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Eight Graphs that Explain Software Engineering Salaries in 2023

Flipboard

Hired’s analysis included data from 68,500 job candidates and 494,000 interview requests collected from the site between January 2021 through December 2022, supplemented by a survey of 1,300 software engineers. Tech Salaries Jump, But Don’t Keep Up With Inflation According to Dice’s numbers , tech salaries grew 2.3

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2021 Data/AI Salary Survey

O'Reilly Media

In June 2021, we asked the recipients of our Data & AI Newsletter to respond to a survey about compensation. It’s easy to hypothesize about this difference, but we’re at a loss to explain it. How do we explain this? In 2021, and without being anywhere near as repulsive, we’d say, “There’s a great future in the cloud.

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NeRFs Explained: Goodbye Photogrammetry?

PyImageSearch

Home Table of Contents NeRFs Explained: Goodbye Photogrammetry? Block #A: We Begin with a 5D Input Block #B: The Neural Network and Its Output Block #C: Volumetric Rendering The NeRF Problem and Evolutions Summary and Next Steps Next Steps Citation Information NeRFs Explained: Goodbye Photogrammetry? How Do NeRFs Work?

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Shooting to score with Scout Advisor’s NLP

IBM Journey to AI blog

As Victor Orta, Sevilla FC Sporting Director, explained at his conference during the World Football Summit in 2023: “We are never going to sign a player with data alone, but we will never do it without resorting to data either. In fact, paperwork is a much more significant part of the job than one might imagine.

NLP 211
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Global executives and AI strategy for HR: How to tackle bias in algorithmic AI

IBM Journey to AI blog

The new rules, which passed in December 2021 with enforcement , will require organizations that use algorithmic HR tools to conduct a yearly bias audit. It is important to choose an auditor that specializes in HR or Talent and trustworthy, explainable AI, and has RAII Certification and DAA digital accreditation.

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The Black Box Problem in LLMs: Challenges and Emerging Solutions

Unite.AI

SHAP's strength lies in its consistency and ability to provide a global perspective – it not only explains individual predictions but also gives insights into the model as a whole. This method requires fewer resources at test time and has been shown to effectively explain model predictions, even in LLMs with billions of parameters.

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Banking on AI: Fraud Detection, Credit Risk Analysis, and the Future of Financial Services

Unite.AI

billion from 2021 to 2026, reflecting the rapid growth and adoption of AI technologies in this domain. One of the key challenges in AI is explainability. If a regulator questions a financial institutions’ decision made with AI, the financial institution needs to be able to explain the rationale behind it.