Explore AIstify's latest reporting, research, and expert analysis tagged with "artificial intelligence", collected in one continuously updated archive.
A foundation model is a large, broadly trained AI model that can be adapted or prompted for many different tasks and applications.
A vector database stores embeddings and performs similarity search for semantic retrieval, recommendations, and retrieval-augmented generation.
A transformer is a neural network architecture built on attention and used in modern language, vision, audio, video, and multimodal AI models.
A yottabyte is 10^24 bytes of digital information, a unit used to describe extremely large data volumes relevant to global computing and AI growth.
Symbolic AI represents knowledge with explicit symbols and rules that a system manipulates through logical reasoning.
Temperature is a generative AI setting that adjusts how strongly a model favors likely tokens, influencing output consistency and variation.
Self-supervised learning trains AI models with labels or objectives created from the data itself, reducing dependence on manual annotation.
Semantic search uses embeddings to retrieve information by meaning and context, finding relevant results even when documents use different words.
YOLO (You Only Look Once) is a real-time computer vision approach that detects and classifies multiple objects in one image processing pass.
Synthetic data is artificially generated information used to train, test, or evaluate AI systems when real data is limited, sensitive, or costly.
Fine-tuning adapts a pretrained AI model to a specific task, domain, or behavior by continuing training on targeted examples.
Regression is a supervised machine learning task that predicts continuous numerical values such as prices, demand, time, or risk.
Neuro-symbolic AI combines neural learning with explicit symbols, rules, or logic to support structured reasoning.
A recurrent neural network (RNN) is a deep learning model that carries information across sequential inputs such as text, speech, and time-series data.
Retrieval-augmented generation (RAG) combines search with generative AI so responses use relevant documents, current data, and source context.