AI-driven system blends literature, experiments and robotics to discover new materials

Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables.

Juicebox raises $30M from Sequoia to revolutionize hiring with LLM-powered search

After attending startup accelerator Y Combinator in the summer of 2022, Paffenholz and Gupta spent a couple more years ...
Techopedia

Inside Fairwater: Microsoft’s Giant Leap in AI Infrastructure

Explore Microsoft’s massive new Wisconsin data center that reveals AI’s new frontier: more power, more processors, and ...
GitHub

LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models

This is the official PyTorch implementation of LLMDet. Recent open-vocabulary detectors achieve promising performance with abundant region-level annotated data. In this work, we show that an ...

How Intuit built custom financial LLMs that cut latency 50% while boosting accuracy—and what enterprise AI teams can learn

Intuit has been on a multi-year journey building its Generative AI Operating System (GenOS) to power AI agents across its platforms which include TurboTax, QuickBooks, Credit Karma and Mailchimp. The ...

Shaking up economic early warning systems with an artificial jellyfish algorithm

A study in the International Journal of Critical Infrastructures discusses a financial early warning system based on an ...
GitHub

Train multi-step agents for real-world tasks using GRPO.

RULER (Relative Universal LLM-Elicited Rewards) eliminates the need for hand-crafted reward functions by using an LLM-as-judge to automatically score agent trajectories. Simply define your task in the ...