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How AI-Led Platforms Are Transforming Business Intelligence and Decision-Making

Unite.AI

Traditional business intelligence processes often involve time-consuming data collection, analysis, and interpretation, limiting an organization’s ability to act swiftly. Traditional customer segmentation methods are limited in scope, often categorizing customers into broad groups.

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The Role of Semantic Layers in Self-Service BI

Unite.AI

This, in turn, empowers business users with self-service business intelligence (BI), allowing them to make informed decisions without relying on IT teams. This article will explain what a semantic layer is, why businesses need one, and how it enables self-service business intelligence. billion by 2032.

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AI Meets Spreadsheets: How Large Language Models are Getting Better at Data Analysis

Unite.AI

Today, the demand for LLMs in data analysis is so high that the industry is seeing rapid growth, with these models expected to play a significant role in business intelligence. These integrations enable generating formulas, categorizing data, and visualizations using simple language prompts.

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ChatBI: A Comprehensive and Efficient Technology for Solving the Natural Language to Business Intelligence NL2BI Task

Marktechpost

As thousands of organizations leverage Business Intelligence (BI) for decision support, industry researchers have honed in on NL2BI, a scenario where natural language is transformed into BI queries. Existing NL2SQL methods primarily handle Single-Round Dialogue (SRD) queries and struggle with MRD scenarios.

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10 Best AI Social Listening Tools (August 2024)

Unite.AI

With a range of affordable pricing plans, it caters to businesses of all sizes, from startups to large enterprises. It enables businesses to monitor brand mentions, track sentiment, and gain audience insights across various social networks and the web.

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AI is coming for the laptop class

Flipboard

The author, Matthew Barnett, uses a commercially available AI model (GPT-4o) to go through a US Department of Labor-sponsored database of over 19,000 job tasks and categorize each of them as doable remotely (writing code, sending emails) or not doable remotely (firefighting, bowling). A task, notably, is not the same as a job or occupation.

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How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning Blog

As AIDAs interactions with humans proliferated, a pressing need emerged to establish a coherent system for categorizing these diverse exchanges. The main reason for this categorization was to develop distinct pipelines that could more effectively address various types of requests. values.tolist()) y_train = df_train['agent'].values.tolist()