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Establishing Knowledge Databases for Amazon Bedrock to comply with GDPR (erasure rights) appeals.

Amazon Bedrock, Best Practices, Compliance, Generative AI, UncategorizedJune 1, 2024212Views0Likes0Comments
The General Data Protection Regulation (GDPR) right to be forgotten gives individuals the power to request the deletion of their personally identifiable information (PII) from the systems of organisations, including third parties with whom the data was shared. This has implications for the usage of artificial intelligence (AI) frameworks in data handling, particularly those using…

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Utilizing AWS HealthOmics and Amazon SageMaker for pre-training genetic language models

Amazon Machine Learning, Artificial Intelligence, AWS HealthOmics, Expert (400), Technical How-to, UncategorizedJune 1, 2024228Views0Likes0Comments
Genomic language models represent a significant development in genomics, interpreting vast amounts of genomic data and allowing scientists to extract valuable insights that contribute to personalized treatment methods, mutation identification, and gene function discovery. In particular, the pre-training of the genomic language model, HyenaDNA, using genomic data in the AWS Cloud, holds immense potential for…

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DAI#41 – Adhesive for Pizza, Prioritizing Safety, and Beneficial Friendships with AI

Roundup, UncategorizedMay 31, 2024178Views0Likes0Comments
This week in AI news, Google's new AI Overview search feature has experienced some humorous and occasionally serious flaws, while OpenAI formed a Safety and Security Committee in response to a safety issue that saw key team members depart. A new frontier model is being trained with "next-level capabilities", warranting hopes that safety protocols will…

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Improving Self-Directed Learning through Automated Data Organization: A Multi-Level K-Means Method.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedMay 31, 2024165Views0Likes0Comments
Researchers from Meta's FAIR, INRIA, Université Paris Saclay, and Google are working on ways to automatically curate high-quality datasets to improve self-supervised learning (SSL). SSL enables models to be trained without human annotations, expanding data and model scalability, but its success often requires careful data curation. The team proposes a clustering-based technique involving hierarchical k-means…

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