Artificial Intelligence (AI) encompasses a wide range of technologies and methodologies. To navigate this complex field, it’s essential to understand the fundamental terms and concepts. This glossary provides a concise overview of key AI terms that are crucial for both newcomers and seasoned professionals in the field.
- Data Augmentation
Data augmentation is a technique used to enhance the diversity and quantity of training data in machine learning and deep learning. By generating new variations of existing data, it helps improve the robustness and generalization of models. - Deep Learning
Deep learning is a subset of machine learning that involves training computers using artificial neural networks to perform tasks by learning from data. It enables systems to make decisions based on complex patterns and large datasets. - Deepfakes
Deepfakes are manipulated videos, images, or audio recordings created using AI to make them appear real. They often use sophisticated algorithms to generate convincing synthetic media. - Deepfake AI
This refers to the AI technology used to produce deepfakes, capable of creating highly realistic images, audio, and videos that may deceive viewers. - Diffusion Model
A diffusion model is a generative model that creates high-quality samples by adding noise to an image and then learning to remove it. This iterative process helps in tasks such as image synthesis. - Discriminative AI
Discriminative AI focuses on distinguishing between different classes of data. It involves identifying patterns to classify or predict outcomes based on observed data. - Discriminative AI Models
These models identify and classify data patterns based on training data, commonly used in prediction and classification tasks. - Foundational Models
Foundational models are AI systems with broad capabilities that can be adapted to create more specialized models for specific applications. - Generative Adversarial Network (GAN)
A GAN comprises two neural networks—a generator and a discriminator. The generator creates samples like text and images, while the discriminator evaluates their authenticity, enhancing the quality of generated content. - Generative AI
Generative AI refers to artificial intelligence that generates new content, including text, images, audio, and video, based on input data. - Generative AI Models
These models understand input context to generate new content, used in automated content creation and interactive applications. - Generative Pre-trained Transformer (GPT)
GPT models, developed by OpenAI, leverage a combination of training and transformers to understand and generate human language. - Hallucinations in AI
Hallucinations occur when AI models produce outputs that are meaningless or incorrect. They can affect various types of data, including text, images, audio, and code. - Large Language Models (LLMs)
LLMs are deep learning models trained on extensive text data to understand language patterns, enabling tasks such as text generation, translation, and sentiment analysis. - Machine Learning
Machine learning is a branch of AI that involves creating algorithms and models that allow computers to learn from data and make decisions or predictions. - Natural Language Processing (NLP)
NLP enables computers to understand, manipulate, and generate human language, facilitating tasks such as translation, sentiment analysis, and conversational AI. - Neural Networks
Neural networks are computational models inspired by the human brain’s structure, fundamental to deep learning and AI. - Personally Identifiable Information (PII)
PII includes data that can directly identify an individual, such as names, addresses, and social security numbers. - Prompt
A prompt is an instruction or query given to a generative AI model to produce content based on specific inputs. - Training Data
Training data consists of large datasets used to teach machine learning models, enabling them to learn and perform tasks effectively. - Transformers
Transformers are deep learning architectures with an encoder-decoder mechanism, used for generating coherent and contextually relevant text. - Variational Autoencoder (VAE)
A VAE is a type of generative model designed to efficiently represent input data by encoding it into a compact space and decoding it back to the original.
This glossary offers a foundational understanding of essential AI concepts, crucial for navigating the evolving landscape of artificial intelligence.