Data Wonderland: Christmas songs from the viewpoint of a data scientist

@machinelearnbot

Whether „Driving Home for Christmas", „Winter Wonderland", „Let it snow!" or „Last Christmas" – every year christmas songs are taking over the charts again. While average Joe is joyfully putting on the next christmas song, the data scientist starts his journey of discovery through the snowy music history.


Why Machine Learning and Big Data need Behavioral Economists

#artificialintelligence

Researchers from Princeton University received mass media attention when they recently predicted the demise of Facebook. Data scientists at Facebook soon hit back with their own'study:' "In keeping with the scientific principle (used by Princeton) 'correlation equals causation,' our research unequivocally demonstrated that Princeton may be in danger of disappearing entirely." Is it surprising that the original Princeton study found its way onto the front pages of newspapers and magazines across the world? Probably not – the fact is statistical results with a causal interpretation have a stronger effect on our thinking than non-causal information. What the data scientists at Princeton relied upon in presenting their paper was our individual human inability to think statistically.


Television And Geography As Big Data: Mapping A Decade Of Television News

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What happens when we begin to think of all information as data that can be explored to yield new insights into our world? What would it look like to take nearly a decade of CNN, Fox News, and MSNBC television broadcasts and two years of BBC News broadcasts and run them through sophisticated natural language processing algorithms to identify every mention of a location on earth in their coverage and then create a series of maps that visualize the places we hear about when we turn to the news? What would those maps look like and what might they tell us about what we see when we turn on our televisions each day? Half a decade ago I began working with the Internet Archive's incredible Television News Archive to explore how powerful computer algorithms could allow us to "see" the news in entirely new ways. From simple longitudinal keyword searches to mass emotion mining to geographic mapping to the most powerful deep learning algorithms watching political ads, television has an incredible amount to teach us as we explore it through the modalities and lenses of massive data mining.


Should I Open-Source My Model? – Towards Data Science

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I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anankumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.


Should I Open-Source My Model? – Towards Data Science

#artificialintelligence

I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anandkumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.