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The importance of data ingestion and integration for enterprise AI

IBM Journey to AI blog

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. In the generative AI or traditional AI development cycle, data ingestion serves as the entry point.

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ApertureData Secures $8.25M Seed Funding and Launches ApertureDB Cloud to Revolutionize Multimodal AI

Unite.AI

The funding will allow ApertureData to scale its operations and launch its new cloud-based service, ApertureDB Cloud, a tool designed to simplify and accelerate the management of multimodal data, which includes images, videos, text, and related metadata. ApertureData’s flagship product, ApertureDB , addresses this challenge head-on.

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AI and Blockchain Integration for Preserving Privacy

Unite.AI

Another subfield that is quite popular amongst AI developers is deep learning, an AI technique that works by imitating the structure of neurons. Artwork metadata and digital tokens are stored in OrbitDB, a database storage system that uses multiple nodes to store the data, and thus ensures data security & privacy.

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Researchers from MIT and Harvard University Work on Enhancing AI Integrity: The Urgent Need for Standardized Data Provenance Frameworks

Marktechpost

This unstructured and obscure data collection poses severe challenges in maintaining data integrity and ethical standards. The research’s core issue revolves around the lack of robust mechanisms to ensure the authenticity and consent of data utilized in AI training.

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eSentire delivers private and secure generative AI interactions to customers with Amazon SageMaker

AWS Machine Learning Blog

eSentire has over 2 TB of signal data stored in their Amazon Simple Storage Service (Amazon S3) data lake. eSentire used gigabytes of additional human investigation metadata to perform supervised fine-tuning on Llama 2. They needed no additional infrastructure for data integration.

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Depth First Search (DFS) Algorithm in Artificial Intelligence

Pickl AI

Access Transparency Users experience seamless access to files, as the system hides the complexities of how data distributed across various servers. Applications of DFS in Artificial Intelligence Distributed File Systems (DFS) play a significant role in enhancing the capabilities of Artificial Intelligence (AI) applications.