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Real-time artificial intelligence and event processing  

IBM Journey to AI blog

Artificial intelligence is also key for businesses, helping provide capabilities for both streamlining business processes and improving strategic decisions. Additionally, developments in both closed and open source large language models (LLM) are enhancing AI’s ability for understanding plain, natural language.

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AFlow: A Novel Artificial Intelligence Framework for Automated Workflow Optimization

Marktechpost

These workflows are modeled as graphs where nodes represent LLM-invoking actions, and edges represent the dependencies between these actions. The key to AFlow’s efficiency lies in its use of nodes and edges to represent workflows, allowing it to model complex relationships between LLM actions.

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CodexGraph: An Artificial Intelligence AI System that Integrates LLM Agents with Graph Database Interfaces Extracted from Code Repositories

Marktechpost

Successfully addressing this challenge is essential for advancing automated software engineering, particularly in enabling LLMs to handle real-world software development tasks that require a deep understanding of large-scale repositories. Check out the Paper and GitHub. All credit for this research goes to the researchers of this project.

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DynamoLLM: An Energy-Management Framework for Sustainable Artificial Intelligence Performance and Optimized Energy Efficiency in Large Language Model (LLM) Inference

Marktechpost

Generative Large Language Models (LLMs) have become an essential part of many applications due to their quick growth and widespread use. This means that the energy consumption of the inference clusters can be optimized by knowing the distinct processing requirements of different LLM tasks and how these requirements vary over time.

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Google AI Proposes USER-LLM: A Novel Artificial Intelligence Framework that Leverages User Embeddings to Contextualize LLMs

Marktechpost

The researchers from Google research have proposed USER-LLM , a framework integrating user embeddings with LLMs to adapt dynamically to user context. The USER-LLM framework involves two stages: embedding generation and LLM contextualization. Also, don’t forget to follow us on Twitter and Google News.

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Top Artificial Intelligence AI Courses from GitLab

Marktechpost

Turn ML Models into Online Apps This course demonstrates how to transform machine learning (ML) models into online apps using the GitLab DevSecOps Platform and Vertex AI. It allows learners to gain practical insights through a detailed demo to integrate ML models into web applications seamlessly.

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MLC LLM: Universal LLM Deployment Engine with Machine Learning ML Compilation

Marktechpost

Introducing MLC LLM, a machine learning compiler and deployment engine that offers a new approach to address these challenges. Designed to optimize and deploy LLMs natively across multiple platforms, MLC LLM simplifies the process of running complex models on diverse hardware setups.