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Big data for the people: it's time to take it back from our tech overlords

The Guardian

Spotify knows your favorite throwback jams. Is this convenient or creepy? One minute, you're grateful for the personalized precision of Netflix's recommendations. The next, you're nauseated by the personalized precision of a Facebook ad. Big data has been around for awhile, but our discomfort with it is relatively recent.


Big data for the people: it's time to take it back from our tech overlords

@machinelearnbot

Spotify knows your favorite throwback jams. Is this convenient or creepy? One minute, you're grateful for the personalized precision of Netflix's recommendations. The next, you're nauseated by the personalized precision of a Facebook ad. Big data has been around for awhile, but our discomfort with it is relatively recent.


'Our minds can be hijacked': the tech insiders who fear a smartphone dystopia

The Guardian

Justin Rosenstein had tweaked his laptop's operating system to block Reddit, banned himself from Snapchat, which he compares to heroin, and imposed limits on his use of Facebook. In August, the 34-year-old tech executive took a more radical step to restrict his use of social media and other addictive technologies. Rosenstein purchased a new iPhone and instructed his assistant to set up a parental-control feature to prevent him from downloading any apps. He was particularly aware of the allure of Facebook "likes", which he describes as "bright dings of pseudo-pleasure" that can be as hollow as they are seductive. And Rosenstein should know: he was the Facebook engineer who created the "like" button in the first place. A decade after he stayed up all night coding a prototype of what was then called an "awesome" button, Rosenstein belongs to a small but growing band of Silicon Valley heretics who complain about the rise of the so-called "attention economy": an internet shaped around the demands of an advertising economy. These refuseniks are rarely founders or chief executives, who have little incentive to deviate from the mantra that their companies are making the world a better place. Instead, they tend to have worked a rung or two down the corporate ladder: designers, engineers and product managers who, like Rosenstein, several years ago put in place the building blocks of a digital world from which they are now trying to disentangle themselves.


Towards a new economic system for the 21st century

Al Jazeera

In his Prison Notebooks, the Italian revolutionary Antonio Gramsci wrote: "The crisis consists precisely in the fact that the old is dying and the new cannot be born; in this interregnum a great variety of morbid symptoms appear." Today, it is the world economy, to be precise, that finds itself once again in the midst of an interregnum. The post-war model of economic growth that produced the golden age of capitalism is long gone, but a new economic system has yet to be born. The morbid symptoms around abound: Intense and growing inequality, massive unemployment and extreme youth idleness in many parts of the world, rapidly declining standards of living, dangerously high levels of both public and corporate debt, a financial system that remains out of whack, and ecological collapse. Moreover, the world economy not only continues to rely on fossil fuels to power growth, but is actually increasing the consumption of primary energy sources - such as coal, oil and natural gas - in spite of the phenomenon of global warming which threatens to destroy human civilisation as we know it.


Leveraging BERT for Extractive Text Summarization on Lectures

arXiv.org Machine Learning

In the last two decades, automatic extractive text summarization on lectures has demonstrated to be a useful tool for collecting key phrases and sentences that best represent the content. However, many current approaches utilize dated approaches, producing sub-par outputs or requiring several hours of manual tuning to produce meaningful results. Recently, new machine learning architectures have provided mechanisms for extractive summarization through the clustering of output embeddings from deep learning models. This paper reports on the project called Lecture Summarization Service, a python based RESTful service that utilizes the BERT model for text embeddings and KMeans clustering to identify sentences closes to the centroid for summary selection. The purpose of the service was to provide students a utility that could summarize lecture content, based on their desired number of sentences. On top of the summary work, the service also includes lecture and summary management, storing content on the cloud which can be used for collaboration. While the results of utilizing BERT for extractive summarization were promising, there were still areas where the model struggled, providing feature research opportunities for further improvement.