The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have a surprisingly large carbon footprint[40]. Ironically, deep learning was inspired by the human brain, which is remarkably energy efficient. Moreover, the financial cost of the computations can make it difficult for academics, students, and researchers, in particular those from emerging economies, to engage in deep learning research.

This position paper advocates a practical solution by making efficiency an evaluation criterion for research along-side accuracy and related measures. In addition, we propose reporting the financial cost or “price tag” of developing,training, and running models to provide baselines for the investigation of increasingly efficient methods. Our goal isto make AI both greener and more inclusive—enabling any inspired undergraduate with a laptop to write high-quality research papers. Green AI is an emerging focus at the Allen Institute for AI. Read More

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Each big step of progress in computing — from mainframe to personal computer to internet to smartphone — has opened opportunities for more people to invent on the digital frontier.

But there is growing concern that trend is being reversed at tech’s new leading edge, artificial intelligence.

Computer scientists say A.I. research is becoming increasingly expensive, requiring complex calculations done by giant data centers, leaving fewer people with easy access to the computing firepower necessary to develop the technology behind futuristic products like self-driving cars or digital assistants that can see, talk and reason.

The danger, they say, is that pioneering artificial intelligence research will be a field of haves and have-nots. Read More

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When Matheus Dominguez was 16, YouTube recommended a video that changed his life.

He was in a band in Niterói, a beach-ringed city in Brazil, and practiced guitar by watching tutorials online.

YouTube had recently installed a powerful new artificial intelligence system that learned from user behavior and paired videos with recommendations for others. One day, it directed him to an amateur guitar teacher named Nando Moura, who had gained a wide following by posting videos about heavy metal, video games and, most of all, politics.

In colorful and paranoid far-right rants, Mr. Moura accused feminists, teachers and mainstream politicians of waging vast conspiracies. Mr. Dominguez was hooked.

As his time on the site grew, YouTube recommended videos from other far-right figures. One was a lawmaker named Jair Bolsonaro, then a marginal figure in national politics — but a star in YouTube’s far-right community in Brazil, where the platform has become more widely watched than all but one TV channel.

Last year, he became President Bolsonaro. Read More

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There is an episode in Black Mirror, the popular Netflix series, in which the scariness of today’s social media comes to life. We are used to hearing slogans from tech companies such as: “Facebook helps you connect and share with the people in your life.” Sounds innocuous enough, right? But as the main character discovers firsthand in the Nosedive episode, platforms designed to enhance social affiliation can be perverted to exaggerate our worst human impulses.

To understand how social media could be used in such a diabolical way, it’s helpful to understand the concept of utilitarianism developed in the 1700s by philosopher Jeremy Bentham. Similar to today’s social media sphere, utilitarianism seems to be an inoffensive way to organize people for their higher good. After all, Bentham held that “The said truth is that it is the greatest happiness of the greatest number that is the measure of right and wrong.” Read More

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