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What LinkedIn learned leveraging LLMs for its billion users

CIO

During the summer of 2023, at the height of the first wave of interest in generative AI, LinkedIn began to wonder whether matching candidates with employers and making feeds more useful would be better served with the help of large language models (LLMs). Those first waves of hype around generative AI didn’t help.

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11 most in-demand gen AI jobs companies are hiring for

CIO

Generative AI is quickly changing the landscape of the business world, with rapid adoption rates across nearly every industry. Businesses are turning to gen AI to streamline business processes, develop proprietary AI technology, and reduce manual efforts in order to free up employees to take on more intensive tasks.

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Salesforce Data Cloud updates aim to ease data analysis, AI app development

CIO

The Einstein 1 platform , in turn, is a data engine with a low code and no code interface that is designed to let enterprises connect data to build AI-based applications. The Einstein Trust Layer is based on a large language model (LLM) built into the platform to ensure data security and privacy.

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How to build a sales development representative strategy that will fill your B2B pipeline

TechCrunch

More posts by this contributor Why it’s so hard to market enterprise AI/ML products and what to do about it Across the dozens of enterprise tech companies that I’ve had the pleasure of working with, pipeline (the quantity and quality of sales qualified opportunities), is the primary driver of go-to-market success.

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Building the Responsible AI Pipeline

Dataiku

This is a guest post from David Ryan Polgar, founder of All Tech Is Human.

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Prioritizing AI? Don’t shortchange IT fundamentals

CIO

Generative AI continues to dominate IT projects for many organizations, with two thirds of business leaders telling a Harris Poll they’ve already deployed generative AI tools internally, and IDC predicting spend on gen AI will more than double in 2024. But the usual laundry list of priorities for IT hasn’t gone away.

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Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering

AWS Machine Learning - AI

This method addressed the RAG limitation and further improved the bot response quality. This method addressed the RAG limitation and further improved the bot response quality. The engineers accessed the pilot system through a web application developed by Streamlit , connected with the RAG pipeline.