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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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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How AI saves money and improves banking complaint handling

Snorkel AI

AI is accelerating complaint resolution for banks AI can help banks automate many of the tasks involved in complaint handling, such as: Identifying, categorizing, and prioritizing complaints. Machine learning to identify emerging patterns in complaint data and solve widespread issues faster. Assigning complaints to staff.

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How AI saves money and improves banking complaint handling

Snorkel AI

AI is accelerating complaint resolution for banks AI can help banks automate many of the tasks involved in complaint handling, such as: Identifying, categorizing, and prioritizing complaints. Machine learning to identify emerging patterns in complaint data and solve widespread issues faster. Assigning complaints to staff.