about.subtitle
When exploring complex systems, it is essential not only to record the outcome but also to understand the logic of their internal structure. This collection features materials where the focus shifts from outward manifestations to the engineering and scientific essence of things. We analyze architectural patterns, algorithmic chains, and fundamental mechanisms that determine the viability of any solution – from software code to urban planning concepts.
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.
Researchers have demonstrated how to fine-tune AI models to replace complex physics simulations – making them faster and cheaper than running calculations from scratch.
AI: Events
Technical context • Development
The TorchAO developers have expanded their toolkit for quantization-aware training, now supporting new architectures, modes, and tasks.
Kubetorch has joined the PyTorch ecosystem, simplifying the process of running ML tasks on Kubernetes by abstracting complex infrastructure behind simple Python code.
What if you could train a massive neural network using half the memory – without breaking anything? That's exactly what the creators of FlashOptim are exploring.
NeuroBlog
Science & Technology • Computer Systems
From the first server at CERN in Switzerland to a global network of five billion users, we break down exactly how the technology we use every day works.
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