A technical blog post (originally from 2018) revisits a classic question about neural network training: why does the backpropagation algorithm invented by Rumelhart et al. in 1986 compute gradients by propagating errors backward through the network rather than forward, given that both directions are mathematically valid applications of the chain rule. The author walks through the notation and structure of a neural network node to set up an explanation of why forward-mode differentiation is computationally suboptimal compared to the standard backward approach.
gregorygundersen.com
· 2026-09-21
A new study introduces patient-specific neural network models that rapidly align 2D X-ray images with 3D CT scans during image-guided surgical procedures. The approach aims to replace slower, less reliable traditional registration methods used in orthopedic, spine and interventional surgeries.
nature.com
· 2026-09-16
OpenAI has reportedly acquired Glass Imaging, a Los Altos-based smartphone camera company, for more than $300 million according to the Wall Street Journal. Founded in 2019 by former Apple engineers Ziv Attar and Tom Bishop, who helped build Portrait Mode, the startup had raised roughly $30 million before the deal and uses neural networks trained on specific camera hardware to improve image quality directly at capture rather than through post-processing edits.
techcrunch.com
· 2026-09-14
A software developer published a personal essay cataloguing mixed emotions about AI: awe at how neural networks produce reasoning without explicit algorithms, fear over unresolved existential-risk arguments, disgust at crawlers overwhelming open web communities, sadness for artists undercut by AI-generated content, and anger at political systems failing to check corporate AI power and resource consumption. The author concludes AI feels promising as technology but troubling for society.
beza1e1.tuxen.de
· 2026-09-06
Julia began in 2009 as an MIT research effort by frustrated researchers who wanted a scientific programming language that was both easy to use and fast, unlike existing tools that forced rewrites for performance. The project evolved from an MIT lab into the company JuliaHub, and Julia is now a free, open-source language used by more than a million people across companies and universities worldwide, applied to everything from modeling jet engines and drugs to imaging black holes.
news.mit.edu
· 2026-08-31
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