science focused on creating systems capable of performing tasks that normally require human intelligence. AI encompasses many subfields such as machine learning, natural language processing, and computer vision, with applications ranging from voice assistants to self-driving cars. It’s like giving machines the ability to learn, understand, and make decisions similar to humans.

Attention
A mechanism in neural networks that allows the model to focus on relevant parts of the input data while ignoring less important information. Attention improves performance in many AI tasks like translation or text generation by dynamically weighting context. Imagine reading a book and paying extra attention to the important sentences that help you understand the story better — AI does something similar with data.

Attention Mechanism
A specific architectural component in neural networks responsible for calculating weights to highlight important information. Popularized by transformer models, it revolutionized many areas by enabling recognition of relationships in data regardless of position. Simply put, it’s like a smart filter that decides which parts of the information deserve more focus when making predictions.

Autoregressive Model
A model that makes predictions based on its previous outputs, generating values step by step. This technique is widely used in language processing, for example in GPT models, to produce coherent text word by word or sentence by sentence. Think of it like writing a story one word at a time, where each new word depends on the ones before it.

B

Backpropagation
An algorithm for training neural networks by propagating the error backward from the output to adjust weights. Backpropagation is the backbone of supervised learning, enabling models to learn from mistakes and improve performance. You can think of it like a student checking their errors on a test and using that feedback to study smarter next time.

Batch Size
The number of training examples processed in one pass (iteration) through a neural network. Choosing the batch size affects training efficiency and model accuracy; larger batches provide more stable estimates but require more memory. It’s similar to learning in groups — bigger groups mean more information shared at once, but they can be harder to manage.

Bias
A systematic distortion in data or models that can lead to unfair or inaccurate outcomes. Bias can arise from unbalanced training data and is a central challenge in developing fair and trustworthy AI systems. For example, if an AI is trained mostly on data from one group, it might not work well or fairly for others — like having a tutor who only knows one type of student.

C

Capsule Network
A special type of neural network that can better model spatial relationships and hierarchies in data compared to traditional CNNs. Capsule networks are promising for object recognition in images, as they explicitly capture object position and orientation. Think of it like recognizing a face not just by its parts, but by understanding how those parts fit together in the right arrangement.

Chatbot
A computer program designed to simulate conversation with human users, often via text or voice. Chatbots use AI techniques to understand questions and provide helpful, relevant responses. Think of them as virtual assistants that can answer your queries or help with tasks, like customer service reps but automated.

Clustering
An unsupervised machine learning technique that groups similar data points together based on shared features. Clustering helps find natural patterns or segments in data without predefined labels. It’s like sorting a mixed bag of fruit into groups based on color or size without knowing their names.

Computer Vision
A field of AI focused on enabling machines to interpret and understand visual information from images or videos. Computer vision powers technologies like facial recognition, object detection, and autonomous driving. Imagine teaching a computer to “see” and recognize the world much like humans do with their eyes.

Convolutional Neural Network (CNN)
A specialized type of neural network designed to process data with a grid-like topology, such as images. CNNs automatically detect important features like edges or textures by applying filters across the input. Think of it as a visual detective that spots patterns and details to identify objects in pictures.

D

Data Augmentation
A technique to artificially increase the size and diversity of a training dataset by creating modified versions of existing data. This helps improve AI model robustness and performance without collecting new data. It’s like practicing a song in different keys or speeds to get better at playing it.

Data Mining
The process of discovering meaningful patterns, trends, or knowledge from large datasets using statistical and computational methods. Data mining helps businesses and researchers make informed decisions based on hidden insights. Imagine digging through a huge pile of information to find valuable gems that weren’t obvious at first glance.

Dataset
A collection of data, often organized in a structured format, used to train or evaluate AI models. Datasets can include images, text, numbers, or other types of information. Think of it as the “training material” or “examples” that teach AI how to perform tasks.

Deep Learning
A subset of machine learning that uses multi-layered neural networks to model complex patterns in data. Deep learning has enabled breakthroughs in speech recognition, image analysis, and more by learning hierarchical representations automatically. It’s like teaching a computer to understand concepts step-by-step, from simple shapes to complex ideas.

