New data analysis competitions

  • ACM RecSys Challenge 2018.

    This year’s challenge focuses on music recommendation, specifically the challenge of automatic playlist continuation. By suggesting appropriate songs to add to a playlist, a Recommender System can increase user engagement by making playlist creation easier, as well as extending listening beyond the end of existing playlists.



  • Inside China's Vast New Experiment in Social Ranking.

    One day a new icon appeared on Liu’s Alipay home screen. It was labeled Zhima Credit (or Sesame Credit). The name, like that of Alipay’s parent company, evoked the story of Ali Baba and the 40 thieves, in which the words open sesame magically unseal a cave full of treasure. When Liu touched the icon, he was greeted by an image of the Earth. “Zhima Credit is the embodiment of personal credit,” the text underneath read. “It uses big data to conduct an objective assessment. The higher the score, the better your credit.” Further down was a button that read, in clean white characters, “Start my credit journey.” He tapped.

    [...] For the Chinese Communist Party, social credit is an attempt at a softer, more invisible authoritarianism. The goal is to nudge people toward behaviors ranging from energy conservation to obedience to the Party. Samantha Hoffman, a consultant with the International Institute for Strategic Studies in London who is researching social credit, says that the government wants to preempt instability that might threaten the Party. “That’s why social credit ideally requires both coercive aspects and nicer aspects, like providing social services and solving real problems. It’s all under the same Orwellian umbrella.”

  • Chinese authorities collecting DNA from all residents of Xinjiang.

    Chinese authorities are collecting DNA samples, fingerprints and other biometric data from every resident in a far western region, Human Rights Watch has said.


  • AI is now so complex its creators can’t trust why it makes decisions. As a Data Scientist myself, this is something I emphasize in every model I have to build: "do you want to be able to understand it?" Then work from there.

    As these artificial neural networks are starting to be used in law enforcement, health care, scientific research, and determining which news you see on Facebook, researchers are saying there’s a problem with what some have called AI’s “black box.” Previous research has shown that algorithms amplify biases in the data from which they learn, and make inadvertent connections between ideas.

  • AI experts caution Senate against heavy regulation. Nice complement to the above link.

    Enterprise and academic experts cautioned the U.S. Senate Committee on Commerce, Science and Transportation against a heavy-handed regulatory approach to AI, citing concerns of stifling innovation and progress. At a hearing Tuesday, experts did call for open data policies, as AI systems will only be as good as the data they can draw insight and learn from. Yet data policies today are also in the midst of change as privacy considerations and fair use policies face scrutiny.

  • Artificial Intelligence, NASA Data Used to Discover Eighth Planet Circling Distant Star.

    The newly-discovered Kepler-90i – a sizzling hot, rocky planet that orbits its star once every 14.4 days – was found using machine learning from Google. Machine learning is an approach to artificial intelligence in which computers “learn.” In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded signals from planets beyond our solar system, known as exoplanets.


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