Practical Tricks for Reward Models

Technical overview of methods to improve reward model robustness for RLHF: quantify preference strength, flip/soften labels, apply adaptive margins, contrastive learning and MetaRM

Future Directions in Lightweight Deep Learning

Review of lightweight deep learning for resource-constrained devices: TinyML, quantization, architectures and deployment strategies for efficient inference.

Resolution-Refutation Logic Reasoning for Large Language Models

GFaiR applies resolution-refutation over natural language to improve first-order logic reasoning in LLMs, boosting generalization and faithfulness with a validator.

Explaining Convolution in Simple Terms

Explains the meaning of convolution—why we flip (fold) and multiply—using signal analysis, dice probability, and image processing kernels as examples.

Medical Robots in Healthcare Applications

Overview of medical robots in digital health, covering surgical and microrobot applications, AI/ML-enabled perception, sensors, system architecture, and market trends.

In-Memory Compute Chips for ML Inference

System-level evaluation of compute-in-memory (CiM) for accelerating GEMM in ML inference: compares analog vs digital CiM, cache-level integration, and optimal dataflows.

Vision Transformer Basics and Object Detection

Overview of Vision Transformer architectures and their use in object detection, covering encoder-decoder design, multi-scale fusion, DETR and Deformable DETR approaches.

Machine Learning Algorithms Implemented with NumPy

Detailed review of a 30k-line NumPy machine learning repository implementing 30+ models with explicit gradient computations, utilities, and test examples.

Passive Component Challenges and Solutions in AI

Overview of passive components for AI systems: material, architectural and process innovations for high-current power inductors and low-ESR polymer tantalum capacitors.

Building Graph Neural Networks with PyTorch

Overview of graph neural networks, graph basics and NetworkX graph creation, GNN types and challenges, plus a PyTorch spectral GNN example for node classification.

Can LLMs Reason and Plan?

Analysis of recent research evaluating whether LLMs can plan or reason, showing limited autonomous planning and that apparent emergent capabilities stem from in-context learning.

AI Applications for Improving Industrial Automation Stability

Predictive maintenance and digital twin applications for AI-driven industrial process stability, enabling real-time parameter optimization and reduced downtime.

Effective Power Management for AI Data Centers

Technical overview of data center power management: hybrid capacitors, low?ESR components, precision resistors and wireless monitoring to improve efficiency and reliability.

F-Learning: Forget-then-Learn for LLM Knowledge Updates

F-Learning: a parameter-based fine-tuning paradigm that subtracts knowledge parameter deltas to forget outdated facts, then fine-tunes (LoRA or full-model) to update LLM knowledge.

AIoT Connecting AI to the Physical World

Explains how AIoT links AI and physical devices via IP-based networks and application layers like Matter, and deployment considerations for scalable device connectivity.

Beginner's Guide to Intelligent Computing Centers

Overview of intelligent computing center architecture and operation: GPU clusters, high-speed storage and networking, distributed frameworks, intelligent OS, and AI access models.

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.

iTOP-RK3568 MobileNet Image Classification Inference

Guide to validating MobileNet image classification inference on the iTOP-RK3568 board, covering RK3568 hardware, NPU use, model files, and execution steps.

Deep Learning Advances in Polarization Image Fusion

Comprehensive review of polarization image fusion and deep learning methods (CNN, GAN), traditional algorithms, datasets, applications, and future research directions.

What Is AI Computation and How Does It Work?

AI overview with latent space representations and practical applications in manufacturing and semiconductor manufacturing, including predictive maintenance and quality assurance.