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Creating value with generative AI in manufacturing

CIO Business Intelligence

Microsoft Copilot can bring to bear a range of capabilities to help manufacturers mitigate risk, manage their inventory, improve planning, and make informed decisions quickly across the entire supply chain. Copilot helps engineers generate code using natural language prompts, automates routine tasks, and improves design efficiency.

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AI and generative AI are revolutionizing manufacturing…here’s how

CIO Business Intelligence

AI and machine learning (ML) can do this by automating the design cycle to improve efficiency and output; AI can analyze previous designs, generate novel design ideas, and test prototypes, assisting engineers with rapid, agile design practices. Generative AI can help mitigate these often serious risks.

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Generative AI use cases for the enterprise

IBM Big Data Hub

It also plays a significant role in identifying and fixing bugs in the code and to automate the testing of code; helping ensure the code works as intended and meets quality standards without requiring extensive manual testing. Generative AI is being used to automatically update and maintain code across different platforms.

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Generative AI’s potential as a force multiplier in defense

CIO Business Intelligence

Since the consequence of failure is high, the defense industry must strike a deft balance between innovation and risk management. MarketResearch.biz forecasts generative AI’s growth in defense at 21% CAGR from 2022-2032, creating a market size of $2.91 billion by 2032.

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Future-Proofing Your Business with Hyperautomation

CIO Business Intelligence

Another research company, Mordor Intelligence, is forecasting annual CAGR of 19.8 They enable greater efficiency and accuracy and error reduction, better decision making, better compliance and risk management, process optimisation and greater agility. Gartner sees these inhibitors driving an annual 11.9 trillion by 2026.

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Automating Model Risk Compliance: Model Validation

DataRobot Blog

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses. Conclusion.

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Manage the Demand of Stress Testing in Financial Services

Cloudera

Risk management is a highly dynamic discipline these days. Stress testing is a particular area that has become even more important throughout the pandemic. Similarly, the European Central Bank is issuing stress testing requirements related to climate risk given the potential economic shifts related to addressing climate change.

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