How do AI coding agents work? We look under the hood.

At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network ...
New Scientist

Can a new book crack one of neuroscience's hardest problems? Not quite

The ideas presented in George Lakoff and Srini Narayanan's The Neural Mind are fascinating, but the writing is far less ...

Kolmogorov-Arnold networks bridge AI and scientific discovery by increasing interpretability

AI has successfully been applied in many areas of science, advancing technologies like weather prediction and protein folding ...

AI uncovers double-strangeness: A new double-Lambda hypernucleus

Researchers from the High Energy Nuclear Physics Laboratory at the RIKEN Pioneering Research Institute (PRI) in Japan and ...
eLife

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images

Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...

Designing Reactive AI With Spiking Neural Nets

Wolfram-like attention framing meets spiking networks: event-triggered, energy-thrifty AI that “wakes” to stimuli.
Interesting Engineering

AlphaFold and the architecture that cracked protein structure

AlphaFold didn't accelerate biology by running faster experiments. It changed the engineering assumptions behind protein structure prediction.
IEEE

C-DNN: An Energy-Efficient Complementary Deep-Neural-Network Processor With Heterogeneous CNN/SNN Core Architecture

Abstract: In this article, we propose a complementary deep-neural-network (C-DNN) processor by combining convolutional neural network (CNN) and spiking neural network (SNN) to take advantage of them.
GitHub

tumftm/radargnn

This repository contains an implementation of a graph neural network for the segmentation and object detection in radar point clouds. As shown in the figure below, the model architecture consists of ...
IEEE

Survey on Graph Neural Networks

Graph Neural Networks (GNNs) have become a powerful tool in order to learn from graph-structured data. Their ability to capture complex relationships and dependencies within graph structures, allows ...
GitHub

Coupling LSTM Neural Networks and State-Space Models through Analytically Tractable Inference

This repo contains the matlab codes to reproduce the results for the paper: Vuong, Nguyen & Goulet (2024), Coupling LSTM Neural Networks and State-Space Models through Analytically Tractable Inference ...