AI has infiltrated every aspect of our lives, But 91% of people don’t know the fundamental AI terms.

June 27, 2023 ebookreaders

WARNING: This article turns you into an AI encyclopedia

As AI becomes more integrated into our lives, it’s important to have a basic understanding of it’s key concepts.

Today, I’m thrilled to introduce you to 43 fundamental AI terms, explained in the most straightforward and easy-to-understand manner.

No jargon or complex explanations — just pure clarity and comprehension.

Say goodbye to confusion and hello to clarity as we explore the key AI terms, each explained in just one line.

Here are the terms in the SIMPLEST way possible:

Artificial Intelligence (AI): Technology that makes machines smart and capable of performing tasks like humans.
Machine Learning (ML): The ability of machines to learn and improve from experience without being explicitly programmed.
Deep Learning: A type of machine learning that uses artificial neural networks to imitate how the human brain works.
Natural Language Processing (NLP): The ability of computers to understand and interpret human language.
Computer Vision: The ability of computers to understand and interpret visual information from images or videos.
Robotics: The field of building and programming robots to perform tasks automatically.
Neural Networks: Computer systems inspired by the human brain that learn patterns & make decisions.
Big Data: Large amounts of data that can be analyzed by computers to find patterns & insights.
Data Science: The study of data to extract meaningful information.
Data Mining: The process of discovering patterns & relationships in large datasets.
Predictive Analytics: Using historical data to make predictions about future events/outcomes.
Supervised Learning: A type of machine learning where models learn from labeled examples.
Unsupervised Learning: A type of machine learning where models learn from unlabeled data to find patterns.
Reinforcement Learning: Teaching machines to make decisions through trial and error and rewards or punishments.
Transfer Learning: Using knowledge from one task to help solve a different but related task.
Artificial Neural Networks: Algorithms that mimic the way human brains process info.
Convolutional Neural Networks: Neural networks designed to analyze visual data.
Recurrent Neural Networks (RNNs): Neural networks that can handle sequential data like language & speech.
Generative Adversarial Networks (GANs): 2 neural networks, 1 generating data and the other evaluating its authenticity.
Support Vector Machines (SVMs): Algorithms used for classification & regression tasks.
Decision Trees: Models that make decisions by following a series of tree-like structures.
Random Forests: Ensembles of decision trees that work together to improve accuracy.
K-Nearest Neighbors(KNN): A simple algorithm that classifies data based on similarity to its neighbors.
Clustering: Grouping data points based on their similarities.
Dimensionality Reduction: Simplifying data by reducing number of features while retaining important info.
Regression: Predicting a numerical value based on input data.
Classification: Assigning categories or labels to data.
Overfitting: When a model becomes too specialized in the training data and fails to generalize to new data.
Underfitting: When a model is too simple & fails to capture important patterns in the data.
Hyperparameters: Settings that control how an ML algorithm learns & makes predictions.
Optimization: The process of finding best settings for a machine learning algorithm.
Gradient Descent: An optimization algorithm used to update the model’s parameters based on the error.
Backpropagation: A method for calculating how errors propagate through a neural network.
Loss Function: A measure of how well a model is performing.
Activation Function: A mathematical function that introduces nonlinearity to neural networks.
Batch Normalization: A technique that normalizes input data to speed up training.
Dropout: Randomly deactivating some neurons during training to prevent overfitting.
Learning Rate: A parameter that controls step size during optimization.
Momentum: A technique that helps optimization algorithms converge faster.
Early Stopping: Stopping training process early to prevent overfitting when model’s performance on validation data declines.
Ensemble Learning: Combining predictions of multiple models to improve accuracy.
Bias: Systematic errors in a model that cause it to deviate from true values.
Variance: Variability of a model’s predictions due to sensitivity to fluctuations in training data.

In conclusion, we’ve explored 50 important AI terms in a way that’s easy to understand. These terms represent the foundations of artificial intelligence, which is a rapidly advancing field with immense potential.

By breaking down complex concepts and providing simple explanations, i hope to have sparked your interest and encouraged further exploration. Whether you’re new to AI or looking to expand your knowledge, these terms serve as a starting point for understanding this transformative technology.

With this knowledge, you can engage in conversations, share insights, and contribute to the ongoing developments in AI.

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AI has infiltrated every aspect of our lives, But 91% of people don’t know the fundamental AI terms. was originally published in Artificial Intelligence in Plain English on Medium, where people are continuing the conversation by highlighting and responding to this story.

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By: EyeingAI
Title: AI has infiltrated every aspect of our lives, But 91% of people don’t know the fundamental AI terms.
Sourced From: ai.plainenglish.io/ai-has-infiltrated-every-aspect-of-our-lives-but-91-of-people-dont-know-the-fundamental-ai-terms-65ec6bbeb1e1?source=rss—-78d064101951—4
Published Date: Mon, 26 Jun 2023 01:56:02 GMT