Mixture-of-Experts (MoE) Models: Structure and Trade-offs

Overview of Mixture-of-Experts (MoE) transformers: sparse routing with gating networks and experts, training and inference trade-offs, and recent Mistral-8x7B-MoE.

OpenAI Sora: a new AI model for video generation

Technical overview of OpenAI's Sora and its video generation capabilities, core machine learning foundations, and potential impacts on production workflows and society.

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.

Sora: OpenAI's Video Model Architecture and Use Cases

Overview of Sora, OpenAI's video diffusion model using a spatiotemporal autoencoder and DiT Transformer to generate high-resolution, minute-long videos.

AI Server PCB Hardware Breakdown

Teardown analysis of NVIDIA DGX A100 AI server PCBs: PCB types, area and per-system value breakdown for GPU board assembly, CPU motherboard, substrates and accessories.

Optimizing Deep Learning Models

Practical deep learning tuning guide covering learning rate selection, batch size effects, weight initialization, optimizers, regularization, data augmentation and training tips.

Understanding the Transformer Neural Network Model

Overview of the Transformer architecture: self-attention, multi-head attention, positional encoding, encoder-decoder stacks, and implications for distributed model training.

How Computer Systems Address Challenges of Large AI Models

Dr. Mao Yang examines how computer systems must evolve - ultra-large-scale computing, cloud redesign, and distributed systems - to support large models and next-gen AI

AI Infrastructure Drives Three Waves of Industrial Change

Technical overview of AI 2.0: how generative AI drives demand for large-scale compute, data pipelines, and Model-as-a-Service (MaaS) to enable industry deployments.

Popular LLM Inference Stacks and Setups

Survey of LLM inference stacks covering throughput, latency and cost; explains hardware constraints, KV cache, quantization, paged/grouped attention, and practical optimizations.