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The importance of diversity in AI isn’t opinion, it’s math

IBM Big Data Hub

.” In an influential study, it was shown that diverse groups of low-ability problem solvers can outperform groups of high-ability problem solvers ( Hong & Page, 2004 ). This holds true in the areas of statistics, science and AI. In mathematical language: the wider your variance, the more standard your mean.

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Smarten Augmented Analytics Receives CERT-IN Certification for Its Products and Services!

Smarten

It was initiated in 2004 by the Department of Information Technology for implementing the provisions of the 2008 Information Technology Amendment Act. All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

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Eli Manning and the power of AI in ESPN fantasy football

IBM Big Data Hub

Today, those insights take the form of two features: Trade Analyzer with Watson , which uses AI to analyze player statistics and media commentary to help team managers understand the value of a potential trade. We’re analyzing the performance statistics of all 1,900 players in the league. Which brings me back to Eli.

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19 organizations advancing women in tech

CIO Business Intelligence

The organization, which was chartered by the National Science Foundation in 2004 and was one of the first organizations to focus on women’s participation in computing fields, also offers support to companies that want to strengthen DEI in their organizations through hiring, awareness, inclusion, and systemic change.

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Modernize Using The BI & Analytics Magic Quadrant

Rita Sallam

Or when Tableau and Qlik’s serious entry into the market circa 2004-2005 set in motion a seismic market shift from IT to the business user creating the wave of what was to become the modern BI disruption. After five minutes of seeing these products back then, I just knew they would change everything!

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New Thinking, Old Thinking and a Fairytale

Peter James Thomas

Of course it can be argued that you can use statistics (and Google Trends in particular) to prove anything [1] , but I found the above figures striking. Feel free to substitute Data Lake for Data Warehouse if you want a more modern vibe, sadly it won’t change the failure statistics. . [5]. The scope is worldwide.

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Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

Hand-Drawn Time Series of Google “Results Pages”, November 1998 through July 2004. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification. Journal of Official Statistics 6.1 Large-Scale Parallel Statistical Forecasting Computations in R ”, Google Research report. [8] 1990): 3. [3]