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Why you should care about debugging machine learning models

O'Reilly Media - Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. 8] Data about individuals can be decoded from ML models long after they’ve trained on that data (through what’s known as inversion or extraction attacks, for example). ML security audits. Discrimination remediation.

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Where’s the ROI for AI? CIOs struggle to find it

CIO

“A chisel in the hands of a trained professional can create amazing things; a chisel in the hands of an amateur can be a lost opportunity.” Kane has seen companies roll out Microsoft Copilot, for example, without any employee training about its uses. Close behind were machine learning and natural language processing.

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The Newest FIFA World Cup Referee: Human-in-the-Loop Machine Learning

Cloudera

C (Cloudera is headquartered in the US, but we also recognize the superiority of the metric system). The data innovation that I was most excited to learn about though is the implementation of a human-in-the-loop (HITL) machine learning (ML) solution to assist referees in more accurately calling offsides.

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Shelf.io closes huge $52.5M Series B after posting 4x ARR growth in the last year

TechCrunch

The company announced an impressive set of metrics this morning, including that from July 2020 to July 2021, it grew its annual recurring revenue (ARR) 4x. Then, after training models and staff, the company’s software can begin to provide support staff with answers to customer questions as they talk to customers in real time.

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Four short links: 8 July 2020

O'Reilly Media - Ideas

When Data is Messy — I love stories that illustrate the ways machine learning can draw the wrong conclusions. Researchers at the University of Tuebingen trained a neural net to recognize images, and then had it point out which parts of the images were the most important for its decision. via Simon Willison ).

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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. billion, net of cash acquired.

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Custom Weather Forecast Model Using ML Net

Perficient

How can I use ML Net? ML Net can be used with Visual Studio 2019 or later, using any version of Visual Studio, and also can be used by Visual Studio Code, but only works on a Windows OS, Its prerequisites are: Visual Studio 2022 or Visual Studio 2019.NET NET Core 3.1 SDK or later.

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