Remove Blog Remove Explainability Remove Metadata
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Google AI Introduces Croissant: A Metadata Format for Machine Learning-Ready Datasets

Marktechpost

Database metadata can be expressed in various formats, including schema.org and DCAT. ML data has unique requirements, like combining and extracting data from structured and unstructured sources, having metadata allowing for responsible data use, or describing ML usage characteristics like training, test, and validation sets.

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Metadata filtering for tabular data with Knowledge Bases for Amazon Bedrock

AWS Machine Learning Blog

However, information about one dataset can be in another dataset, called metadata. Without using metadata, your retrieval process can cause the retrieval of unrelated results, thereby decreasing FM accuracy and increasing cost in the FM prompt token. This change allows you to use metadata fields during the retrieval process.

Metadata 118
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How IBM and the Data & Trust Alliance are fostering greater transparency across the data ecosystem

IBM Journey to AI blog

Companies developing or deploying responsible AI must start with strong data governance to prepare for current or upcoming regulations and to create AI that is explainable, transparent and fair. Strong data governance is foundational to robust artificial intelligence (AI) governance.

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How to use audio data in LlamaIndex with Python

AssemblyAI

The metadata contains the full JSON response of our API with more meta information: print(docs[0].metadata) The metadata needs to be smaller than the text chunk size, and since it contains the full JSON response with extra information, it is quite large. You can read more about the integration in the official Llama Hub docs.

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Bring light to the black box

IBM Journey to AI blog

Consistent principles guiding the design, development, deployment and monitoring of models are critical in driving responsible, transparent and explainable AI. Building responsible AI requires upfront planning, and automated tools and processes designed to drive fair, accurate, transparent and explainable results.

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Delivering responsible AI in the healthcare and life sciences industry

IBM Journey to AI blog

There are many elements required to earn people’s trust, including making sure that your AI model is accurate, auditable, explainable, fair and protective of people’s data privacy. To earn the trust of the communities it serves, AI must have proven, repeatable, explained and trusted outputs that perform better than a human.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. The development and use of these models explain the enormous amount of recent AI breakthroughs. AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities.

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