Deep Reinforcement Learning
A technique that combines deep learning with reinforcement learning, where an AI learns to make decisions by trial and error through rewards and penalties. It’s used in applications like game playing and robotics to develop strategies without explicit instructions. Imagine training a dog by rewarding good behavior and correcting mistakes to teach complex tricks.

Distributed Learning
A method where the training of AI models is spread across multiple computers or devices to speed up processing or handle large datasets. Distributed learning allows collaboration without sharing raw data, enhancing privacy and scalability. It’s like a team working together on different parts of a big project to finish faster.

E

Early Stopping
A technique used during training of AI models to stop the process before overfitting occurs—that is, before the model starts to memorize the training data instead of learning general patterns. Early stopping helps improve how well the model performs on new, unseen data. Think of it like knowing when to stop practicing a skill so you don’t get stuck only being good at specific examples.

Embedding
A way to convert complex data such as words, images, or items into numerical vectors that capture their meaning or characteristics. Embeddings allow AI models to work with and compare different types of data effectively. Imagine turning words into points on a map where similar words are closer together, helping the AI “understand” relationships.

Epoch
One complete pass through the entire training dataset during the learning process of an AI model. Multiple epochs allow the model to gradually improve by seeing the data again and again. It’s like reading a book several times to fully understand it.

Ethics (AI Ethics)
The study and practice of ensuring AI systems are developed and used in ways that are fair, transparent, and respect human rights. AI ethics tackles challenges like bias, privacy, and accountability to prevent harm. It’s about making sure AI benefits society without causing unintended problems.

Explainability
The ability to understand and interpret how an AI model makes its decisions or predictions. Explainability is important for trust and verification, especially in critical areas like healthcare or finance. It’s like having a clear explanation for why a doctor chose a specific treatment.

Explainable AI (XAI)
AI methods and techniques designed to make the behavior and decisions of AI systems understandable to humans. XAI helps users and developers see the reasoning behind AI outputs, making systems more transparent and trustworthy. Think of it as a “glass box” instead of a “black box” AI.

F

Feature
An individual measurable property or characteristic used as input for AI models. Features could be anything from pixel brightness in images to word counts in text. They are the pieces of information that help the AI learn and make predictions.

Feature Engineering
The process of selecting, modifying, or creating features from raw data to improve AI model performance. Good feature engineering often makes the difference between mediocre and excellent results. It’s like preparing and organizing ingredients carefully before cooking a meal.

Fine-tuning
Adjusting a pre-trained AI model on a new, specific dataset to improve performance on a particular task. Fine-tuning allows leveraging existing knowledge while adapting to new requirements efficiently. Think of it like customizing a general skill to fit a special project.

G

GAN Training
The process of training Generative Adversarial Networks (GANs), where two neural networks — a generator and a discriminator — compete with each other. The generator tries to create realistic fake data, while the discriminator tries to detect fakes. This “adversarial” training improves the generator’s ability to produce high-quality synthetic data. It’s like a forger and an art expert challenging each other to get better and better.

Generative Adversarial Network (GAN)
A type of neural network architecture consisting of two competing models: a generator that creates fake data and a discriminator that evaluates its authenticity. GANs are used to generate realistic images, videos, or audio by improving through this adversarial process. Think of it like a game between a counterfeiter trying to make fake money and a detective trying to spot it — both getting better over time.

Generative AI
AI systems designed to create new content such as text, images, music, or code, rather than just analyzing existing data. Generative AI models can produce creative outputs that often resemble human work. Imagine an AI that can write stories, compose music, or design art from scratch.

GPT (Generative Pre-trained Transformer)
A popular family of large language models developed by OpenAI that generate coherent and context-aware text. GPT models have revolutionized natural language generation by enabling machines to produce human-like text across many applications, from chatbots to content creation. They work by predicting the next word in a sentence based on the words that came before.

Gradient Descent
An optimization algorithm used to minimize the error of AI models by iteratively adjusting parameters in the direction that reduces loss. It’s fundamental for training neural networks effectively. You can think of it as a hiker trying to find the lowest point in a valley by taking careful steps downhill.

Gradient Vanishing
A problem during training deep neural networks where gradients (used to update weights) become extremely small, slowing or stopping learning in early layers. This can make it hard for the model to learn complex features. It’s like a whisper that becomes too faint to hear as it travels across a long line of people.

