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ML Used to Decode How Brain Interprets Different Sounds

Analytics Vidhya

In a groundbreaking study published in Communications Biology, neuroscientists at the University of Pittsburgh have developed a machine-learning model that sheds light on how brains recognize and categorize different sounds.

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Microsoft Researchers Introduce Advanced Query Categorization System to Enhance Large Language Model Accuracy and Reduce Hallucinations in Specialized Fields

Marktechpost

Researchers at Microsoft Research Asia introduced a novel method that categorizes user queries into four distinct levels based on the complexity and type of external data required. The categorization helps tailor the model’s approach to retrieving and processing data, ensuring it selects the most relevant information for a given task.

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This AI Paper Presents SliCK: A Knowledge Categorization Framework for Mitigating Hallucinations in Language Models Through Structured Training

Marktechpost

This methodology stands out by categorizing knowledge into distinct levels, ranging from HighlyKnown to Unknown, providing a granular analysis of how different types of information affect model performance. The study’s findings demonstrate the effectiveness of the SliCK categorization in enhancing the fine-tuning process.

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Meta AI Introduces MLGym: A New AI Framework and Benchmark for Advancing AI Research Agents

Marktechpost

This system, the first Gym environment for ML tasks, facilitates the study of RL techniques for training AI agents. A six-level framework categorizes AI research agent capabilities, with MLGym-Bench focusing on Level 1: Baseline Improvement, where LLMs optimize models but lack scientific contributions.

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Unstructured data management and governance using AWS AI/ML and analytics services

Flipboard

However, with the help of AI and machine learning (ML), new software tools are now available to unearth the value of unstructured data. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data. Understanding the data, categorizing it, storing it, and extracting insights from it can be challenging.

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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?

ESG 238
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Five machine learning types to know

IBM Journey to AI blog

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.