A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

Probabilistic programming has emerged as a powerful paradigm that integrates uncertainty directly into computational models. By embedding probabilistic constructs into conventional programming ...

(Nanowerk News) We are in a fascinating era where even low-resource devices, such as Internet of Things (IoT) sensors, can use deep learning algorithms to tackle complex problems such as image ...

You can’t cheaply recompute without re-running the whole model – so KV cache starts piling up Feature Large language model ...

This brute-force scaling approach is slowly fading and giving way to innovations in inference engines rooted in core computer systems design.

Data-driven AI systems increasingly influence our choices, raising concerns about autonomy, fairness, and accountability. Achieving algorithmic autonomy requires new infrastructures, motivation ...

Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design” was published by researchers at ...

Energy is no longer a background input but a defining constraint and increasingly, a performance metric, shaping how AI ...