H

Heuristic
A practical approach or rule-of-thumb used to solve problems or make decisions more quickly when perfect solutions are too costly or complex. Heuristics provide good-enough answers efficiently but don’t guarantee optimal results. It’s like using a shortcut or educated guess instead of checking every possibility.

Hyperparameter
A configuration setting in AI models that is set before training begins, such as learning rate or batch size. Hyperparameters control how the model learns but are not updated during training. Think of them like knobs you adjust on a machine to tune its performance before running it.

Hyperparameter Tuning
The process of finding the best hyperparameters to improve AI model performance, often by testing different combinations systematically. Effective tuning can significantly boost accuracy and efficiency. It’s like adjusting the recipe’s ingredients and cooking time to get the perfect dish.

I

Image Recognition
A technology within computer vision that enables AI to identify and classify objects, people, or features within images. It’s used in applications like facial recognition, medical imaging, and autonomous vehicles. Imagine teaching a computer to spot your friend in a crowd just by looking at a photo.

Inductive Bias
The set of assumptions a learning algorithm uses to generalize from limited training data to unseen situations. Inductive bias helps AI models make predictions beyond the examples they have seen. It’s like a gardener’s belief that plants need sunlight, guiding how they care for different flowers even without exact instructions.

Inference
The process of using a trained AI model to make predictions or decisions based on new input data. Inference is what happens when the model applies what it has learned to real-world tasks. Think of it as asking a well-trained expert for advice when faced with a new problem.

Instance
A single example or data point within a dataset used for training or testing AI models. Instances can be individual images, sentences, or records. It’s like one photo in an album or one row in a spreadsheet.

K

Knowledge Graph
A structured representation of information that connects entities (like people, places, or things) and their relationships, enabling AI to understand context and make informed connections. Knowledge graphs power search engines and recommendation systems by organizing facts like a web of related concepts.

L

Label
The correct output or category assigned to a data instance, used to teach supervised AI models. For example, labeling images as “cat” or “dog” helps the model learn to recognize them. Labels act like answer keys that guide the AI during learning.

Large Language Model (LLM)
A type of AI model trained on vast amounts of text data to understand and generate human-like language. LLMs, like GPT, can write, summarize, translate, and answer questions with impressive fluency. They’re like language experts who have read millions of books and articles.

Latent Variable
A hidden or unobserved variable inferred from observed data that helps explain patterns or structures in the data. Latent variables capture underlying factors that are not directly measurable. Think of it as the secret ingredient influencing a recipe’s flavor that isn’t listed outright.

Learning Rate
A hyperparameter that controls how much the AI model’s parameters are updated during each training step. Setting the learning rate too high can cause unstable learning; too low can make training very slow. It’s like deciding how big a step to take when trying to reach a destination.

Loss Function
A mathematical function that measures how well an AI model’s predictions match the actual results during training. The model tries to minimize this loss to improve accuracy. Think of it as a scorecard showing how many mistakes the model makes.

M

Machine Learning (ML)
A subset of AI focused on developing algorithms that enable computers to learn patterns from data and make decisions without being explicitly programmed. ML powers many everyday technologies, like spam filters and recommendation systems. It’s like teaching a child to recognize animals by showing many examples instead of giving strict rules.

Markov Decision Process (MDP)
A mathematical framework for modeling decision-making where outcomes depend on both current actions and states, with probabilities defining transitions. MDPs are foundational in reinforcement learning, helping AI plan optimal strategies over time. Imagine playing a board game where each move changes the situation and future options.

Meta-learning
Often called “learning to learn,” meta-learning is an approach where AI models improve their ability to learn new tasks quickly by gaining experience from previous tasks. It helps AI adapt faster to new situations with less data. Think of it like a student who learns study techniques to pick up new subjects more efficiently.

Mini-batch
A small subset of the training data processed together in one step during model training, smaller than a full batch. Mini-batches help balance training speed and stability. Imagine tasting small spoonfuls of a recipe repeatedly to adjust seasoning rather than eating the whole dish at once.

