machine-learning.
73 writings found
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When AI Models Escape: The Alignment Crisis That OpenAI Is Ignoring
OpenAI's response to a model breach reveals a dangerous split in how the industry thinks about AI safety. Here's why containment alone won't work.
Meta's Hierarchical Interest Representation: A Shift in Graph-Based Ranking
How Meta's new representation layer tackles sparse signals in deep funnel ads using hierarchical clustering, multimodal features, and efficient transformers.
Meta's Hierarchical Interest Representation: What It Means for Ad Tech
Inside Meta's new representation learning system that maps user intent across billions of entities. A deep dive into graph learning at scale.
Grabette: Making Robot Learning Data Collection Accessible
An open-source handheld gripper system that lets anyone record manipulation demonstrations for robot training, democratizing data collection beyond expensive labs.
NVIDIA NeMo Automodel: Production-Grade Diffusion Training for Everyone
NVIDIA and Hugging Face collaborate on distributed diffusion training that scales from one GPU to hundreds, with zero checkpoint conversion and native Diffusers support.
NeMo Automodel brings production diffusion training to Hugging Face
NVIDIA and Hugging Face collaborate to make distributed diffusion model training accessible, scalable, and checkpoint-conversion-free for any Diffusers model.
Building AI responsibly: lessons from Microsoft's NIST approach
Sarah Bird on why irresponsible AI stems from experimentation without impact consideration, and how developers can adopt NIST principles for thoughtful AI workflows.
Meta's Hierarchical Interest Representation: A New Approach to Graph-Scale ML
Exploring Meta's breakthrough in recommendation systems that combines sparse engagement signals with world knowledge to power ads across billions of users.
Meta's AI Layoff System Ignored Protected Leave, Now Faces Lawsuit
26 former Meta employees sue over AI performance ranking that allegedly penalized workers on medical and parental leave during 2024 layoffs.
When AI Has No Source: Implications for Developers
Exploring what it means for developers and the AI industry when models generate content with no traceable source material.
When AI Has No Source: Implications of Empty Context
Exploring what it means for developers and the AI industry when models operate with no grounding context or source material at all.
What Bayer's PRINCE Teaches Us About Building Agentic AI Systems
A deep dive into the engineering decisions behind Bayer's agentic RAG system for preclinical research
When AI Infrastructure Meets Community Backlash: What Developers Need to Understand
The Shelbyville data center controversy reveals a growing tension between AI's infrastructure hunger and local communities. Here's what it means for developers.
How Meta Rewrote Recommendation Systems From Scratch With Index as Model
Inside SilverTorch: Meta's radical shift from microservices to a single neural network for retrieval
Microsoft's MAI-Thinking-1 Just Changed the Game, and Most People Don't Get Why
Microsoft's new flagship AI model MAI-Thinking-1 marks a major shift in the AI landscape. Here's what it means for developers.