We’ve been promised a revolution in how and why nearly everything happens. But the limits of modern artificial intelligence are closer than we think. 我们已经预见几乎所有事情如何发生以及为什么发生的变革。但现代人工智能的极限比我们想象的要近得多。 By JASON PONTIN [an Ideas contributor for WIRED. He is a senior partner at Flagship Pioneering, a firm in Boston that creates, builds, and funds ... Read more 02 Feb 2018 - 27 minute read
Supersmart algorithms won’t take all the jobs, but they are learning faster than ever, doing everything from medical diagnostics to serving up ads. 绝顶聪明的算法不会拿走所有的工作，但它们在做从医疗诊断到广告推送等一切事情时，学习得比以往更快。 By TOM SIMONITE [an Ideas contributor for WIRED. He is a senior partner at Flagship Pioneering, a firm in Boston that creates, builds, and funds co... Read more 01 Feb 2018 - 40 minute read
综述 Medical Image Analysis with Deep Learning 深度学习下的医学图像分析: Medical Image Analysis with Deep Learning的翻译版 解析深度学习如何改变医疗成像领域 【PPT】深度学习技术在医学影像CAD中的应用 [过去 20 个月，影像全球医学界的 11 大 AI 事件 IEEE Spectrum](https://www.toutiao.com/a6507480449529414152/?tt_from=weixin&utm_campaign=client_share×tamp=1515205426&app=news_a... Read more 30 Dec 2017 - 4 minute read
1 引言 Knet (pronounced “kay-net”) is the Koç University deep learning framework implemented in Julia by Deniz Yuret and collaborators. Knet uses dynamic computational graphs generated at runtime for automatic differentiation of (almost) any Julia code. This allows machine learning models to be implemented by only describing the forward calculati... Read more 11 Dec 2017 - 2 minute read
06 Dec 2017 Any sufficiently complicated machine learning system contains an ad-hoc, informally-specified, bug-ridden, slow implementation of half of a programming language.1 By Mike Innes (Julia Computing), David Barber (UCL), Tim Besard (UGent), James Bradbury (Salesforce Research), Valentin Churavy (MIT), Simon Danisch (MIT), Alan Edelm... Read more 06 Dec 2017 - 43 minute read