Model
A mathematical representation or algorithm trained to perform a specific task, such as recognizing images or generating text. Models learn patterns from data and make predictions or decisions. It’s like a digital brain that processes input to produce meaningful output.

Multi-task Learning
A training approach where a single AI model learns to perform several related tasks simultaneously, sharing knowledge across them. This often leads to better overall performance and efficiency. It’s like a student studying multiple subjects together, benefiting from the overlap in skills.

N

Natural Language Processing (NLP)
A field of AI focused on enabling computers to understand, interpret, and generate human language in text or speech form. NLP powers applications like chatbots, translators, and voice assistants. Imagine teaching a computer to read and write human languages.

Neural Architecture Search (NAS)
An automated process of finding the best neural network design or architecture for a specific task using algorithms. NAS reduces the need for manual trial-and-error in model design. Think of it like using a smart assistant to find the perfect recipe instead of experimenting blindly.

Neural Network
A computational model inspired by the human brain’s interconnected neurons, consisting of layers of nodes (neurons) that process and transform data. Neural networks are the backbone of many AI systems, especially in deep learning. They work by passing signals through connections to recognize patterns.

Node
Also called a neuron, a node is a basic unit in a neural network that receives input, processes it, and passes the output to the next layer. Nodes apply mathematical functions to incoming data to help the network learn. Think of nodes as tiny decision-makers working together inside the AI.

Normalization
A technique to adjust and scale input data or intermediate values within a neural network to improve training speed and stability. Normalization helps prevent issues like exploding or vanishing values during learning. It’s like making sure ingredients are measured consistently before cooking to get reliable results.

O

Object Detection
A computer vision task that involves locating and identifying objects within images or videos. Unlike image recognition, which only classifies, object detection tells both what objects are present and where they are. Imagine a system that can spot and draw boxes around people, cars, or animals in a photo.

One-shot Learning
A learning technique where an AI model can recognize or learn a new concept from just one or very few examples. This contrasts with traditional models that need large amounts of data. Think of it like recognizing a new face after seeing it only once.

Optimization
The process of adjusting a model’s parameters to minimize errors and improve performance, often by finding the best possible settings. Optimization algorithms guide how models learn from data. It’s like tuning a car engine to get the best speed and fuel efficiency.

Outlier
A data point that differs significantly from other observations and may indicate noise, error, or rare events. Outliers can affect AI model training and predictions if not handled properly. Imagine a single unusually tall person in a group where everyone else is of average height.

Overfitting
When an AI model learns the training data too well, including its noise and details, causing poor generalization to new data. Overfitting means the model performs well on known examples but poorly on unseen ones. It’s like memorizing answers instead of understanding concepts, so you fail on new questions.

P

Parameter
A variable within a model that is adjusted during training to learn from data, such as weights in a neural network. Parameters determine how input data is transformed into output predictions. Think of them as dials a model turns to fit the data.

Parameter Sharing
A technique where certain parameters are reused across different parts of a model to reduce complexity and improve generalization. It’s commonly used in convolutional neural networks. Imagine using the same stencil to paint multiple patterns instead of creating a new one each time.

Perceptron
The simplest type of artificial neural network, consisting of a single layer of nodes that can perform basic binary classification tasks. It’s the building block for more complex neural networks. Think of it as an early “yes/no” decision-maker.

Precision and Recall
Two key metrics for evaluating classification models: precision measures the proportion of correct positive predictions, while recall measures the proportion of actual positives correctly identified. Balancing both is important depending on the application. For example, in disease detection, high recall ensures sick patients aren’t missed, while high precision avoids false alarms.

Predictive Model
A model trained to forecast outcomes or labels based on input data, used in various domains like finance, healthcare, and marketing. Predictive models help anticipate future trends or behaviors. It’s like a weather forecast predicting rain based on atmospheric data.

Pre-training
The initial phase where a model is trained on a large generic dataset before being fine-tuned on a specific task. Pre-training helps the model learn general features that transfer well to other problems. It’s like learning general math before focusing on physics problems.

Principal Component Analysis (PCA)
Principal Component Analysis is a dimensionality reduction technique that transforms complex datasets into a smaller set of uncorrelated variables called principal components, capturing the maximum variance in the data. This method helps simplify models, improve computational efficiency, and reveal underlying patterns without significant loss of information. In everyday terms, PCA is like summarizing a long book into a few key chapters while still keeping the essence of the story.

