2025-08-18 Jamie Lord Every textbook showed the same inexorable curve: small models underfit, optimal models generalise, large models catastrophically overfit. End of story. … In 2019, a group of researchers committed the ultimate sin: they ignored the warnings and kept…
2025-04-29 Vítor Fróis Building an intuitive understanding of how Linear Regression works and how it leads to Gradient Descent
2024-07-28 Gary Marcus My strong intuition, having studied neural networks for over 30 years (they were part of my dissertation) and LLMs since 2019, is that LLMs are simply never going to work reliably, at least not in the general…
2024-05-05 Miguel Grinberg
Visualizations of low dimension artificial neural networks transforming the input into a representation that can be separated by a line with the different classes of data on each side. Neural Networks, Manifolds, and Topology 2014-04-06 Christopher Olah
2022-12-08 by Synced there is increasing interest in whether the biological brain follows backpropagation or, as Hinton asks, whether it has some other way of getting the gradients needed to adjust the weights on its connections. In this regard, Hinton…
2021-12-13 Anil Ananthaswamy
2021-12-09 Martin Anderson Researchers from MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) have experimented with using random noise images in computer vision datasets to train computer vision models , and have found that instead of producing garbage, the method…
2020-09-01 Samuel K. Moore It combines resistance, capacitance, and what’s called a Mott memristor all in the same device. Memristors are devices that hold a memory, in the form of resistance, of the current that has flowed through them. Mott…
2020-09-27 Andrey Kurenkov The story of how neural nets evolved from the earliest days of AI to now.