AI Glossary & Dictionary for “H”
Find the Flux+Form AI glossary & dictionary to help you make sense of common AI terms. Below you can find a AI Glossary & Dictionary for “H”:
Hallucination — In AI, hallucination occurs when a model generates plausible-sounding but factually incorrect or made-up information. Think of it like a student confidently giving a wrong answer that sounds reasonable – the response is fluent but not accurate.
Hard Attention — Hard attention forces a model to focus completely on specific parts of input data, ignoring others entirely. Think of it like using a spotlight in a dark room – you can only see what’s directly illuminated, while everything else is completely dark.
Hard Example Mining — Hard example mining focuses training on the most difficult cases a model struggles with. Think of it like a teacher giving extra attention to the problems students consistently get wrong – it helps improve performance on challenging cases.
Hard Negative — A hard negative is a training example that’s particularly challenging for a model to classify correctly. Imagine having two very similar-looking objects that should be classified differently – these challenging distinctions help refine the model’s understanding.
Hardware Acceleration — Hardware acceleration uses specialized computer hardware to speed up AI computations. Imagine having a dedicated calculator for complex math instead of doing it by hand – the specialized tool makes the process much faster.
Hardware Optimized — Hardware optimized refers to AI systems designed to run efficiently on specific hardware. Think of it like having a car engine perfectly tuned for a particular type of fuel – it’s customized to perform best with specific hardware capabilities.
Harmonic Mean — Harmonic mean is a type of average particularly useful for rates and speeds in AI metrics. Think of it like calculating your average speed on a round trip – it handles reciprocal values in a way that makes more sense than a simple average.
Hashing — Hashing converts data into a fixed-size value that represents the original data. Think of it like assigning a unique locker number to store your belongings – the number serves as a consistent way to reference and find your items.
Heap Memory — Heap memory is a pool of memory used for dynamic allocation in AI systems. Imagine having a flexible storage space where you can grab or release space as needed – it’s like having an expandable closet that grows or shrinks based on your needs.
Heat Map — A heat map visualizes data values using color intensity. Imagine using different colors to show temperature across a map – warmer colors might indicate higher values while cooler colors show lower values.
Hebbian Learning — Hebbian learning strengthens connections between neurons that fire together. Think of it like the saying “practice makes perfect” – connections that are used together become stronger over time.
Heterogeneous Computing — Heterogeneous computing uses different types of processors together for better performance. Imagine having a team with different specialists – each type of processor handles the tasks it’s best suited for.
Heuristic Function — A heuristic function makes educated guesses to find approximate solutions quickly. Think of it like using rules of thumb to make decisions – it might not be perfect, but it’s usually good enough and saves time.
Hidden Layer — A hidden layer in a neural network processes information between the input and output layers. Think of it like the behind-the-scenes workers in a factory – they process materials but aren’t directly visible from the outside.
Hidden Markov Model — A Hidden Markov Model predicts sequences of hidden states based on observable events. Imagine trying to guess someone’s activities based only on their energy usage throughout the day – you’re inferring hidden behavior from visible signals.
Hidden State — A hidden state maintains information about past inputs in a neural network. Think of it like your short-term memory while reading a book – it keeps track of context from previous pages to understand the current page better.
Hierarchical Clustering — Hierarchical clustering groups data into a tree-like structure of nested clusters. Imagine organizing a family tree – you start with individuals, group them into immediate families, then extended families, and so on, creating levels of organization.
Hierarchical Model — A hierarchical model organizes learning tasks in levels of increasing complexity. Imagine learning math starting with addition, then multiplication, then algebra – each level builds on the previous ones.
High-Dimensional Data — High-dimensional data has many attributes or features for each data point. Think of it like describing a car using hundreds of characteristics – color, speed, weight, fuel efficiency, and many more details all at once.
High-Level Feature — A high-level feature represents complex, abstract concepts in data. Think of it like recognizing a face – instead of seeing individual lines and shapes, you’re perceiving the higher-level concept of a specific person.
Highway Network — A highway network allows information to flow directly from earlier to later layers. Imagine having express lanes on a highway – some traffic can bypass the regular routes and move directly to its destination.
Hilbert Space — Hilbert space is a mathematical framework for working with infinite-dimensional spaces. Think of it like having a universe where you can measure infinite different properties of objects – it provides rules for working in such complex spaces.
