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4 ways generative AI addresses manufacturing challenges

IBM Services

The manufacturing industry is in an unenviable position. Manufacturers are being called to reduce their carbon footprint, adopt circular economy practices and become more eco-friendly in general. And manufacturers face pressure to constantly innovate while ensuring stability and safety.

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The most valuable AI use cases for business

IBM Services

When thinking of artificial intelligence (AI) use cases, the question might be asked: What won’t AI be able to do? The easy answer is mostly manual labor, although the day might come when much of what is now manual labor will be accomplished by robotic devices controlled by AI.

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Revolutionize Maintenance: Predictive Maintenance with Oracle Cloud Maintenance & Power of AI and ML

Perficient Digital Transformation

Traditional maintenance strategies, based on scheduled inspections or reactive fixes, often fall short in preventing costly downtime and equipment failures. This is where Predictive Maintenance, powered by Artificial Intelligence (AI) and Machine Learning (ML), comes into play.

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Enhancing Maintenance Efficiency: Predictive vs. Preventive Maintenance in Oracle Cloud Maintenance

Perficient Digital Transformation

In the world of maintenance management, two key strategies have emerged as game-changers – Predictive Maintenance and Preventive Maintenance. The goal is to perform maintenance only when necessary, minimizing downtime and reducing costs.

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Building a Smarter Future at the Convergence of Generative AI and Automation

Zinnov

What does the Oracle of Delphi and Generative AI have in common? Just as the Oracle was known for her prophetic abilities and wise counsel, Generative AI , with its ability to analyze vast amounts of data and make predictions, is the modern-day Oracle. But the term “Generative AI” wasn’t introduced until 2014.

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Life Sciences Smart Manufacturing Services PEAK Matrix® Assessment 2023

Everest Group

Life Sciences Smart Manufacturing Services In the past, the manufacturing industry primarily focused on designing standardized manufacturing procedures and managing labor and mechanical systems. Nevertheless, the life sciences industry has been slow in embracing technology to modernize its manufacturing setups.

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Getting ready for artificial general intelligence with examples

IBM Services

While AGI remains theoretical, organizations can take proactive steps to prepare for its arrival by building a robust data infrastructure and fostering a collaborative environment where humans and AI work together seamlessly. This gap highlights the vast difference between current AI and the potential of AGI.