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When is data too clean to be useful for enterprise AI?

CIO

For many organizations, preparing their data for AI is the first time they’ve looked at data in a cross-cutting way that shows the discrepancies between systems, says Eren Yahav, co-founder and CTO of AI coding assistant Tabnine. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

Data 211
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Low code/no code tools reap IT benefits—with caveats

CIO

Low-code/no-code visual programming tools promise to radically simplify and speed up application development by allowing business users to create new applications using drag and drop interfaces, reducing the workload on hard-to-find professional developers. So there’s a lot in the plus column, but there are reasons to be cautious, too.

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Valence raises $25M to track team performance with continuous reviews

TechCrunch

Valence lets managers track team performance by certain metrics and, if they deem it necessary, intervene with “guided conversations.” Valence , a growing teamwork platform, today announced that it raised $25 million in a Series A round led by Insight Partners. What constitutes a “teamwork platform,” exactly?

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Book review: Accelerate

Henrik Warne

The book Accelerate details the findings of four years of research on how DevOps affects various outcomes, such as software delivery tempo and stability, as well as the organizations’ profitability and market share. Software Delivery Performance. Many aspects of software development are hard to measure.

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Glass rethinks the smartphone camera through an old-school cinema lens

TechCrunch

Smartphone cameras have gotten quite good, but it’s getting harder and harder to improve them because we’ve pretty much reached the limit of what’s possible in the space of a cubic centimeter. It may not be obvious that cameras won’t get better, since we’ve seen such advances in recent generations of phones.

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Interpreting predictive models with Skater: Unboxing model opacity

O'Reilly Media - Data

There is also a trade off in balancing a model’s interpretability and its performance. A deep dive into model interpretation as a theoretical concept and a high-level overview of Skater. There is often a need to verify the reasoning of such ML systems to hold algorithms accountable for the decisions predicted.

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Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval

AWS Machine Learning - AI

This post focuses on evaluating and interpreting metrics using FMEval for question answering in a generative AI application. FMEval is a comprehensive evaluation suite from Amazon SageMaker Clarify , providing standardized implementations of metrics to assess quality and responsibility. Question Answer Fact Who is Andrew R.