Breaking Compute Bottlenecks for Large Language Models

Analysis of large-model scaling: how parameter count and training tokens drive compute requirements, showing compute grows ~quadratically with model size.

Greg Wilson on Public Sector AI Responsibility

Microsoft’s government CTO details public sector AI adoption, cloud migration, ethical AI governance, and cybersecurity practices to enable secure, data-driven government services.

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.

What Is an RNN (Recurrent Neural Network)?

Technical overview of RNNs and LSTM architectures, how they model sequential data, application areas like signal and text processing, and MATLAB-based implementation.

Key Factors in Designing an AI Chip

Review of neural network quantization and numeric formats, covering floating vs integer, block floating point, logarithmic systems, and inference vs training trade-offs.

Deep Learning for Radar Target Detection

Survey of deep learning approaches for radar target detection, comparing two-stage and single-stage detectors (Faster R-CNN, YOLOv5), preprocessing, and deployment results.

Relationship Between AI, Machine Learning, and Deep Learning

Overview of artificial intelligence and its relationship to machine learning and deep learning, covering AI categories, ML workflow, and common deep architectures.

What Is Automatic Speech Recognition and GPU-Accelerated ASR

Overview of ASR (speech-to-text): pipeline, acoustic and language models, CTC training, decoding strategies, and GPU acceleration using NVIDIA NeMo and toolkits.

Top 10 AI Challenges for Cybersecurity

Technical overview of AI cybersecurity risks: automated attacks, deepfakes, adversarial examples, privacy and data security, ethics, legal challenges, and system fragility.

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.

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