# Botober 2025: Terrible recipes from a tiny neural net After seeing generated text evolve from the days of tiny neural networks to today's ChatGPT-style large language models, I have to conclude: there's something special about the tiny guys. Maybe it's the way the tiny neural networks string together text letter by letter just based on what you've given it, rather than drawing from prior internet training. It's not secretly drawing on some dark corner of the internet, it's just mashing together statisti
University of Minnesota Duluth scientist Vitaly Vanchurin thinks the universe may be a neural network, and suggests a "theory of everything
A team of neuroscientists at the Champalimaud Centre for the Unknown, in Lisbon, has been able to map single neural connections over long distances in the brain. "These are the first measurements of neural inputs between local circuits and faraway sites", says Leopoldo Petreanu, who led the research. In doing so, Petreanu and co-authors Nicolás Morgenstern and Jacques Bourg have also discovered that the wiring of the brain is more complex than previously thought. Their results have been published in the
In this article, we will introduce some AI chips which can improve your experience to run deep neural network models
The recommendation system is not only a problem of inductive statistics from data but also a cognitive task that requires reasoning ability. The most advanced graph neural networks have been widely used in recommendation systems because they can capture implicit structured information from graph-structured data. However, like most neural network algorithms, they only learn matching patterns from a perception perspective. Some researchers use user behavior for logic reasoning to achieve recommendation predic
Today’s machine learning (ML) solutions are complex and rarely use just a single model. Training models effectively requires large, diverse datasets that may…
# Auditing Systemic Strategic Imperatives for Sustainable Artificial Neural Network Models Software Support: Developing inefficient hardware libraries and tools that oppose Machine Learning operations is an ongoing area of research. Developing old experimental techniques: To probe the activity of inhibitory interneurons and their synaptic connections in greater detail. Reduced Computational Cost: With fewer inactive connections, the number of calculations optional during forward and backward passes is Ba
Open Menu Proceedings of the AAAI Conference on Artificial Intelligence Search Login Home / Archives / Vol. 33 No. 01: AAAI-19, IAAI-19, EAAI-20 / AAAI Technical Track: Machine Learning Interpretation of Neural Networks Is Fragile Authors Amirata Ghorbani Stanford University Abubakar Abid Stanford University James Zou Stanford University DOI: https://doi.org/10.1609/aaai.v33i01.33013681 Abstract In order for machine learning to be trusted in many applications, it is critical to be able to reliably explain w
The discussion revolves around methods for predicting movement direction and strength using neural networks. Options include single or multiple outputs, normalization challenges, and the trade-off between model complexity and interpretability. The key idea is that the probability of class assignment can reflect movement strength if the training data is appropriately normalized
Value Iteration Networks Tamar et al., NIPS 2016 ‘Value Iteration Networks’ won a best paper award at NIPS 2016. It tackles two of the hot issues in reinforcement learning at the moment: incorporating longer range planning into the learned strategies, and improving transfer learning from one problem to another. It’s two for the price of