Remove 2023 Remove Experimentation Remove Testing Remove Uncertainty
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Generative AI is the biggest and hottest trend in AI (Artificial Intelligence) at the start of 2023. 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. Test and refine the chatbot.

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

CIO Business Intelligence

The use of AI-generated code is still in an experimental phase for many organizations due to numerous uncertainties such as its impact on security, data privacy, copyright, and more. Best practices and education Currently, there are no established best practices for leveraging AI in software development.

Software 116
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CIOs press ahead for gen AI edge — despite misgivings

CIO Business Intelligence

If anything, 2023 has proved to be a year of reckoning for businesses, and IT leaders in particular, as they attempt to come to grips with the disruptive potential of this technology — just as debates over the best path forward for AI have accelerated and regulatory uncertainty has cast a longer shadow over its outlook in the wake of these events.

Risk 132
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20 issues shaping generative AI strategies today

CIO Business Intelligence

Just look at the stats:Some 45% of 2,500 executives polled for a May 2023 report from research firm Gartner said the publicity around ChatGPT prompted them to increase their AI investments, 70% said their organization is already exploring gen AI, and 19% are in actual pilot or production mode.

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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. In this section we’ll discuss how we approach these two kinds of uncertainty with QCQP.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. The AGI would need to handle uncertainty and make decisions with incomplete information.