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Measuring Validity and Reliability of Human Ratings

The Unofficial Google Data Science Blog

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Researchers and practitioners have been using human-labeled data for many years, trying to understand all sorts of abstract concepts that we could not measure otherwise. That’s the focus of this blog post.

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PODCAST: Exploring Data, Digital and Artificial Intelligence through a Holistic Lens

bridgei2i

In the latest episode of ‘The Data Strategy Show’, host Samir Sharma engages Prithvijit(Jit) Roy and Pritam K Paul, Co-Founders of BRIDGEi2i, in a riveting discussion. It’s also crucial for enterprises to plan for contingencies and take preventive measures to ensure biases do not creep into datasets after implementing algorithms into pilots.

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CIO Cloud Transformation Summit – taking the digital enterprise to new heights

CIO Business Intelligence

Corey hosts the podcast “Screaming in the Cloud” and “AWS Morning Brief” podcasts; and curates “Last Week in AWS,” a weekly newsletter summarising the latest in AWS news, blogs, and tools, sprinkled with snark and thoughtful analysis in equal measure. million in 2021 to 4 million by 2025.

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What to Do When AI Fails

O'Reilly on Data

And last is the probabilistic nature of statistics and machine learning (ML). Because statistics: Last is the inherently probabilistic nature of ML. Data sensitivity also tends to be a helpful measure for the materiality of any incident. First and foremost is the tendency for AI to decay over time.

Risk 361
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4 Key Elements of Enterprise AI Strategy

bridgei2i

In the latest episode of ‘The Data Strategy Show’, host Samir Sharma engages Prithvijit(Jit) Roy and Pritam K Paul, Co-Founders of BRIDGEi2i, in a riveting discussion. It’s also crucial for enterprises to plan for contingencies and take preventive measures to ensure biases do not creep into datasets after implementing algorithms into pilots.

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Data Science Journey Walkthrough – From Beginner to Expert

Smart Data Collective

Data science needs knowledge from a variety of fields including statistics, mathematics, programming, and transforming data. Mathematics, statistics, and programming are pillars of data science. In data science, use linear algebra for understanding the statistical graphs. It is the building block of statistics.

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Rebranding IT for the modernized IT mission

CIO Business Intelligence

A 1958 Harvard Business Review article coined the term information technology, focusing their definition on rapidly processing large amounts of information, using statistical and mathematical methods in decision-making, and simulating higher order thinking through applications. What comes first: A new brand or operating model?

IT 108