Remove Experimentation Remove Risk Remove Testing Remove Uncertainty
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). encouraging and rewarding) a culture of experimentation across the organization. Test early and often.

Strategy 289
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Lessons from the field: How Generative AI is shaping software development in 2023

CIO Business Intelligence

While the potential of Generative AI in software development is exciting, there are still risks and guardrails that need to be considered. Risks of AI in software development Despite Generative AI’s ability to make developers more efficient, it is not error free. To learn more, visit us here. Artificial Intelligence, Machine Learning

Software 115
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Belcorp reimagines R&D with AI

CIO Business Intelligence

These circumstances have induced uncertainty across our entire business value chain,” says Venkat Gopalan, chief digital, data and technology officer, Belcorp. “As That, in turn, led to a slew of manual processes to make descriptive analysis of the test results. The team leaned on data scientists and bio scientists for expert support.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Crucially, it takes into account the uncertainty inherent in our experiments. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. It is a big picture approach, worthy of your consideration.

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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. This has serious implications for software testing, versioning, deployment, and other core development processes. Underneath this uncertainty lies further uncertainty in the development process itself. Models within AI products change the same world they try to predict.

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Machine Learning Product Management: Lessons Learned

Domino Data Lab

Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. It is more experimental because it is “an approach that involves learning from data instead of programmatically following a set of human rules.”

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20 issues shaping generative AI strategies today

CIO Business Intelligence

As vendors add generative AI to their enterprise software offerings, and as employees test out the tech, CIOs must advise their colleagues on the pros and cons of gen AI’s use as well as the potential consequences of banning or limiting it. The CIO’s job is to ask questions about potential scenarios.