Probabilistic Model
A probabilistic model represents systems or processes using probability distributions to capture uncertainty and variability in data. By assigning likelihoods to different outcomes, these models enable informed predictions and reasoning under uncertainty. For a non-technical view, it’s like a weather forecast that tells you there’s a 70% chance of rain—it doesn’t guarantee the outcome but gives you a calculated expectation.

Prompt
In AI, a prompt is the input or instruction given to a model that guides its response. The quality, clarity, and structure of a prompt significantly influence the model’s output. For ordinary life, think of it as the way you phrase a question to a friend—you’ll get different answers depending on how you ask.

Q

Quantum Computing (in AI context)
Quantum computing leverages quantum mechanical phenomena, such as superposition and entanglement, to perform computations far faster than classical computers for certain tasks. In AI, it promises accelerated optimization, faster training for complex models, and enhanced problem-solving capacity for high-dimensional data. In simple terms, it’s like upgrading from a single-lane road to a multi-dimensional highway where cars can take multiple routes at the same time.

R

Recurrent Neural Network (RNN)
A Recurrent Neural Network is a type of neural architecture designed to process sequential data by retaining information from previous inputs through hidden states. This makes RNNs particularly suited for tasks like language modeling, speech recognition, and time-series prediction. In plain words, it’s like a storyteller who remembers past events to make the current chapter of the story more meaningful.

Regularization
Regularization is a set of techniques used to prevent overfitting in machine learning models by adding penalties or constraints during training. This encourages simpler, more generalizable models that perform better on unseen data. In everyday terms, it’s like keeping your suitcase light so you can travel easily, rather than overpacking with things you won’t need.

Reinforcement Learning
Reinforcement Learning is a machine learning paradigm where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. Over time, the agent optimizes its strategy to maximize cumulative rewards. For non-experts, it’s similar to training a dog: good behavior is rewarded, mistakes are corrected, and skills improve through trial and error.

Reinforcement Signal
A reinforcement signal is the feedback given to an AI agent in reinforcement learning, indicating how good or bad a specific action was in relation to the overall goal. This signal guides the agent in refining its decision-making policy. In everyday terms, it’s like the applause or boos an actor hears during a performance—it tells them whether to keep going or change their approach.

Representation Learning
Representation learning is the process by which a model automatically discovers and encodes relevant features from raw data in a way that makes downstream tasks easier. This reduces the need for manual feature engineering and often improves performance. For a simple analogy, it’s like learning to recognize faces—you stop focusing on every detail and instead remember key patterns that make each person unique.

S

Sample Complexity
Sample complexity refers to the amount of training data a machine learning model needs to achieve a desired level of accuracy. Models with lower sample complexity can learn effectively from fewer examples, which is valuable when data is scarce or expensive. For everyday life, it’s like how some people can learn to cook a dish after seeing it once, while others need several practice attempts.

Self-supervised Learning
Self-supervised learning is a machine learning approach where models generate their own labels from input data, using part of the data to predict another part. This reduces the need for large manually labeled datasets while still enabling high-quality representations. Think of it like solving half-finished crossword puzzles—using the known words to figure out the missing ones.

Semi-supervised Learning
Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data to train a model. It leverages the labeled examples to guide learning while extracting patterns from the unlabeled portion. Imagine learning a new language from a handful of translated sentences and then inferring the meaning of the rest by recognizing patterns.

Sequence-to-sequence (Seq2Seq)
Sequence-to-sequence is a neural network architecture designed to transform one sequence of data into another, often used in tasks like translation or summarization. It uses an encoder to process the input sequence and a decoder to generate the output. It’s like having a skilled interpreter who listens to a speech in one language and then delivers it fluently in another.

Stochastic Gradient Descent (SGD)
Stochastic Gradient Descent is an optimization algorithm that updates model parameters by calculating gradients using random subsets (batches) of the training data. This speeds up training and helps escape local minima. It’s like adjusting your route to a destination after checking only a small, random part of the map each time, making progress step by step.

