Learn about AI

Explore the dynamic world of generative AI and programming with our comprehensive A to Z guide. From foundational concepts to advanced techniques, this page offers clear explanations of essential terms, empowering you to navigate and innovate in the AI landscape with confidence.

Scripter AI’s Glossary of Generative AI terms

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Term	Description	Example/Company
Activation Function	A function that determines the output of a neural network node.	Sigmoid and ReLU are common activation functions.
Algorithm	A step-by-step procedure for calculations.	Google’s PageRank algorithm.
Artificial Intelligence (AI)	The simulation of human intelligence in machines.	Siri by Apple.
Artificial Neural Network (ANN)	A computing system inspired by the human brain’s neural networks.	Used in image recognition.
Autoencoder	A type of neural network used to learn efficient codings of input data.	Anomaly detection systems.
Autonomous Systems	Systems capable of performing tasks without human intervention.	Tesla’s self-driving cars.
Backpropagation	A method to calculate the gradient of the loss function in neural networks.	Used in training deep learning models.
Bias	An error introduced by approximating a complex problem.	Can lead to overfitting or underfitting.
Bias and Variance	Measures of an algorithm’s accuracy.	Balancing bias and variance is key for model performance.
Chatbot	A program designed to simulate conversation with users.	ChatGPT by OpenAI.
Classification	The task of predicting the category of a given data point.	Spam detection in emails.
Clustering	Grouping sets of objects in such a way that objects in the same group are more similar.	Customer segmentation in marketing.
Cognitive Computing	Systems that simulate human thought processes in a computerized model.	IBM Watson.
Computer Vision	Enabling computers to interpret and make decisions based on visual data.	Optical character recognition.
Convolutional Neural Networks (CNNs)	A class of deep neural networks commonly used to analyze visual imagery.	Image classification in Google Photos.
Corpus	A large collection of texts used for linguistic analysis.	Wikipedia’s text corpus for NLP training.
Data	Information used as input for algorithms to produce insights.	Customer data in a CRM system.
Data Augmentation	Techniques used to increase the diversity of a dataset.	Rotating and flipping images for CNN training.
Data Mining	The process of discovering patterns in large datasets.	Market basket analysis in retail.
Dataset	A collection of data used for analysis.	MNIST dataset for digit recognition.
Decision Tree	A model used for classification and regression.	Used in loan approval processes.
Deep Learning	A subset of ML using neural networks with many layers.	AlphaGo by DeepMind.
Diffusion Models	Probabilistic models used for generative tasks.	Used in generating high-quality images.
Dropout Regularization	A technique to prevent overfitting in neural networks.	Applied in training deep learning models.
Epoch	One complete pass through the training dataset.	50 epochs in a training cycle.
Expert System	AI that mimics the decision-making ability of a human expert.	MYCIN for medical diagnosis.
Feature Extraction	Identifying key attributes from raw data for analysis.	Edge detection in image processing.
Generative Adversarial Networks (GANs)	A type of neural network used for generative tasks.	Used in deepfake creation.
Generative AI	AI models that generate new content from data inputs.	DALL-E by OpenAI.
Generative Code	AI-driven code generation.	GitHub Copilot.
Gradient Descent	An optimization algorithm for minimizing the loss function.	Used in training machine learning models.
Hyperparameter	Parameters set before the learning process begins.	Learning rate in neural networks.
Hyperparameters	Variables that define model structure and training conditions.	Number of trees in a random forest.
Intelligent Agent	An autonomous entity which observes through sensors and acts upon an environment.	Autonomous drones.
Label	The output or outcome for each input data point in a dataset.	Tagging images as “cat” or “dog” in training data.
Latent Space	A representation used to encode features in lower dimensions.	Used in autoencoders and GANs.
Loss Function	A method of evaluating how well a specific algorithm models the data.	Mean squared error in regression.
Machine Learning (ML)	A subset of AI focused on building systems that learn from data.	Netflix’s recommendation system.
Model	A statistical representation learned from data.	A trained decision tree.
Model Optimization	The process of improving a machine learning model’s performance.	Hyperparameter tuning.
Natural Language Processing (NLP)	A field of AI that focuses on the interaction between computers and humans through language. This field often overlaps with translation related fields.	Google’s BERT model.
Neural Architecture Search	Automating the process of selecting optimal neural network architectures.	Google’s AutoML.
Neural Network	A series of algorithms that mimic the operations of a human brain.	Used in deep learning applications.
Overfitting	When a model learns the noise in the training data too well.	Occurs with overly complex models.
Predictive Analytics	The use of data, statistical algorithms, and ML techniques to identify the likelihood of future outcomes.	Forecasting stock prices.
Recurrent Neural Networks (RNNs)	A class of artificial neural networks where connections between nodes form a directed graph.	Used in language modeling and text generation.
Regression	A type of predictive modeling technique that estimates the relationship between variables.	House price prediction.
Regularization	Techniques used to reduce a model’s complexity to prevent overfitting.	Lasso and Ridge regression.
Reinforcement Learning	An area of ML where an agent learns to make decisions through trial and error.	Training robots to walk.
Robotics	The branch of technology that deals with the design, construction, operation, and application of robots.	Boston Dynamics’ robots.
Scripter AI	A startup dedicated to implementing Generative AI programming at scale.	Scripter AI
Supervised Learning	A type of learning where the model is trained on labeled data.	Image recognition models.
Swarm Intelligence	The collective behavior of decentralized, self-organized systems.	Optimization algorithms inspired by flocks of birds.
Testing Set	A subset of the dataset used to evaluate the final model’s performance.	20% of data reserved for testing.
Tokenization	The process of splitting text into discrete units for analysis.	Breaking a sentence into words in NLP.
Training	The process of teaching a machine learning model using data.	Running multiple epochs on a dataset.
Training Dataset	The subset of data used to train a model.	80% of data used in training a model.
Transfer Learning	A technique where a pre-trained model is adapted to a new task.	Using ImageNet models for new image classification tasks.
Transformers	A deep learning model architecture primarily used in NLP.	BERT and GPT models.
Turing Test	A test to determine a machine’s ability to exhibit intelligent behavior indistinguishable from a human.	Proposed by Alan Turing.
Underfitting	When a model is too simple to capture the underlying trend in the data.	Occurs with overly simplistic models.
Unsupervised Learning	A type of learning where the model learns from unlabeled data.	Clustering algorithms like K-Means.
Validation Set	Data used to tune the model’s parameters and avoid overfitting.	Separate from training and testing datasets.

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