Big Data Statistics Testing Uncertainty 
IBM Big Data Hub
SEPTEMBER 28, 2023
Based on initial IBM Research evaluations and testing , across 11 different financial tasks, the results show that by training Granite-13B models with high-quality finance data, they are some of the top performing models on finance tasks, and have the potential to achieve either similar or even better performance than much larger models.
Sisense
OCTOBER 6, 2020
Let’s consider the differences between the two, and why they’re both important to the success of data-driven organizations. Digging into quantitative data. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?” Quantitative data is the bedrock of your BI and analytics.
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IBM Big Data Hub
APRIL 18, 2024
LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. The AGI would need to handle uncertainty and make decisions with incomplete information. The AGI analyzes the data and identifies a rare genetic mutation linked to a specific disease.
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.
Occam's Razor
APRIL 8, 2013
We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature.
The Unofficial Google Data Science Blog
JULY 22, 2020
For example, imagine a fantasy football site is considering displaying advanced player statistics. 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. We offer two examples where this may be the case.
The Unofficial Google Data Science Blog
MARCH 31, 2016
Far from hypothetical, we have encountered these issues in our experiences with "big data" prediction problems. We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all.
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