Experimentation Measurement Risk Uncertainty 
Rocket-Powered Data Science
FEBRUARY 15, 2023
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.
O'Reilly on Data
MARCH 31, 2020
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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The Unofficial Google Data Science Blog
APRIL 23, 2024
the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Crucially, it takes into account the uncertainty inherent in our experiments. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.
CIO Business Intelligence
OCTOBER 18, 2023
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.
CIO Business Intelligence
SEPTEMBER 12, 2022
Prioritize time for experimentation. It requires bold bets and a willingness to persevere despite setbacks, criticism, and uncertainty,’’ wrote McKinsey senior partners Laura Furstenthal and Erik Roth in a recent blog post. “By Here, they and others share seven ways to create and nurture a culture of innovation.
IBM Big Data Hub
APRIL 18, 2024
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.
The Unofficial Google Data Science Blog
JANUARY 14, 2016
Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.
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