The automatic detection of surface-level irregularities—defects or anomalies—in 3D data is of significant interest for various real-world purposes, such as industrial quality inspection, ...

Together, these three visual and methodological perspectives do not merely coexist; they form a holistic constellation.

Responsible AI is an investment in long-term sustainability. The absence of governance can lead to model drift, eroding customer trust and increasing risk.

Databricks and Tonic.ai have partnered to simplify the process of connecting enterprise unstructured data to AI systems to reap the benefits of RAG. Learn how in this step-by-step technical how-to.

Google’s DORA first report on AI coding maturity provides a valuable starting point and a bevy of helpful metrics. It also ...

The transition to S/4HANA represents a transformation for the entire company. The associated challenges can be effectively ...

Introduction A significant proportion of adults in England and Wales report experiencing childhood trauma, which is often associated with poor health and negative social outcomes including a ...

A new framework for assessing transthoracic echocardiography (TTE) image quality revealed that only 30% of studies achieved clear inner-edge-to-outer-edge delineation of at least five segmental ...

The MarketWatch News Department was not involved in the creation of this content. Inconsistent data quality, unclear data governance, and low data literacy are continuing to undermine AI readiness and ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

A reusable, configurable data quality framework using Great Expectations, designed for Microsoft Fabric environments and usable across all your HS2 projects. fabric_data_quality/ ├── README.md # This ...