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3 areas where gen AI improves productivity — until its limits are exceeded

CIO Business Intelligence

So when on-boarding potential vendors, productivity has increased by 70 to 80% as a result of using gen AI to help analyze masses of documents. “We We did side-by-side testing,” he says. In testing, gen AI was also particularly good at generating test cases and creating dummy data for testing.

IT 127
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What to Do When AI Fails

O'Reilly on Data

If our model generates false negative predictions for tumor detection, organizations could combine automated imaging results with activities like follow up radiologist reviews or blood tests to catch any potentially incorrect predictions—and even improve the accuracy of the combined human and machine efforts. How Material Is the Threat?

Risk 359
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6 best practices to develop a corporate use policy for generative AI

CIO Business Intelligence

But it doesn’t always work, so don’t forget to test ChatGPT’s output before pasting it somewhere that matters.” This may include developing training videos and hosting live sessions. When AI-generated code works, it’s sublime,” says Cassie Kozyrkov, chief decision scientist at Google.

Risk 121
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Applying cyber resilience to DORA solutions

IBM Big Data Hub

The Digital Operational Resilience Act , or DORA, is a European Union (EU) regulation that created a binding, comprehensive information and communication technology (ICT) risk-management framework for the EU financial sector. It offers more control and flexibility for comprehensive testing and validation.

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Examples of IBM assisting insurance companies in implementing generative AI-based solutions  

IBM Big Data Hub

The most common insurance use cases include optimizing processes that require processing large documents and large blocks of text or images. Customer engagement Providing insurance coverage involves working with numerous documents. IBM works with several insurance companies to identify high-value opportunities for using generative AI.

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Why you should care about debugging machine learning models

O'Reilly on Data

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. 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]. Sensitivity analysis.

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The biggest enterprise technology M&A deals of the year

CIO Business Intelligence

Even though Nvidia’s $40 billion bid to shake up enterprise computing by acquiring chip designer ARM has fallen apart, the merger and acquisition (M&A) boom of 2021 looks set to continue in 2022, perhaps matching the peaks of 2015, according to a report from risk management advisor Willis Towers Watson. Precisely buys PlaceIQ.