about.subtitle
This collection brings together materials where we explore the mechanisms for extracting patterns from data sets. Rather than providing a superficial overview of applied solutions, we focus on the internal logic of the processes: from system architecture design to the selection of loss functions and regularization methods. This section features research and deep dives covering both classical statistical approaches and current neural network architectures.
AI: Events
Infrastructure
AMD explains how to run ensemble weather forecasting on its GPUs – an approach that acknowledges and works with the inherent imprecision of forecasting.
Lab
Computer Science
How feminist principles and collaborative workshops are changing the approach to data annotation – and why it matters for fair AI.
AI: Events
Technical context • Development
The TorchAO developers have expanded their toolkit for quantization-aware training, now supporting new architectures, modes, and tasks.
This article examines the accuracy of AI transcription for pharmaceutical names, identifies which models perform best, and explains the importance of this for medicine.
Kubetorch has joined the PyTorch ecosystem, simplifying the process of running ML tasks on Kubernetes by abstracting complex infrastructure behind simple Python code.
AI: Events
Technical context • Research
AMD researchers have demonstrated how an AI agent can iteratively optimize high-performance code without human intervention.
Want to dive deeper into the world
of neuro-creativity?
Be the first to learn about new books, articles, and AI experiments
on our Telegram channel!