Machine Learning
Displaying 10 of 24 articles
Why AI Stumbles on Certain Games and How to Fix It
Why AI Stumbles on Certain Games and How to Fix It Developers want game agents that win, learn, and entertain, yet AI game playing limitations keep showing up in titles that reward improvisation or hi
How to Fine-Tune Qwen 3.5 on Your Own Data
How to Fine-Tune Qwen 3.5 on Your Own Data Alibaba’s Qwen 3.5 is the strongest open-weight multimodal model available today. But a base model trained on general data will not match the performance you
NVIDIA Rubin Platform Explained: What the Next-Gen GPU Means for AI Training
NVIDIA Rubin Platform Explained: What the Next-Gen GPU Means for AI Training NVIDIA announced the Rubin platform at CES in January 2026 and has been rolling out technical details since. The platform r
Alibaba’s Qwen 3.5 Is the Strongest Open-Weight Multimodal Model Yet
Alibaba’s Qwen 3.5 Is the Strongest Open-Weight Multimodal Model Yet Alibaba released Qwen 3.5 in March 2026 and it immediately set new benchmarks for open-weight multimodal AI. The model processes te
The AI Energy Bottleneck: How Grid-Scale Batteries Fit In
AI Cannot Scale Without Solving the Power Problem First AI runs on electricity, and there is not enough of it. Goldman Sachs projects that AI data center power consumption will rise 175% by 2030. Sigh
What Trainium3’s Neuron Switches Mean for AI Infrastructure
Amazon’s Neuron Switches Are Changing How AI Chips Communicate Individual AI chips are fast. But in a data center, what matters most is how thousands of chips work together. Amazon’s custom Neuron swi
AI Data Collection at Scale: From Delivery Routes to Training Sets
Gig Workers Are Becoming AI’s Data Collection Network Training AI systems that interact with the physical world requires real-world data that cannot be scraped from the internet. DoorDash and Uber hav
Compressing Large AI Models Without Losing Performance
How Quantum-Inspired Compression Shrinks AI Models Large AI models like GPT-4 and Llama require massive computational resources. They run on GPU clusters in data centers, consuming significant power a
How Amazon Builds and Tests AI Chips from Scratch
Inside Amazon’s Chip Lab: Where Trainium Gets Built Amazon’s custom AI chip program started in 2015 when the company acquired Israeli chip designer Annapurna Labs for $350 million. More than 10 years
Gemini 3.1 Flash-Lite Cuts Inference Costs While Doubling Speed
Google shipped Gemini 3.1 Flash-Lite in March 2026 as a purpose-built model for high-volume inference workloads. The model delivers throughput roughly double that of Gemini 3.1 Flash while maintaining