Remove 2020 Remove Machine Learning Remove System Architecture
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Unbundling the Graph in GraphRAG

O'Reilly Media

at Facebook—both from 2020. What’s old becomes new again: Substitute the term “notebook” with “blackboard” and “graph-based agent” with “control shell” to return to the blackboard system architectures for AI from the 1970s–1980s. Here’s a simple rough sketch of RAG: Start with a collection of documents about a domain.

Database 127
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Reduce call hold time and improve customer experience with self-service virtual agents using Amazon Connect and Amazon Lex

AWS Machine Learning Blog

The lack of flexibility in the existing customer service system prevented them from providing their customers the best user experience and from innovating further by introducing features like the ability to handle redundant queries via chat. He is focusing on system architecture, application platforms, and modernization for the cabinet.

AWS 101
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Google Research, 2022 & beyond: Robotics

Google Research AI blog

In previous machine-learned approaches, robots were limited to short, hard-coded commands, like “Pick up the sponge,” because they struggled with reasoning about the steps needed to complete a task — which is even harder when the task is given as an abstract goal like, “Can you help clean up this spill?”

Algorithm 139
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Mitigating risk: AWS backbone network traffic prediction using GraphStorm

Flipboard

In this post, we show how you can use our enterprise graph machine learning (GML) framework GraphStorm to solve prediction challenges on large-scale complex networks inspired by our practices of exploring GML to mitigate the AWS backbone network congestion risk. Patrick Taylor is a Senior Data Scientist in AWS networking.

AWS 139