machine-learning.
29 writings found
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Meta's AI is Reshoring American Concrete, One Mix at a Time
How Bayesian optimization is helping U.S. concrete producers ditch imported cement and redesign mixes in days instead of months.
How Facebook Built Friend Bubbles: A Deep Dive into Social ML Architecture
Meta's friend bubbles system combines closeness prediction models, ranking optimization, and performance engineering to surface friend-driven content at scale.
Facebook's Friend Bubbles: When Social Graphs Meet Recommendation Systems
Meta's friend bubbles on Reels reveal how social signals and ML models can coexist in video recommendations without destroying performance.
Facebook's Friend Bubbles: A Masterclass in Social Graph ML
How Meta blends closeness prediction models, multi-task ranking, and prefetch optimization to surface friend-driven content at scale on Reels
Anthropic vs Pentagon: What the Technical Evidence Actually Shows
Anthropic's court filings reveal technical misunderstandings in the Pentagon's national security case. What this means for AI companies working with government.
Facebook's Friend Bubbles: When Social Graphs Meet Video Recommendations
Meta's approach to blending relationship closeness with content relevance reveals hard truths about building social features at scale.
Google's Flash Flood AI: Training on News Reports to Predict Urban Disasters
Google Research uses Gemini to extract flood data from news articles, creating an AI model that predicts flash floods 24 hours early across the Global South
Google's Flash Flood AI: Training Neural Networks on News Articles
Google Research uses Gemini to scrape news reports for flood data, training ML models that predict urban flash floods 24 hours ahead. Here's why that's wild.
Google's WAXAL Dataset: Why African Language AI Actually Matters
WAXAL brings speech recognition to 27 African languages. Here's why this dataset matters more than just being another AI research release.
Teaching AI to Read Maps: Google's MapTrace Pipeline
Google's synthetic data approach teaches language models spatial reasoning through 2M generated map paths, revealing a fundamental gap in AI capabilities.
Teaching AI to Navigate: Why Path Tracing on Maps Is Harder Than It Looks
Google's MapTrace reveals a surprising gap in AI capabilities: multimodal models can recognize images but struggle with basic spatial navigation on maps.
Teaching AI to Navigate Maps Like Humans Do
Google's MapTrace shows how synthetic data generation can teach multimodal models spatial reasoning they never learned from training data alone.
Teaching AI Models to Actually Read Maps: Google's MapTrace Pipeline
Google researchers built a synthetic data pipeline to teach multimodal LLMs spatial reasoning. Turns out, tracing paths on maps is surprisingly hard for AI.
The Multi-Agent Myth: Why More AI Agents Aren't Always Better
Google Research reveals the first quantitative scaling principles for AI agents, showing when multi-agent systems help and when they catastrophically fail.