Histogram — A histogram shows the distribution of data by grouping values into ranges. Think of it like sorting coins into different cups based on their value – you can quickly see how many items fall into each range.
Histogram Equalization — Histogram equalization adjusts image contrast by better distributing intensity values. Imagine adjusting the lighting in a photo to make both dark and bright areas more visible – it helps bring out details in all parts of the image.
Holdout Set — A holdout set is data reserved for final model evaluation that’s never used in training. Think of it like a final exam that tests how well you’ve learned – it’s completely separate from the practice materials you used while studying.
Holistic Processing — Holistic processing considers entire patterns rather than just individual components. Think of it like appreciating a symphony as a whole rather than focusing on individual instruments – you’re looking at how all parts work together.
Homogeneous Network — A homogeneous network consists of similar types of nodes or components. Think of it like a team where everyone has the same role – all parts of the network operate in similar ways.
Homography — Homography is a transformation that maps points between two planes while preserving straight lines. Imagine taking a photo of a painting at an angle and then correcting it to look like a straight-on view – that’s a homographic transformation.
Hopfield Network — A Hopfield Network is a type of neural network that can recover learned patterns from partial or noisy input. Think of it like reconstructing a memory – given a fragment of information, it can recall the complete pattern it learned before.
Hostility Detection — Hostility detection identifies aggressive or harmful content in text or speech. Think of it like having a moderator who can spot unfriendly behavior in a conversation – it helps maintain a safe and respectful environment.
Hot Deck Imputation — Hot deck imputation fills missing data with values from similar complete cases. Think of it like borrowing an ingredient from a neighbor who’s cooking the same recipe – you’re using similar, real values to fill in what’s missing.
Hot Start — Hot start initializes a model using parameters from a previously trained model. Imagine starting a new job with experience from a similar previous position – you’re not starting from scratch but building on existing knowledge.
Hough Transform — Hough Transform detects shapes like lines and circles in images. Think of it like having a template that you slide over an image to find where certain shapes match best – it’s particularly good at finding regular geometric patterns.
Human Feedback — Human feedback incorporates human responses and corrections into AI learning. Think of it like having a mentor who provides guidance and corrections – the system learns from human expertise to improve its performance.
Human-in-the-Loop — Human-in-the-loop systems combine AI with human oversight and input. Think of it like having an AI assistant that makes initial recommendations but checks with a human supervisor before making final decisions.
Hybrid Architecture — A hybrid architecture combines different types of AI approaches in one system. Imagine having both a map and a GPS in your car – you’re using multiple tools together to get better overall navigation results.
Hybrid Cloud — Hybrid cloud combines private and public cloud resources for AI computing. Think of it like having both a personal workshop and access to a public makerspace – you can use your own resources or tap into shared ones as needed.
Hybrid Model — A hybrid model combines multiple AI techniques to solve complex problems. Think of it like using both a map and local knowledge to navigate – different approaches work together to provide better results.
Hyperbolic Tangent — Hyperbolic tangent is an activation function that maps values between -1 and 1. Think of it like a volume control that can make sounds either louder or softer – it can emphasize or diminish signals in a balanced way.
Hyperparameter — A hyperparameter is a configuration value set before training begins. Think of it like setting the temperature and time when baking – these are the controls you adjust before the process starts to get the best results.
Hyperparameter Optimization — Hyperparameter optimization automatically finds the best configuration settings for a model. Imagine having a smart oven that tests different temperature and time combinations to find the perfect settings for baking a specific type of bread.
Hyperplane — A hyperplane is a decision boundary that separates different classes in high-dimensional space. Think of it like drawing a line between two groups in a photograph – but imagine doing this with many dimensions instead of just two.
Hypothesis Space — Hypothesis space includes all possible models or solutions a learning algorithm can consider. Think of it like having all possible recipes a chef could create with given ingredients – it’s the complete set of possibilities to explore.
Hypothesis Testing — Hypothesis testing evaluates whether observed results support or reject a particular theory. Think of it like a detective testing a theory about a case – you’re systematically examining evidence to determine if your hypothesis holds up.
This includes our AI Glossary & Dictionary for “H.”
Browse AI Terms by Letter
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z