GoodAI Meta-learning & Multi-agent Learning Workshop attracts researchers from across the world

August 21, 2020

The GoodAI team taking part in the Meta-learning & Multi-agent Learning Workshop

Last week GoodAI organized the first Meta-Learning & Multi-Agent Learning Workshop which throughout the week saw over 60 participants from across the world take part including speakers from Google Brain, DeepMind, OpenAI, University of Oxford, Stanford University, MIT and many more.

The workshop consisted of 19 talks as well as discussions and Big Picture panels and was inspired by GoodAI’s Badger Architecture. It covered topics such as: 

  • Modular meta-learning
  • Multi-agent learning 
  • Reinforcement learning 
  • Deep learning 
  • Open-Ended learning
  • And much more. 

At the start of the workshop, GoodAI announced the new GoodAI Grants initiative, a $300,000 grant fund that will be used to support researchers or research groups working on topics that can build on and improve GoodAI’s Badger Architecture.

Jan Feyereisl, Senior Research Scientist (left) and Marek Rosa, Founder and CEO of GoodAI (right) announce AI Grants

Below you can see a list of all of the talks along with videos of 14 of them.

Day 1

  • Workshop & Badger Introduction and Principia Badgerica (VIDEO) (Jan Feyereisl, GoodAI & Marek Rosa, GoodAI)


  • End to end differentiable Self organising systems (Ettore Randazzo, Google Research & Alexander Mordvintsev, Google Research)
  • Discovering Reinforcement Learning Algorithms  (Junhyuk Oh,  DeepMind)
  • Intelligence Without a Brain (VIDEO) (Deepak Pathak, CMU)


Day 2

  • A multi-agent perspective to AI (VIDEO) (Anuj Mahajan, University of Oxford)


  • Multi-Agent Learning 2 (Jun Wang, UCL)
  • Learned Communication (Angeliki Lazaridou, DeepMind)
  • Self-Play and Zero-Shot Human AI Coordination in Hanabi (VIDEO) (Jakob Foerster, The Vector Institute)


Day 3

  • Relative Overgeneralization in Distributed Control (VIDEO) (Wendelin Boehmer, TU DELFT)


  • Modular Meta-Learning: Learning to build up knowledge through modularity (VIDEO) (Ferran Alet, MIT)


  • Modular & Compositional Computation (VIDEO) (Clemens Rosenbaum, ASAPP Inc)


  • Complexity: Concepts, Abstraction, and Analogy in Natural and Artificial Intelligence (VIDEO) (Melanie Mitchell, The Santa Fe Institute)


Day 4

  • Measuring growth of complexity (VIDEO) (Tomas Mikolov, CIIRC)


  • Why Think? (VIDEO) (Nicholas Guttenberg, GoodAI & Cross Compass)


  • AI Learning Environments and PCG and Open-endedness and…the Extended Mind? (VIDEO) (Julian Togelius, New York University)

  • The Importance of Open-Endedness in AI and Machine Learning (VIDEO) (Kenneth Stanley, OpenAI)


Day 5 

  • Towards the Science of Deep Learning – The Loss Landscape Geometry (VIDEO) (Stanislav Fort, Stanford)

  • Learned Learning Algorithms (VIDEO) (Luke Metz, Google Brain)

  • Understanding Neural Networks via Pruning (Jonathan Frankle, MIT)


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