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Real-time Data, Machine Learning, and Results: The Evidence Mounts

CIO

From delightful consumer experiences to attacking fuel costs and carbon emissions in the global supply chain, real-time data and machine learning (ML) work together to power apps that change industries. more machine learning use casesacross the company. The study results don’t surprise us.

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Small Business, Big Threats: INE Security Launches Initiative to Train SMBs to Close a Critical Skills Gap

CIO

We know that cybersecurity training is no longer optional for businesses – it is essential. Our mission is to provide accessible, effective, and affordable training to these businesses so they can close the gap, ultimately enhancing their defensive capabilities.”

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Article: Testing Machine Learning: Insight and Experience from Using Simulators to Test Trained Functionality

InfoQ Culture Methods

When testing machine learning systems, we must apply existing test processes and methods differently. Machine Learning applications consist of a few lines of code, with complex networks of weighted data points that form the implementation.

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Scalable training platform with Amazon SageMaker HyperPod for innovation: a video generation case study

AWS Machine Learning - AI

Luma AI’s recently launched Dream Machine represents a significant advancement in this field. Trained on the Amazon SageMaker HyperPod , Dream Machine excels in creating consistent characters, smooth motion, and dynamic camera movements. The process extends image generation techniques to the temporal domain.

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Scaling Media Machine Learning at Netflix

Netflix Tech

We have been leveraging machine learning (ML) models to personalize artwork and to help our creatives create promotional content efficiently. We will then present a case study of using these components in order to optimize, scale, and solidify an existing pipeline.

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Patients may suffer from hallucinations of AI medical transcription tools

CIO

An AI-powered transcription tool widely used in the medical field, has been found to hallucinate text, posing potential risks to patient safety, according to a recent academic study. In a separate study, researchers found that AI models used to help programmers were also prone to hallucinations.

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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. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8]. Not least is the broadening realization that ML models can fail. ML security audits.