Remove Categorization Remove Explainability Remove Information
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SEC’s climate disclosure rule proposal explained

IBM Journey to AI blog

Scope 3 emissions disclosure Envizi’s Scope 3 GHG Accounting and Reporting module enables the capture of upstream and downstream GHG emissions data, calculates emissions using a robust analytics engine and categorizes emissions by value chain supplier, data type, intensities and other metrics to support auditability.

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AI for Universal Audio Understanding: Qwen-Audio Explained

AssemblyAI

  This problem is harder for audio because audio data is far more information-dense than text. A joint audio-language model trained on suitably expansive datasets of audio and text could learn more universal representations to transfer robustly across both modalities. 

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Amazon AI Introduces DataLore: A Machine Learning Framework that Explains Data Changes between an Initial Dataset and Its Augmented Version to Improve Traceability

Marktechpost

There are major worries about data traceability and reproducibility because, unlike code, data modifications do not always provide enough information about the exact source data used to create the published data and the transformations made to each source. This information will then be indexed as part of a data catalog.

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Judicial systems are turning to AI to help manage its vast quantities of data and expedite case resolution

IBM Journey to AI blog

The Ministry of Justice in Baden-Württemberg recommended using AI with natural language understanding (NLU) and other capabilities to help categorize each case into the different case groups they were handling. Explainability will play a key role. The courts needed a transparent, traceable system that protected data.

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Accelerating scope 3 emissions accounting: LLMs to the rescue

IBM Journey to AI blog

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Why are Scope 3 emissions difficult to calculate?

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Using Comet for Interpretability and Explainability

Heartbeat

In the ever-evolving landscape of machine learning and artificial intelligence, understanding and explaining the decisions made by models have become paramount. Enter Comet , that streamlines the model development process and strongly emphasizes model interpretability and explainability. Why Does It Matter?

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Can CatBoost with Cross-Validation Handle Student Engagement Data with Ease?

Towards AI

This story explores CatBoost, a powerful machine-learning algorithm that handles both categorical and numerical data easily. CatBoost is a powerful, gradient-boosting algorithm designed to handle categorical data effectively. CatBoost automatically transforms them, making it ideal for datasets with many categorical variables.