Issue #84
January 3, 2021
- 1. State machines are wonderful tools [nullprogram.com]
- 2. It takes a lot of energy for machines to learn – here’s why AI is so power-hungry [theconversation.com]
- 3. Reboot the Computing-Research Publication Systems [cacm.acm.org]
- 4. A Different Kind of Theory of Everything [newyorker.com]
- 5. The Pile - An 800GB Dataset of Diverse Text for Language Modeling [pile.eleuther.ai]
- 6. Science Is Getting Less Bang for Its Buck [theatlantic.com]
- 7. The Big Little Guide to Message Queues [sudhir.io]
- 8. Introduction to Reinforcement Learning [deepmind.com]
- • Mastering Atari, Go, chess and shogi by planning with a learned model (J. Schrittwieser, I. Antonoglou, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, E. Lockhart, D. Hassabis, T. Graepel, T. Lillicrap, D. Silver)
- • True scale-free networks hidden by finite size effects (M. Serafino, G. Cimini, A. Maritan, A. Rinaldo, S. Suweis, J. R. Banavar, G. Caldarelli)
- • The prevalence of dyads in social life (L. S. Peperkoorn, D. V. Becker, D. Balliet, S. Columbus, C. Molho, P. A. M. Van Lange)
- • Thinking Fast and Slow in AI (G. Booch, F. Fabiano, L. Horesh, K. Kate, J. Lenchner, N. Linck, A. Loreggia, K. Murugesan, N. Mattei, F. Rossi, B. Srivastava)
- • Fairness in Machine Learning (L. Oneto, S. Chiappa)
- • Non-pharmaceutical interventions during the COVID-19 pandemic: a rapid review (N. Perra)
- • An algorithm for network community structure determination by surprise (D. Gamermann, J. A. Pellizaro)
- • Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning (A. X. Lu, A. X. Lu, A. Moses)
- • Graph Convolutional Networks for traffic anomaly (Y. Hu, A. Qu, D. Work)
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