no code implementations • 13 Dec 2024 • Beth Goldberg, Diana Acosta-Navas, Michiel Bakker, Ian Beacock, Matt Botvinick, Prateek Buch, Renée DiResta, Nandika Donthi, Nathanael Fast, Ravi Iyer, Zaria Jalan, Andrew Konya, Grace Kwak Danciu, Hélène Landemore, Alice Marwick, Carl Miller, Aviv Ovadya, Emily Saltz, Lisa Schirch, Dalit Shalom, Divya Siddarth, Felix Sieker, Christopher Small, Jonathan Stray, Audrey Tang, Michael Henry Tessler, Amy Zhang
Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares.
no code implementations • 23 Apr 2024 • Raphael Koster, Miruna Pîslar, Andrea Tacchetti, Jan Balaguer, Leqi Liu, Romuald Elie, Oliver P. Hauser, Karl Tuyls, Matt Botvinick, Christopher Summerfield
A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves.
2 code implementations • 22 Feb 2022 • Joao Carreira, Skanda Koppula, Daniel Zoran, Adria Recasens, Catalin Ionescu, Olivier Henaff, Evan Shelhamer, Relja Arandjelovic, Matt Botvinick, Oriol Vinyals, Karen Simonyan, Andrew Zisserman, Andrew Jaegle
This however hinders them from scaling up to the inputs sizes required to process raw high-resolution images or video.
1 code implementation • NeurIPS 2021 • DJ Strouse, Kevin R. McKee, Matt Botvinick, Edward Hughes, Richard Everett
Here, we study the problem of how to train agents that collaborate well with human partners without using human data.
no code implementations • 8 Mar 2021 • Kevin R. McKee, Edward Hughes, Tina O. Zhu, Martin J. Chadwick, Raphael Koster, Antonio Garcia Castaneda, Charlie Beattie, Thore Graepel, Matt Botvinick, Joel Z. Leibo
Collective action demands that individuals efficiently coordinate how much, where, and when to cooperate.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
2 code implementations • 24 Feb 2021 • David Raposo, Sam Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt Botvinick, Hado van Hasselt, Francis Song
We propose state-associative (SA) learning, where the agent learns associations between states and arbitrarily distant future rewards, then propagates credit directly between the two.
1 code implementation • NeurIPS 2021 • David Ding, Felix Hill, Adam Santoro, Malcolm Reynolds, Matt Botvinick
Neural networks have achieved success in a wide array of perceptual tasks but often fail at tasks involving both perception and higher-level reasoning.
Ranked #4 on
Video Object Tracking
on CATER
no code implementations • ICLR 2021 • Sam Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matt Botvinick, David Raposo
We show that EPNs learn to execute a value iteration-like planning algorithm and that they generalize to situations beyond their training experience.
5 code implementations • 22 Jan 2019 • Christopher P. Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, Alexander Lerchner
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence.
1 code implementation • NeurIPS 2018 • DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, David Schwab
We show how to optimize these regularizers in a way that is easy to integrate with policy gradient reinforcement learning.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
31 code implementations • 4 Jun 2018 • Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
no code implementations • 3 Apr 2018 • Luis Piloto, Ari Weinstein, Dhruva TB, Arun Ahuja, Mehdi Mirza, Greg Wayne, David Amos, Chia-Chun Hung, Matt Botvinick
While some work on this problem has taken the approach of building in components such as ready-made physics engines, other research aims to extract general physical concepts directly from sensory data.
1 code implementation • 28 Mar 2018 • Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matt Botvinick, Demis Hassabis, Timothy Lillicrap
Animals execute goal-directed behaviours despite the limited range and scope of their sensors.
9 code implementations • 17 Nov 2016 • Jane. X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, Matt Botvinick
We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.
no code implementations • ICML 2017 • Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matt Botvinick, Nando de Freitas
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent.