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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. It is also a sound strategy when experimenting with several parameters at the same time. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages.

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Uncertainties: Statistical, Representational, Interventional

The Unofficial Google Data Science Blog

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

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Generative AI: now is the time to ‘learn by doing’

CIO Business Intelligence

By Bryan Kirschner, Vice President, Strategy at DataStax Today, we’re all living in a world in which “humans with machines will replace humans without machines”—for the second time. And CIOs are already playing a vital role in putting enthusiasm and talent to work: 43% said that AI strategy is led by IT.

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20 issues shaping generative AI strategies today

CIO Business Intelligence

They note, too, that CIOs — being top technologists within their organizations — will be running point on those concerns as companies establish their gen AI strategies. Here’s a rundown of the top 20 issues shaping gen AI strategies today. says CIOs should apply agile processes to their gen AI strategy. It’s not a hammer.

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How to Set AI Goals

O'Reilly on Data

This in turn would increase the platform’s value for users and thus increase engagement, which would result in more eyes to see and interact with ads, which would mean better ROI on ad spend for customers, which would then achieve the goal of increased revenue and customer retention (for business stakeholders).

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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.

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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.