Focuses on the latest large-model trends, applications, technical architectures, and related information.
The author details the deployment of an office AI system on an internal network using eight NVIDIA A800 GPUs, explaining model choices, inference engines, GPU allocations, compatibility issues, and presenting the overall architecture diagram.
The article links Qian Xuesen’s 1954 work on engineering control theory to today’s challenges in large‑model training, arguing that a three‑step framework—ontology (defining what to control), control theory (designing how to control), and harness (accurate measurement)—is essential for reliable AI systems across domains such as medicine, law, and multimodal perception.
The paper formulates LLM hallucination as systemic noise, builds a forward‑feedback‑adaptive control loop using Prompt engineering, Retrieval‑Augmented Generation and a hallucination detector, proves global asymptotic stability via Lyapunov theory, designs an LQR optimal controller and an MRAC adaptive scheme, and demonstrates up to 5 dB SNR improvement and sub‑5% hallucination rates on standard benchmarks.
Huawei’s He Tingbo announced at ISCAS 2026 that Moore’s geometric scaling has reached a physical ceiling and introduced the “Tau Law,” a time‑scaling strategy that leverages multi‑layer optimization, logic folding, and the Lingqu bus to achieve 1.4 nm‑equivalent performance without EUV, outlining a roadmap to 2026 and 2031.
The article explains how traditional data governance, which merely cleans and organizes files, fails to meet AI’s need for semantic understanding, and argues that adopting ontology‑based governance—building a “cognitive dictionary” of entities, relationships, and rules—enables machines to truly comprehend and reason over enterprise data.
The article presents a comprehensive AI‑driven framework for unmanned underwater vehicles, detailing a three‑layer decision architecture, human‑machine collaboration models, conflict‑resolution mechanisms, data acquisition and simulation pipelines, ontology‑based knowledge graphs, and self‑evolution processes to enable reliable autonomous perception, planning, and actuation in complex marine environments.
This roundup covers OpenAI’s autonomous proof of the Erdős unit‑distance conjecture, Anthropic’s first quarterly profit and a $300 billion‑plus valuation, major AI‑related policy moves, product integrations, and a series of strategic acquisitions and funding announcements across the AI ecosystem.
The article chronicles Palantir’s two‑decade saga of high‑profile setbacks—from a $5 billion, six‑year military AI project and a failed financial platform to stalled consumer data alliances—showing how advanced algorithms falter when detached from real‑world business needs.
Deploying Dify in an offline environment is fraught with hidden traps—from missing Docker images and vector‑database dependencies to network subnet conflicts, plugin‑daemon crashes, and silent external service time‑outs—requiring careful preparation, configuration, and maintenance to achieve a stable setup.
The article explains why complex intelligent decisions—such as those in autonomous driving—must be decomposed, outlines five key challenges, presents four classic decomposition paradigms, discusses interface design principles, and identifies scenarios where splitting may be counterproductive.
