AI Applications in Wargaming: Case Studies

Analysis of AI applications in wargaming: case studies, benefits like enhanced scenario realism and decision support, and limitations such as black-box effects and cost.

DeepPointMap: LiDAR SLAM Framework Using Deep Learning

DeepPointMap is a LiDAR SLAM framework using sparse neural descriptors for memory-efficient map representation and multi-scale localization including odometry and loop closure.

Survey of LLM Inference on Tabular Data

Survey of table inference using large language models: tasks, datasets, methods (fine-tuning, in-context learning, tools), benchmarks and future research directions.

AIGC basics and industry applications

Overview of AIGC and ChatGPT: technologies, industry chain, applications in text/image/video, e-commerce impact, and prompt engineering best practices.

Benchmarks for Harmlessness Evaluation in the LLM Era

Overview of FFT benchmark: a 2,116-instance evaluation of LLM harmlessness assessing factuality, fairness, and toxicity to reveal model harms and mitigation gaps.

AI Chip Ecosystem: Analysis and Future Outlook

Analysis of the AI chips ecosystem: technical, commercial, and strategic factors shaping developer affinity, multi?generation platform continuity, and ecosystem influence.

Artificial Intelligence in Military Intelligence

Review of artificial intelligence applications in military intelligence: current uses, an AI-driven intelligence service model, US projects, trends and technical challenges.

GPU Clusters for Very Large AI Models

Summary of TensorNODE, a TensorWave bare-metal AI cloud using AMD MI300X GPUs and a PCIe Gen5 memory fabric to enable petabyte-scale GPU memory pools.

PixelLM: First Efficient Pixel-Level Inference Model Without SAM

PixelLM is an efficient pixel-level multimodal large model for open-domain multi-object reasoning segmentation without SAM, accompanied by the MUSE dataset.

LLM Programming: Lessons from Redis's Creator

Programmer's account of using large language models to accelerate Python one-off scripts and ONNX model interpretation, and limits in systems programming.

Four Methods for Fine-Tuning Large Models

Technical overview of large-model fine-tuning and PEFT approaches, covering prompt/prefix tuning, P-tuning v2, AdaLoRA, adapter/LoRA methods and standard training workflow.

Probability Distributions for Machine Learning

Technical overview of probability distributions for urban research, explaining random variables and density functions (PMF, PDF, CDF) and common discrete cases like Bernoulli.

Fixing ChatGPT API Timeout Issues

Mitigating ChatGPT API streaming timeouts: shorten retry intervals and monkey-patch APIRequestor.arequest_raw to set aiohttp.ClientTimeout (connect, total, sock_read).

17 Common Machine Learning Algorithms

Overview of machine learning algorithms and learning paradigms, detailing 17 algorithm families, applications, strengths, and limitations for engineers.

NVIDIA's Three Core Technologies: CUDA, NVLink, InfiniBand

Technical overview of CUDA and NVLink for GPU-accelerated AI: architecture, interconnect bandwidth, and scalable multi-GPU networking.

PanopticNeRF-360: Panoptic Labels from 3D Annotations

PanopticNeRF-360 uses coarse 3D annotations and NeRF-based joint geometry-semantic optimization to synthesize RGB images and panoptic segmentation from novel views.

Global and China AI Server Market Size

Analysis of AI server market dynamics, NVIDIA dominance, and Huawei Ascend's role in domestic substitution of AI chips amid export controls and foundry capacity gains.

Length Extrapolation with Positional Encoding

Survey of length extrapolation approaches analyzing positional encoding methods and their effects on extending model context windows for long-sequence inference.

Self-Supervised Monocular Depth Estimation in Dynamic Scenes

Review of Dynamo-Depth: a self-supervised monocular depth method that jointly learns depth, 3D independent flow and motion segmentation to handle dynamic scenes.

Inside Amazon's AI Chip Architecture

Analysis of AWS Trainium2 architecture and its relationship to Inferentia, with performance projections, core/memory scaling, NeuronLink bandwidth and instance implications.