From the Nature 17 April Issue:
- the “usual” wishful tribunes with no implementation spreadsheet (incentivizing bug tech companies to make the digital world safer, make quantum tech sustainable and ethical)
- Trump’s bull-in-a-China-shop attitude towards science and academia (US pullback from Antarctica, NSF halving PhD fellowships in 2025, reflecting on how the US became a science superpower, till Trump administration wreaked havoc, with a comparison of how US and UK sciences differ, starting during WW II and seing for the US a $200 billion funding from US governmental research agencies)
- top-cited papers (in the 21st century and overall) including many machine-learning and statistics newcomers, with the top 21st century pretender being an IEEE CVPR 2016 conference paper by Kaiming He, Xiangyu Zhang, Shaoqing Ren & Jian Sun from Microsoft, Deep residual learning for image recognition, plus a 2015 Nature paper by Le Cun, Bengio and Hinton, Deep learning, a 2017 NeurIPS paper by mostly Google researchers, Vaswani et al., Attention is all you need, and… Leo Breiman’s 2001 Random forests. The article goes on given reasons why AI is so over-represented in the list, one being the huge number of conference publications and hence reference opportunities, as well as the practice of the culture of posting preprints (even though this complicates the task of scientometricians. The most highly cited paper in the scientific literature remains Lowry et al.’s 1951 Protein measurement with the Folin phenol reagent, in the Journal of Biological Chemistry, with 1996 generalized gradient approximations made simple (Physical Review Letters) by Perdew et al. coming fourth. With a mention that R does not come up in the list because there is no single paper or book to cite.
- a machine-learning paper on Mastering diverse control tasks through world models, by Danijar et al. (mostly from GoogleDeepMind), which highlighted achievement is collecting diamonds in Minecraft (!). But more fundamentally improving reinforced learning towards robustness in the task handled. The model is an encoder-decoder structure, with three intermediary random predictor functions
- a supportive review of the book More Everything Forever: AI Overlords, Space Empires, and Silicon Valley’s Crusade to Control the Fate of Humanity by Adam Becker (despite the endless title!), written before Elon Musk took over the DOGE (for a while).
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