Explore latest news about algorithms, model architectures, and data-driven techniques that power today's most capable predictive and analytical systems. Covering machine learning and the latest advances in model training and optimization.
Argonne National Laboratory repurposed ChatGPT's transformer architecture to simulate fluid dynamics in nuclear reactors, achieving high accuracy at unprecedented speeds and paving the way for real-time digital twins.
Moonshot AI has released the complete technical architecture for Kimi K3, a 2.8 trillion-parameter multimodal model with a one-million-token context window, but training recipes remain closed.
A cascade architecture for RAG generation starts with cheap local models and escalates to hosted flagships only when validation fails, slashing costs while preserving correctness.
Caltech spinout Oratomic raised $300M to build a quantum computer with just 20,000 qubits by 2030, using AI-driven design and mobile atoms for efficient error correction. The approach could break RSA encryption far sooner than expected.
A new study measures the actual GPU electricity cost of running local LLMs on an RTX 3090, finding that some models are cheaper than cloud APIs while others are surprisingly expensive. The key factor is effective throughput, not model size.
As AI matures, CPUs are emerging as a cost-effective, energy-efficient alternative for inference workloads. This shift could democratize AI deployment across industries.
A Peking University team has created an all-optical link for standard chips, achieving a 100x speedup in distributed AI inference with drastically lower resource use. The breakthrough could reshape data center design amid soaring AI demand.
Despite rapid progress, frontier AI models still hallucinate with alarming confidence. This article dissects recent embarrassing failures, explains the underlying mechanisms, and offers practical mitigation strategies.
Ant Group's AI unit, Robbyant, has developed LingBot-Vision, a model that detects object edges with sub-pixel precision. It outperforms larger models on depth benchmarks while using far less data, potentially reducing costly glass crashes in robotics.