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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. 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems.

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CIOs must beware committing ‘AI washing’ themselves

CIO

With the current AI gold rush, companies may be tempted to exaggerate their AI implementations to lure investors and customers, a practice called “AI washing,” but they should think twice before doing so, says David Shargel, a regulatory compliance lawyer with law firm Bracewell.

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How to build an intelligent enterprise

CIO

Significantly, this definition suggests that hyperautomation isn’t siloed but a set of integrated processes, systems, and technologies for automating operations, interacting with customers, viewing and managing the supply chain, and more. Consider operational efficiency, customer experience, compliance requirements, and business strategy.

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10 most in-demand generative AI skills

CIO

These skills include expertise in areas such as text preprocessing, tokenization, topic modeling, stop word removal, text classification, keyword extraction, speech tagging, sentiment analysis, text generation, emotion analysis, language modeling, and much more.

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IBM’s watsonx.governance takes aim at AI auditing

CIO

IBM is betting big on its toolkit for monitoring generative AI and machine learning models, dubbed watsonx.governance , to take on rivals and position the offering as a top AI governance product, according to a senior executive at IBM. watsonx.governance is a toolkit for governing generative AI and machine learning models.

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Protect AI lands a $13.5M investment to harden AI projects from attack

TechCrunch

Seeking to bring greater security to AI systems, Protect AI today raised $13.5 Protect AI claims to be one of the few security companies focused entirely on developing tools to defend AI systems and machine learning models from exploits. A 2018 GitHub analysis found that there were more than 2.5

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5 IT risks CIOs should be paranoid about

CIO

Mounting technical debt from mission-critical systems CIOs have good reason to stress out over rising technical debt and the impact of supporting legacy systems past their end-of-life dates. Legacy hardware systems are a growing problem that necessitates prompt action,” says Bill Murphy, director of security and compliance at LeanTaaS.