Introducing v0.5 of the AI Safety Benchmark from MLCommons

1 code implementation • 18 Apr 2024 • Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, Kurt Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Sujata Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse khomh, Bhavya Kailkhura, Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H. Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Yifan Mai, Priyanka Mary Mammen, Kelvin Manyeki, Sean McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh Jinenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr, Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh, Eleonora Presani, Fabrizio Puletti, Paul Röttger, Saurav Sahay, Tim Santos, Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss, Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav, Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy Liang, Peter Mattson, Joaquin Vanschoren

We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.

Detecting Natural Language Biases with Prompt-based Learning

no code implementations • 11 Sep 2023 • Md Abdul Aowal, Maliha T Islam, Priyanka Mary Mammen, Sandesh Shetty

In this project, we want to explore the newly emerging field of prompt engineering and apply it to the downstream task of detecting LM biases.

Language Modelling Prompt Engineering

WiSleep: Inferring Sleep Duration at Scale Using Passive WiFi Sensing

no code implementations • 7 Feb 2021 • Priyanka Mary Mammen, Camellia Zakaria, Tergel Molom-Ochir, Amee Trivedi, Prashant Shenoy, Rajesh Balan

With a motivation to expand sleep monitoring capabilities at a large scale and contribute sleep data to public health understanding, we present Wisleep, a system for inferring sleep duration using smartphone network connections that are passively sensed from WiFi infrastructure.

Change Point Detection

Federated Learning: Opportunities and Challenges

no code implementations • 14 Jan 2021 • Priyanka Mary Mammen

Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server.

BIG-bench Machine Learning Federated Learning

Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids

no code implementations • 19 Oct 2019 • Hareesh Kumar, Priyanka Mary Mammen, Krithi Ramamritham

Motivated by recent advancements in Deep Reinforcement Learning (RL), we have developed an RL agent to manage the operation of storage devices in a household and is designed to maximize demand-side cost savings.

reinforcement-learning Reinforcement Learning (RL)