🌾 Innovations in spatial imaging could unlock higher wheat yields
#farming
“While governments and regulators act to structurally reduce electricity prices, industry has an opportunity to be innovative with its heating processes, using flexible operational strategies to improve the near-term business case for electrification. This briefing takes stock of this potential, and introduces recommendations on how best to capitalise on it.”
GitHub says it will use Copilot interaction data, including inputs, outputs, and code snippets, to train its AI models starting April 24, unless users opt out (Corbin Davenport/How-To Geek)
https://www.howtogeek.com/githubs-copilot-will-use-you…
"From April 24 onward, interaction data—specifically inputs, outputs, code snippets, and associated context—from Copilot Free, Pro, and Pro users will be used to train and improve our AI models unless they opt out." #github
Nothing says "smart data vision for 2035" like a PDF-only policy paper.
https://www.gov.uk/government/publications/smart-data-strategy
The Diffusion Duality, Chapter II: $\Psi$-Samplers and Efficient Curriculum
Justin Deschenaux, Caglar Gulcehre, Subham Sekhar Sahoo
https://arxiv.org/abs/2602.21185 https://arxiv.org/pdf/2602.21185 https://arxiv.org/html/2602.21185
arXiv:2602.21185v1 Announce Type: new
Abstract: Uniform-state discrete diffusion models excel at few-step generation and guidance due to their ability to self-correct, making them preferred over autoregressive or Masked diffusion models in these settings. However, their sampling quality plateaus with ancestral samplers as the number of steps increases. We introduce a family of Predictor-Corrector (PC) samplers for discrete diffusion that generalize prior methods and apply to arbitrary noise processes. When paired with uniform-state diffusion, our samplers outperform ancestral sampling on both language and image modeling, achieving lower generative perplexity at matched unigram entropy on OpenWebText and better FID/IS scores on CIFAR10. Crucially, unlike conventional samplers, our PC methods continue to improve with more sampling steps. Taken together, these findings call into question the assumption that Masked diffusion is the inevitable future of diffusion-based language modeling. Beyond sampling, we develop a memory-efficient curriculum for the Gaussian relaxation training phase, reducing training time by 25% and memory by 33% compared to Duo while maintaining comparable perplexity on OpenWebText and LM1B and strong downstream performance. We release code, checkpoints, and a video-tutorial on: https://s-sahoo.com/duo-ch2
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#CFP: SUMMER SCHOOL ON THE KYOTO SCHOOL 2026 (1-12 June 2026)
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#CFP: SUMMER SCHOOL ON THE KYOTO SCHOOL 2026 (1-12 June 2026)
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DESCRIPTION While major Western philosophical movements in the 20th century looked upon claims to…
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