Remove Experimentation Remove Optimization Remove Risk Remove Uncertainty
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). Crucially, it takes into account the uncertainty inherent in our experiments.

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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.”

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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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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 Finally, our goal is to diminish consumer risk evaluation periods by 80% without compromising the safety of our products.” This allowed us to derive insights more easily.”

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How to create a culture of innovation

CIO Business Intelligence

Prioritize time for experimentation. One instance of how that exploration led to real business benefits was with the application of machine learning to predict optimal product formulation using a set of desired consumer benefits. Here, they and others share seven ways to create and nurture a culture of innovation.

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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.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. For example, imagine a fantasy football site is considering displaying advanced player statistics.