GoodAI announces $300,000 AI Grant fund
August 10, 2020
Today we announced a $300,000 fund to support AI researchers who are pushing the boundaries of modern artificial intelligence.
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Benefits of modular approach – generalization
August 09, 2020
Research
One of the properties of the Badger architecture is modularity: instead of using one big neural network, the Badger should be composed of many small Experts which solve the whole task in a collaborative manner.
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Workshop on Collective Meta-Learning and the Benefits of Deliberation
June 08, 2020
Research
GoodAI recently hosted a virtual workshop with a number of external collaborators in order to address some of the crucial open questions related to our Badger Architecture.
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Trainability of Badger – Why is Badger so hard to train?
May 20, 2020
Research
To understand why Badger is hard to train, we need to understand first how Badger learns, using a toy task. We try to understand the plateaus, what happens during this period, and why the plateaus are there.
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GoodAI’s ToyArchitecture published in PLOS ONE
May 20, 2020
Research
Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are often uncomputable, or lack practical implementations.
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Internal Badger Workshop – Summary
May 07, 2020
Research
We recently organized an internal workshop with a number of external collaborators to advance the progress of various challenging topics related to the Badger architecture. In this post, we would like to share the outcomes of sessions.
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5 ways COVID-19 is changing the future
May 04, 2020
AI Policy and Society
The emergence and global spread of COVID-19 has already changed the world in ways that were unimaginable at the start of the year. However, in many areas, it seems to have sped up changes that were on the horizon anyway.
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Distributed Evolutionary Computation on Deep Reinforcement Learning Tasks
April 30, 2020
Research
Currently, we are experimenting with an experimental setup proposed in our Badger paper. One of the areas of explorations is an evaluation of suitability of various training settings: supervised learning, Deep Reinforcement Learning (RL), and evolutionary optimization.
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