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Algorithms, Microcontent, and the Vanishing Distinction between Platforms and Creators Authors: Liu Leqi, Dylan Hadfield-Menell, and Zachary C. Lipton To appear in Communications of the ACM (CACM) and available on arXiv.org. Ever since social activity on the Internet began migrating from the wilds of the open web to the walled gardens erected by so-called platforms (think … Continue reading "When Curation Becomes Creation"
Full PDFs free on GitHub. To support us, visit Patreon. Related Posts Hope Returns to the Machine Learning Universe 5 Habits of Highly Effective Data Scientists Is This a Paper Review? AI Researcher Joins Johnson & Johnson, to Make More than $19 Squillion ICML 2018 Registrations Sell Out Before Submission Deadline Death Note: Finally, an … Continue reading "Superheroes of Deep Learning Vol 1: Machine Learning Yearning"
If you’re not living under a rock, then you’ve surely encountered the Heroes of Deep Learning, an inspiring, diverse band of Deep Learning all-stars whose sheer grit, determination, and—[dare we say?]—genius, catalyzed the earth-shaking revolution that has brought to market such technological marvels as DeepFakes, GPT-7, and Gary Marcus. But these are no ordinary times. … Continue reading "Hope Returns to the Machine Learning Universe"
While COVID has negatively impacted many sectors, bringing the global economy to its knees, one sector has not only survived but thrived: Data Science. If anything, the current pandemic has only scaled up demand for data scientists, as the world’s leaders scramble to make sense of the exponentially expanding data streams generated by the pandemic. … Continue reading "5 Habits of Highly Effective Data Scientists"
On Thursday, OpenAI announced that they had trained a language model. They used a large training dataset and showed that the resulting model was useful for downstream tasks where training data is scarce. They announced the new model with a puffy press release, complete with this animation (below) featuring dancing text. They demonstrated that their model … Continue reading "OpenAI Trains Language Model, Mass Hysteria Ensues"
Whether you are speaking to corporate managers, Silicon Valley script kiddies, or seasoned academics pitching commercial applications of their research, you’re likely to hear a lot of claims about what AI is going to do. Hysterical discussions about AI machine learning’s applicability begin with a breathless recap of breakthroughs in predictive modeling (9X.XX% accuracy on … Continue reading "When are predictions policies?"
What is a conference? Common definitions provide only a vague sketch: “a meeting of two or more persons for discussing matters of common concern” (Merriam Webster a); “a usually formal interchange of views” (Merriam-Webster b); “a formal meeting for discussion” (Google a). What qualifies as a meeting? Are all congregations of people in all places conferences? How … Continue reading "The Greatest Trade Show North of Vegas (Pressing Lessons from NeurIPS 2018)"
With paper submissions rocketing and the pool of experienced researchers stagnant, machine learning conferences, backs to the wall, have made the inevitable choice to inflate the ranks of peer reviewers, in the hopes that a fortified pool might handle the onslaught. With nearly every professor and senior grad student already reviewing at capacity, conference organizers … Continue reading "Is This a Paper Review?"
By Zachary C. Lipton & Jacob Steinhardt *equal authorship Originally presented at ICML 2018: Machine Learning Debates [arXiv link] Published in Communications of the ACM 1 Introduction Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire to any subset of … Continue reading "Troubling Trends in Machine Learning Scholarship"