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Transforming AI Accuracy: How BM42 Elevates Retrieval-Augmented Generation (RAG)

Unite.AI

BM42 is a state-of-the-art retrieval algorithm designed by Qdrant to enhance RAG's capabilities. This algorithm addresses the limitations of previous methods, making it a key development for improving the accuracy and efficiency of AI systems. This is where BM42 comes into play.

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AI News Weekly - Issue #338: Marvel faces backlash over AI-generated opening credits - Jun 22nd 2023

AI Weekly

What kind of plan Lenovo has for its AI systems Lenovo worked with 45 software partners to release. gizchina.com AI in Packaging Market is expected to hit US$ 6,015.6 It’s too new and we don‘t even know what we’re regulating, says one school of sceptics. techxplore.com What Is Unsupervised Machine Learning?

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What is Artificial General Intelligence (AGI) and Why It’s Not Here Yet: A Reality Check for AI Enthusiasts

Unite.AI

Despite achieving remarkable results in areas like computer vision and natural language processing , current AI systems are constrained by the quality and quantity of training data, predefined algorithms, and specific optimization objectives.

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Unbundling the Graph in GraphRAG

O'Reilly Media

The “distance” between each pair of neighbors can be interpreted as a probability.When a question prompt arrives, run graph algorithms to traverse this probabilistic graph, then feed a ranked index of the collected chunks to LLM. One way to build a graph to use is to connect each text chunk in the vector store with its neighbors.

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AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?

IBM Journey to AI blog

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. What is artificial intelligence (AI)?

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A Guide to Mastering Large Language Models

Unite.AI

Powerful approximate nearest neighbor algorithms like HNSW , LSH and PQ enable fast semantic search even with billions of documents. Responsible AI tooling remains an active area of innovation. Retrieval Large vector databases called semantic indexes store embeddings for efficient similarity search over documents.

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Understanding the Core Limitations of Large Language Models: Insights from Gary Marcus

ODSC - Open Data Science

This blog explores Marcus’s insights, addressing LLMs’ inherent limitations, the need for hybrid AI approaches, and the societal implications of current AI practices. The Case for Hybrid AI Models A significant portion of Gary Marcus’s discussion revolves around hybrid AI as a necessary evolution.