General-purpose models struggle with messy, industry-specific data. A three-layer AI stack from Trunk Tools cut document review cycles from 60 days to 10.

txtai is an all-in-one AI framework for semantic search, LLM orchestration and language model workflows. The key component of txtai is an embeddings database, which is a union of vector indexes ...

Training deep learning models for semantic occupancy prediction is challenging due to factors such as a large number of occupancy cells, severe occlusion, limited visual cues, complicated driving ...

Sophisticated AI models tend to require a lot of memory and take up a lot of storage space. One of the ways to reduce that ...

AI will not replace dashboards; instead, analytics is evolving into a hybrid model combining dashboards, semantic layers, and conversational AI.

Traditional semantic segmentation models typically require substantial training data and are constrained to categories that have been learned. Recently, few-shot learning has garnered increasing ...

Abstract: Deep hashing has been intensively studied and successfully applied in large-scale image retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that the ...

Local AI inference at 32B-parameter quality, no cloud API required: University of Waterloo researchers released PAW on July 2 ...

Local AI inference at 32B-parameter quality, no cloud API required: University of Waterloo researchers released PAW on July 2, 2026, a system that compiles any natural-language task spec into a 23MB ...

Unit4's Claus Jepsen on why semantic layers, deterministic guardrails, and vertical depth are what it takes to move from a ...

Uncover powerful insights and turn them into impact with a unified analytics platform. Visualize any data and use Copilot in Microsoft Fabric to quickly explore, explain, and take action. Connect ...

Physical AI raised $10B+ in 2025, but robots still train on under 5,000 hours of real-world data. Who's funding the race to ...