Explore AIstify's latest reporting, research, and expert analysis tagged with "artificial intelligence", collected in one continuously updated archive.
AI alignment is the practice of directing AI systems toward intended goals and human values while keeping their behavior safe and controllable.
Multimodal AI is artificial intelligence that understands or generates multiple data types, including text, images, audio, video, and sensor input.
OpenAI has launched GPT-5.5, a new flagship model designed for coding, computer use, knowledge work, and scientific research, with stronger performance, lower token usage, and broader real-world autonomy than GPT-5.4.
Model quantization lowers the numerical precision of AI weights or calculations to reduce memory, inference time, energy use, and deployment cost.
Model drift is the decline or change in AI performance that occurs when real-world data and relationships move away from training conditions.
A world model is an AI representation that predicts how an environment may change, helping agents simulate outcomes before choosing an action.
A loss function measures model error during training and gives an optimization algorithm the objective used to adjust AI model parameters.
A diffusion model generates new content by learning to transform random noise into structured images, audio, video, or other data.
Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning method that trains small adapter weights instead of updating an entire AI model.
Workflow automation coordinates tasks, data, approvals, and AI-assisted actions through a repeatable process with defined rules and oversight.
An expert system uses encoded knowledge and inference rules to reproduce specialist decision-making within a narrow domain.
Word error rate (WER) measures speech-recognition accuracy from word substitutions, deletions, and insertions compared with a reference transcript.
Latent space is a learned numerical representation where an AI model organizes hidden features and relationships within images, text, or other data.
K-means clustering is an unsupervised learning algorithm that groups similar data points around k centroids without requiring labeled examples.
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