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NIST AI 600-1 - Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile
The document provides a profile of risks unique to or exacerbated by generative artificial intelligence (GAI) and offers actions to manage these risks.
airc.nist.gov2006, Geoff Hinton, then at the University of Toronto, made a key tweak to this method, which he dubbed “deep learning.” He was able to mathematically optimize results from each layer so that the learning accumulated faster as it proceeded up the stack of layers.
Kevin Kelly • The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future
Artificial intelligence
Seth Werkheiser • 19 cards
We introduce the Open X-Embodiment Dataset, the largest open-source real robot dataset to date. It contains 1M+ real robot trajectories spanning 22 robot embodiments, from single robot arms to bi-manual robots and quadrupeds.
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Machines Learning
Victor Wagner • 2 cards
deep neural nets
Prashanth Narayan and • 2 cards
We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms.