LearnLM: Improving Gemini for Learning

no code implementations • 21 Dec 2024 • LearnLM Team, Abhinit Modi, Aditya Srikanth Veerubhotla, Aliya Rysbek, Andrea Huber, Brett Wiltshire, Brian Veprek, Daniel Gillick, Daniel Kasenberg, Derek Ahmed, Irina Jurenka, James Cohan, Jennifer She, Julia Wilkowski, Kaiz Alarakyia, Kevin R. McKee, Lisa Wang, Markus Kunesch, Mike Schaekermann, Miruna Pîslar, Nikhil Joshi, Parsa Mahmoudieh, Paul Jhun, Sara Wiltberger, Shakir Mohamed, Shashank Agarwal, Shubham Milind Phal, Sun Jae Lee, Theofilos Strinopoulos, Wei-Jen Ko, Amy Wang, Ankit Anand, Avishkar Bhoopchand, Dan Wild, Divya Pandya, Filip Bar, Garth Graham, Holger Winnemoeller, Mahvish Nagda, Prateek Kolhar, Renee Schneider, Shaojian Zhu, Stephanie Chan, Steve Yadlowsky, Viknesh Sounderajah, Yannis Assael

Today's generative AI systems are tuned to present information by default rather than engage users in service of learning as a human tutor would.

Instruction Following

Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach

no code implementations • 21 May 2024 • Irina Jurenka, Markus Kunesch, Kevin R. McKee, Daniel Gillick, Shaojian Zhu, Sara Wiltberger, Shubham Milind Phal, Katherine Hermann, Daniel Kasenberg, Avishkar Bhoopchand, Ankit Anand, Miruna Pîslar, Stephanie Chan, Lisa Wang, Jennifer She, Parsa Mahmoudieh, Aliya Rysbek, Wei-Jen Ko, Andrea Huber, Brett Wiltshire, Gal Elidan, Roni Rabin, Jasmin Rubinovitz, Amit Pitaru, Mac McAllister, Julia Wilkowski, David Choi, Roee Engelberg, Lidan Hackmon, Adva Levin, Rachel Griffin, Michael Sears, Filip Bar, Mia Mesar, Mana Jabbour, Arslan Chaudhry, James Cohan, Sridhar Thiagarajan, Nir Levine, Ben Brown, Dilan Gorur, Svetlana Grant, Rachel Hashimshoni, Laura Weidinger, Jieru Hu, Dawn Chen, Kuba Dolecki, Canfer Akbulut, Maxwell Bileschi, Laura Culp, Wen-Xin Dong, Nahema Marchal, Kelsie Van Deman, Hema Bajaj Misra, Michael Duah, Moran Ambar, Avi Caciularu, Sandra Lefdal, Chris Summerfield, James An, Pierre-Alexandre Kamienny, Abhinit Mohdi, Theofilos Strinopoulous, Annie Hale, Wayne Anderson, Luis C. Cobo, Niv Efron, Muktha Ananda, Shakir Mohamed, Maureen Heymans, Zoubin Ghahramani, Yossi Matias, Ben Gomes, Lila Ibrahim

A major challenge facing the world is the provision of equitable and universal access to quality education.

Zero-Shot Reward Specification via Grounded Natural Language

no code implementations • 29 Sep 2021 • Parsa Mahmoudieh, Sayna Ebrahimi, Deepak Pathak, Trevor Darrell

Reward signals in reinforcement learning can be expensive signals in many tasks and often require access to direct state.

Reinforcement Learning (RL)

Weakly-Supervised Trajectory Segmentation for Learning Reusable Skills

no code implementations • 25 Sep 2019 • Parsa Mahmoudieh, Trevor Darrell, Deepak Pathak

Instead of direct manual supervision which is tedious and prone to bias, in this work, our goal is to extract reusable skills from a collection of human demonstrations collected directly for several end-tasks.

Multiple Instance Learning Segmentation

Zero-Shot Visual Imitation

1 code implementation • ICLR 2018 • Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell

In our framework, the role of the expert is only to communicate the goals (i. e., what to imitate) during inference.

Imitation Learning

Loss is its own Reward: Self-Supervision for Reinforcement Learning

no code implementations • 21 Dec 2016 • Evan Shelhamer, Parsa Mahmoudieh, Max Argus, Trevor Darrell

Reinforcement learning optimizes policies for expected cumulative reward.

reinforcement-learning Reinforcement Learning +2