Supervised Dataset
A supervised dataset contains input data paired with corresponding labels that indicate the correct output. These labeled examples guide the model during training to learn accurate mappings. Picture it as a set of flashcards with questions on one side and answers on the other—perfect for studying.

Supervised Learning
Supervised learning is a machine learning method where models learn from labeled datasets to make predictions or classifications. The model uses the known answers to adjust its parameters until it can generalize to new, unseen data. It’s like a student practicing with an answer key until they can solve similar problems on their own.

Support Vector Machine (SVM)
A Support Vector Machine is a supervised learning algorithm that finds the optimal hyperplane to separate data into distinct categories. It maximizes the margin between classes, improving classification accuracy. Imagine drawing the widest possible line in the sand that cleanly separates red shells from blue shells on a beach.

Swarm Intelligence
Swarm intelligence refers to the collective problem-solving behavior of decentralized, self-organized systems, often inspired by nature. It’s used in optimization, robotics, and data analysis. Think of how a flock of birds or a school of fish moves in harmony without a leader, yet achieves coordinated goals.

T

Tokenization
Tokenization is the process of breaking text or data into smaller units, such as words, subwords, or characters, for processing by a model. It prepares raw input for analysis and understanding. It’s like chopping up a paragraph into individual puzzle pieces so a computer can understand how they fit together.

Training
Training in machine learning is the process of adjusting a model’s parameters using data so it can make accurate predictions. It involves iterative optimization to minimize errors. It’s like teaching someone to play piano—practice, feedback, and refinement lead to better performance over time.

Training Set
A training set is the portion of data used to teach a machine learning model how to make predictions. It contains input examples along with the correct answers (labels) the model should learn from. Think of it like flashcards for a student—the more varied and relevant the examples, the better the “student” will understand the subject.

Transfer Learning
Transfer learning is a method where a model trained on one task is adapted for a different, but related, task. Instead of starting from scratch, the model reuses learned patterns, saving time and resources. Imagine learning to play the piano and then picking up the organ quickly—you already know about scales, chords, and rhythm.

Transformer
A transformer is a deep learning architecture that processes data in parallel and uses attention mechanisms to focus on the most relevant information in a sequence. Originally designed for natural language processing, transformers power tools like ChatGPT. For a non-technical view, it’s like reading a book but instantly knowing which sentences matter most to understand the plot.

Turing Test
The Turing Test, proposed by Alan Turing, evaluates whether a machine can exhibit intelligent behavior indistinguishable from that of a human. If a person chatting with the machine cannot reliably tell it apart from a human, it “passes” the test. Think of it as the ultimate disguise—if you can’t spot the robot, it’s doing a good job.

U

Unsupervised Learning
Unsupervised learning is when a model learns patterns from data without being told the correct answers in advance. It clusters, groups, or finds structures in the data on its own. It’s like walking into a party, not knowing anyone, and instinctively spotting groups of friends based on who’s talking to whom.

V

Validation
Validation is the process of evaluating a trained model’s performance on data it hasn’t seen before, but still within the training phase, to fine-tune parameters and avoid overfitting. Think of it like rehearsing for a play in front of a small audience before the big premiere—you see how it works under pressure without risking the final show.

Variance
Variance measures how much a model’s predictions change if trained on different subsets of data. High variance means the model is sensitive to small changes, leading to inconsistent results. For a simple analogy, it’s like a chef who cooks the same dish differently every time—sometimes it’s great, sometimes not so much.

Variational Autoencoder (VAE)
A Variational Autoencoder is a type of neural network that learns to compress data into a smaller, structured form (encoding) and then reconstruct it (decoding), while also modeling uncertainty. In non-technical terms, it’s like an artist who can create a simplified sketch of a scene and later recreate the full painting, adding creative variations each time.

W

Weight
In machine learning, a weight is a numerical value that determines the importance of an input in making predictions. During training, weights are adjusted to minimize errors. Think of weights as the knobs on a sound mixer—tweak them right, and the music (prediction) sounds just right.

Z

Zero-shot Learning
Zero-shot learning allows a model to perform a task it hasn’t been explicitly trained on by leveraging knowledge from related tasks and general understanding. It’s like being asked to cook a dish you’ve never made before, but you succeed because you already understand cooking techniques and similar recipes.