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. You’ll become familiar with the problems that real-world data presents.
CIO Business Intelligence
MAY 19, 2022
They’re people — each with their own unique circumstances at home, families to support, and worries about the uncertainty that comes with a volatile global pandemic. By investing in an engaged workforce, service leaders provide their agents with an opportunity to be more creative, present, and honestly, themselves.
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CIO Business Intelligence
NOVEMBER 12, 2024
The risk is exemplified by the case of an executive canceling Microsoft Copilot subscriptions supposedly because “he compared the slide-generation capability of Microsoft’s AI tools to ‘middle school presentations.’” That presentation in question sits inside two workflows. or the dreaded ‘ meeting before the meeting ’)?”
Andrew White
MAY 4, 2020
COVID-19 and the related economic fallout has pushed organizations to extreme cost optimization decision making with uncertainty. Everything Changes. As a result, Data, Analytics and AI are in even greater demand. Every decision by every executive leader need information: What investments to furlough or delay, or accelerate?
CIO Business Intelligence
JANUARY 6, 2023
The economists lament, “ A thick fog of uncertainty still surrounds us.” Uncertainty is our jam.” minutes of downtime per year), and expanding digital capabilities in a world characterized by massive economic, political, social, and technological uncertainty. After all, uncertainty is the one certainty.
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
JANUARY 22, 2019
All models, therefore, need to quantify the uncertainty inherent in their predictions. These factors lead to profound epistemic uncertainty about model parameters. Fundamental to the valuation of any financial asset, interest rates are used to discount uncertain future cash flows of the asset and estimate its value in the present.
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