1. Friend of a Friend: The Facebook That Could Have Been by Sinclair Target at Two-Bit History. Alternate History: What if Resource Description Frameworks (RDFs) had succeeded over database-backed software services? That difference would have broken down the monopoly powers of Facebook by connecting social networks among each other. “If users do not value control enough to stomach additional complexity, and if centralized systems are more simple than distributed ones—and if, further, centralized systems tend to be closed and thus the successful ones enjoy powerful network effects—then social networks are indeed natural monopolies.”
  2. 10 ML & NLP Research Highlights of 2019 by Sebastian Ruder. Each of the highlights includes a short summary, links, and an outlook for the future.
  3. Machine Learning 4 Science Newsletter by Charles Yang. In the first edition of his ML4SCI newsletter, Charles Yang covers applications of machine learning in science, from materials science to climate change.
  4. Climate Change AI Interactive Summary at Climate Change AI, linked from the ML4SCI newsletter: An overview of ML applications in the fight against climate change, marked by level and speed of impact.
  5. Scientists Give Cuttlefish 3D Glasses and Shrimp Films for Vision Study by Ian Sample for The Guardian. Sometimes important scientific findings are serious and stern matter – and sometimes it’s all about putting glasses on a cuttlefish. 
  6. A Scalable Pipeline for Designing Reconfigurable Organisms by Kriegman et al. (2019). They trained an algorithm to create virtual organisms and then turned the results into organic copies and tested how they performed.
  7. Met Begins Operational Use of Live Facial Recognition (LFR) Technology. In some areas of London, the police will start using live facial recognition to detect offenders. Interesting quote by the assistant commissioner: “We all want to live and work in a city which is safe: the public rightly expect us to use widely available technology to stop criminals.” Yikes.
  8. The 19th Edelman Trust Barometer. The research shows trust has shifted towards a closer circle and the level of trust has become more diverse across segments.
  9. Competing in the Age of AI by Marco Iansiti and Karim R. Lakhani for Harvard Business Review. The continuous loop from more data to better algorithms to better products transforms traditional operating models with diminishing marginal returns into digital operating models with increasing value.
  10. Ironies of Automation by Adrian Colyer at The Morning Paper, review of the paper with the same title by Bainbridge (1983). The more advanced our automations, the more we depend on equally advanced human operators, while their skills will simultaneously deteriorate as their work is taken over by automation. Quoting from her paper: “Perhaps the final irony is that it is the most successful automated systems, with rare need for manual intervention, which may need the greatest investment in human operator training […]”.