Remove AI Development Remove Categorization Remove Data Quality
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Will the EU’s AI Act Set the Global Standard for AI Governance?

Unite.AI

Risk-Based Categorization of AI Technologies Central to the Act is its innovative risk-based framework, which categorizes AI systems into four distinct levels: unacceptable, high, medium, and low risk. This includes AI systems used for indiscriminate surveillance, social scoring, and manipulative or exploitative purposes.

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This AI Paper by Alibaba Introduces Data-Juicer Sandbox: A Probe-Analyze-Refine Approach to Co-Developing Multi-Modal Data and Generative AI Models

Marktechpost

Models are trained on these data pools, enabling in-depth analysis of OP effectiveness and its correlation with model performance across various quantitative and qualitative indicators. In their methodology, the researchers implemented a hierarchical data pyramid, categorizing data pools based on their ranked model metric scores.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning Blog

Some components are categorized in groups based on the type of functionality they exhibit. Prompt chaining – Generative AI developers often use prompt chaining techniques to break complex tasks into subtasks before sending them to an LLM. The standalone components are: The HTTPS endpoint is the entry point to the gateway.

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LG AI Research Releases EXAONE 3.5: Three Open-Source Bilingual Frontier AI-level Models Delivering Unmatched Instruction Following and Long Context Understanding for Global Leadership in Generative AI Excellence

Marktechpost

Steps were taken to de-identify sensitive data and ensure that all datasets met strict ethical and legal standards. Models were categorized into three groups: real-world use cases, long-context processing, and general domain tasks. Benchmark Evaluations: Unparalleled Performance of EXAONE 3.5 across nine benchmarks, while the 7.8B

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Snorkel AI partners with Snowflake to bring data-centric AI to the Snowflake Data Cloud

Snorkel AI

Snorkel AI has teamed with Snowflake to help our shared customers transform raw, unstructured data into actionable, AI-powered insights. Users are able to rapidly improve training data quality and model performance using integrated error analysis to develop highly accurate and adaptable AI applications.

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Snorkel AI partners with Snowflake to bring data-centric AI to the Snowflake Data Cloud

Snorkel AI

Snorkel AI has teamed with Snowflake to help our shared customers transform raw, unstructured data into actionable, AI-powered insights. Users are able to rapidly improve training data quality and model performance using integrated error analysis to develop highly accurate and adaptable AI applications.

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Building AI Applications with Foundation Models: Key Insights from Chip Huyen

ODSC - Open Data Science

Unlike traditional machine learning tasks, where outputs are binary or categorical, foundation models produce nuanced, open-ended outputs that are harder to assess. Focus on data quality over quantity. Use quantization libraries to reduce the computational load, making it feasible to train and deploy models on limited hardware.