
Software provider Graitec has unveiled a three-stage artificial intelligence strategy aimed at the architecture, engineering, construction and operations (AECO) sector, arguing that the industry’s biggest challenge is not generating content with AI but ensuring the results can be trusted in real-world projects. The company says it is embedding AI directly into engineering, fabrication and construction […]
Will Graitec release a new AI-powered product update before September 30, 2026?
Resolves by Sep 30, 2026
A software provider in the architecture, engineering, construction and operations sector has announced a three-stage strategy for integrating AI into its products, with emphasis on ensuring AI-generated results can be trusted and audited for real-world projects. The company argues that the main challenge in this industry is not creating content with AI but guaranteeing accountability, since engineering software must comply with strict technical and regulatory standards unlike consumer AI applications. The strategy progresses from AI-assisted workflows that improve productivity, to automation of repetitive tasks, to eventually generating optimized designs that meet building codes directly from project requirements. This approach matters because engineers and contractors face pressure to boost productivity while maintaining compliance with design standards and safety regulations, and the company believes introducing AI earlier in projects could reduce costly rework and errors.

The all-cash deal gives MoEngage access to technology that assigns AI agents to individual customers.

A new update for Google Home could make it less likely your smart home cameras mistake you for someone else, just because you're facing away from the camera. Starting June 23rd, Google's expanding its facial recognition feature so that people you've tagged in your Familiar Faces library can continue to be identified when their faces aren't clearly visible, using "additional non-biometric signals (body size, clothing color, etc.)." The Familiar Faces library will also begin aut

In this tutorial, we build a speech recognition and translation workflow using NVIDIA Canary-1B-v2. We begin by setting up the required audio, NeMo, NumPy, and SciPy dependencies, then load the Canary model on a GPU-enabled runtime for efficient inference. From there, we prepare audio into a clean 16 kHz mono format, perform English ASR, translate speech into multiple languages, generate word and segment timestamps, export translated subtitles as an SRT file, test long-form transcription, run b
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