Blog

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

Usually, in Deep Learning, the tasks are solved by a big monolithic Artificial Neural Network.

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Workshop on Collective Meta-Learning and the Benefits of Deliberation

June 08, 2020 ResearchTechnical blogs

There seem to be at least two views on the topic of multi-agentness/collectivity in badger, depending on whether we are talking about the potential of badger vs. the technical details of how learning in badger occurs, i.e. the why and.

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Trainability of Badger – Why is Badger so hard to train?

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 in the first.

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GoodAI’s ToyArchitecture published in PLOS ONE

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

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 posed questions and the outcomes of the nine.

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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 ResearchTechnical blogs

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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