Remove Data Quality Remove Explainable AI Remove LLM
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This AI newsletter is all you need #93

Towards AI

As we have discussed, there have been some signs of open-source AI (and AI startups) struggling to compete with the largest LLMs at closed-source AI companies. This is driven by the need to eventually monetize to fund the increasingly huge LLM training costs. This would be its 5th generation AI training cluster.

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Maximizing compliance: Integrating gen AI into the financial regulatory framework

IBM Journey to AI blog

By leveraging LLMs, institutions can automate the analysis of complex datasets, generate insights for decision-making, and enhance the accuracy and speed of compliance-related tasks. These use cases demonstrate the potential of AI to transform financial services, driving efficiency and innovation across the sector.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning Blog

This includes handling unexpected inputs, adversarial manipulations, and varying data quality without significant degradation in performance. When you ask the model to explain the process it used to generate the output, the model has to identify different the steps taken and information used, thereby reducing hallucination itself.

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GPT-4o

Bugra Akyildiz

Automated Query Optimization: By understanding the underlying data schemas and query patterns, ChatGPT could automatically optimize queries for better performance, indexing recommendations, or distributed execution across multiple data sources. The BPE algorithm is "byte-level" because it runs on UTF-8 encoded strings.

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ODSC Europe 2024 Virtual Sessions Now Available On-Demand!

ODSC - Open Data Science

In this talk, Stefanie will discuss Data Morph, an open-source package that builds on previous research from Autodesk (the Datasaurus Dozen) using simulated annealing to perturb an arbitrary input dataset into a variety of shapes, while preserving the mean, standard deviation, and correlation to multiple decimal points.