Spending more money on paint can save you hundreds of dollars

USATODAY

The holiday season is coming soon, and maybe you're thinking about painting your living room to spread some joy and impress the guests. But before you choose between Oceanside and Caliente, there's something you need to think about: What type of paint should you use?


Are You Still in the Dark About the Quality of Your Data?

@machinelearnbot

More and more businesses are waking up to the threat of poor data quality. We're gradually seeing the risk being taken more seriously as the shockwaves of poor management are felt. Yet for many businesses, data quality is seen as an abstract concept; difficult to understand, and impossible to value. When the business formulates its budgets for the year, data quality is often skipped over, because nobody really knows what's wrong. Sure: they can see emails bouncing, and their customers are drifting away to competitors, but the root cause hasn't been fully determined.


Are You Still in the Dark About the Quality of Your Data?

@machinelearnbot

More and more businesses are waking up to the threat of poor data quality. We're gradually seeing the risk being taken more seriously as the shockwaves of poor management are felt. Yet for many businesses, data quality is seen as an abstract concept; difficult to understand, and impossible to value. When the business formulates its budgets for the year, data quality is often skipped over, because nobody really knows what's wrong. Sure: they can see emails bouncing, and their customers are drifting away to competitors, but the root cause hasn't been fully determined.


Allergies are the worst — this smart air quality monitor wants to help.

Mashable

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Learning to Predict the Quality of Contributions to Wikipedia

AAAI Conferences

Although some have argued that Wikipedia's open edit policy is one of the primary reasons for its success, it also raises concerns about quality -- vandalism, bias, and errors can be problems. Despite these challenges, Wikipedia articles are often (perhaps surprisingly) of high quality, which many attribute to both the dedicated Wikipedia community and "good Samaritan" users. As Wikipedia continues to grow, however, it becomes more difficult for these users to keep up with the increasing number of articles and edits. This motivates the development of tools to assist users in creating and maintaining quality. In this paper, we propose metrics that quantify the quality of contributions to Wikipedia through implicit feedback from the community.