How Google’s WeatherNext 2 AI Model Delivers 15-Day Forecasts 8× Faster
2 days ago 高效码农From 32-Dimensional Noise to 15-Day Forecasts: Inside Google DeepMind’s WeatherNext 2 What makes a brand-new AI weather model worth replacing Google’s own flagship? WeatherNext 2 answers with three numbers: 8× faster, 99.9 % better CRPS, and a single TPU that spits out 56 global scenarios in under a minute—without ever seeing a joint-distribution label. What problem is WeatherNext 2 trying to solve? Medium-range forecasts must quantify uncertainty, but classic physics ensembles cost a super-computer and most ML ensembles are either slow (diffusion) or spatially disjoint (point-wise noise). WeatherNext 2 delivers physically coherent, high-resolution ensembles in one forward pass by injecting …
Gemini 2.5 Computer Use: The Revolutionary AI That Finally Uses Your Computer Like a Human
1 months ago 高效码农Gemini 2.5 Computer Use Model: The Revolution That Teaches AI to “Use Computers” Is Here “ As you read this, you might be tired of repetitive web operations or frustrated with tedious UI testing. Now, there’s a new solution to these challenges. Ten years ago, we dreamed of AI assistants that could handle repetitive computer tasks. Today, Google has turned that dream into reality. Based on Gemini 2.5 Pro, the Gemini 2.5 Computer Use model doesn’t just understand your instructions—it actually “sees” the screen and performs clicks, typing, and scrolling like a human, accomplishing tasks that were once strictly manual. …
Gemma 3n: How Google DeepMind Redefines On-Device AI for Real-Time Multimodal Tasks
6 months ago 高效码农Google DeepMind Unveils Gemma 3n: Redefining Real-Time Multimodal AI for On-Device Use Introduction: Why On-Device AI Is the Future of Intelligent Computing As smartphones, tablets, and laptops evolve at breakneck speed, user expectations for AI have shifted dramatically. The demand is no longer limited to cloud-based solutions—people want AI to run locally on their devices. Whether it’s real-time language translation, context-aware content generation, or offline processing of sensitive data, the vision is clear. Yet, two critical challenges remain: memory constraints and response latency. Traditional AI models rely on cloud servers, offering robust capabilities but introducing delays and privacy risks. Existing …