Experimentation Measurement Presentation Uncertainty 
The Unofficial Google Data Science Blog
DECEMBER 14, 2021
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.
O'Reilly on Data
MARCH 31, 2020
In the best case scenario, the trained neural network accurately represents the underlying phenomenon of interest and produces the correct output even when presented with new input data the model didn’t see during training. Machine learning adds uncertainty. Models within AI products change the same world they try to predict.
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The Unofficial Google Data Science Blog
JULY 22, 2020
Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. Just as in ramp-up, making inferences while ignoring the complexity of time-based confounders that are present can lead to biased estimates.
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.
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.
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.
Domino Data Lab
JUNE 23, 2019
Pete Skomoroch presented “ Product Management for AI ” at Rev. Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Session Summary.
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